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International Journal of Developmental Disabilities logoLink to International Journal of Developmental Disabilities
. 2018 Mar 15;65(4):220–230. doi: 10.1080/20473869.2018.1439819

Technology use to support employment-related outcomes for people with intellectual and developmental disability: an updated meta-analysis

Despoina Damianidou 1,*, Michael Arthur-Kelly 1, Gordon Lyons 1, Michael L Wehmeyer 2
PMCID: PMC8115596  PMID: 34141342

Abstract

Objectives

The aim of this study is to update and extend an original meta-analysis which included papers published up to and including 2003 and investigated the impact of technology use on employment-related outcomes for people with intellectual and developmental disability.

Methods

Following on from the original meta-analysis, this study is a meta-analysis of pertinent single-subject experimental design studies conducted from 2004 to 2016 and employs the same metric methods as the original contribution.

Results

The results are generally consistent with those of the original meta-analysis, namely, applied cognitive technology effectively supports employment-related outcomes for people with intellectual and developmental disability. Nevertheless, significant differences in the intervention effects were found (a) between groups of individuals with varying levels of disability, and (b) between interventions utilizing technology with and without universal design features.

Conclusions

In line with the original contribution, applied cognitive technology seems to support people with intellectual and developmental disabilities to better achieve employment-related outcomes. More research is needed though to explore the impact of different types of technology on employment-related outcomes.

Keywords: intellectual and developmental disability, applied cognitive technology, assistive technology, meta-analysis, employment-related outcomes

Introduction

Having a job is a fundamental adult human right. More particularly, for people with intellectual and developmental disability (IDD), employment enables progress toward independent living, increased income, better social inclusion, more meaningful reciprocal relationships, and improved quality of life.

Notwithstanding international legislation, employment rates for people with IDD are low and have been falling in many countries (OECD 2010). Verdonschot et al. (2009) found that people with IDD are three to four times less likely to have a job compared with people without IDD. Furthermore, most people with IDD who work do so in segregated employment settings and not in mainstream settings, thereby lagging significantly behind people without disability in terms of wages and social inclusion (Migliore et al. 2007).

Various factors affect these low employment rates. These mostly relate to the transition of people with IDD from formal education to employment (Rusch and Wolfe 2008). People with IDD have more functional and adaptive skill challenges (Carter et al. 2012; Emerson 2007), as well as more cognitive function challenges (Alloway et al. 2009; Edgin et al. 2010; Hartman et al. 2010). These factors need to be addressed to achieve more successful transitions.

This study is an update and extends the original meta-analysis of Wehmeyer et al. (2006). Wehmeyer et al. concluded that the support then provided by technology use was ‘fair’. More specifically, the potential of technology to enable people with IDD to achieve more positive employment-related outcomes was evident.

Despite the limited research evidence, Wehmeyer et al.’s (2006) meta-analysis identified the potential of technology (including audio prompting devices, palmtop computers, communication devices, and desktop computers) to improve employment-related outcomes, particularly when Universal Design (UD) features were incorporated. This potential was particularly evident in improving task performance with reduced human support in work settings (Wehmeyer et al. 2008).

In the years since the original meta-analysis carried out between 1977 and 2003 (Wehmeyer et al. 2006), studies using technology that is more sophisticated, namely smartphones, augmented-reality (AR), watches, and portable video prompting devices (e.g. iPod), have been conducted. The following selection is representative. Chang et al. (2014) trained three individuals with IDD using a computer-based interactive game with Augmented Reality designed to provide task prompts, identify incorrect task steps, and help users to make corrections. The authors found that participants with intellectual disability (IDD) improved their vocational job skills during the intervention phases and maintained these after the intervention. Van Laarhoven et al. (2009) found that using an iPod as a video prompting device to teach three job-related tasks to a young man with IDD in a community-based employment setting was associated with immediate and substantial gains in more independent task engagement. In another investigation, Cannella-Malone et al. (2012) found that using iPod video prompting during in vivo instruction improved skill acquisition for three students with IDD.

In addition, studies that used technology to teach self-determination skills (e.g. self-instruction) regarding employment-related outcomes were more widely used in the current literature. Green et al. (2011) examined the impact of the use of a vibrating watch programmed to vibrate at the appropriate time and provide a visual prompt to indicate a transition to another place. The results indicated an improvement in time management skills for a participant with IDD. Smith et al. (2015) evaluate the use of a mobile device for video self-instruction of a functional skill. The findings demonstrated that participants with IDD independently initiated the use of the mobile device, and acquired the novel functional skill following self-instruction.

The aim of this study

The aim of this study is to update and extend the original contribution of Wehmeyer et al. (2006), which was a meta-analysis of papers published up to and including 2003. The analyzed studies were predominantly single-subject experimental design (SSED) studies. This study includes a search of the relevant literature to identify relevant studies on the use of applied cognitive technology (henceforth technology) and its impact on vocational-related outcomes for people with IDD. Wehmeyer and Shogren (2013) suggested the term Applied Cognitive Technology to refer to ‘technology supports that enable people with cognitive disabilities to successfully function in inclusive environments, to increase participation in tasks and activities in inclusive environments, and to promote social inclusion, self-determination, and quality of life’ (92). Such technology includes traditional assistive technology, but other forms of technology, such as computer technology, electronic and information technology, and so forth. Following on, updating and extending from the original work of Wehmeyer et al. (2006), this paper included only SSED published from 2004 up to and including 2016.

Method

The method used in this study was designed to be as similar as possible to that used by Wehmeyer et al. (2006). Necessary points of difference will be noted and explained in this section. This method is best described in eight parts. These are (1) literature search strategy, (2) criteria for inclusion of studies, (3) criteria for exclusion of studies, (4) search results, (5) information on the studies, (6) intervention effects, (7) post hoc analyses, and (8) percentage of non-overlapping data (PND) and universal design (UD) measures of reliability. PRISMA reporting guidelines have been taken into consideration during the analysis and reporting of these results (Moher et al. 2009).

Literature search strategy

A search for pertinent articles published in peer-reviewed journals was conducted. The search covered the EBSCO, ProQuest and Scopus databases for articles published from 2004 to 2016. A decision rule applied whereby the abstracts of selected articles would include at least one keyword from each of the three categories of keywords (diagnosis, technology, and employment). The third category of keywords, ‘employment’, was applied from the initial search to limit the number of articles found. Furthermore, the list of keywords in this study was updated, taking into account those more currently used in the field.

All full text articles meeting the keyword search criteria were entered into a Microsoft Excel database along with their bibliographic data. These data were: article title, authors’ names, journal name, volume number, and page numbers. This search delivered 441 articles (available from the first author).

Criteria for inclusion of studies

Essentially the same inclusion criteria adopted by Wehmeyer et al. (2006) were applied. These are: (1) at least one participant in the study had an IDD based on either clinical diagnosis or an IQ assessment; (2) the study employed a SSED (i.e. pre-experimental (AB), withdrawal (ABA/ABAB), multiple baseline, multiple-probe, reversal, changing criterion, multiple-treatment, alternating treatments, adapted alternating treatments); (3) the study’s intervention phase involved the use of technology; (4) the intervention phase targeted employment-related outcomes; (5) the results of the study were presented in a line graph format consistent with the usual protocols for SSED studies; (6) the study was published in a peer-reviewed journal; (7) the study was published from 2004 up to and including 2016; and (8) the study was written in English.

Criteria for exclusion of studies

Essentially the same exclusionary criteria were applied. These are: (1) use of a group design, opinion articles, position statements, qualitative studies, group design studies, literature review articles; (2) inappropriate design (e.g. lack of baseline or less than 3 baseline scores and 2 baseline scores for multiple probe design); (3) participants were identified as having Autism Spectrum Disorder (ASD) with IQ score above 75; (4) results of the study were not presented in a line graph consistent with SSED studies; and (5) data involved ‘floor’ or ‘ceiling’ effects in graphed data which made the calculation of the intervention effect inaccurate (Scruggs et al. 1987).

Search results

Of the initial 441 studies, 41 studies were identified once all the inclusion and exclusion criteria were applied (Figure 1). Wehmeyer et al. (2006) analyzed 13 studies. In the present study, this determination was made using a three-step process: (1) the lead researcher excluded studies that did not meet the inclusion criteria, (2) two expert colleagues (2nd and 3rd author) worked independently through a sample of 12 (out of the 41) randomly selected studies (28% of total studies), and (3) 2nd and 3rd author cross-checked each study, measuring the application of inclusionary and exclusionary criteria. Table 1 shows the bibliographic details of the remaining 41 studies.

Figure 1.

Figure 1

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PRISMA 2009 flow diagram of the papers selection

Table 1. Studies included in the meta-analysis.

Authors and year Participants Type of SSED Type of technology Employment-related goals Setting
1. Gilson and Carter (2016) 1 male (Age: 20), IDD MPD across participants Auditory prompting device (two-way radio device (i.e. ‘walkie-talkie’) as part of a fade-out process Work-related social skills Real
2. Smith et al. (2015) 2 males (Age: 16, 16), ASD MPD across participants Smartphone (mobile device) Vocational task performance and completion Simulated
3. Cihak et al. (2015) 3 males (Age: 22, 23, 22) and 1 female (Age: 21), IDD MPD across different devices or platforms Desktop and laptop computers (desktop, laptop and iPad) Computer-use itself Simulated
4. Collins et al. (2014) 3 males (21, 21, 22), IDD ATD Video-assisted training (iPod-Functional Planning System application) Vocational task performance and completion Simulated
5. Rouse et al. (2014) 1 male (Age: 12), DS MPD across behaviors Pictorial Prompts (self-monitoring picture prompt checklist) Vocational task performance and completion Simulated
6. Chang et al. (2014) 2 males (Age: 20, 25) and 1 female (Age: 21), IDD MPD across participants Augmented Reality device (personal computer, LCD monitor, web-camera, in-house developed software, AR tags, ARToolKit) Vocational task performance and completion Simulated
7. Wu et al. (2016) 2 males (Age: 14, 17) ASD/IDD, Prader–Willi Syndrome/IDD MPD across participants Video-assisted training (iPod) General cleaning skills Simulated
8. Goh and Bambara (2013) 2 males (Age: 53, 47) and 1 female (Age: 28), IDD, IDD, IDD/ASD MPD across targeted job asks, replicated across the participants Video-assisted training (DVD) Vocational task performance and completion Real
9. Mechling et al. (2013) 4 males (Age: 15, 19, 16, 18), ASD, William syndrome/ASD, ASD, Fragile X syndrome AATD Desktop and laptop computers (laptop) Food preparation skills Simulated
10 Chang et al. (2013) 2 males (Age: 20, 25) and 1 female (Age: 21), IDD MPD across participants Augmented Reality device (personal computer, LCD monitor, web-camera, in-house developed software, AR tags, ARToolKit) Vocational task performance and completion Simulated
11. Johnson et al. (2013) 2 males (Age: 17), IDD/ASD, IDD/CP MPD across behaviors Video-assisted training (iPod) Food preparation skills Simulated
12. Cannella-Malone et al. (2013) 3 males (Age: 16, 16, 15) 1 female (Age: 17), CP/IDD, IDD, DS/IDD, ADHD/IDD MPD across students Video-assisted training (iPod) General cleaning skills Simulated
13. Allen et al. (2012) 2 males (Age: 18, 17) and 1 female (Age: 16), ASD/IDD ABCAC withdrawal design Auditory prompting device (Radio Shack TRC-508 s FM transceiver with microphone and earphones) Vocational task performance and completion Simulated
14. Payne et al. (2012) 1 male (Age: 19), PDD/IDD/ADHD MPD across students Video-assisted training (iPod and inPromptu application) Food preparation skills Simulated
15. Mechling and Ayres (2012) 4 males (Age: 19, 19, 21, 20), IDD/ASD AATD Palmtops (PDA) and desktop and laptop computers (laptop) Vocational task performance and completion Simulated
16. Cannella-Malone et al. (2012) 2 males (Age: 15, 15) and 1 female (Age: 15), Noonan Syndrome-IDD, Trisomy X syndrome/CP, IDD/ASD AATD within MPD across participants Video-assisted training (iPod) General cleaning skills Simulated
17. Mechling and Collins (2012) 3 males (Age: 22, 20,21) and 1 female (Age: 19), IDD AATD Desktop and laptop computers (laptop) Food preparation skills NR
18. Green et al. (2011) 1 female (Age: 22), IDD ABAB sequence Time management tasks (vibrating watch) Time management tasks Real
19. Taber-Doughty et al. (2011) 1 male (Age: 12) and 2 females (Age: 12, 13), IDD ATD Video-assisted training (iPod) Food preparation skills Simulated
20. Chang et al. (2011) 1 male (Age: 27), IDD ABAB sequence Smartphone (mobile device) Vocational task performance and completion Real
21. Lancioni et al. (2011) 1 male (Age: 38) and 2 female (Age: 33, 36), Congenital encephalopathy and IDD ATD (Part I) and MPD (Part II) Auditory prompting devices (two sets of radio-frequency photocells and light-reflecting paper, an amplified MP3 player with USB pen drive connection, a pen containing the verbal instructions) Food preparation skills Simulated
22. Cannella-Malone et al. (2011) 5 males (Age:12, 12, 12, 13, 13) and 2 females (Age:13, 11), ASD/IDD, ASD/IDD/fragile X syndrome, ASD/IDD ATD within an MPD across participants Desktop and laptop computers (laptop) General cleaning skills (one task) and Vocational task performance and completion (one task) Simulated
23. Mechling and Savidge (2011) 2 males (Age: 14, 14) and 1 female (Age: 14), ASD/IDD MPD across tasks and replicated with the students Palmtops (PDA) Task sequencing and transition skills and Vocational task performance and completion Simulated
24. Van Laarhoven et al. (2010) 2 males (Age: 13, 14), IDD AATD within-subject Pictorial prompts and desktop and laptop computers (laptop – PowerPoint) Vocational task performance and completion Simulated
25. Shih, Shih and Wu (2010) 2 males (Age: 12, 15), IDD, CP/IDD MPD across participants Desktop and laptop computers (computer and a mouse) Computer-use itself Simulated
26. Shih and Shih (2010) 2 males (Age: 17, 16), IDD MPD across participants Desktop and laptop computers (computer and a trackball) Computer-use itself Simulated
27. Mechling et al. (2010) 1 male (Age: 15) and 2 females (17,17), Williams syndrome/IDD, DS/IDD, IDD MPD across the recipes and replicated with the students Palmtops (PDA) Food preparation skills Simulated
28. Shih, Shih and Chiu (2010) 2 males (Age: 17, 15), IDD MPD across participants Desktop and laptop computers (computer and mouse) Computer-use itself Simulated
29. Bennett et al. (2010) 3 males (Age: 22, 30, 42), ASD, IDD, IDD MBD across participants and work tasks Auditory prompting devices (two-way radio and headset) Vocational task performance and completion Real
30. Shih, Li, et al. (2010) 2 females (Age: 17, 15), IDD MPD across participants Desktop and laptop computers (computer and mouse) Computer-use itself Simulated
31. Shih, Chiu, et al. (2010) 1 male (Age: 14), IDD MBD across participants Desktop and laptop computers (computer and mouse) Computer-use itself Simulated
32. Shih, Huang, et al. (2010) 2 males (Age: 14, 13), IDD MPD across participants Desktop and laptop computers (computer and mouse) Computer-use itself Simulated
33. Van Laarhoven et al. (2009) 1 male (Age: 17), 1p36 Deletion Syndrome MPD across tasks Video-assisted training (iPod) General cleaning skills Real
34. DiPipi-Hoy et al. (2009) 4 males (Age: 17, 16, 17, 17), IDD MBD across participants Time management task (alarm watch) Time management tasks Real
35. Shih et al. (2009) 2 males (Age: 17, 16), IDD MPD across participants Desktop and laptop computers (computer and mouse) Computer-use itself Simulated
36. Mechling and Stephens (2009) 2 males (Age: 20, 22) and 2 females (Age: 19, 19), IDD, IDD, IDD, IDD/ASD/Williams syndrome AATD Pictorial prompts (cookbook) and video-assisted training (DVD) Food preparation skills Simulated
37. Mechling et al. (2009) 3 males (Age: 17, 16, 17), ASD/IDD, ASD, ASD/IDD MPD across cooking recipes and replicated across the students Palmtops (PDA) Food preparation skills Simulated
38. Mechling et al. (2008) 1 male (Age: 22) and 2 females (Age: 20, 19), IDD/DS, IDD/ADHD, IDD MPD across cooking recipes and replicated across the students Video-assisted training (DVD) Food preparation skills Simulated
39. Minarovic and Bambara (2007) 2 males (Age: 40, 56) and 1 female (Age: 32), IDD MPD across participants Pictorial prompts (sight-word checklist) Task sequencing and transition skills Real
40. Cihak et al. (2007) 3 males (Age: 19, 19, 18) and 1 female (Age): 19, IDD MPD across tasks Palmtop (handheld computer) Vocational task performance and completion Real
41. Riffel et al. (2005) 1 male (Age 16) and 3 females (Age: 20, 20, 20), IDD, IDD, ASD, Prader-Willi MPD across participants Palmtops (hand-held computer/visual assistant application) Vocational task performance and completion Simulated
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Key:

Participants. DS: Down Syndrome; ADHD: Attention Deficit Hyperactivity Disorder; ASD: Autism Spectrum Disorder; CP: Cerebral Palsy.

Single Subject Experimental Design. MPD: Multiple Probe Design; MBD: Multiple Baseline Design; ATD: Alternating Treatments Design; AATD: Adapted Alternating Treatments Design.

Types of Technology. PDA: personal digital assistant; AR: Augmented Reality.

NR: not reported.

Information on the studies

The aspects of these 41 studies were compared and contrasted with the ones included in the original contribution (Wehmeyer et al. 2006). Aspects analyzed included: article title, authors names, journal name, volume number, page numbers, keywords, the type of technology used, the type of employment-related outcomes measured, the level and type of IDD, the research design, and the presence and nature of UD features.

As in Wehmeyer et al. (2006), the studies were examined to ascertain the degree to which any of the seven features of UD was identified or discussed by the studies’ authors as a feature of the technology used. According to Connell et al. (1997), these features were: (1) equitable use (the design is useful and marketable to people with diverse abilities), (2) flexible use (the design accommodates a wide range of individual preferences and abilities), (3) simple and intuitive use (use of the design is easy to understand, regardless of the user’s experience, knowledge, language skills, or current concentration level), (4) perceptible information (the design communicates necessary information effectively to the user, regardless of ambient conditions or the user’s sensory abilities), (5) tolerance for error (the design minimizes hazards and the adverse consequences of accidental or unintended actions), (6) low physical/cognitive effort (the design can be used efficiently and comfortably and with a minimum of fatigue), (7) size, and space (appropriate size and space is provided for approach, reach, manipulation, and use regardless of user’s body size, posture, or mobility).

Intervention effect

As in Wehmeyer et al. (2006) the common outcome metric, PND was calculated. The outcome metric Percentage of Zero Data (PZD), was not calculated since there was only one study where technology was used to reduce problem behavior in a vocational-related activity (PZD scores). PND is an outcome metric for aggregating data across SSED studies and provides a measure of the proportion of non-overlapping data between baseline and treatment phases. PND measures the number of observations in the treatment phase presented in a visual graph that exceed the highest point in the baseline phase (Scruggs et al. 1987). PND scores range between 0 and 100, with higher scores reflecting treatments that are more effective. Scores above 90 indicate very effective treatments, scores from 70 to 90 indicate effective interventions, scores from 50 to 70 suggest questionable interventions, and scores below 50 signal ineffective interventions (Scruggs and Mastropieri 1998). PZD is an outcome metric that represents the degree to which the targeted behavior is eliminated in treatment. It is measured by identifying the first data point to reach zero in a treatment phase and then by calculating the percentage of data points that remain at zero from the first zero point onwards. PZD scores range from 0 to 100, with higher scores reflecting more effective treatments. PZD scores that fall below 18 indicate ineffectiveness, between 18 and 54 questionable effectiveness, between 55 and 80 fair effectiveness, and over 80 the treatment can be assessed as highly effective (Scotti et al. 1991).

Generally, the method for calculating PND in the present study followed the same conventions as used by Wehmeyer et al. (2006). For each unique treatment phase and each preceding baseline identified, the PND scores were measured. It is important to note that each baseline and treatment phase was addressed as a unique case. Therefore, in the 41 studies, 347 unique treatment phases (95 in Wehmeyer et al. 2006) resulted in PND scores.

Post hoc analyses

The impact of two factors (the participants’ level of IDD, and the presence of UD features) on study outcomes (PND scores) was analyzed. The same non-parametric Kruskal–Wallis test, as used by Wehmeyer et al. (2006), was used specifying PND scores as dependent variables and participants’ characteristics and UD features of technology as independent variables.

PND and UD reliability measures

The reliability of the PND scores was investigated as per Wehmeyer et al. (2006). In this study, three experienced raters (the three authors) independently measured the PND scores for each unique treatment phase and its preceding baseline. Agreement was reached when the three raters obtained identical PND scores. This procedure included three steps. First, the lead researcher (author 1) calculated all PND scores and excluded studies that did not meet data criteria. Second, author 3 worked collaboratively with the lead researcher through a random sample of 12 of the 41 studies and cross-checked the PND calculations. Finally, author 2 independently verified each decision by reviewing the same sample of 12 papers and the decisions made in the first two steps.

The assessment of the reliability of identified UD features followed the same steps as mentioned above. The three authors independently evaluated the technology’s UD features identified by each study’s authors. Reliability was calculated by dividing the number of agreements by the total number of agreements and disagreements, multiplied by 100.

Results

The results are reported in six sub-sections: (1) descriptive statistics pertaining to participants’ characteristics, (2) descriptive statistics on: Types of technology, employment-related goals, and work settings, (3) intervention effects, (4) post hoc analyses, (5) presence of various UD features, and (6) PND and UD reliability measures.

Descriptive statistics: participants’ characteristics

These included the number of participants, the age range, and the IQ scores range and were reported both for the total sample and by gender, as per Wehmeyer et al. (2006). The 41 articles involved 112 unique study participants, aged from 11 to 56 years old (M = 19.57, SD = 7.86). All participants were engaged in employment-related activities. IQ scores were identified for only 65 participants, ranging from 16 to 75 (M = 50.45, SD = 10.53). There were 83 male and 29 female participants. The male participants’ age range was from 12 to 56 (M = 19.6, SD = 8.43) and IQ scores from 16 to 75 (M = 50, SD = 10.5). The females participants’ age range was from 11 to 36 (M = 19.48, SD = 6.04) and IQ scores from 36 to 75 (M = 51.76, SD = 10.83).

By comparison, Wehmeyer at al. (2006) presented 42 unique study participants in their 13 articles included, aged from 12 to 37 years old (M = 20.23, SD = 6.89). IQ scores were reported for 30 participants and ranged from 28 to 72 (M = 42.86, SD = 11.75). There were 22 males and 20 females with males ranging in age from 14 to 34 (M = 18.52, SD = 4.51) and IQ scores from 28 to 72 (M = 44.68, SD = 11.76), while females ranged in age from 12 to 37 (M = 22.12, SD = 8.54) and IQ scores from 29 to 72 (M = 40.78, SD = 11.82).

Descriptive statistics on: types of technology used, employment-related goals, and work settings

Overall, the 41 SSED studies used a variety of types of technology in a range of employment-related goals and work settings (see Table 1). The types of technology included (a) auditory prompting devices, (b) video-assisted training (e.g. DVD, iPod), (c) palmtops (e.g. PDA, handheld computers), (d) desktop and laptop computers, (e) pictorial prompts, (f) augmented reality devices, (g) smartphones, and (h) watches.

These technology supports were used for a range of employment-related goals in different work settings. These included: (a) work-related social skills, (b) task sequencing and transition skills, (c) vocational task performance and completion, (d) food preparation skills, (e) computer-use itself, (f) general cleaning skills, and (g) time management skills. The work settings ranged from simulated to real.

In Wehmeyer et al. (2006), the range of technology supports, and the employment-related goals differed somewhat from the present study. The range of technology included the first four aforementioned types ((a) auditory prompting, (b) video-assisted training, (c) palmtops, (d) desktop computers) but with the addition of ‘augmentative and alternative communication’. The ‘pictorial prompts’, ‘augmented reality device’, ‘smartphones’ and ‘watches’ were the additional types found in this study.

In Wehmeyer et al. (2006), the employment-related goals included the first five aforementioned goals ((a) work-related social skills, (b) task sequencing and transition skills, (c) vocational task performance and completion, (d) food preparation skills, and (e) computer-use itself) and two other goals, ‘the vocational assembly skills’ and ‘requesting assistance on vocational task’. The ‘general cleaning skills’ and ‘time management skills’ were the additional goals found in the present study.

Intervention effects

A visual analysis of the graphic data was performed and PND scores for each baseline and treatment phase were aggregated. The mean PND scores associated with the effectiveness of technology were calculated. The mean PND score for all 347 treatment phases was 87 (SD = 29.9). By comparison, the mean PND score reported by Wehmeyer et al. (2006) was 93 (SD = 0.14) for 95 unique treatment phases.

Post hoc analyses

The Kruskal–Wallis non-parametric test revealed significant differences in the PND scores based on level of IDD. From the 347 unique treatment phases, 292 included information about the participants’ level of IDD. The Kruskal–Wallis test found significant differences between the groups (p = 0.047, r = 0.12), with participants with mild/moderate levels of IDD (n = 271) having an average PND score of 86 (SD = 30.85), and participants with severe/profound levels of IDD (n = 21) having an average PND score of 71 (SD = 43.7). As an extension to the Wehmeyer et al. (2006) approach, and consistent with current APA guidelines, effect sizes have been added where appropriate.

A Kruskal–Wallis test for PND scores was also employed based on the identified UD features incorporated into the technology. The findings revealed significant differences. Two groups were formed according to the presence of UD features as in Wehmeyer et al. (2006). The first one had no UD features (n = 67), and the second one had one or more (n = 280). The results of the Kruskal–Wallis test revealed significant differences between these groups (p = 0.000, r = 0.29). In particular, cases with UD features had an average PND score of 92 (SD = 22.43) and cases without UD features had an average of 67 (SD = 45).

By comparison, in Wehmeyer et al. (2006), there were some points of difference regarding the findings of the impact of (a) the level of IDD functioning, and (b) the presence of UD features on the PND scores. The impact of level of IDD functioning on the PND scores differed from the results in the present study since no significant differences were found. The participants with mild/moderate IDD (n = 42) had an average PND score of 94 (SD = 0.11), and participants with severe/profound IDD (n = 53) had an average PND score of 92 (SD = 0.16). Regarding the impact of the presence of UD features on PND scores, significant differences between the groups (p = 0.035) were found, with the studies incorporating UD features having an average PND score of 97 (SD = 0.08), and studies not addressing UD features having an average PND score of 91 (SD = 0.18).

Presence of UD features

The presence of UD features as discussed or identified by the studies’ authors as an incorporated feature into the technology was measured. It was found that 13% of the 347 treatment phases had none, 24% had one, 38% had two, and 25% had three or more UD features identified.

The degree to which each UD feature was discussed or identified by the authors was also measured. The following frequencies were calculated: ‘Equitable Use’ 14%, ‘Flexible Use’ 20%, ‘Simple Intuitive Use’ 22%, ‘Size and Space’ 0%, ‘Perceptible Information’ 32%, ‘Tolerance for Error’ 2%, and ‘Low Phys/Cog Effort’ 10%.

By comparison, in Wehmeyer et al. (2006) there are points of difference regarding the presence of UD features. It was demonstrated that 42% of the 95 cases had none, 15% had one, 40% had two, and 3% had three UD features identified. In addition, even though the presence of the principles ‘Equitable Use’ (14%), ‘Flexible Use’ (28%), ‘Simple Intuitive Use’ (24%) and ‘Size and Space’ (0%) were similar to the findings in this study, the UD features ‘Perceptible Information’ (16%), ‘Tolerance for Error’ (20%) and ‘Low Phys/Cog Effort’ (0%) differ substantially.

PND and UD reliability measures

The three authors agreed on 100% of the included and excluded articles and on the calculations of the PND scores. The percentage of agreement regarding the identified UD features incorporated into the technology was initially 82%. After two rounds, the raters came to a consensus on the remaining data point calculations in order to ensure the most accurate and representative result.

Discussion

Four major points of discussion emerge. These concern: (1) the overall impact of technology on employment-related outcomes, (2) the functional impact of levels of IDD, (3) the presence of UD features, and (4) limitations to the study.

Overall impact of technology on employment-related outcomes

This meta-analysis supports the earlier findings of Wehmeyer et al. (2006) that the use of technology is effective for individuals with IDD. This can be considered in terms of achieving more positive employment-related outcomes, as technology has a significant and positive impact on the enhancement of targeted employment skills and competencies. The overall PND score of 87 for all 347 treatment phases is considered in the upper half of the ‘effective’ treatments range (Scruggs and Mastropieri 1998). It is in reasonable proximity to the 93 documented by Wehmeyer et al. (2006) for their 95 unique cases. It seems that the use of technology enables, enhances or extends the functional capabilities of people with IDD. Nevertheless, the findings are based only on SSED studies and, thus, more research in the field, including more sophisticated group experimental design studies, is needed to explore and extend these parameters.

The standard deviation for the mean PND scores in the current meta-analysis (SD = 29.9) is larger than in Wehmeyer et al. (2006) (SD = 0.14). This SD indicates that the PND scores are more spread out over a wider range of values. The larger number of studies and the range of different types of technology included in this meta-analysis, resulting in more or less effective treatments might be a possible explanation. A further analysis of the impact of particular types of technology is needed to investigate this.

The functional impact of levels of IDD

The level of IDD was shown to be a significant factor influencing the effectiveness of technology use with people with mild/moderate levels of IDD who exhibited higher PND average scores than people with severe/profound levels of IDD (86 vs. 71, respectively). A possible explanation might be that the level of sophistication of available applied cognitive technology is higher than in the past and strategic use of this technology has become more complex. This suggests that differences between levels of IDD should be taken into account when selecting a technology support and designing an intervention, by providing more options for accommodations to meet the needs of people with IDD.

The presence of UD features

Significant differences were found between studies using technology with (one or more) and without UD features, with the technology incorporating UD features resulting in a higher PND score (92 vs. 67, respectively). The range of current technology user options and the concomitant higher level of sophistication might account for these differences. In this context, interventions with technology should be designed to meet the unique needs of people with IDD by incorporating more UD features. The UD features ‘flexibility in use’, ‘simplicity and intuitiveness’, and ‘perceptible information’ were most frequently incorporated into the technology used. These UD features might be particularly important for these individuals, as Wehmeyer et al. (2006) also suggested.

The presence of two particular UD features differed from the findings of the original contribution of Wehmeyer et al. (2006), with ‘Perceptible Information’ having a much lower percentage in (16% vs. 32% in the present study) and ‘Low Physical/Cognitive Effort’ being absent (0% vs. 10% in the present study). One possible explanation for this finding may be the use of more high-tech, sophisticated technology in the last decade than in the past. One representative example of applied cognitive technology might be the use of electronic and information technology and palmtop or hand-held computers (e.g. iPads and iPhones) providing multiple modes (pictorial, verbal, tactile) for presentation of essential information, while also accommodating variations in hand and grip size. More research is needed to understand the impact of the use of different types of technology on employment-related outcomes for people with IDD.

Limitations of the study

This study has three discernible limitations. First, the studies examined were of a relatively limited number (41). Nonetheless, they resulted in 347 unique treatment phases, which are of a sufficient size for a meta-analysis, given that the use of technology for improving employment outcomes for people with IDD is a relatively new area of research. Thus, new peer-reviewed evidence is needed in order to address this issue. As noted earlier, more adventurous group experimental research design studies are also needed to extend the field.

Second, the work settings included in the studies ranged from simulated to real locations, making it difficult to generalize the present findings to a specific work setting. An analysis of the impact of ‘work setting’ as the independent variable on intervention effectiveness (PND scores) is warranted in order to reach a conclusion.

Third, the initial inter-rater agreement (82%) for the evaluation of the degree to which UD features were discussed or identified by the authors of each study was reasonable. However, the three authors found it difficult to objectively ascertain the use of UD principles in this context. Thus, there is a need of a development of a more specific evaluation tool focused on interpreting and associating the UD principles with the features of technology devices or applications to make the evaluation more accurate.

Conclusion

This study has updated and extended the original contribution of Wehmeyer et al. (2006) and demonstrated that technology use by people with IDD continues to be a promising research area. The intervention effects in the relevant studies from 2004 to 2016 were identified as ‘effective’. This is in line with the original contribution of Wehmeyer et al. (2006) that included studies published until 2003. This study taken together with the original meta-analysis of Wehmeyer et al. (2006) indicates that technology use by people with IDD has the potential to enable, enhance and/or extend adaptive functions in order to achieve more positive employment-related outcomes.

Conflict of interest

No potential conflict of interest was reported by the authors.

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