assistive technology research paper topics

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Assistive Technology Interventions for Adolescents and Adults with Learning Disabilities: An Evidence-Based Systematic Review and Meta-Analysis

2. introduction.

People with specific learning disabilities (LD) have learning difficulties that are specific to reading, writing or mathematics, and are not explained by another condition ( APA, 2013 ). Students with LD are increasingly seeking postsecondary education ( Joyce & Rossen, 2006 ). In fact, the largest group of incoming postsecondary students with disabilities are students with LD, not students with sensory or motor disabilities ( Gregg, 2009 ). Creating inclusive learning environments for students at all educational levels is an issue at the forefront of discourse today. In countries like the United States, there are also legal requirements for inclusion at the postsecondary level, as mandated by the Americans with Disabilities Act of 1990 (42 U.S.C.§12101). Therefore it is important to investigate all aspects of inclusive learning environments, including how assistive technology (AT) can serve the needs of adolescent and adult learners - our focus in this paper.

AT options have proliferated in recent years, due to the greater availability of ever more powerful computers and other forms of technology ( Alper & Raharinirina, 2006 ). Survey studies like Abreu-Ellis & Ellis (2006) have documented the use of several types of AT accommodations for higher education students with LD: text-to-speech and voice recognition software, outlining programs, and various word-processing-based accommodations. But do these accommodations improve learning for students with LD, and how do they impact students’ educational experience?

The meta-analysis is the primary tool of choice for collating results from disparate studies in a systematic, quantitative way ( Borenstein et al., 2009 ). Currently, research on AT supports reveals mixed success of AT for students with LD ( Holmes & Silvestri, 2012 ; Lewandowski et al., 2016 ). However, there is a lack of meta-analyses and systematic evaluations of this work. This situation impedes students and educational technologists from making evidence-based decisions about AT interventions.

Relatedly, although quantitative research can tell us about the effectiveness of AT, we need qualitative research to find out about the lived experience of participants ( Van Manen, 2015 ), a crucial consideration for inclusive education. If AT helps learning purely in a numerical sense, but is uncomfortable or socially stigmatizing to use, then advocating for it might cause more harm than good. Reviews including qualitative research are becoming widespread, especially in the health sciences ( Andrew et al., 2009 ). However, we could not locate any research synthesis incorporating relevant qualitative studies.

To address these limitations, we present a systematic review of both quantitative and qualitative work, with meta-analyses where appropriate, to help us determine which particular AT interventions are effective for adolescents and adults with LD – a question which cannot be answered on the basis of previous reviews.

Meta-analyses help address a major issue in both learning disabilities research and education research with atypically developing groups: the issue of small sample sizes. Meta-analyses provide a formal framework of collating data from separately conducted, preexistent studies. Their primary limitation is that often they compare studies that have many differences in design, with the assumption that those differences are not central ( Borenstein et al., 2009 ). Therefore, in every meta-analysis, a case-by-case decision must be made about inclusion criteria related to study design, outcome variables and sample characteristics. Here we will explain our decisions by including data about each paper in detailed tables, and discussing commonalities in outcome variables and design in each category.

2.1. An overview of previous work

The current paper builds on previous work on pedagogical uses of digital technology for students with disabilities by providing a review of the effectiveness of AT for adolescent and adult students with LD. The earliest relevant reviews, like Brown et al. (1989) , did not focus specifically on LD, but surveyed all disabilities. A more recent review, Alper & Raharinirina (2006) , included more papers about students with LD than any other disability, but only 25% of those papers featured students with LD who were over 21 years of age.

The use of AT in writing interventions for students with LD has received attention in the research literature. Some authors considered specific interventions in their reviews. Peterson-Karlan (2011) examined writing interventions for students of all ages with LD. That review was one of the very few that explicitly followed evidence-based guidelines, but it also concluded that in this specific area, there were an insufficient amount of studies at that time to draw any systematic conclusion (even with this broader age range). Freeman et al. (2005) reached a similar conclusion about keyboarding interventions. Wanzek et al. (2006) reviewed spelling interventions for K-12 students. This was another of the few relevant evidence-based publications, and the only one where effect sizes were calculated; however, the authors did not provide forest plots or meta-analyses of these effect sizes, possibly due to the heterogeneity of studies. Together, these reviews yielded support for the use of AT for students who struggle with writing, but the researchers noted many limitations – like the large amount of non-standardized outcome measures used in the papers –, and a lack of generalizability to older students.

The research team of MacArthur also produced a series of reviews about writing interventions for students of all ages ( MacArthur, 1996 ; Macarthur, 1999 ; MacArthur et al., 2001 ), predominantly focusing on their own studies. Other researchers like Raskind & Higgins (1998) also primarily reviewed their own work, and did not seek to provide a systematic survey of the field. Their findings were mixed, with AT interventions often beneficial, but in limited ways.

Some articles focused on specific LDs, only finding a handful of papers. Lindstrom (2007) discussed accommodations specifically for postsecondary students with reading and written expression disorders, finding that there was little empirical evidence to guide judgment. We also located an amount of articles enumerating the various extant AT devices and approaches, without systematically assessing their effectiveness – like Lewis (1998a) , Mull & Sitlington (2003) . These were usually aimed at educators, though some of them appeared in academic journals with an audience mostly composed of researchers. Some articles for educators, like Martinez-Marrero & Estrada-Hernandez (2008) , discussed both reviews and individual studies.

We also located review papers with broader foci. Rath & Royer (2002) surveyed all accommodations for college students with LD, including assistive technology; with the conclusion that at that date, very few relevant studies existed. More recently, Gregg (2011) focused on all sorts of accommodations for adolescent and adult students with LD – including and emphasizing testing accommodations like extended time, which do not fall under the AT umbrella. There was insufficient information to draw conclusions; even though the authors had previously been able to conduct a meta-analysis on the effectiveness of extended time for adolescents and adults with LD ( Gregg, 2009 ). Lang et al. (2014) was the closest to our review, but it summarized various AT interventions in only postsecondary students with LD, and with a non-systematic literature search. The authors argued that AT was a viable option. Another recent publication, Lewandowski et al. (2016) discussed AT options both for students with LD and those with attention deficit hyperactivity disorder (ADHD). Its authors likewise did not perform a systematic search and assessment, and did not focus on any particular age group. They also concluded AT was helpful, but did not attempt to draw firm conclusions about the type of AT that was useful, and the extent of its usefulness.

As the above demonstrate, systematic evaluations have been lacking to this date. Most reviews were limited in scope. Often they listed studies with no search process described, and no attempt to produce a meta-analytic summation. Holmes & Silvestri (2012) criticized this lack and pointed out the mixed success of previous work. Furthermore, few reviews used an evidence-based framework ( Wanzek et al., 2006 ; Peterson-Karlan, 2011 ), and none of them used formal assessments of study quality or meta-analytic tools – even though many of these articles were produced after evidence-based practice has developed reviewing standards; see for example Dollaghan (2004) . Finally, qualitative and survey-based studies in particular have not been reviewed at all, to our knowledge. Some have argued that investment in AT is justified despite the meager systematic evaluation of the evidence base ( McIntosh, 2009 ).

Considering these factors, we deemed that a systematic review of all available work was warranted, and would in fact be necessary to guide pedagogical decisions and disability support services in an evidence-based way.

2.2. Questions and hypotheses

We were interested in knowing whether AT interventions are effective for adolescents and adults with LD in secondary and post-secondary settings. On the basis of the above literature, we predicted mixed results, with some interventions more useful than others, and some possibly detrimental to learning. We sought to find out exactly which interventions were likely to be helpful.

We were likewise interested in the lived experience of people with LDs using these technological supports, and the broader social context of AT intervention. Therefore, we also assessed available qualitative and survey-based literature.

Our systematic review and meta-analysis addressed the limitations of past research by:

• Conducting a broad search of research on the effectiveness of AT for students with LD and providing a description of our search.
• Focusing on students at the secondary and post-secondary levels.
• Focusing on students with LD.
• Identifying which ATs are effective at enhancing learning for students with LD.
• Providing both an analysis of the quality of the studies reviewed as well as a meta-analysis of the study results.
• Including both qualitative and quantitative studies.

By focusing our review in this way, we aimed to provide the most useable information for students with LD as well as individuals who support their learning. Likewise, this systematic review and meta-analysis allowed us to identify limitations in the type of studies that are designed and conducted on this topic.

3.1. Inclusion criteria

Our goal was to locate AT interventions for students with LD in secondary and post-secondary settings; therefore, we searched for studies that primarily included participants who were in Grade 9 or higher or who were 14 years or older. To be as comprehensive as possible, we included peer reviewed, published studies as well as Ph.D. theses that were available on-line. We included both quantitative and qualitative work. We did not place geographic, cultural or time limitations on studies. We focused on research published in English, but we did not restrict our search procedure by language, meaning a study in potentially any language could have been included if the English-language papers cited it, or if it was indexed in the search engines we used by English keywords. During our queries we located and included only one non-English publication, a paper in Swedish ( Milrad, 2010 ).

We included studies where some kind of AT was used. We used the definition of assistive technology from the Technology-Related Assistance for Individuals with Disabilities Act of 1988, commonly found in the literature: ”Any item, piece of equipment, or product system, whether acquired commercially off the shelf, modified or customized, that increases, maintains, or improves functional capabilities of individuals with disabilities.” (Sec. 3.) We excluded prototype design studies.

The definition of learning disabilities can vary from publication to publication; American studies often use the federal legal definition of LD:

”General. The term means a disorder in one or more of the basic psychological processes involved in understanding or in using language, spoken or written, that may manifest itself in an imperfect ability to listen, think, speak, read, write, spell, or to do mathematical calculations, including conditions such as perceptual disabilities, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia.

Disorders not included. The term does not include learning problems that are primarily the result of visual, hearing, or motor disabilities, of mental retardation, of emotional disturbance, or of environmental, cultural, or economic disadvantage. (34 C.F.R. § §300.7 and 300.541)”

We did not favor a specific definition and included studies with participants with ”learning disabilities” or ”learning difficulties” in general, alongside papers where participants had a specific disability usually considered to fall under LDs, like dyslexia. Studies of participants with attention deficits or autism spectrum conditions but no concurrent LDs were not included. Studies where the term ”learning disability” was used in a different way, e.g., to refer to general intellectual impairment, were not included. Studies were participants of multiple disabilities were grouped together were likewise not included, unless at least two thirds of the group had a diagnosis of LD. LD frequently co-occurs with other conditions, and thus we did not want to exclude studies with participants who also have further diagnoses.

3.2. Search method

We reported our literature search and data analysis process in the systematic reviews prospective database PROSPERO, registration number: CRD42015015918. The search was conducted in January 2015 and updated with 2015–2017 data due to peer reviewer requests in February 2017. Here we present the updated results.

For finding relevant literature, we used a combination of PubMed, Google Scholar (multiple methods), ERIC, manual citation search, and journal-based search. We provide the number of works yielded by each search method, with the caveat that these numbers can also overlap in some cases; better-cited articles can be located in multiple ways. In PubMed, we used the queries ”((”learning disabilities”) AND ”assistive”) AND ”technology”” which produced 18 articles, and ”((”learning disabilities”) AND ”assistive”)” (PubMed format), which produced 24 articles.

In Google Scholar, we used the queries ”learning disab* technolog*” and ”learning disab* technolog* college” (in 2015) and ”learning disabilities technology” and ”learning disabilities technology college” (in 2017, as the asterisk feature had been removed). The first twenty pages were evaluated (200 articles each page).

We also used another method of literature search: we chose older papers in the field, and examined the more recent papers that cited them using Google Scholar’s Cited By function. This allowed us to locate articles that either had low citation numbers, or were too recent to be indexed properly in topical databases, and thus might not be found using other methods. The articles we used as our starting point in Google Scholar Cited By were Day & Edwards (1996) , which yielded 88 articles, and Yau et al. (1990) , which yielded 6 articles. (Note that Yau et al. (1990) itself used a younger age group than our area of interest and thus was not included in this review.)

In the educational database ERIC we searched for the descriptor ”Learning Disabilities” with the field ”Assistive Technology”, an education level of ”Postsecondary Education”, a descriptor of ”College”, and we restricted the search to ”Peer Reviewed Only”. This yielded 113 articles. Since many articles in ERIC mention LD in passing, and more topical articles are not prioritized, less restricted search queries yielded thousands of results.

We also examined the citations of related review papers. Alper & Raharinirina (2006) yielded 96 articles. Bryant & Bryant (1998) yielded 98 articles. Freeman et al. (2005) yielded 96 articles. Peterson-Karlan (2011) yielded 123 articles. Holmes & Silvestri (2012) yielded 54 articles. Lang et al. (2014) yielded 55 articles.

To cover gaps in our more recent coverage, we also perused January 2012 – March 2017 articles in journals which had published at least two relevant articles in the past, based on the list of articles acquired during the previous steps. These journals included: the Journal of Learning Disabilities (248 articles), Learning Disability Quarterly (108 articles), Assistive Technology (195 articles), Annals of Dyslexia (70 articles), Assistive Technology Outcomes and Benefits (13 articles), Journal of Postsecondary Education and Disability (149 articles), Computers & Education (1339 articles), Journal of Vocational Rehabilitation (275 articles), and the Journal of Special Education Technology (94 articles).

We excluded five studies on the basis of them being prototype design studies aimed at developing a device or an interface – Lannen et al. (2002) , Savidis et al. (2007) , Mendi & Bayrak (2013) , Williams & Hennig (2015a) , Williams & Hennig (2015b) .

The full text of all possibly relevant articles could be located after using our university’s InterLibrary Loan, with only a single exception where the citation itself was incomplete and was missing the publication name.

3.3. Data extraction

Author1 extracted multiple parameters of each article manually: the full citation, study method, diagnostic criteria used for inclusion, study context, sample size, sample gender distribution, sample age range, and geographic location of the study (on the country or where available, state or region level). Study methods were categorized as survey-based, qualitative, single-subject intervention, or group-design intervention. Study contexts were categorized as university, high school, special high school (for students with LD and/or other disabilities), or other.

3.4. Quality assessment

3.4.1. assessing survey studies.

There is a limited amount of research on the formal assessment of survey quality in systematic reviewing. Systematic reviews often explicitly exclude survey studies. General-purpose quality checklists used in evidence-based research do not tap the specific characteristics of survey studies. Although there is some precedent for using the Modified Downs-Black Checklist ( Justice et al., 2008 ) for assessing survey studies, we have opted not to do this for multiple reasons. First, as we have used the same checklist for quantitative intervention studies, readers might conclude that the numbers would be comparable. However, as the survey and qualitative categories include theses, which often have lengthier methods sections, the quality ratings would probably be inflated compared to the quantitative studies. This means that even inside the survey studies and qualitative categories, ratings for articles and theses cannot be compared. (We did not locate any theses that featured quantitative studies.)

3.4.2. Assessing qualitative studies

Qualitative studies were assessed in a purely qualitative manner. The methodology of qualitative reviewing is much less defined at present than that of quantitative reviewing. Study quality assessment checklists like Tong et al. (2007) or Anderson (2010) generally have not been tested psychometrically – for example, metrics like inter-rater reliability are not provided. Therefore we cannot assess the usefulness of these checklists sufficiently.

After consultations with qualitative methods expert Renita R. Schmidt (personal communication), we designed the following procedure. Each paper was read, in temporal order of publication, and analytic notes were written by Author1 after each article – not upon finishing the entire set of papers, so as to allow what has been learned from each paper to influence the interpretation of the next. This is similar to the constant comparative method of analysis that is pervasive in qualitative research (for a recent description, see ( Merriam, 2009 )). We did not code articles line by line, as we deemed this unfeasible with the amount of data at hand. We performed two readings of each paper: the first in chronological order, then we sorted publications into topics and read them by topic group in chronological order.

3.4.3. Assessing group-design and single-subject studies

Two of us (BP, KRG) rated all group-design and single-subject intervention studies using the Modified Downs-Black Checklist ( Justice et al., 2008 ). Both of us have a background in quantitative study design and developmental disabilities research.

The Modified Downs-Black Checklist was designed to assess the quality of treatment research in speech-language pathology. It has 25 binary yes-no items, for example Does study provide clear description of actual probability values of main outcomes or Are participants randomized to treatment conditions . We decided to add another item to this scale. We split the item Does study provide clear description of participant characteristics into demographic characteristics (age, gender, ethnicity etc.) and diagnosis characteristics. Therefore our maximum score for a given paper was 26.

To estimate inter-rater reliability, we calculated the intraclass correlation coefficient for average measures using a two-way random effects model. We determined inter-rater reliability for average quality scores was high (ICC = 0.828, 95% CI: 0.672–0.910, p -value for the F test < 0.001.). After this phase, we built item by item consensus, and the consensus ratings were used in the following analyses.

We will proceed in this section in the following order: survey studies, qualitative studies, single-subject and group-design intervention studies (these last two grouped together). This way we proceed from studies with a broader focus toward studies with more circumscribed goals. Survey studies tend to have broad research questions, which is also true of some qualitative studies examining the lived experience of students with LD using assistive technology, whereas some qualitative studies have a narrower goal of assessing specific devices. The experimental and quasi-experimental publications tend to have very circumscribed aims. Publications with multiple components will be discussed in multiple sections.

4.1. Survey studies

4.1.1. inclusion.

Based on abstracts, we located 9 studies with a marked survey component; 5 met inclusion criteria. Table 1 presents information on the included survey studies. We will describe them in chronological order.

Survey-based studies in our review

4.1.2. Summary

Only two of the survey studies we located used statistical hypothesis testing: Parker & Banerjee (2007) and Heiman & Shemesh (2012) . Other survey studies like Gaiters-Fields (2005) , Abreu-Ellis & Ellis (2006) , Klemes et al. (2006) reported only descriptive statistics.

The studies were often dated, presenting information from a time before major changes in university disability services. Further, the publications did not form a cumulative body of science and did not build on each other: each discussed a separate question, and they did not tend to cite each other. This restricted the conclusions we could draw from them.

Based on the two studies that contained inferential statistics, we can state that there is tentative data about the following: students with LD show a different technology use profile than students with ADHD and typical development (for both assistive and general-use technology); for example, students with ADHD are more comfortable with presentation software than either the LD or TD groups. Data are mixed on whether students with LD are more likely to use internet-based technology than the other two groups – this might depend on the particular kind of technology and how much reading it requires. There is a small amount of data related to students’ well-being and hope: for example, there is some evidence that students with LD might self-report better well-being and a more hopeful outlook, connected to AT use. This latter result might be culture-specific to Israel, as we have seen conflicting data from the United States in less controlled studies and a large-scale quantitative survey from our laboratory ( McGregor et al., 2016 ).

The three studies that included only descriptive statistics also provided information on disparate topics. Gaiters-Fields (2005) queried African-American students with LD at a Historically Black University, as part of a larger project. All students ( n = 10) used AT, but self-funded their AT due to little support from the university. Abreu-Ellis & Ellis (2006) surveyed professionals who worked in disability services offices in 17 universities of Ontario, Canada. Most participants strongly agreed that incoming students with LD needed to be trained in using AT and indicated that their university provided this service on a one-on-one basis. About half of the respondents stated that their office also provided training in small group settings. Challenges the participants listed in response to open-ended questions included ”consistency in assistive technology use by the students, effective training while semester coursework is in progress, and fitting unique individuals with very unique needs to the available technology” (p. 39). Klemes et al. (2006) examined postsecondary students with LD using a distance-learning course with electronic units that featured multimedia presentation of the subject matter. Students self-reported in the survey that they spent less time studying with these materials than with conventional materials. They also claimed to use many of the electronic components (like search or copy-paste). Most students stated they enjoyed the electronic units, though a minority expressed dislike, primarily about the inconvenient format.

4.2. Qualitative studies

4.2.1. inclusion.

We located 15 primarily qualitative studies based on their abstracts, and excluded two after reading them. One thesis, Roberts (2003) was also published in shortened form as an article Roberts & Stodden (2005) . Table 2 shows descriptive information.

Qualitative studies in our review

4.2.2. Studies: AT as one component of accommodations

Four of the qualitative studies had a broader focus, with AT only one of the areas investigated: Bradshaw (2001) , Gaiters-Fields (2005) , Milrad (2010) , Dodge (2012) . These studies examined the impact of all accommodations on the lived experience of university students with LD, primarily using interviews, but also with various forms of participant observation.

Bradshaw (2001) provided two detailed case studies of university students with LD in Northern Virginia, to identify which factors were important for their academic success. One student mentioned using technological accommodations, but stated he did not benefit from most of them, with the exception of a tape recorder. Accommodations and support strategies other than AT were much more helpful in his university studies; he especially benefited from anxiety-related counseling, and reading remediation so that he eventually no longer needed to rely on books on tape. The second student benefited more from AT than the first student. Although her university had accommodations for students with LD, these were not technological. Instead, she tape-recorded her classes and learned how to use a laptop computer with the help of her husband. The laptop was also useful because she could look up resources for people with LD, including information about her legal rights as a student.

Gaiters-Fields (2005) provided case studies of three African-American undergraduate students with LD at a Historically Black University, who were chosen from the ten survey respondents mentioned above. One of the students, Kensley, was described as heavily relying on technology ”to assist him in his studies and personal management.” The university did not provide students with AT at the time of the study, and the researcher mentioned this as an area of concern: if Kensley had not been able to afford his AT supports himself, he would have had great difficulty coping with the university environment. The specific details of Kensley’s technology use were not described. The other two students’ AT use was not emphasized in the study.

Milrad (2010) , focused on Swedish higher education students with dyslexia. Out of nine students interviewed, two did not use any AT. All others used audiobooks, although one student stated he tried this type of AT, but it did not work well for him. The other students spoke highly of audiobooks. Students often used spell checkers, either Word’s built-in spell checker or Stava Rex / Stava Rätt, a spell checker developed in Sweden specifically for people with dyslexia that can be integrated into various word processing programs (including Word). Two people had problems with Stava Rex: one student liked it but could not get it on his computer, and the other did not find it good enough for her purposes. Some students also used text-to-speech. Two people mentioned Quicktionary, a handheld scanner that translates English words into Swedish. Students reported mixed experiences with AT; they tried forms of AT that they ultimately did not adopt, and they sometimes abandoned AT altogether.

The two students who claimed they had never used AT received an intervention where they were provided a speech-to-text program, Voice Xpress. Both students used Voice Xpress extensively to write essays, but both reported that it often had trouble understanding their speech. They found it especially problematic that the errors it produced were of the type not recognized by spell checkers. Eventually, both students abandoned the software.

Dodge (2012) interviewed eight students with reading comprehension disabilities at a US Midwestern community college. Students received a variety of accommodations, including AT: for example, books on CD, smart pens, laptops, and recorded lectures. All students used multiple accommodations. Overall, they perceived the non-AT accommodations to be more valuable than the AT accommodations. A participant mentioned that books on CD were often hard to use. Other non-AT accommodations like reading the test out loud, dictating answers, and extended time on tests were described as more useful. In general, testing accommodations were more appreciated than classroom accommodations, but testing accommodations were also primarily not technological.

4.2.3. Studies: Perspectives about AT

Young (2013) also addressed a broader theme: what do high school students and their parents think about AT? Twelve Canadian students who each had LD with or without ADHD and their parents participated in the study. All students attended a special school for people with LD. Both groups were asked about their experience of AT use prior to and during their attendance of the special school. The overall tone of the responses was positive, but some drawbacks of AT were noted. Students and parents reported that AT helped the students finish tasks, demonstrate their academic ability, and improve their writing. It let them compensate for their difficulties, and increased their confidence. Some students felt AT increased motivation, but others felt it decreased motivation, because it was often a hassle. Two students also reported they felt stigmatized by their use of AT. Three parents and four students mentioned that they found AT frustrating.

4.2.4. Studies: Technological course supports for students with LD

Four qualitative studies examined technology-mediated course design or complex technologically supported studying for students with LD.

Anderson-Inman et al. (1999) featured three case study vignettes of students with LD who were enrolled in two intervention programs in a high school, community college or a university focusing on computer-assisted studying. Some of the students received a laptop and participated in a course titled ”Computer-Based Study Strategies.” Instruction was personalized to specific students’ needs. Other students participated in a networked note-taking intervention helping students with LD learn efficient note-taking by sharing a virtual workspace with a note-taker. Out of the three vignettes presented, one student received both the laptop and strategies intervention and the networked note-taking intervention, one received only the laptop and strategies intervention, and one received only the networked note-taking intervention. All three students achieved more academic success than before the intervention, but some initially struggled with the technology.

Woodfine et al. (2008) investigated text-based synchronous e-learning in university students with dyslexia. The authors found that students with dyslexia struggled more in text-based synchronous e-learning contexts than students with typical development. Participants had to collaborate in groups of three to solve a survival scenario: one student with dyslexia and two without. The interactions took place in a WebCT environment, which had descriptions of the problem to be solved and a chat room where participants could collaborate. Students were interviewed after the problem-solving exercise. Participants with dyslexia reported difficulty with typing and spelling and also with reading other students’ responses. They also experienced negative emotions related to their performance. They felt isolated and embarrassed, especially by their spelling, and they thought they often failed to convey their meaning. Participants who were typically developing noticed and remarked on the lack of participation on the part of the students with dyslexia, but they sometimes misinterpreted it, for example by assuming that the student was not computer literate.

Graves et al. (2011) examined asynchronous online access. The asynchronous online component involved recordings of class presentations, specifically intended to be an accommodation for students with disabilities. Postsecondary students with LD or ADHD who were enrolled in STEM courses which had an asynchronous online component were interviewed about their experience. Students were overall favorably disposed toward this accommodation. They claimed that having access to course recordings increased clarity, convenience and comfort, and helped them study at their own pace. Participants anticipated higher grades for themselves and felt that the accommodation helped them cope with their disability. On the other hand, some students found the structure of the downloadable materials confusing. They also experienced some technological issues.

Dziorny (2012) designed a course module in Second Life™ (Linden Labs) with the assumption that learning in a virtual spatial environment would be beneficial for students with dyslexia. This study contained a lengthy survey segment, but survey data were presented with all participants grouped together, the majority of whom did not have a learning disability. In its qualitative segment, participant data were presented individually, with students with dyslexia clearly identified. Eight people participated in a single module of an introduction to communications course, three of whom had dyslexia and five who were typically developing. They were observed while participating, both inside and outside the virtual environment, and they were also interviewed twice about their experiences, learning history and preferences. Participants both with and without dyslexia liked the course, and most of them felt it had met their needs. However, all of them experienced technical difficulties with Second Life, and one participant (without dyslexia) was unable to use it.

4.2.5. Studies: Specific assistive supports

Two studies investigated specific kinds of AT: Roberts & Stodden (2005) , Chiang & Liu (2011) . Roberts (2003) ’s thesis, later published as an article in Roberts & Stodden (2005) examined the use of a voice recognition system, Dragon Naturally Speaking™ (Nuance), in a sample of 15 students with LD at different postsecondary institutions. Dragon™ is the current market leader in voice recognition. Data were gathered using a variety of methods including interviews, focus groups, participant observation, and writing samples. Participants received training in the use of the software as well as ongoing support. The main research questions involved the continued use of the system, and the variables influencing it. Only two students continued to use Dragon after their training was completed. Important variables related to continued use were ”time, access to a personal computer, ease of use, personal issues, use of standard English, the specific limitations associated with a person’s disability, whether or not the subjects had any other compensatory strategies in place, and the acquisition of skills necessary to use the software” (p. iv).

Chiang & Liu (2011) sampled Taiwanese high school students having both LD and dyslexia diagnoses, who were studying English as a second language. During their English classes, they had the opportunity to use Kurzweil 3000™ (Kurzweil Educational Systems) text-to-speech software, and after two weeks, they were interviewed about their experience. They liked that the software could be customized to their own preferences (for example, in reading speed) and that they could ”use this software to read repetitiously” (p. 202). Several participants reported that they also used Chinese-English electronic dictionaries, and they all preferred the Kurzweil 3000 to their electronic dictionary. However, they also complained that it was very hard to use it to look up dictionary definitions, as the software did not have Chinese-English vocabulary definitions, unlike their preexistent devices. Students also felt that the software helped them both with spelling and with pronunciation, but they did not report an improvement of their general academic performance. The authors speculated this was probably due to the short length of the study. Readers should note that the authors also had a separate publication with quantitative results from this intervention, included in our group designs section Chiang et al. (2012) .

4.2.6. Quality assessment

The quality of the qualitative studies summarized above was mixed due to multiple factors. Participant recruitment and follow-up was a notable issue. For instance, in Graves et al. (2011) , even though many more students participated in a larger implementation study, only about 25% of them were available for interview; and in Dziorny (2012) , the author could locate only three participants with dyslexia, and they were grouped together with typically developing students (for this reason we could not use the survey segment from this publication, only the qualitative segment). This difficulty in recruitment resulted in a lack of data saturation – participants each had markedly different reactions, and we could not estimate whether additional participants would follow similar patterns or provide altogether different data. Participant attrition was likewise problematic. For example, in Roberts & Stodden (2005) , acquiring quantitative data of improvement proved largely unsuccessful due to difficulties in reaching participants for follow-up.

Triangulation was sometimes lacking due to reasons not related to recruitment: for example in Graves et al. (2011) , there was no independent assessment of whether students’ performance improved, or whether there was a relationship between positive experiences with the technology and performance improvement. Further, data were on occasion selectively reported: for example, Anderson-Inman et al. (1999) described ”stories of successful transition from secondary to postsecondary education” in the course of a complex, federally funded intervention project. Unsuccessful stories were not mentioned.

Theoretical grounding was also a relative weakness. Sometimes the reason for performing a qualitative study was unclear in itself: intervention projects like Woodfine et al. (2008) or Dziorny (2012) seemed to lend themselves more to quantitative research. In some cases – for instance, Woodfine et al. (2008) – the conclusions sections were brief and non-analytical.

Despite these deficiencies, we found valuable data in the qualitative literature, often focusing on aspects of the academic experience of students with LD that went unreported elsewhere. An important conclusion we could draw was whenever triangulation was performed, results almost always supported the students’ own claims. This was true even when students offered strongly negative opinions of official support. For example, in Bradshaw (2001) , one of the participants claimed that he did not benefit from most AT at the university: the institution either did not have current computer software or his counselor did not know how to use it. The counselor independently confirmed this.

4.2.7. Summary

Qualitative researchers often explicitly claimed that they had no intent to provide generalizable data (for example, Dziorny (2012) ) or stated that they would have liked to provide generalizable data, but they were not able to do so (for example Dodge (2012) ). Yet we saw many commonalities in the papers we described.

More recent studies revealed that higher education institutions provided more AT supports than older studies – this could be expected. However, even in recent studies, it was a consistent theme that students with LD did not necessarily appreciate or utilize AT supports provided by the institution. Some students were heavy users of AT, but others found it a hassle. Those who were AT users often acquired and set up AT independently of their institution.

Many participants tried but subsequently abandoned AT. In some cases, AT became unnecessary with practice of the original skill it supported. Even on the post-secondary level, we saw mentions of reading remediation being beneficial, somewhat contrary to the view that these kinds of interventions are no longer useful with adult students and AT use should be preferred ( Reis et al., 2000 ). In other cases, AT was abandoned because of frustration. Technical difficulties were reported with every device or software, and different participants had different difficulties with the same intervention. Often, university professionals were unable to provide effective technical support for AT. Negative emotions associated with AT were mostly connected to these technical issues, but some students also felt stigmatized because of their AT use.

Even when participants enjoyed using their AT devices, it was hard to tell from these studies whether this resulted in a performance improvement; in any case, the improvement in subjective well-being was marked (also in line with the small amount of quantitative survey data relevant to this question). AT use also had advantages beyond academic support, for example by helping students advocate for themselves.

We can conclude that, based on extensive and varied qualitative data, some students clearly benefited from AT use, but AT use should be custom-tailored to the individual, and technical support should be provided. Even when technical support was available, some people failed to respond to AT interventions. Negative emotions about AT were expressed by many students, even students who benefited from AT. Finally, some kinds of AT could be harmful to students with LD; most notably, synchronous online course components, as these required rapid reading and writing. In contrast, asynchronous online course components enabled students to work through the material at their own pace and were perceived by students to be more useful.

4.3. Group-design and single-subject intervention studies 4.3.1. Inclusion

After reading the abstracts, 45 papers were included as either single-subject (n = 9) or group-design (n = 36) interventions. Upon reading the full text of articles, 7 single-subject publications and 31 group-design publications met criteria for inclusion. These were then grouped by topic on the basis of which AT device/s they used for intervention. Six groups were produced:

• Text-to-speech, including complex computer-based interventions with a primary text-to-speech component – 11 publications (13 studies)
• Speech-to-text – 6 publications (6 studies)
• Word processing, including spell or grammar check – 5 publications (5 studies)
• Multimedia and hypertext – 7 publications (8 studies)
• Smart pens – 4 publications (4 studies)
• Other computer-based interventions – 4 publications (4 studies)

We used these groups to present and analyze our data. Most publications contained only a single study. For the exceptions, publication quality ratings refer to the entire paper, not to individual studies.

Despite categorization by AT type, some categories were too heterogeneous to support a metaanalysis. Table 3 shows the analyses we applied by topic. We calculated effect sizes in all cases where sufficient data were available from the original publications, and where the outcome variable was suitable. (In the case of single-subject studies, we used Beeson & Robey (2006) where the design permitted it, with adjustment for bias as in the group-design Hedges’ g ; we note where we departed from this.)

Analyses performed on topic groups

For each study, we will proceed to report the major significant differences found, with p -values, and the size of said differences in the format provided in the publication itself (for example, raw means difference or percentage change). We provide standardized effect sizes (Hedges’ g ) for our chosen outcome variable, calculated with the software package Comprehensive Meta-Analysis, version 3.

Where multiple comparisons or contrasts existed, we used only an intervention / no intervention measure, and where there were both pre- and post- intervention and no intervention measures, we used the post-measures. We did not use data from typically developing controls or controls with other disabilities or remedial education. Where there were different kinds of interventions (for example, a word processor with various features turned on/off), we used a contrast between no intervention and maximal intervention.

In all cases we used random effects models for metaanalyses due to heterogeneity in the data. Because the number of studies in each subgroup was small, we opted not to report funnel plots for publication bias.

4.3.2. Text-to-speech (speech synthesis, computer-assisted reading, screen reading)

Text-to-speech (TTS) interventions use software or, in early cases, combined software/hardware devices to provide synthesized speech. The computer reads out text to the user with reading difficulties.

Table 4 provides demographic and other background information on the studies, while Table 5 features methodological data and the results of the quality assessment.

Text-to-speech publications – demographic and other background information

Text-to-speech publications – design and quality

We chose reading comprehension as our outcome variable. When some academic examination or testing score of the material was provided instead of a direct reading comprehension metric, we took that also as a measure of reading comprehension.

The large number of studies is deceptive: out of the 13 unique studies, several reported no statistics useful for calculation of effect sizes – Olson et al. (1986) , Studies 3–4 of Elkind et al. (1996) , Lange et al. (2006) , Lewis (1998b) and Floyd & Judge (2012) . A further study (Study 2 of ( Elkind et al., 1996 )) was an expansion of a previous one. In the remaining studies, outcome variables were often hard to compare – for example, Fälth & Svensson (2015) did not provide a reading comprehension metric per se, only a word boundary detection metric (though with a moderately positive effect size of g = 0.716).

Selective presentation of data was also an issue: for instance, Olson et al. (1986) only presented data from 9 out of 26 participants, as other participants either experienced a floor or ceiling effect, and Lewis (1998b) and Lange et al. (2006) did not provide standard deviations or enough data to calculate them.

Table 6 shows the forest plot with all studies we could include. The overall effect was small (Hedges’ g ) of 0.445 ( p = 0.06, just over the common threshold of significance), and this effect further decreases if we exclude the one large effect, Chiang et al. (2012) as an outlier (0.167; p = 0.094) ( Table 7 ). With the removal of that study, the null effect falls into the 95% confidence interval.

Forest plot and metaanalysis of text-to-speech studies

Results with one study excluded

At the group level, text-to-speech interventions have small effects. That said, there are patterns within groups that suggest benefits for some individuals. Therefore, more data should be gathered. To ensure maximum comparability with previous results, a reading comprehension measure should be included among the set of outcome measures, and the size of improvements should be correlated with unassisted / baseline performance.

Some studies also investigated which variables can lead to intervention success. Elkind et al. (1996) and Higgins & Raskind (1997) reported large negative correlations where the lower the initial score, the more the improvement from TTS. However, Calhoon et al. (2000) reported higher initial reading levels associated with more improvement. There can also be an age-dependent effect. Lewis (1998b) found that in secondary school students, as opposed to primary school students, performance decreased in response to intervention. These issues can all potentially produce the relatively small effect size in our metaanalysis, because the interactions might obscure the main effect.

A large amount of papers in this category featured very old papers and/or papers with low quality ratings; this happened primarily because for historical reasons, text-to-speech was one of the first forms of AT applied in LD. The question arises whether we are able to draw any conclusion from this data set. TTS systems have undergone vast improvement in the last two decades, and this might also mean present-day TTS interventions could be more effective. This is very tentatively supported by the fact that the most recent cases where we were able to calculate effect sizes from papers were also higher ( Chiang et al., 2012 ; Fälth & Svensson, 2015 ) – though one needs to note that Chiang et al. (2012) found a null effect on one of the three metrics the authors used.

Given the small positive effect, we recommend more causal experimentation, with a specific focus on performance interactions. We hypothesize that older learners and/or more advanced readers are less likely to benefit from TTS.

4.3.3. Speech-to-text (speech recognition, computer-aided dictation)

Speech-to-text interventions use software to recognize the user’s voice and translate it into computer commands. Speech-to-text is used as AT by people with various disabilities, including motor conditions which prevent users from typing, but this kind of software can also facilitate writing and computer use in LDs. All studies in this category used a version of the Dragon™ voice recognition software.

Table 8 provides demographic and other background information on the studies, while Table 9 features methodological data and the results of the quality assessment.

Speech-to-text publications – demographic and other background information

Speech-to-text publications – design and quality

Overall, the speech-to-text studies were surprisingly heterogenous considering they used the same software. Designs were different and outcome variables were not directly comparable. Even total error rate, a very straightforward measure, was only reported by two studies. Therefore, we opted not to include a forest plot or to conduct a meta-analysis.

One further issue with the studies reviewed in this section is that Dragon underwent great improvement in the time span of these studies (1995–2005) and since then ( Huang et al., 2014 ). Many of the studies contained remarks about how the technology is often unreliable and does not recognize voices of certain people. The field of machine learning has seen a great deal of growth in the past decade, so we have no way of knowing if we can extrapolate from any of the earlier technological failures and shortcomings to present-day speech recognition technology. In any case, the studies we located seemed to have positive outcomes, but research was not cumulative, and two studies out of a total of five did not present enough information for us to evaluate. Still, we can a fortiori assume that if a technology was successful in its previous iterations, it is likely to be at least as much, and probably more successful in current, more improved iterations. Thus we cautiously recommend both the use of STT to assist in learning, and more investigation as it is likely to lead to further positive results.

4.3.4. Word processing, including spell and grammar check

The built-in features of modern word processing software like spelling or grammar checks, or composition aids, are usually designed for typically developing users. However, many people with LDs report that they use these software features extensively and find them beneficial. Interventions in this category examine the effect of word processing aids on the learning outcomes of the LD population. Table 10 provides demographic and other background information on the studies, while Table 11 features methodological data and the results of the quality assessment.

Word processing publications – demographic and other background information

Word processing publications – design and quality

There are many possible outcome measures in this avenue of research, which adds heterogeneity. Four out of the five studies reported some kind of final error rate measure, and three out of the five reported some kind of quality measure. Quality measures varied, so we opted to use error rate changes in response to interventions as our outcome variable. We produced effect sizes where possible; in the single subject design study McNaughton et al. (1997) , for one student (Case 2), there was only one post-intervention measurement, and thus we could not produce a standard deviation to calculate an effect size.

Even though there were only a handful of studies, we still decided to run a metaanalysis, as the beneficial effects were large. As shown in Figure 12, the overall effect is a large −1.626 (Hedges’ g ), with a p -value of 0.002. All studies in the plot show a negative effect because the error rate decreases; meaning the intervention was successful.

These interventions have been successful, but we must note their limitations regardless. We did not manage to locate many studies, and many that we did find were dated, a worry given that word processing technology has changed rapidly in the past decades. Still, we can safely assume that if this technology was highly effective in the past, its effectiveness is unlikely to decrease.

4.3.5. Multimedia and hypertext

Multimedia and hypertext interventions are more heterogenous than either speech-to-text or text-to-speech. They usually use some form of multimedia, such as illustrated hypertext or audiovisual presentations, to facilitate the learning of people with LDs. 7 papers in this category presented information about 8 distinct studies. Table 13 provides demographic and other background information on the studies, while Table 14 features methodological data and the results of the quality assessment.

Multimedia publications – demographic and other background information

Multimedia publications – design and quality

Interventions took disparate forms in this category. Quite a few studies used multimedia presentations, but even these were designed differently. In the Higgins studies ( Higgins & Boone, 1990 ; Higgins et al., 1996 ), the multimedia presentations were interactive ”hypertext study guides”, but in the Kennedy studies ( Kennedy et al., 2014 , 2015 ), they were non-interactive ”content acquisition podcasts”; essentially slide-based presentations. But these studies were otherwise relatively homogenous: they all included high school students, outcome variables were similar to or identical with preexistent school testing, and even the subject material was similar (history lessons).

Two interventions by another research group, Satsangi & Bouck (2015) and Satsangi et al. (2016) took an entirely different approach. Both studies examined whether students with a mathematics disability could benefit from learning about geometry using virtual, computer-based manipulatives - both compared to no intervention and to physical manipulatives (the physical manipulatives were slightly more effective.) A further, unique intervention ( Straub & Vasquez III, 2015 ) examined whether synchronous online collaborative learning could help students with LD in learning writing strategies.

Multiple publications also investigated the performance of remedial education students as distinct both from students with LD and typical development: the two studies reported in Higgins & Boone (1990) , and Higgins et al. (1996) . We did not consider data from remedial education students.

Although these studies were thematically similar, their interventions were too different to include in one meta-analysis. Therefore we only opted to produce a table of effect sizes, as seen in Figure 15. Effects tended to be strongly positive where they were possible to calculate; in some case we could not calculate them, usually due to too few or no outcome measurements in a single-subject trial. Some of the very large positive effects were due to participants not being able to perform the experimental task at all before the intervention, and able to perform perfectly after the intervention.

The only article we could not include in the table of effect sizes was Kennedy et al. (2015) , because it did not feature a no-intervention condition, therefore we describe it separately. This study compared ”content acquisition podcasts” (similar to Powerpoint presentations) produced with different methods. There were four kinds of podcasts: explicit instruction only (with adherence to Mayer’s Cognitive Theory of Multimedia Learning), keyword mnemonics only, explicit + keyword, and explicit instruction (with no adherence to Mayer’s Cognitive Theory of Multimedia Learning). Effect sizes were provided with Cohen’s d . In a 4 × 2 split-plot, fixed-factor repeated measures ANOVA, group or time effects were not significant, but the group × time interaction was significant at p < 0.001. Post hoc pairwise comparisons showed that students with LD in the explicit + keyword group had significantly higher scores than students with LD in the non-Mayer group, even after Bonferroni correction (Cohen’s d = 1.97). Students with LD in the explicit + keyword group scored higher than students with LD in the explicit only and keyword only groups, but these results did not reach significance after correction (Cohen’s d = 1.09 and 1.40, respectively.)

4.3.6. Smart pens

Smart pens are handheld devices with built-in scanning and character recognition features. Users can scan individual words or lines of text with the pen, and the device can provide speech synthesis, dictionary definitions, translations or syllabification, depending on model and make. Smart pens are mostly used as AT for people with dyslexia, or for typically developing learners of a foreign language.

Table 16 provides demographic and other background information on the publications, and Table 17 features methodological information and the results of the quality assessment.

Smart pen publications – demographic and other background information

Smart pen publications – design and quality

Most studies of smart pen interventions included some kind of reading comprehension measure as their outcome variable, with the exception of Belson et al. (2013) focusing on the quality of notes that the students produced. Therefore we opted for reading comprehension as our outcome variable. Two studies used group designs: Higgins & Raskind (2005) , Johnson (2008) ; and one study used a single-subject design: Schmitt et al. (2012) . As this latter study had no A-B-A phases, we calculated effect sizes not using the method in Beeson & Robey (2006) , but rather by recording the individual data points from the graph, and producing their means and standard deviations. The effect sizes we gained this way were similar to those provided by the authors.

Belson et al. (2013) used a different outcome measure than the above publications: the quality of notes taken by high school students with LD. The content and selectivity of notes significantly improved in response to using the Livescribe Echo pen combined with notetaking instruction, with a difference of 0.56 and 0.64 on a scale of 1–5 ( p = 0.0499 and 0.0209, respectively). Unfortunately, notes were rated by non-blinded observers.

Figure 18 shows our forest plot and meta-analysis. The combined effect size (Hedges’ g ) of the studies was 0.449, quite small, but significant at p = 0.029.

In this subset of studies, more research is likewise warranted, but results so far allow us to be optimistic. Again not everyone responds to this kind of intervention favorably, but the overall effect is positive.

4.3.7. Other computer-based interventions

These interventions are too diverse to divide into further subgroups. Due to the heterogeneity of the research, no meaningful quantitative summation is possible; therefore we will discuss each study separately. Table 19 presents demographic and other background information of these studies, while Table 20 provides information on study design and quality.

Miscellaneous publications – demographic and other background information

Miscellaneous publications – design and quality

Okolo et al. (1990) examined which formats of keyboarding instruction are most helpful for students with LD. Good keyboarding skills are a prerequisite of using many of the above forms of AT, but students with LD can also struggle with learning to type. Two keyboard teaching interventions were used in the study: a more conventional drill, and game-based learning. Students’ typing speed increased in both conditions (from 5.44 to 8.25 wpm in the drill, and from 6.45 to 8.60 in the game, at p < 0.001), although their typing accuracy did not change significantly. Their attitudes toward computers also became more positive. There was no significant performance difference between the two interventions. However, students in the game-based learning condition completed fewer training sessions on their own after the intervention had concluded.

Anderson et al. (1996) taught complex laptop-based study strategies to high school students with LD. Students were sorted into three post hoc groups based on their technology adoption: power users, prompted users and reluctant users - this related both to the frequency of their technology use and their attitudes toward it. Using one-way ANOVA, lower IQ scores on verbal, performance and full-scale measures were associated with lower levels of adoption at p < 0.001. There was also a difference in some of the recorded measures of literacy and skill test scores, usually disfavoring reluctant users, but no difference in others. The post hoc grouping seemed less suited to interpreting the results than for example, correlation-based reporting would have been, so these data were not straightforward to evaluate.

Berninger et al. (2015) investigated tablet-based writing instruction for students with LD. The intervention used researcher-designed interventions and measured outcomes with a variety of standardized tests like the Clinical Evaluation of Language Fundamentals-4 conducted pre- and post-intervention. Effect sizes (Cohen’s f 2 ) were reported ranging from 0.17 to 0.40 (p. 9), in the medium to large range. An analysis of individual students by their specific LD also showed that most, but not all of them responded to instruction aimed at their specific areas of concern. These results were quite favorable, but the intervention itself was quite sparsely described, and only named in figure captions, as ”HAWK (Help Assistance for Writing Knowledge)” (p. 6–7).

Lin & Lin (2016) was a study with an epidemiological approach. All grade 10 students taking the Ontario Secondary School Literacy Test in a year (n = 208,289) were subdivided post hoc into groups based on whether they had learning disabilities, ”emotional or behavioral exceptionalities” or ”multiple exceptionalities”, or none of these conditions. Then the researchers assessed whether accommodations they received on the test resulted in better performance. Many of the accommodations were outside our scope (e.g., extended time), but both ”computer” and ”assistive technology” accommodations were listed. (The further ”scribe” category combined both STT software and human scribes, so we could not make use of it.) As the precise nature of these accommodations was not further explained, we could not sort this study into our above categories. Computer-based and other AT accommodations were helpful in LD, with the accommodation combinations most likely to be positively related to performance being computer + setting-based accommodations (e.g., quiet room) and computer + extended time. Overall the accommodation combination with the highest rate of success was also the most resource-intensive: scribe + setting-based, followed by the two computer-based accommodations we mentioned. The researchers reported multiple methods of calculating odds ratios and also used the data for a methodological discussion on effect sizes.

4.4. Summary of the experimental and quasi-experimental intervention studies

We located studies of various kinds of AT. Whereas to our knowledge, there has been no recent research on how frequently students with LD use these kinds of AT, we found experimental or quasi-experimental studies related to five different types of AT: text-to-speech systems, speech-to-text systems, word processing interventions, multimedia interventions, smart pens; and some miscellaneous studies using computers and tablets that did not fit into these five categories.

The numerous text-to-speech studies demonstrated a small overall effect, likely due not to the lack of effectiveness of the technology itself, but because of an interaction with baseline reading ability. The fact that some students found this intervention helpful, but for others it was not helpful or even detrimental, obscured the effect in positive responders. Some studies found that students with lower ability ( Elkind et al., 1996 ; Higgins & Raskind, 1997 ) or younger students ( Lewis, 1998b ) benefited more from this technology, but there was also a contrary result from Calhoon et al. (2000) where better initial performance also predicted better response to the intervention. We would need more information on the nature of this interaction to make a recommendation. Students with LD might try this accommodation, but the students and the university disability services personnel who work with them are likely to find it helpful for some but not others.

Speech-to-text studies were fewer in number and overall quite heterogenous, despite using the same software, Dragon. There was not enough information for us to produce a meta-analysis. Studies did tend to present positive effects, so we would recommend more investigation of this type of AT, and making it available to students.

Word processing interventions focused on different features of word processors (for example, spell check or grammar check). Word processing seemed to lead to better writing outcomes, often with large effect sizes. We located four studies using a comparable outcome metric, error rates, and found a large positive effect in our meta-analysis. But this result unfortunately has limited usefulness: first, because the meta-analysis only included a small number of studies; and second, because the research was conducted in the 1990s. Since then, word processing aids have become extremely commonplace in higher education, to the extent that they might not be seen as AT accommodations anymore. Nevertheless, these supports are clearly helpful and, thus, should be considered when planning accommodations for students with LD. They can also be targets for further study, and application development, to determine exactly which aspects are helpful.

We further recommend that modern word processors with all their features enabled serve as a control condition in future studies, as a baseline against which other AT interventions are measured – similar to the ”treatment gold standard” in medical intervention trials. Lange et al. (2006) was the only study we could locate that used a similar control.

Multimedia and hypertext interventions had a strong positive effect, across all subtypes of these very varied interventions. These presentations of subject material seemed to lead to better learning. These types of presentations are also becoming common in higher education, and making headway in secondary school settings, so what needs to be researched is not whether multimedia is helpful at all, as in practice, its use is a given. Rather, what we need to know is which kinds of presentations we should use. One recent study, Kennedy et al. (2015) , did compare different kinds of multimedia presentations, and another ( Satsangi et al., 2016 ) compared virtual and physical presentations of the same objects.

Smart pens seemed to have a small, but significant effect on reading comprehension. As in other computer-reading interventions (text-to-speech above), some students responded unfavorably to this kind of AT, but the overall effect was positive. In addition to more research, we recommend that smart pens be added to the repertoire of possible secondary and postsecondary education accommodations for LD.

Even though our meta-analyses could be best described as tentative due to the small number of studies, we believe they do offer one additional considerable benefit. By assembling comparable studies and choosing outcome variables that are most frequent in the literature, we provided a framework for future studies. If outcome variables already present in previous studies are chosen for new projects, the results will fit into preexistent data, enabling researchers to produce cumulative metaanalyses at later points.

5. Conclusions

Our study was the first to formally survey and evaluate AT interventions for adolescents and adults with LD. We located a sizable body of research that has not been previously evaluated in this manner, and could draw both quantitative and qualitative conclusions, in addition to providing future directions.

Word processing-based AT interventions had a large positive effect on writing error rates. Text-to-speech systems had a small positive effect on reading comprehension, with some evidence that an interaction with baseline reading ability was obscuring a larger effect. Smart pens also had a small positive effect. The use of speech-to-text systems led generally to positive outcomes, but outcome variables were too different for meta-analysis. In the case of multimedia / hypertext interventions, effects were very strongly positive, but the interventions themselves were too disparate to perform a meta-analysis.

Table 21 shows a brief summary of these results. Where we could not perform a meta-analysis, we provide only the range. (Note: speech to text effect sizes are reported by the original authors in various formats; as we could not find comparable outcome variables in that case, we did not standardize effect sizes.)

Summary of effects

Convergent results from both quantitative and qualitative data demonstrated that AT supports can be effective, but they need to be customized to the person. Some forms of AT could be unhelpful or harmful for some participants; for example, text-to-speech systems tended to hinder students who had relatively high baseline reading ability. Negative emotions were predominantly connected to frustration with the technical aspects of specific AT solutions, though a minority of participants also reported social stigmatization. Often, students did not receive sufficient support from their educational disability services personnel, especially with the technical aspects of AT. Many students who reported success with AT used systems they had set up at their own time and cost.

We can conclude that AT use is helpful to adolescents and adults with LD in multiple ways: it can produce better quantitative educational outcomes, and it can also lead to increased satisfaction with learning and improved quality of life. But to reach these goals, certain conditions need to be met. The systems need to be customized to the individual, and educational professionals should not expect that one-size-fits-all solutions will be suitable for everyone in the LD population. The largest drawbacks of AT are abandonment due to technical issues, and the lack of suitability of specific interventions to specific students. These drawbacks can both be mitigated with increased institutional support, and we also found evidence that support has indeed been increasing over the past decades.

6. Limitations

A systematic review can only draw conclusions from the available publications. Many assistive devices or methods had little to no effectiveness testing in the preexistent literature-The interventions were quite disparate even when the AT systems tested were similar, and effectiveness was often measured using metrics that were not comparable. This meant that we were not able to include several of the available studies in the meta-analyses.

Overall, research was often not cumulative: authors either did not refer to other scholarly efforts, or deliberately chose different paradigms and designs for their own projects. Many of the articles were published in low or no impact factor journals and venues with little visibility. Several relevant papers were published in defunct journals and/or journals without DOI; thus it is not surprising that other scholars were less aware of them.

It is imperative to produce quantitative summations of AT effectiveness in adolescent and adult samples, as an evidence-based approach can be used to argue for higher-quality supports in secondary and postsecondary education, and also in the workplace. Unfortunately, at present, data primarily provide evidence in favor of AT supports that have already been widely acknowledged by the public to be effective (for example, word processing).

An especially large gap remains related to individualized supports. Although it is apparent that some people with LD do not benefit from the same kinds of AT that are effective for others, we know little about which factors influence AT effectiveness. Both qualitative and quantitative studies have tried to address this: for instance, Elkind et al. (1996) , Higgins & Raskind (1997) , Lewis (1998b) , Calhoon et al. (2000) , Roberts & Stodden (2005) , but so far there have been few of them, and results are sometimes conflicting.

Another gap relates to the development of new technologies. Many kinds of AT currently in use have been in development for decades, and thus it is relatively easy to generalize from earlier studies to current technology - especially considering that the effects we found tended to be positive overall. If even the more rudimentary forms of AT tested in older articles were usually found to be effective, then all the more so for more advanced forms of the same technology. For instance, speech synthesis in TTS systems has progressed from monotoneous, robotic computer voices to natural-like speech in the present day.

By contrast, there are very few technologies that are currently in use and are entirely new. Tablets are probably the devices that differ the most from AT widely available a decade or more ago. Tablets are handheld computers with a touch interface. Research about tablets currently mostly focuses on typical development (for a systematic review, see Haßler et al. (2016) with 23 studies) or other conditions like autism ( Lorah et al. (2015) with 17 studies). We managed to locate very few studies using tablet apps for adolescents and adults with LD, and sometimes even those apps were used for purposes already described in the previous literature - like Fälth & Svensson (2015) featuring a text-to-speech app.

It is unknown how likely are students with LD to use tablets for academic purposes outside a research context - in our mixed methods study in preparation, very few young adult college students with language-related LD reported using tablets for studying, while most of them reported using laptops. Mobile phones were mostly mentioned in the context of scheduling (Google Calendar, etc.) and only infrequently as a study aid (e.g., as a spell checker or dictionary).

Unfortunately, tablets and other mobile devices running apps have not yet had substantive effectiveness testing in our age and diagnostic groups. Many tablet studies with atypically developing groups focus on children, and LD research in general has a similar age bias ( Conti-Ramsden & Durkin, 2016 ). When these devices run software that fulfils a similar function as preexistent AT software, it is not a stretch to assume that effectiveness can be similar, but it is important to know that this has seldom been empirically tested.

It is a possible major criticism that the data reported above often stem from older studies. Should earlier studies be incorporated into meta-analyses when technology is rapidly developing?

Including older data is common in meta-analyses. Patsopoulos & Ioannidis (2009) empirically examined the issue of older studies in meta-analyses of healthcare interventions. They gathered a random Cochrane sample of 157 meta-analyses, and found that only a quarter of the studies included in these meta-analyses were published in the last five years of the literature search period. Only 8% of all reviews discussed the age of studies as a methodological concern. In 82.1% of studies, post hoc excluding studies older than 10 years did not change the p-value of the effect; in 10.1% of studies, the effect lost significance and in 7.8%, it gained significance. The authors could not compare effect size changes between older and newer studies, because usually the more recent publications were so few that such a comparison was underpowered. According to Dechartres et al. (2016) ’s meta-meta-analysis of which variables affect meta-analysis treatment effect estimates, study age was one of the very few variables which did not seem to influence results.

Patsopoulos & Ioannidis (2009) concluded that ”The amount of data, regardless of year of publication, is limited for most health care topics […] and we do not have the luxury of discarding trials simply because of their calendar year.” They also pointed out that excluding older studies can be a form of selective reporting that introduces positive bias. Further, they tentatively noted that when a comparison is possible, older studies tend to have slightly larger effect sizes. In our meta-analyses, technology has improved considerably and therefore we would expect newer studies to have larger effect sizes, so these biases are likely to cancel each other out.

Therefore we opted not to exclude older data, with the caveat that more research would definitely be welcome. By gathering all available studies, our review hopefully also serves to point out the gaps in literature and inspire more AT intervention research.

Despite these limitations, we successfully conducted meta-analyses and draw conclusions from both quantitative and qualitative studies. The quality of these studies was also similar to that of studies on other types of interventions ( Justice et al., 2008 ).

7. Future directions

This summary of the literature on the effectiveness of AT interventions for adolescents and adults with LD reveals a number of gaps. Methodologically, there is little quantitative survey-based research, research that could document type, frequency, purpose, and satisfaction with AT use. Outcome measures should be carefully considered in all types of research designs. The more we, as a field, can select common outcomes, the more we will enable meta-analytic conclusions. Gaps in the types of questions we ask are also worth considering. For example, questions concerning the effectiveness of tablets or contemporary word processing are rare, despite the ubiquity of these products in high school and post-secondary classrooms. Finally, it would be useful to ask how the utility of AT accommodations vary depending on the purpose of the intervention: to compensate for a disability, to scaffold coursework, or to aid skill development. This issue bears further investigation.

Forest plot of error rate effects

Testing score changes in response to multimedia interventions

Forest plot and metaanalysis for smart pen studies

• ✧ Assistive technology can be useful for adolescents and adults with LD
• ✧ Word processing, multimedia and hypertext interventions were the most effective
• ✧ Speech-to-text interventions had a small effect
• ✧ Smart pens and text-to-speech systems had mixed results
• ✧ Interactions with baseline ability or age can obscure intervention success

Acknowledgments

This research was supported by the University of Iowa Presidential Graduate Research Fellowship awarded to Bogi Perelmutter and by NIH grant R01DC011742-02 awarded to Karla K. McGregor.

We acknowledge the help of University of Iowa Libraries, Hardin Library Interlibrary Loan in obtaining many of the articles listed. We also acknowledge the help of Renee Perelmutter and Amanda Van Horne.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflict of interest statement

The authors report no financial or other conflicts of interest.

Ethical adherence

This manuscript does not contain any studies with human participants or animals performed by any of the authors. For this type of study, formal consent is not required.

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Review of substitutive assistive tools and technologies for people with visual impairments: recent advancements and prospects

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• Published: 19 December 2023
• Volume 18 , pages 135–156, ( 2024 )

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• Zahra J. Muhsin   ORCID: orcid.org/0000-0003-3309-1706 1 ,
• Rami Qahwaji 1 ,
• Faruque Ghanchi 2 &
• Majid Al-Taee 1  

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The development of many tools and technologies for people with visual impairment has become a major priority in the field of assistive technology research. However, many of these technology advancements have limitations in terms of the human aspects of the user experience (e.g., usability, learnability, and time to user adaptation) as well as difficulties in translating research prototypes into production. Also, there was no clear distinction between the assistive aids of adults and children, as well as between “partial impairment” and “total blindness”. As a result of these limitations, the produced aids have not gained much popularity and the intended users are still hesitant to utilise them. This paper presents a comprehensive review of substitutive interventions that aid in adapting to vision loss, centred on laboratory research studies to assess user-system interaction and system validation. Depending on the primary cueing feedback signal offered to the user, these technology aids are categorized as visual, haptics, or auditory-based aids. The context of use, cueing feedback signals, and participation of visually impaired people in the evaluation are all considered while discussing these aids. Based on the findings, a set of recommendations is suggested to assist the scientific community in addressing persisting challenges and restrictions faced by both the totally blind and partially sighted people.

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1 Introduction

1.1 background.

Globally, more than 2.2 billion people are living with visual impairment; nearly half of these cases could have been avoided or are being unaddressed [ 1 ]. An estimated 2.28 million persons in the UK are thought to have moderate to severe vision impairment; of these, around 171 thousand individuals were registered as totally blind [ 2 ]. This has a significant negative impact on people’s quality of life (QoL) and imposes a significant economic and financial burden with an estimated annual global cost of $411 billion [ 1 ]. Additionally, delays in a child’s physical, verbal, emotional, social, and cognitive development might have long-term consequences if there is a visual impairment. Adults with vision impairment, on the other hand, have lower rates of labour force involvement and productivity, as well as greater prevalence of anxiety and depressive disorders. Also, elderly people with visual impairment are more likely to fall and break bones, feel lonely, and enter nursing homes sooner [ 1 , 2 , 3 , 4 ].

The main causes of visual impairment are [ 5 ]: uncorrected refractive errors, cataracts, age-related macular degeneration, glaucoma, and diabetic retinopathy [ 1 , 4 ]. Poor diabetes awareness [ 6 ], long-standing uncontrolled diabetes [ 3 ] and higher rates of retinopathy [ 7 ] have all been associated to visual impairment in diabetics. People with diabetes mellitus frequently experience preventable vision damage due to diabetic retinopathy [ 8 ]. Based on the type of damage they cause; visual impairments can be divided into [ 9 , 10 ]: (i) visual acuity deficits and (ii) visual field deficits. Visual acuity deficits can result in a variety of defects such as myopia (near-sightedness), hypermetropia (far-sightedness), and astigmatism. On the other hand, depending on where in the visual loss occurs, the visual field problems can be classified as central and peripheral vision impairments. Visual field defects are more difficult to rehabilitate than visual acuity defects, which can be corrected with a variety of procedures and conventional options such as eyeglasses. This is because the bulk of these deficits originate from brain injuries or eye conditions that cause persistent damage to parts of the visual system [ 11 ].

Today’s technology advances can help visually impaired people (VIP) in different ways such as going to school, finding jobs, and successfully performing daily activities of living. Low-vision rehabilitation aids can be categorised into two groups [ 10 ]: (i) visual field deficit aids and (ii) visual impairment (partial or total impairment) aids. According to several studies, about a quarter of those with low vision suffer from peripheral vision loss, with the remainder from central vision loss [ 12 ]. As a result, people who have central vision loss receive the most rehabilitation and assistive technologies. Scientists and clinicians proposed different aids to help with visual field loss problems. These aids that aimed at compensating the visual field and increasing the individual’s awareness of their surroundings are widely used and their benefits are well documented [ 13 ]. Despite the clinical distinction between low vision (partial impairment) and blindness (total impairment), these terms have been interchangeably used in the literature. In the context of assistive technology, both terms refer to visual impairment that affects the patient’s ability to perform their daily tasks [ 14 ].

The term assistive technology (AT), which is used to describe both hardware and software that enables people with disabilities to utilise technology in a way that improves their quality of life, encompasses a variety of devices, systems, services, and applications. [ 15 ]. Based on this definition, AT were broadly categorised into [ 16 ]: (i) traditional (e.g., eyeglasses, prisms, white canes, occupational therapy, etc.) and (ii) mobile IT-based (e.g., navigation & wayfinding devices, screen & text readers, object & facial recognition, etc.). As VIP have difficulties in utilising devices that are visually demanding, researchers began looking into other possibilities for AT development. Speech recognition [ 17 ], text-to-speech [ 18 ], haptics feedback [ 19 ], multimodal feedback [ 20 , 21 , 22 ], and gesture identification [ 23 ] are examples of non-visual sensory modalities that have been used to make AT more accessible for VIP.

Based on the examined research, it was difficult to find a unified classification approach in the literature that covers all previously reported studies linked to vision impairment rehabilitation aids. In some studies [ 10 ], these aids were categorised into: (i) navigation & wayfinding, (ii) obstacle detection and (iii) scene perception, depending on the main functions they perform. Other studies classified them according to the type of data gathering devices or sensing input [ 24 , 25 , 26 , 27 ], or according to the purposes for which they were intended [ 24 ]. For example in [ 25 ], they were classified them into systems with 3D Sound, map-based systems, visual imagery systems and non-visual data systems.

1.2 Libraries and search strategy

In this article, the relevant libraries and publishers are searched using combinations of relevant keywords including assistive technology/tools, human–computer interaction (HCI), human interface modalities, smartphone aids/apps, substitutive interventions, visually impaired and their cognate variations. The main digital libraries searched are ACM Library, IEEE Xplore, ISI Web of Science, ITU Publications, Biomed Central, BMJ Best Practice, British Standard Online, ProQuest, Pubmed, Springer link, ScienceDirect, Scopus, and Springer link.

A combination of the following keywords is used to search the literature:

“assistive aids” or “assistive technologies” or “assistive devices” or “substitutive assistive interventions”;

“blind” and/or “visually impaired “;

visually impaired“ and/ “human-computer interaction” or “human interface modalities”;

visually impaired“ and/ “smartphone assistive technology” or mobile assistive technology”.

Initially, over 250 papers are collected and after evaluating the relevance of the searched studies with the subjects of interest (by reading their abstracts) and excluding the unsuitable ones, a pool of 180 review and experimental research papers are considered relevant, thus they are used and cited in this study. Next, the following inclusion and exclusion criteria is applied:

The review publication date must be within the last five years (2018–2023), and the experimental research publication date must be within the period from 2010 to 2023.

The research study is disqualified if it does not satisfy one or more of the following requirements:

Pertinent to the topic covered in this article.

A full-length research publication.

Contains laboratory experiments with user-system interaction and system validation.

Written in English.

As a result, 52 research projects are included for further analysis in this review, as well as 18 review papers are explored to identify the topics they covered and their contributions, as shown Table  1 .

1.3 Objective and article structure

The VIP rehabilitation industry is highly diverse and can be seen from a variety of angles. Its focus spans a variety of topics, including medical and adaptability interventions, social and psychological factors, and technological aspects of creating technology-based assistive devices. In this study, the assistive aids are broadly categorized into three main interventions, as opposed to the prior studies: compensatory, restitution, and substitutive. These interventions are outlined briefly as follows:

Compensatory interventions – a group of tools that help VIP compensate for or adapt to their impairments [ 42 ], thus making their daily tasks easier to do. Ong et al. [ 43 , 44 ] reported that considerable developments in eye-search as well as reading-writing tasks were achieved as a result of using online tools such as eye-search [ 45 ] and read-right [ 46 ]. In these interventions, some form of audio-visual stimulation was utilised [ 47 ] to increase saccadic movements [ 48 , 49 ], and improve eye motions into the defective field [ 50 , 51 , 52 ]. Other studies considered specific interventions that occupational therapists offer to patients in their daily lives. For example, Scheiman [ 53 ] described some guidelines for occupational therapists to improve independent movement and training in instrumental daily living skills.

Restitution interventions – a therapeutic concept that suggests damaged neurons in the visual cortex can regenerate from light stimulation. It was thought to have a limited effect on visual rehabilitation for decades [ 42 , 46 ]. Recent research, however, has revealed that the visual field can be enlarged following brain or optic nerve damage with the right use of certain therapies [ 47 ]. This kind of interventions involved a series of treatments in which the defective visual field is repeatedly trained or activated [ 54 ]. The vision restoration therapy (VRT) is one of the most described treatments in the literature. Numerous studies [ 55 , 56 ] found that VRT improved QoL assessments. Other studies [ 54 , 57 ], however, reported that VRT is ineffectual when compared to placebo or no treatment, taking visual field outcomes into account [ 42 ].

Substitutive interventions – a strategy that uses technology assistance and sensory substitution devices for the rehabilitation of the VIP [ 58 ]. The scope of these assistive technologies covers a wide range of applications including: mobility, wayfinding, object and human activity recognition, information access, interaction, education, wearable & handheld devices, and others [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].

In addition to the human-machine interface modalities, the main objective of this article is to review the recent advancements in the substitute tools and technologies, focusing on the developed research with experimental prototypes. Even though the real-world perception is generally multimodal, the use of various technologies that produce independent visual, auditory, and tactile feedback encourages the discussion of these modalities individually. Depending on the primary cueing feedback signal offered to the user as an input, the substitutive tools and technologies are categorized as visual, auditory, or haptics-based aids. The dominant feedback signal will be considered for classifying the multimodal systems that may mix multiple feedback signals. The context of use as well as the participation of VIP in the evaluation are also considered while discussing these aids. In light of the findings, several recommendations are also made to help the scientific community address the persisting challenges and restrictions faced by both totally blind and partially sighted people.

The rest part of this article is structured as follows. The characteristics of the various human-machine interface modalities are detailed in Sect.  2 . Sections  3 – 5 cover the visual, haptics, and auditory-based aids, respectively. The primary cueing feedback in these aids is produced by computer devices other than smartphones. In contrast, Sect.  6 describes additional substitutive aids in which a smartphone’s sensor provides the user with cueing feedback signal(s). In Sect.  7 , the challenges and limitations of the tools and technologies addressed in this article are discussed along with some recommendations for the likely future course of substitute AT.

2 Human-machine interface modalities

Recent technical developments in numerous domains and an increase in processing power have led to the emergence of new human-computer interaction (HCI) modalities. If these modalities are successfully combined into one interface, it might be able to alleviate the HCI bottleneck that has evolved with the development of computing and communication [ 59 ]. Through an interface modality, a user and a computing device can exchange sensory data. [ 32 ]. The interface modality can be unimodal (i.e., employs single sensory channel) or multimodal that relies on multiple channels. The term “multimodal” stands for the simultaneous utilization of different modalities to perform a functionality [ 60 ]. Gathering data from different input modalities ( m i ) and integrating them together into a particular format for further processing is termed as a fusion process. [ 61 ]. In contrast, the process of fission occurs when the resulting command is subsequently carried out in multiple output modalities ( m o ) or devices [ 62 ]. Combining this input (sensing) and output (action) modalities in a single system is called multimodality. A schematic for a multimodal system is shown in Fig.  1 .

Schematic of a multimodal sensing/action system

According numerous studies in [ 32 , 63 ], multimodal systems can offer more flexibility and reliability than that of unimodal systems. Furthermore, it has become increasingly clear that combining different sensing modalities into a multimodal interface can solve the problems related to the interpretation and processing of each type of sensing modality. In order to provide complementary solutions to a task that may be redundant in function but communicates information to the user more effectively, HCI designers and developers have tried to leverage a variety of modalities [ 64 ]. Modalities can be roughly divided into two categories based on how information is perceived: human-computer (input) and computer-human (output) [ 60 ]. The system responds to the user using a range of output modalities while the user interacts with the system utilising the available input modalities [ 65 ]. A multimodal system has the potential to improve accessibility for users by utilising a variety of interface modalities. Additionally, the advantages of combining multimodal inputs and outputs have led to the adoption of multimodal fusion in a number of applications to support user needs [ 66 ]. To enable their interpretation, multimodal systems must be able to recognize a variety of input modalities and combine them in line with temporal and contextual boundaries [ 67 , 68 , 69 ]. Figure  2 shows an example of a multimodal HCI system, in which the two-level flow of modalities (action and perception) provides an overview of how the user interacts with a multimodal system and the numerous activities that are carried out throughout the HCI process. [ 32 ]. These modalities work in a complementary manner to create interfaces that are more adaptable and trustworthy and to improve the perception of “reality.“

Schematic of a multimodal HCI system; adapted from [ 32 ]

In the context of AT, multimodal systems have been proposed and used frequently to communicate with VIP [ 65 , 70 ]. Depending on the contextual usage, audio benefits from rich interaction experiences and aids in the creation of more reliable systems when combined with other modalities while vibrations and other tactile sensations are used in haptic communication. According to Stanton & Spence [ 71 ], the brain continuously prioritizes, filters, and integrates a wide variety of incoming input cues. It then combines these inputs with knowledge and experience from the past to produce a perceptual inference, which is a singular perception of the human body and its surroundings. For example, when presenting feedback of movement, the discrepancy between the visual, proprioceptive, tactile, and audible information may be related to valence and the failure to match expectations (i.e., motor prediction error) [ 72 ].

The most widely used modality in contemporary mainstream technology is visual, followed by audio, and haptics [ 32 ] that has been recently receiving more and more attention [ 73 ]. These main modalities are covered in more detail in the sections that follow. Other modalities that use other senses like taste, heat, and smell are less used in interactive systems [ 74 ], thus they are not covered in this article.

3 Visual-based aids

This section provides a review of research projects that seek to improve or correct the visual perception of the user. Eyeglasses were used in the early attempts to extend field of vision; the idea was increasing the field of view (FoV) to shift a person’s peripheral area of view inward. This would improve the overall functional field [ 12 ]. Peli et al. [ 75 , 76 ] proposed glasses with high-power prism-segments, offering the user a quick peek at the lacking information in the periphery area. In the latter study, different multiplexing prism (MxP) glasses for acquired monocular visual field extension were tested, with each user’s performance being measured perimetrically (for a total of four individuals). Because MxP glasses provide a wider field extension than other devices, the contrast and monocular visual disorientation were trade-offs. Additionally, despite the fact that MxP glasses expanded the visual field to a range of about 20°, users had poorly adapted [ 77 ]. More recently, Jung, et al. [ 78 ] refined their earlier work in [ 76 ] by suggesting a new field expansion aid with MxP glasses to increase pedestrian detection for acquired monocular vision. In three dimensions, a clip-on MxP holder that can be adjusted for a specific user’s facial features was developed. To investigate the effect of MxP field expansion on the identification of an approaching person arriving from different initial bearing angles and courses, virtual reality (VR) walking scenarios were developed. The pedestrian detection rates and response times were evaluated with volunteers who had one eye covered and three visually impaired users. It was reported that the proposed aid provided a field expansion of roughly 25°. Also, the participants with MxP performed better than those without MxP in the pedestrian identification test on their blind field, while their performance on the healthy field was not substantially different.

Other visual-based research studies incorporated smart glasses that have a range of functions to improve how the user gets information and engages with the environment. Digital eyewear models utilized partially transparent digital screens that transfer visual data without obstructing the user’s FoV. A few of the several techniques utilized to accomplish augmented reality (AR) smart glasses include the half-mirror [ 79 , 80 ], retina scanning [ 81 , 82 ], geometric waveguide [ 83 , 84 ], and diffractive waveguide [ 85 , 86 ]. However, these devices always have a sizable volume and weight, thus it is difficult to improve user experiences with AR display systems based on half-mirror and freeform optical prisms. Miniaturization, compactness, and mobility are the current research topics for AR head-mounted display (HMD) systems to meet the expectations of daily use of wearable consumer electronics devices. Diffractive waveguide-based AR-HMD devices have the advantages of being lightweight and compact [ 86 , 87 ]. Because there is only a piece of glass in front of the user’s eyes, these devices can easily have positive wearing experiences. A recent study by Wu, et al. [ 88 ] described a compact gating waveguide AR display system using curved variable-period gating as in-coupler. According to the authors, this technology can significantly lower the thickness of optical systems by 39% when compared to traditional grating waveguide systems with the same collimating system focal length. Additionally, the system’s diagonal FOV may reach 36.6 degrees, and the average in-coupling efficiency can approach 70%. There is, however, no proof that the proposed technology was made into a product or tried out using VIP.

In recent years, a different choice called waveguide holographic optics [ 89 , 90 , 91 ], which digitally overlays text and images in the FoV, was proposed to enhance the user’s experience. Near-eye displays (NEDs) is another technology that provides VR, AR, and mixed reality (MR) [ 92 ]. AR-NEDs can superimpose virtual images onto real scenes to provide a combination of virtual and real scenes. Thus, it is particularly important to develop lightweight NEDs with high optical transmittance and high image fidelity performance. Typical AR-NED solutions include freeform-based prism systems [ 93 , 94 ], hybrid reflective-refractive systems [ 80 , 95 ], and optical waveguides [ 83 , 96 , 97 , 98 , 99 , 100 ]. In order to make AR glasses portable and wearable, Ni et al. [ 101 ] proposed the most recent option, which is based on the optical waveguide technology. In this study, a 2D eye box expansion (2D-EBE) holographic waveguide prototype with an integrated micro projection optical system is created and its functionality was tested experimentally. The presented results look promising with a wide diagonal FOV of 45°. However, there was no mention that the developed prototype was evaluated with visually impaired users.

For those with tunnel vision, Elango and Murugesan [ 102 ] presented an AR system that utilizes cellular neural network and HMD to enhance the knowledge of VIP. The user’s understanding of the environment was enhanced by using a model that has a camera and a microcomputer for image processing to superimpose useful information from the environment on the user’s observation. The developed prototype did, however, have some shortcomings: (i) the CNN architecture needs to be optimised by lowering the resource requirements and making it practical for parallel implementation; and (ii) the presented visuals essentially project what the camera sees into the user’s central view, resulting in the generation of superfluous data, which could be distracting and diminish performance of the user’s healthy eyesight. Also, the was no mention that the system was tested with VIP. Recently, Younis et al. [ 103 ] proposed a context-awareness outdoor navigation aid for people with peripheral vision impairment. The context-awareness concept—which denotes the system’s ability to learn about its surroundings and adjust behaviour accordingly—was used to develop a hazard detection and tracking system. The system utilizes smart glasses that have a tiny camera attached to them to capture and process videos in real-time and provide suitable output warnings depending on pre-established rules and extracted object’s attributes. The glasses can deliver the output warnings without obstructing the user’s normal vision because the display was built inside the transparent lenses. Real-time processing begins with identifying the type of head motion, followed by the detection, tracking, and classification of the risks surrounding the subject. The system then generates a warning notification that is coloured (red, orange, and green) according to the risk level (high, medium, and low, respectively). Based on predetermined danger thresholds, the risk levels—which rely on the speed of the object—are determined. Finally, the generated notification is positioned in front of the central visual field. The initial experiments suggested relatively slow performance due to the low processing capability of the smart glasses utilized in this study, and there was no conclusive evidence that the prototype was tested with VIP.

4 Auditory-based aids

Auditory cues, such as the sound made when a user moves their body or interacts with their environment, can provide important information to affect how people perceive the items they engage with [ 71 ]. Research studies that use auditory cues as the primary feedback to the user in various circumstances, such as navigation, obstacle detection and avoidance, and scene perception, are reviewed in this section, as follows.

To assist totally blind people in their navigation, Yánez [ 104 ] proposed an IoT-based solution based on Blind Guide technology, an artifact that helps blind people navigate both indoors and outdoors scenarios. The developed system was modular, making it adaptable to the needs of the user and compatible with other solutions like the white cane. The blind guide wireless sensor in the forehead can identify impediments at the head level in addition to the white cane’s ability to detect obstacles below waist level. This feature was deemed especially crucial because some sightless people may feel uncomfortable without their white cane. When an obstacle is discovered, a wireless signal is delivered to a central processing unit (a Raspberry Pi board), and the user is provided with a voice feedback message containing the obstacle’s name and its location in relation to them. It was reported that the developed prototype was tested with a group of sightless volunteers from different ages, and the obtained results suggested successful detection of incoming obstacles and the system was received positively by the participants. The operation of this system, however, is restricted to locations with data network access because the obstacles recognition requires internet connectivity.

To help completely blind persons navigate the outdoors, Kammoun et al. [ 105 ] created a prototype called NAVIG to enhance traditional mobility aids (e.g., a white cane) by offering guiding and navigational information via binaural 3D audio sceneries, it takes advantage of the human hearing ability, particularly spatial audition. With the intention of giving the user the knowledge essential to create cognitive maps of the environment, it provides spatial information regarding the trajectory, position, and significant landmarks. There was no evidence that this prototype was evaluated with VIP. Sohl-Dickstein et al. [ 106 ] presented a tool to assist VIP in using ultrasonic echolocation for indoor navigation and object perception. It consists of a headgear that can be worn, stereo microphones with attached artificial pinnae, and an ultrasonic emitter. Ultrasonic pulse echoes were recorded, their frequencies were time-extended to make them human-audible, and they were then played back to the user. It was mentioned that volunteers wearing blindfolds were used to test this prototype. The findings were interpreted to indicate that while some echoic cues delivered by the device are immediately and intuitively apparent to users, perceptual acuity is potentially highly trainable, thus, it could be a helpful aid for VIP.

A wearable stereovision system that can help VIP to avoid obstacles at outdoors settings was proposed by Lin et al. [ 107 ]. It consists of eyeglasses with two tiny cameras on one end for stereo imaging, as well as Field Programmable Gate Arrays (FPGAs) integrated circuits and first-in, first-out buffers on the other end to synchronise and integrate the stereo images. The video captured by the cameras was broadcasted to a mobile device over 3G network. This technology has a notable function in that a healthy sighted person can use the live video feed to give logistical advice to a visually impaired user. However, the running cost of the mobile connectivity was considered as key limitations for the developed prototype. Also, there was no evidence that this prototype was tested with VIP. A light-weight smart glasses with a front camera was proposed by Lan et al. [ 108 ] to assist VIP with the recognition of the public street signs in cities. It was based on a tiny computer offered by Intel (named Edison) as a development system for wearable devices. When Edison receives the video stream from the camera (USB video class) via the UVC (USB video class) module, Opencv routines are called to process and analyze the images. When a public sign is matched, the system provides the user with voice hints through wireless bone conduction headphones. The presented results suggested that the system was successfully implemented but there was no mention that it was tested with VIP.

Mahmud et al. [ 109 ] proposed a navigation aid for totally blind people at indoor and outdoor navigation environments. A microcomputer and ultrasonic device were utilised to identify a variety of obstacles and provide the user with vibration and speech warning feedback. As long as the user is within 70 cm of the obstruction, the feedback remains active. The navigation aid was attached with sonars for sensing obstacles in certain directions, thus the user didn’t need to move the cane around to detect barriers like they would with a regular cane. This prototype was not tested with VIP. Pundlik et al. [ 110 , 111 ] also built a collision-warning system to assist people with peripheral vision impairment in avoiding objects in an indoor navigation environment. It comprises of a portable video camera coupled to a microcomputer to predict approaching collisions based on time to collision rather than proximity. In the case of a prospective collision, a simple audio warning message is provided to the user. According to the authors, 25 visually impaired users successfully completed four consecutive loops both with and without the device. This system was regarded as a significant contribution in the application of computer vision to wearable devices for VIP. However, this prototype had some limitations, including the exclusion of impediments at the floor level, the detection is limited to stationary obstacles, and the absence of information on the projected collision’s direction, which can be crucial for safe navigation.

Fiannaca et al. [ 112 ] developed a wearable technology that helps VIP navigate open environments. Google smart glasses and OpenCV blob detection algorithm were utilised to lead VIP towards doors using the shortest path possible. It explores the environment and provides audio feedback to guide the user towards the desired landmark. According to the authors, the usability and efficacy of two types of auditory feedback (sonification and text-to-speech) for leading a user across an open space to a doorway were examined satisfactorily with eight totally blind individuals using the built prototype. However, the system was only capable of identifying doors as landmarks and was not capable of avoiding hazards in the user’s environment. Tsirmpas et al. [ 113 ] also presented an indoors navigation aid for VIP and elderly individuals based on passive radio frequency identification (RFID) tags. These tags that were placed in various locations across the user’s path. In their study, the authors utilized RFID tags in 40 × 40 cm cells, which is considered a short range and so necessitates the addition of more tags in large environments. Also, there was no mention that the developed prototype was evaluated with VIP or elderly people.

Bai et al. [ 114 ] proposed an additional travel aid for completely blind folks to use indoors. It came with a depth camera to gather depth information from the environment, an ultrasonic rangefinder to determine obstacle distance, an embedded microcontroller board acting as main processing module to perform operations (e.g., depth image processing, data fusion, AR rendering, guiding sound synthesis, etc.), a set of AR glasses to display the visual information, and an earphone to listen to the guiding sound. Algorithms based on multi-sensor fusion and depth images were developed to address the challenges with avoiding translucent, small obstacles.

Three auditory cues can be provided by the guiding sound synthesis module: a stereo tone [ 115 ], recorded instructions, and variable frequency beeps. However, the wayfinding and route-following features of this prototype prevented it from assisting the user in avoiding dynamic barriers or providing location data. The authors therefor created an improved version of this device [ 116 ] a year later to overcome these drawbacks. They addressed user identification, object recognition, navigation, and obstacle avoidance using a mapping technique and a SLAM, or simultaneous localization and mapping, algorithm. The depth and fisheye cameras were utilized to create the virtual blind path and to locate the user utilizing the SLAM algorithm. They were paired with a set of optical see-through (OST) glasses. These glasses contained two loudspeakers and earbuds so the user can hear directions. According to the authors, both prototypes were tested on a group of VIPs who were free to move around on their own.

Li et al. [ 117 ] described a wearable obstacle stereo feedback system to assist VIP in their indoor navigation based on 3D space obstacle detection. Depth information was utilised to detect obstacles in the user’s path and provide the user with auditory feedback notifications. The developed prototype was put to the test on a user who was wearing a blindfold and carrying a laptop in a backpack. The results showed that the approach for detecting barriers and representing their positions by auditory perception was effective. In the developed prototype, however, the detection was limited to stationary obstacles and the user’s movement was not considered. Kang et al. [ 118 , 119 ] and Chae et al. [ 120 ] also investigated an obstacle detection method, called deformable grid (DG). Obstacle avoidance employing the shape change of the DG was then proposed to the VIP navigate both indoors and outdoors. When compared to other equivalent methods, which typically only use two consecutive frames to estimate the risk, this method updates the risk continually, thus it is more resistant to motion tracking mistakes and offers an improved detection rate. A prototype that comprises a camera, WiFi module and Bluetooth earphone were developed and mounted into eyeglasses. The acquired videos are transmitted to a laptop, which performs the necessary computations for the obstacle identification and avoidance, and then provides the user with auditory feedback on the estimated risk of collision. The produced prototype, which was evaluated with blindfold volunteers, had some limitations since the motion tracking with deformable grid sometimes fails when the user approaches non-textured barriers such as a door or a wall.

To help totally blind people with their indoor navigation, Everding et al. [ 121 ] proposed a lightweight wearable device. It made use of two depth cameras that operated on the vision stream before it was sent to a computing stick for depth extraction. The detected events were transformed into virtual spatial sounds using event-based algorithms, and then provided as auditory feedback to the user’s ear via headphones attached to a USB sound adapter. The operating principle of the depth cameras in this project differs from frame-based cameras. Every pixel on the chip runs independently from the others and creates an event each time it detects a change in luminance that exceeds a certain threshold, simulating the visual processing of animal eyes. It was reported that the developed prototype was evaluated with 11 VIP. However, these tests were limited to static subjects and did not account for moving objects which limits their implementation in the real-world scenarios. Tapu et al. [ 122 ] also created a navigation system to assist VIP when navigating in crowded urban scenes. The proposed system utilized an object recognition that has the advantage of recognising both moving and stationary items [ 123 ]. This system employed two convolutional networks to detect and track objects in real-time. After detecting an object, the system classifies it using its type, location, and distance attributes, and then generates a set of acoustic warnings provided to the user through bone conducting headphones. This prototype, however, was not tested in real-life scenarios with VIP.

Yang et al. [ 124 ] described a framework to help VIP in the indoor and outdoor pathfinding tasks. It comprises wearable smart glasses integrated with a waist-worn pathfinder that was composed of RGB-D sensor, Intel RealSense RS410 depth camera, an inertial measurement sensor MPU6050, and a bone-conduction headphone. The bone-conduction headphone that was utilized to transfer sound from the processing units to the user would not prevent the users from hearing environmental sounds. However, neither the detected barriers nor the motion model of the dynamic objects was mentioned in the framework that was offered. Also, it was not validated with real VIP. Aladrén et al. [ 125 ] proposed another system to guide VIP navigation of indoor settings by means of sound commands. The system uses an RGB-D camera, from which they fuse a range of information and colour information to detect obstacle-free paths. It recognizes and categorizes the primary scene structural components, giving the user with clear paths to safely travel across unknown scenarios. It was claimed that the created algorithm had successfully segmented floors in real-life scenarios using a public data set, but there was no mention that this system was tested with VIP.

Mekhalfi et al. [ 126 ] also described an indoor navigation system for totally blind people, which offers a set of navigation features such as obstacle detection and avoidance, multi-object recognition and path planning. The recognition model includes a portable chest-mounted camera that the user employs to capture an inside scene. This captured image is then sent to a microcomputer where the proposed multi-object recognition algorithms are implemented. The output of this algorithm was then translated into an audible voice. According to the author, the developed prototype was tested successfully in an indoor setup, and appropriate voice navigation instructions and warning messages were generated and fed back to the user via earphones. However, the proposed multi-object recognition method is limited by real-time processing constraints of the computing device. Additionally, there was no mention that this system was tested with VIP. Another group of researchers [ 127 ] also built a context-aware indoor navigation system (named ISANA), which was based on the Google Tango AR platform that made it feasible for mobile devices to locate themselves in respect to their surrounding environment without the use of the GPS by relying solely on their hardware and software resources. It incorporated obstacle detection algorithms and semantic map editors. This system was proposed to provide indoor navigation path for VIP. A speech-audio interface employs a priority-based strategy was used to deliver real-time guidance and alert cues, while reducing the cognitive strain on the user. According to the authors, field trials with blindfolded and VIP suggested that the developed prototype was successful at performing context-aware and secure indoor aided navigation. However, this prototype cannot be utilized without a pre-planned user path.

In the context of scene perception, Yang et al. [ 128 , 129 ] proposed a navigational framework that was based on deep neural networks and depth sensory segmentation to assist VIP in both indoor and outdoor settings. A functional prototype was developed in a wearable device to provide efficient semantic comprehension of the surrounding world. The device comprises a portable microcomputer, and a set of smart glasses that integrate a RealSense R200 camera, RGB-D sensor, and bone-conducting earphones. Obstacles such as stairs, sidewalks, water hazards, pedestrians, and cars were all incorporated into a single device that functions in a real-time navigational support framework. According to the authors, the developed prototype was evaluated successfully with six VIP.

5 Haptics-based aids

Haptic/tactile feedback (or haptics, a Greek for “I touch”) is the use of complex vibration patterns and waveforms to communicate information to a user. Haptics utilizes a vibrating component, sometimes referred to as an actuator or a linear resonant actuator. A microcontroller typically decides when and how to vibrate, with a specific haptic driver chip controlling the actuator. Although haptics is not yet well established, they are being used more frequently to give users a better sense of “reality.“ A haptic feedback based on tactile (touch) and kinaesthetic (force) input can therefore be used as an alternative to establish a tactile connection with the user. Understanding how various modalities interact to enhance the user experience is urgently needed given that haptic (vibrotactile) feedback is now a standard feature of consumer VR equipment [ 73 ]. Haptics feedback was also proposed to assist VIP in navigation scenarios dealing with dynamic settings that may require multimodalities. Due of this, it is simple to assume that “adding haptics” will inevitably enhance the user experience [ 73 , 130 ]. The research studies that use haptics as the primary feedback to VIP in various circumstances, such as navigation, obstacle detection and avoidance, and scene perception, are reviewed in this section, as follows.

Prattico et al. [ 131 ] also developed a navigational device to lead totally blind individuals in indoor situations. Four vibrating motors were utilised to alert the user when an impediment was encountered, together with a pair of infrared sensors, an ultrasonic sensor, and a microcontroller were used to make the proposed device. These elements are all included in an easy-to-wear belt. The built prototype was put to the test on a walking distance that had a wall and other barriers. The results suggested that as the user approached the obstacle, the vibration intensity was raised. The created prototype, however, had some drawbacks due to its poor response, noise filtering, and short range of obstacle detection. This prototype was not evaluated with VIP. A similar system was developed by Nada et al. [ 132 ] to aid totally blind people with indoor navigation via haptic feedback. This system was made up of a laser stick with an ultrasonic obstacle detector and a micromotor that vibrated to alert the user when an obstacle was detected. Once an obstruction is detected, the ultrasonic sensor provides a signal to the system, activating the haptic feedback via the user’s stick. According to the authors, this prototype was tested with six VIP volunteers, and the system was able to identify the majority of the obstacles that were put in the user’s way.

Rizvi et al. [ 133 ] proposed a wearable glove to help totally blind people navigate indoor spaces. It was made up of a microprocessor, short- and long-range ultrasonic sensors, a buzzer, and vibrational haptic feedback for the user. The microprocessor triggered one of the ultrasonic sensors based on the user’s selection, which then released sound pulses and waited for echoes to bounce to reflect from obstacles. The microcontroller was then fed the received echoes in the form of PWM (Pulse-Width Modulation) pulses in order to determine the distance of the obstacle by measuring the width of these pulses. The vibrating motor begins with a beep to signal the existence of a hurdle (if exists) and lies within the predetermined range. Additionally, it used GSM (Global System for Mobile Communication) to provide the user’s carer location data. However, the developed prototype only covered a small area for obstacle identification and did not support the detection of moving obstacles. Also, there was no mention that the created prototype was evaluated with VIP. Bharambe et al. [ 134 ] also proposed a system (known as a “substitute eyes”) to help the totally blind navigate outdoor. They employed a microcontroller, a couple of ultrasonic sensors and an Android app to detect nearby obstructions. The haptic feedback was provided to the user’s fingers via three vibrator motors. Depending on the estimated distance between the user and the obstacle, different vibration frequencies and intensities are produced. The Android app is used to provide complementary feedback on navigation directions. It was reported that one person wearing a blindfold tested the produced prototype successfully. Based on the photographs provided, the prototype appears to be in its early stage, cumbersome and difficult for VIP to use.

To help VIP indoor mobility in uncommon circumstances, an obstacle detection and warning system was proposed by Hoang et al. [ 135 ]. To help VIP indoor mobility in uncommon circumstances, an obstacle detection and warning system was proposed. The suggested system was built on a mobile Kinect (a line of motion-sensing devices produced by Microsoft in 2010) and an electrode matrix. It was composed of two primary parts: first, an obstacle detection unit that uses the mobile Kinect to gather scene data, which is then processed on a laptop computer to identify predefined obstacles like stairs, doors, chairs, and other obstructions. The second unit was used to encode the obstacle information (colour image, depth image, and accelerometer information) and represent it to the user as stimuli in touch with his or her body (through the user’s tong). In this project, the vision information was converted to stimulation of the vibrotactile or electro-tactile matrix using the electrode matrix (a set of electrodes used for detecting electric current or volage and it can stimulate patterns). After that, the authors expanded on this prototype [ 136 ] to detect moving items (like humans) as well as new static objects (such trash, plant pots, and fire extinguishers) and reduce the miss rate of the obstacle identification method. The tactile-visual substitution method, which makes use of the tongue as a human-machine interface was reused for the obstacle warning. According to the authors, the developed prototypes were evaluated with 20 young VIP participants who managed to walk independently in an indoor environment on one floor. However, this system requires user pre-training to be able to interpretate the system’s feedback correctly.

Katzschmann et al. [ 137 ] presented a wearable navigation solution for completely blind individuals in confined and open indoor environments. For local navigation, it enables users to feel physical boundaries in their immediate environment as well as low- and high-hanging impediments. The proposed system was made up of a sensor belt and a haptic strap. The sensor belt is an ensemble of time-of-flight distance sensors worn around the user’s waist, and the infrared light pulses it emits enable accurate estimates of the distance between the user and any nearby objects or surfaces. The haptic strap, on the other hand, transmits the measured distances through a network of vibrating motors worn around the user’s upper abdomen, providing the user haptic sensation. According to the authors, the developed prototype was evaluated with 12 totally blind users who were able to navigate through hallways, avoid obstacles, and recognize staircases. The viewing range of the array of vibrotactile and lidar units, however, still had to be improved so that it can be automatically altered according to the user’s speed, whether they are advancing or sidestepping.

Mancini et al. [ 138 ] proposed a vision system to aid completely blind people in soft jogging and walking in outdoor settings. The user’s position in relation to the desired lines or lanes that were used as a reference path was adjusted by sending vibration feedback to specially designed gloves that the user is wearing. The user then responds to vibrations by accelerating or decelerating, or by turning left or right. It is simple for a human to change pace or turn left or right in this scenario where the user behaves like a differential wheeled robot. The created system’s various design elements are clearly presented, however there was no mention that the developed prototype was tested with VIP. Also, the vibration bracelets were not tested to determine the wearer’s sensitivity to vibration.

To help totally blind people navigate a path and avoid hazards, a handheld force feedback device has been proposed by Amemiya & Sugiyama [ 139 ]. A kinesthetics perception approach (called the “pseudo-attraction force”) that the haptic direction indicator employs to create a force sensation by taking advantage of the nonlinear relationship between perceived and actual acceleration was utilized in this study. This kind of haptics modality helped the users to experience the kinaesthetic illusion of being pulled or pushed towards the correct path and avoiding collisions by speeding further in the correct direction. According to the authors, the developed prototype was put to the test by 23 VIP subjects who received the developed device positively [ 140 ]. Sharma et al. [ 141 ] developed a smart stick to assist VIP navigation in an unstructured indoor settings. The stick detects both dynamic and static obstacles and provide a fair idea about the distance and the location of obstacles through vibration in hand, as well as auditory feedback to the user. The audio signal was provided to the user via Bluetooth connection between the stick and the user’s earphone. It was reported that the developed prototype was tested successfully using different vibration frequencies and tracks of the audio alerts. It was unclear whether the developed prototype was tested on VIPs or just blindfold ones.

Li et al. [ 142 ] also proposed an indoor navigation system (named ISANA) to assist totally blind people with independent indoor travel utilizing Google Tango AR platform. In this study, the authors combined feature-based localization maps from Tango devices with semantic maps to provide semantic localization, navigation, and context awareness information. A multimodal human-machine interface (haptics as well as audio) was designed for interactions through an electronic SmartCane. The produced prototype reportedly underwent evaluations with blindfold totally blind users in a variety of contexts, including both single- and multi-floor scenarios. Feedback from the test subjects indicated some limitations in terms of speech recognition in noisy environments. Additionally, the semantic map annotation feature needs to be made simpler for users who are completely blind to utilize, especially when adding point-of-interest markers. The audio feedback also needs user-dependent frequency customization.

6 Smartphone-based aids

The operating systems of modern smartphones (or mobile phones) include numerous features that make them accessible. The option to increase text size, speak to text communication, vibration alerts rather than ringtones, and the facial recognition software on the most recent smartphones are notable features [ 143 ]. VIP can use accessibility tools like screen readers, magnifying glasses, and high-contrast screens to interact with these tools [ 144 ]. The screen reader provides audio feedback of the interface elements that are in focus, the magnifier enlarges the visible elements on the screen, and the increased contrast changes the colors of the user interface elements. The emergence of new tech-based assistance for VIP has also been made possible by these recent technology developments [ 145 , 146 , 147 ]. Given that senses other than vision (touch, hearing, smell, and taste) have smaller bandwidths, one major difficulty is how to convey information to the user in a clear and understandable method [ 148 ]. However, despite these usability and accessibility issues [ 149 , 150 ] that VIPs encounter while interacting with smartphones, a variety of applications (apps) have been proposed to aid them in their everyday tasks. In contrast to the studies covered earlier in Sects.  3 – 5 , this section presents additional substitutive aids that use a smartphone’s sensor to deliver the cueing feedback signal to the user. The aids presented in this section can use visual, auditory, haptic, or a mix of these modalities as cueing feedback to the user.

Senarathne et al. [ 151 ] proposed a mobile software, named BlindAid, to assist VIP in both indoor and outdoor settings with a variety of tasks, including face recognition, mobility (employing distance measurement to distinguish objects from obstacles), and extracting data from signboards and product labels. These tasks were processed in real-time using mobile devices only. The user received audio messages through headphones or the device’s speakers after the built-in camera and deep learning algorithms completed these tasks. The authors claim that depending on the ambient lighting, the produced app showed various degrees of accuracy. Testing in VIP was not performed in this prototype. Patel et al. [ 152 ] presented a real-time system to assist completely blind individuals in spotting potholes and other obstacles in their path while they walked through an unknown outdoor environment. Two ultrasonic sensors, an Arduino Nano microcontroller, a Bluetooth module, an accelerometer, and a smartphone with a camera and software application constitute the suggested system. One ultrasonic sensor was attached to the bottom of a one-foot-long stick, while the other is affixed to the stick so that it faces the user’s front. These sensors were utilized to locate impediments and gauge their distance from the user, and image processing methods were applied to the images captured by smartphone camera to identify objects in the user’s immediate environment. The smartphone app received the data from the ultrasonic sensors (through Bluetooth), processed it using an obstacle detection algorithm, and then provided the user a vibration or voice warning feedback. In this project, the user can also capture pictures with the smartphone’s camera to have a better understanding of their surroundings. The captured photo was then analysed using image processing algorithms to identify objects. Regarding the evaluation of this prototype with or without VIP, insufficient data was provided.

Uddin et al. [ 153 ] created a smartphone-based system to assist totally blind people in their outdoor navigation. It generated vocal commands and used an ultrasonic sensor to find holes (laying impediments) and obstacles. The shortest path between source and destination once the user speaks the destination location as the initial input. If an obstacle is detected by an ultrasonic sensor, its distance is calculated by a microcontroller and communicated to a smartphone (through Bluetooth), where it is converted into a voice that the user could hear. The developed prototype did, however, have some limitations, including power consumption, as well as dependency on the accuracy and coverage of the Microsoft Bing Map and the Global Positioning System, both of which are impacted by weather. It was reported that the developed prototype was tested with five volunteers, but it was not clear whether they were VIP, blindfold or sighted subjects. Another smartphone-based guidance for navigation and obstacles avoidance for VIP was proposed by Lin et al. [ 154 ]. The system was created using a smartphone app utilizing image recognition algorithms. The smartphone was connected to a remote server to execute the obstacle recognition task and the server communicates the findings back to the smartphone app, which then delivers the user audible warning messages. Since this application was only intended to serve as a proof of concept, it was not evaluated with VIP.

Croce et al. [ 155 ] built an indoor navigation aid (named ARIANNA) for totally blind people. It allows users to navigate various indoor areas of interest by following a pre-planned path that is panted or sticked to the floor. It can be deployed on smartphones or other handheld devices with augmented reality capabilities. This technology utilizes computer vision to detect the navigation path and provide haptic feedback signals in the form of vibration that the user can utilize to correct his or her direction. It was reported that the user can walk normally while using the smartphone to examine his/her immediate surroundings. The location of the hand in relation to the body suggested, through proprioception, what was the direction to follow. The early experiments pointed out some limitations in the smartphone camera and the optical flow accuracy. A couple of years later, the authors reported another version of ARIANNA [ 156 ] to address these limitations by using an extended Kalman filter and weighted moving average filtering, together with topological information available on the path. However, there was no evidence that those applications were tested with VIP.

An e-stick module was proposed by Bharatia et al. [ 157 ] to assist totally blind people with their outdoor navigation using an Android app and Google’s cloud vision. A vision API was used to capture and process images taken with a portable camera on the stick for object recognition. For each functionality, specific keywords from the voice command were recognized and provided as feedback to the user via a smartphone. It was reported that the primary goal of this project was to provide a simple and affordable solution by keeping the stick structurally like the traditional stick, that is thin, lightweight, and easy to handle, as well as optimizing its performance and efficiency. However, no clear optimization and performance metrices were presented and there was no evidence that this prototype was tested with VIP. Another wayfinding application (named GuideBeacon) was proposed by Cheraghi et al. [ 158 ] to aid VIP’s mobility in large indoor spaces. It enables smartphone-equipped users to communicate using inexpensive Bluetooth beacons placed strategically across a desired indoor location, and it also provided the users with directions over the speaker of their smartphones. It was reported that both sighted and visually impaired individuals successfully tested the developed prototype. However, this prototype had insufficient testing with various situations and some infrastructure deployment factors, such as reduction of the speech distortion and timeliness of the user’s instructions.

Another mobile app (known as HandyAPPs) proposed by Chuckun et al. [ 159 ] to assist people with different impairments (visual, speech or hearing). Therefore, it had multiple features, including text recognition, face detection and recognition, object recognition, speech-to-text, text-to-speech, and other functionalities. Talkback, a voice assistant, was offered for VIP to help them navigate its different features. Vibrant buttons, sounds, and using a large touch area were provided to make it easier for users to interact with the app. According to the authors, the created app underwent testing with VIP as well as those who had hearing and speech impairments, employing the functionalities offered to each disability, and the participants seemed to like the device. However, this app did have two potential limitations: (i) the functionality provided to the VIP is dependent on the availability and quality of the smartphone camera; and (ii) the object recognition, and face detection and recognition functionalities require an internet connection to process images at a remote server.

Kaushalya et al. [ 160 ] offered another smartphone app (named AKSHI) to aid completely blind people navigate outside while they are unaware of their surroundings and without the assistance of a sighted person. It delivers early obstacle recognition, gives the user auditory tones to indicate how far away obstacles are, gives spoken directions to a specific spot, recognizes pedestrian crossings, and sends position information and emergency SMS messages to the VIP’s guardian. The authors claim that the preliminary investigation showed acceptable functioning and accessibility. The small range of the RFID scanners and tags as well as the battery life, however, are limitations of this technology. Additionally, there was no indication of VIP testing this prototype. A similar application (known TransmiGuia) crested by Landazabal et al. [ 161 ] to assist totally blind people with public transportation services in the city of Bogotá D.C., using voice commands. The system directs the user using sound emissions that specify the available routes in accordance with the required route, the user’s location in the city, the hour, and the day. The users must enter the target path after locating the closest station. This is done using a set of buttons with Braille surfaces and continues in this manner until they arrive at their intended location. The system’s effectiveness in noisy surroundings, weather conditions and whether the developed prototype was evaluated with VIP were not mentioned, though.

Sumanasekera et al. [ 162 ] proposed a voice-based smartphone application (known as Kawulu) to address social isolation of VIP in Sri Lanka. The proposed app eliminates the issues of sifting through pointless content in existing social media networks, which was time-consuming and of little interest to the user, by allowing the user to select and share information in line with their preferences. Although there were certain limitations in terms of the application’s usability and the evaluation process, it was mentioned that this prototype was tested with 11 participants who had varying degrees of visual impairment.

Kardyś et. al. [ 163 ] proposed an Android application that allows VIP to use voice commands to access the phonebook, make calls, send and receive text messages, as well as other features such as current time, location and battery monitoring without a significant engagement. Many pre-defined voice commands were used to create these activations. If the user forgets any commands, they should say “help” or “help me.“ The user will then hear a list of the possible commands along with brief usage instructions such as “close, “switch,“ and “turn off” and others. This application reportedly worked; however, VIP testing was not done on it. Total blind people had also been assisted in recognising common money notes by smartphone applications that use machine vision [ 164 , 165 , 166 ].The notes’ recognized value was translated from text to speech, which was then provided to the user via the smartphone’s speaker. These applications’ recognition accuracy is generally impacted by the lightning conditions and mobile phone’s processing power.

In the field of mHealth (mobile health), a term for the application of wireless technology and mobile phones in healthcare [ 167 ], drug information, medicine identifications, and insulin dosage calculation were among a small number of applications in the context of VIP’s healthcare services. Madrigal-Cadavid et al. [ 168 ] created a mobile drug information application for VIP, so they could access a device on how to utilize their medications. A user-cantered process was adopted to design and develop a functional prototype of this application, highlighting the importance of involving users in the process. However, the developed prototype is restricted to drugs with barcodes, which is unusual for many manufacturers. Additionally, there was no information regarding the application’s VIP test. A similar mobile application was also proposed by Almuzaini et al. [ 26 , 169 ] to aid VIP in identifying and managing their medications, using an object recognition technique based on feature matching. The pharmaceutical pictures were detected and described using a fast-rotating detector and descriptor. Using a Brute-force matcher, the detected feature is matched with the feature of the medication box in the scene. It was mentioned that the proposed application was susceptible to lightning variation, and the established algorithm needs more work to cut down on the frequency of false matches. Additionally, there was no proof that the produced prototype had been tried out on VIP.

In the context of providing aids beyond the visual impairment, a few applications have also been reported in the literature for those with partial visual impairments. Radfar et al. [ 170 ] proposed a voice-activated mobile app for calculating insulin dosage for the VIP with diabetes. The user can interact with the app by saying the name of the meal and how much of it they want to eat. Following that, a speech recognition system compares the spoken name to the one already stored in the meal database. This work was a proof-of-concept demonstration of an insulin bolus dose (a quick-acting insulin that is administered at mealtimes to keep blood sugar levels under control following a meal) calculator using a voice-based interface. Additionally, this app was not tested on visually impaired diabetics. Additionally, Muhsin et al. [ 171 ] have recently proposed another voice-activated smartphone software for insulin-dependent diabetics with visual impairment. The insulin doses for each meal can be calculated automatically by this application while taking into consideration any remaining insulin in the body. It recognized digital readings from a range of popular blood glucose (BG) monitors, blood pressure monitors, and weight scales using machine vision, and it stored those readings as text in a smartphone database. Then, utilizing voice-driven dialogues, the amount of carbohydrate consumption and level of physical activity were acquired from the user and recorded in a smartphone’s database. Eventually, the user is given a spoken message containing the estimated insulin dose. According to the authors, the created prototype can improve blood sugar control, boost trust in dosage accuracy, and lessen anxiety over hypoglycemia brought on by a potential insulin overdose. However, there was no mention that this prototype was evaluated with the intended user.

7 Discussion

A wide range of AT devices and applications were explored in this review, and one of the primary difficulties is that most of these aids were focused on the functioning aspects of the services rather than on the human aspects of the user experience. The examined research revealed that despite the development of a range of important technological solutions for substitutive assistive devices and smartphone apps, the user acceptance of these solutions is still relatively low [ 25 , 172 ]. This was mainly because they were mostly conceived, built, and tested in various settings with poor VIP participation, thus they were underutilised due to challenges in terms of human factors of the user experience [ 172 , 173 ]. Nevertheless, this does not negate the existence of other popular apps that have favorably impacted the VIP’s social integration [ 174 ]. On the other hand, the lack of a common wearable platform for AT, also makes the development of compatible and interoperable tools and technologies a challenging task for the scientific and research community. Another shortcoming is attributed to the lack of utilizing emerging technologies such as IoT, 5G, and big data. According to recent studies [ 175 , 176 , 177 ], these technologies can result in significant improvements in the tools and technologies that support the VIP’s daily activities.

The employment of various types of sensors, wireless networks, speech-text-speech, and computer vision algorithms has advanced significantly, particularly in navigation and wayfinding support systems for the totally blind people. Many of these systems solved technology issues, but they also had limitations in terms of usability and accessibility, learning and adaption time to the new system, and other factors [ 178 ]. In addition, most of the proposed aids assumed that users were totally blind, and as a result, people with other visual impairments such as colour blindness and those with normal vision in specific portions of their visual field have received little attention in the literature. Specifications of assistive devices could also differ based on whether the VIP are adults or children, as well as whether they are partially or totally blind. Additionally, only a few studies addressed difficulties other than visual impairments, such as managing diabetes, which is one of the leading causes of vision loss, in partially sighted people.

Smartphone platforms enabled development of a variety of AT for VIP, using built-in sensors, as discussed earlier in Sect.  6 . Depending on the system complexity as well as availability of the tools/libraries in the mobile development platform, the developed apps were either (i) native mobile apps that are installed directly onto the smartphone and can work, in most cases, with no internet connectivity, (ii) web-based mobile apps in which the smartphone is linked to a remote web server and used to perform secondary tasks (e.g., user terminal, wireless communications gateway) or (iii) hybrid apps that are part native apps, part web-based apps. In complex navigation and wayfinding support systems [ 104 ], the core functionalities of the mobile apps were performed by a local external processing unit and/or a web server. Unlike the web-based and hybrid mobile apps, the native apps have several limitations including camera optics and sensors, computation speed, power requirements and the challenges of obtaining sufficient information about image processing pipelines for mobile vision problems. As a result, the algorithms used in native mobile apps must be both reliable and efficient. The researchers and developers employed a combination of algorithms, heuristics, refinements, and know how to achieve these objectives [ 179 ].

Based on the analysed research in this review, only 35.2% of the of the created assistive aids were tested on VIP and 64.8% were tested with blindfolded or sighted participants, as shown in Fig. 3 . Without testing with VIP, it is difficult to assess whether an AT is useful for visually impaired people and easy to use, assume it is wearable, whether it is bulky or heavy, and whether it can timely respond. In addition, it is found that only a few of the analysed projects (less than 4%) were involved VIP in the design and development process. Participation of VIP in these activities also requires clinical or ethical approval. As a result, these findings clearly demonstrate that the VIP’s needs were not effectively communicated to the system’s designers and developers.

Based on the findings highlighted in this discussion, we believe that the following ideas can improve future design and development of AT for VIP:

Participation of VIP in the design, development, and testing of AT aids

To produce successful and acceptable AT for VIP, the development method must follow a user-centred agile method. Figure  4 shows a simplified flow diagram for the suggested method, in which the development process follows an iterative and incremental model [ 180 ]. In this process, the required functionality is divided into small increments that can be delivered independently. Instead of developing a complete prototype and asking for feedback, the outcome of each increment is shared with the end-users to obtain their evaluation and. As a result, it is simpler to identify the user’s desires in a timely manner and to deliver small parts of the design to the development team for faster execution. Additionally, this process focuses on deep understanding of the user’s need and their context in all stages of the design and development stages. These necessities an awareness of the types of interface modalities and interactions that make learning and using the AT with physical convenience easier for the intended users. This goal can be met by gathering data from the intended users and their carers regarding the problems they face in managing their daily tasks, and the changes they believe the proposed system will help them achieve. The findings of such investigations could inspire more people with visual impairments to utilise these devices and to provide a solid foundation for the development process.

After identifying the actual need, the usage context the application is used to determine who the application is intended for, why users are using it, what they require, how and where they are using it. The context of utilizing the application provides information about the tasks, setting, VIP attributes that can be utilized to create a user’s persona. Next, the application requirements are specified based on information gathered from the user (via observations and interviews) and connected to the environment in which the application will be developed. All the following design, development, and evaluation stages in each increment will be affected by these needs. In the design and development stage, the user interface modalities that we discussed earlier in Sects.  3 – 5 are created according to user’s needs that are defined in the previous stages of the development process. In the evaluation stage, the USE Questionnaire approach (Usefulness, Satisfaction, and Ease of Use) [ 173 ] can be implemented to assess the user perception. As shown in Fig.  4 , this process cycle continues until a suitable result is attained before releasing the product for usage by the targeted users.

The user interface modalities must be embedded in the design and development process to improve the system’s accessibility and usability. Existing aids, particularly the smart-phone-based ones, can be enhanced in terms of usability, accessibility, learnability, and time to adapt to new systems by adopting more efficient user interfaces and human-computer interaction techniques. Interactions with others and body awareness are fundamentally multimodal experiences. [ 71 ]; thus, AT interface with VIP can be multimodal to enhance the user experience, with the preferences of the user directing the selection. More research is required to produce a general-purpose vision-to-touch and vision-to-audio translator that is reliable and robust for everyday use. To attain this goal, multidisciplinary research efforts, funding, and a universal wearable platform combining advances in wireless networks, GPS, voice recognition, and other essential technologies are still needed. In addition, for the vision-based AT, more efficient algorithms for more accurate interpretation of visual information and contents of an image or a scene are still needed for vision-based assistive devices.

To address the challenges of the white cane, the most widely used tool by VIP to detect obstacles below waist level, there is a need to utilize renewable energy source like solar energy, innovate concepts built on low-cost technology, effective algorithms, and low-power consumption devices. Recent studies reported that the white cane can be integrated with other technologies such as the IoT-based Blind Guide [ 104 ], which allows utilization of a wireless sensor in the forehead to identify obstacles at the head level as well, as explained earlier in Sect.  4 .

It is critical to separate the needs of VIPs who have normal vision in some areas of their visual field or colour-blind from those who are totally blind. This is likely to open up new research opportunities to address challenges other than complete blindness. The difficulties faced by people with partial vision impairments as well as other chronic diseases, such as diabetes, have also received little coverage in the literature [181–184]. Despite recent initiatives in this approach, more effort is still required to fill the AT research vacuum. AT aids are projected to minimise dependence of VIP on others in terms of the prevention (or delay) of disease progression, its related complications (including vision impairment), and the long-term treatment expenses.

Well-designed smartphone-based apps that focus on the needs and expectations of users are a viable path towards building adaptable and acceptable aids for the VIP community. More research is therefore needed to improve present smartphone technologies as well as programming tools/libraries. Potential upgrades include computer vision sensors and algorithms, as well as voice and text detection capabilities. Technology experts [ 67 ] believe that users have become more ready for trying out various new modalities as the use of smartphones and other mobile devices expands. Others started utilizing voice assistants like Siri, Alexa, Cortana, and Google Home as an alternative to using computers and other digital devices for communication after these tools were introduced [ 151 , 153 ]. This demonstrates how specific modalities with varied intensities are advantageous in a variety of contexts [ 154 ]. Other modalities, such as computer vision sensors, can be used to gather three-dimensional movements with depth cameras like the Microsoft Kinect [ 74 ]. Voice and text detection capabilities are also being explored as prospective enhancements.

There are numerous research prototypes developed by the academic community, but the VIP community was not given access to these technological advancements. The challenges of moving from a research prototype to production were significantly impacted by a lack of available resources or the necessary knowledge to do so. This challenge can be mitigated by including stakeholders, like industry partners, in the prototyping and design process and raising public awareness for the quality of prototyping and yielded benefits on many levels. The standard of the AT research, on the other hand, is negatively impacted by the sometimes challenging and drawn-out processes of gaining authorisation to access the VIP group in order to involve them in the development or testing. The healthcare sector should be more eager to adopt more effective, more efficient procedures to promote collaboration between the research and VIP groups.

Existing technology advancements in AT are helpful in preserving the health and comfort of those with visual impairments or chronic diseases. However, to ensure a respectable standard of living and accessible medical care, AT needs to develop further in the healthcare sector. People with visual impairments have the ability to make themselves visible in a manner they never could since they now have access to smartphones and other tools and technologies (e.g., IoT, big data, and machine intelligence), which are becoming more and more accessible, affordable and reliable.

A simplified diagram of the suggested user-centred agile method

8 Conclusion

Based on the reviewed research, there is no tool or technology is considered ideal. In order to assist those with visual impairments, it is therefore crucial to develop more intelligent systems that can address standing challenges including participation of the intended users in the design, development and evaluation of these systems. This review analysed state-of-the-art technology aids that have been proposed by the research community to assist people with visual impairments, as well as posing critical questions concerning the direction substitution aids may go in the future. Over the last 10 years, we have seen numerous technological developments in the creation of research prototypes with user-system interaction and system validation for VIP. Most of these aids addressed technical problems and assumed that the users were totally blind, while few other aids were proposed for children and those with healthy eyesight in parts of their visual field. Previous studies demonstrated that developing navigation systems for the totally blind people has been the most active research topic as well as a difficult one in which human aspects of the user experience must be considered. The analysed research studies revealed that neither the researchers nor the AT developers were able to successfully identify the needs of VIP in terms of human factors of the user experience such as usability, learnability, and time to user adaption. This is supported by the fact that many technical aids fall short by the poor participation of VIP in the development process or the evaluation of the developed prototypes. As a result of these limitations and others, most of the created research prototypes are still far from seeing systems used in everyday by the totally blind community.

We believe that future assistive tools and technologies should take advantage of technological advancements in and apply them to create a globally accessible navigation aids, creating new concepts based on low-cost technology, efficient algorithms, and low-power consumption. Moreover, distinguishing between the needs of the people who have normal vision in some portions of their visual field and colour-blind from those who are totally blind would open new research opportunities to address challenges beyond the visual impairment.

We hope that the findings and recommendations presented in this article will open new discussions among the research community, advance the development of AT aids that are more adaptable for VIP, and encourage further research into challenges these people face beyond their primary visual impairments.

Blindness and vision imparent [ https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment ]

Number of individuals affected with vision loss in the United Kingdom from 1990 to 2020, by severity [ https://www.statista.com/statistics/1200836/vision-impairments-in-the-uk/ ]

Blindness and vision loss [ https://www.nhs.uk/conditions/vision-loss/ ]

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Muhsin, Z.J., Qahwaji, R., Ghanchi, F. et al. Review of substitutive assistive tools and technologies for people with visual impairments: recent advancements and prospects. J Multimodal User Interfaces 18 , 135–156 (2024). https://doi.org/10.1007/s12193-023-00427-4

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DOI : https://doi.org/10.1007/s12193-023-00427-4

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Assistive technology products: a position paper from the first global research, innovation, and education on assistive technology (GREAT) summit

Affiliations.

• 1 a RESNA & University of Wisconsin-Milwaukee , USA.
• 2 b Institute for Matching Person & Technology, Inc. , Webster , NY , USA.
• 3 c University of Pittsburgh , USA.
• 4 d Centre for Rehabilitation Studies, Stellenbosch University , Cape Town, South Africa.
• 5 e Flinders University , Australia.
• 6 f Department of Architecture and Civil Engineering, Chalmers University of Technology , Goteborg, Sweden.
• 7 g Motivation Charitable Trust , South Africa.
• 8 h Lund University , Sweden.
• 9 i University of Pennsylvania , USA.
• 10 j University at Buffalo , USA.
• 11 k Indian Institute of Technology , Madras , India.
• 12 l Indian Institute of Technology , Delhi , India.
• 13 m Jordan University of Science & Technology, Jordan, and University of Wisconsin-Milwaukee , USA.
• 14 n Assisting Living & Learning (ALL) Institute, Maynooth University , Maynooth , Ireland.
• 15 o National Institute on Disability, Independent Living, and Rehabilitation Research, University at Buffalo , USA.
• PMID: 29873268
• DOI: 10.1080/17483107.2018.1473895

This paper is based on work from the Global Research, Innovation, and Education on Assistive Technology (GREAT) Summit that was coordinated by WHO's Global Cooperation on Assistive Technology (GATE). The purpose of this paper is to describe the needs and opportunities embedded in the assistive product lifecycle as well as issues relating to the various stages of assistive product mobilization worldwide. The paper discusses assistive technology product terminology and the dangers of focusing on products outside the context and rolling out products without a plan. Additionally, the paper reviews concepts and issues around technology transfer, particularly in relation to meeting global needs and among countries with limited resources. Several opportunities are highlighted including technology advancement and the world nearing a state of readiness through a developing capacity of nations across the world to successfully adopt and support the assistive technology products and applications. The paper is optimistic about the future of assistive technology products reaching the people that can use it the most and the excitement across large and small nations in increasing their own capacities for implementing assistive technology. This is expressed as hope in future students as they innovate and in modern engineering that will enable assistive technology to pervade all corners of current and potential marketplaces. Importantly, the paper poses numerous topics where discussions are just superficially opened. The hope is that a set of sequels will follow to continue this critical dialog. Implications for Rehabilitation Successful assistive technology product interventions are complex and include much more than the simple selection of the right product. Assistive technology product use is highly context sensitive in terms of an individual user's environment. The development of assistive technology products is tricky as it must be contextually sensitive to the development environment and market as well. As a field we have much to study and develop around assistive technology product interventions from a global perspective.

Keywords: Assistive technology; global; products; technology transfer; worldwide.

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• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

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Acknowledgements

This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu

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Conceptualization, XS, YW, CW; methodology, XS, ZL, CG, CW; software, XS, CG, YW; writing-original draft preparation, XS, CW; writing-review and editing, XS, CG, ZH, CW; supervision, ZL, ZH, CW; project administration, ZL, ZH, CW; funding acquisition, XS, CG. All authors read and approved the final manuscript. All authors have read and approved the re-submission of the manuscript.

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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Received : 20 June 2024

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DOI : https://doi.org/10.1057/s41599-024-03658-2

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Assistive technology for higher education students with disabilities: a qualitative research.

1. Introduction

2. materials and methods, 2.1. participants, 2.2. instruments and procedures, 4. discussion, 5. conclusions, author contributions, institutional review board statement, data availability statement, acknowledgments, conflicts of interest.

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Papadopoulos, K.; Koustriava, E.; Isaraj, L.; Chronopoulou, E.; Manganello, F.; Molina-Carmona, R. Assistive Technology for Higher Education Students with Disabilities: A Qualitative Research. Digital 2024 , 4 , 501-511. https://doi.org/10.3390/digital4020025

Papadopoulos K, Koustriava E, Isaraj L, Chronopoulou E, Manganello F, Molina-Carmona R. Assistive Technology for Higher Education Students with Disabilities: A Qualitative Research. Digital . 2024; 4(2):501-511. https://doi.org/10.3390/digital4020025

Papadopoulos, Konstantinos, Eleni Koustriava, Lisander Isaraj, Elena Chronopoulou, Flavio Manganello, and Rafael Molina-Carmona. 2024. "Assistive Technology for Higher Education Students with Disabilities: A Qualitative Research" Digital 4, no. 2: 501-511. https://doi.org/10.3390/digital4020025

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