The Effects of Technology Supports on Community Grocery Shopping Skills for Students With Intellectual and Developmental Disabilities: A Meta-Analysis

Abstract

In this meta-analytic review, we investigated the effects of technology supports on the acquisition of shopping skills for students with intellectual and developmental disabilities (IDD) between the ages of 5 and 24. Nineteen single-case experimental research studies, presented in 15 research articles, met the current study’s inclusion criteria and the What Works Clearinghouse (WWC) standards. An analysis of potential moderators was conducted, and we calculated effect sizes using Tau-U to examine the impact of age, diagnosis, and type of technology on the reported outcomes for the 56 participants. The results from the included studies provide evidence that a wide range of technology interventions had a positive impact on shopping performance. These positive effects were seen for individuals across a wide range of ages and disability types, and for a wide variety of shopping skills. The strongest effect sizes were observed for technologies that provided visual supports rather than just auditory support. We provide an interpretation of the findings, implications of the results, and recommended areas for future research.

Keywords

shopping skills assistive technology instructional technology developmental disability autism spectrum disorder intellectual disability community-based settings

Many individuals with intellectual and developmental disabilities (IDD), such as autism spectrum disorder and Down syndrome, require support to perform functional life skills (Alwell & Cobb, 2009; Chiang et al., 2017; Heward et al., 2016). Functional life skills are defined as skills or tasks which contribute to the successful and independent functioning of an individual in adulthood (Browder et al., 2004; Brown et al., 1979). These include those skills which are needed for successful community living, including planning menus, shopping, and preparing meals (Browder et al., 2004; Brown et al., 1979; Cronin, 1996).

Grocery shopping is frequently identified as a critical functional life skill, as it is a key determinant of an individual’s self-sufficiency, nutrition, and health (Hall et al., 2003; Morse et al., 1996; Mojtahedi et al., 2008). There are many component skills within grocery shopping which can be challenging for individuals with IDD. For example, to grocery shop successfully, one has to complete several tasks such as navigating the store, selecting desired items, and monitoring the cost of purchases. These tasks place demands on the individual’s attention, working memory, multi-tasking abilities, and problem-solving skills, all areas of challenge for persons with IDD (Carulla et al., 2011). In addition, omitting just one of these tasks (e.g., failing to pay for selected items) results in an overall negative shopping experience.

Previous research has examined the use of a wide variety of technologies to improve the grocery shopping performance of individuals with IDD (Xin et al., 2005). These technologies can be categorized by their features (Odom et al., 2015). For example, low-tech approaches are not electronically powered, and would include the provision of visual schedules (i.e., organized series of images to depict a sequence of events; Cihak et al., 2004), flashcards with the name of a target food item (Colyer & Collins, 1996), or text-based shopping lists (Goo et al., 2016) as supports for shopping. More recently, there has been considerable attention to the use of high-tech approaches: electronically powered technologies such as audio recorders (Bouck et al., 2012, 2013) and tablet computers with apps (Cakmak & Cakmak, 2015; Douglas et al., 2015; Hsu et al., 2014).

Technology can also be categorized by its function. In an instructional technology (IT) intervention, the technology functions as “a tool designed to help a learner acquire a set of responses, (and is) typically used for a short period of time during the acquisition or fluency phase of learning” (Ayres et al., 2016, p. 132). A common example is the use of computer-based instruction to teach students how to shop for groceries (e.g., how to make a shopping list, how to navigate the store). In IT interventions, the technology will no longer be used after the learner can perform the target behavior independently (Ayres et al., 2016; Shepley et al., 2017). In an assistive technology (AT) intervention, the technology is used as an ongoing cognitive, physical, or communication support for a desired function. For example, an individual with disability and complex communication needs can use an augmentative and alternative communication (AAC) application on a mobile device to communicate with others (e.g., deli staff, cashiers) at the store. The expectation is that the AT will be used by the individual with IDD to provide support for activities on an ongoing basis (Edyburn, 2013; Shepley et al., 2017).

Participation in necessary and socially valued activities such as grocery shopping is an important goal for individuals with disabilities, families, and educators (Aljehany & Bennett, 2019; Park et al., 2019; Simplican et al., 2015). This emphasis on meaningful life outcomes has prompted educators and researchers to examine ways in which technology might support the performance of key life skills in community settings (Edyburn, 2013; Shepley et al., 2017). Although recent reviews have examined the role of technology for persons with disabilities in community activities such as employment, transportation, and social participation (Chanana et al., 2017; Morash-Macneil et al., 2018; Owuor et al., 2018), there is a need to examine how technology can support successful performance for students with IDD in specialized functional life skill activities, including grocery shopping.

The purpose of this meta-analytic review, therefore, is to examine the effect of technology on the grocery shopping performance of individuals with IDD, as well as the presence of moderator effects. This review addresses the following questions:

What populations have participated in grocery shopping intervention research?

In what settings have interventions occurred?

What skills have been targeted in grocery shopping intervention research?

What types of technology were used?

What sensory modalities (e.g., vision, hearing) were targeted with the technology?

What was the impact of the technology intervention for improving shopping skills in community-based settings?

What was the reported social validity for the interventions?

Method

To complete this review and analysis, the following procedures were conducted: (a) creation of definitions for key terms, (b) systematic search of electronic databases, (c) screening of potential studies against initial inclusion and exclusion criteria, (d) application of design quality standards, (e) calculation of inter-rater reliability, (f) extraction of descriptive information and coding of potential moderators, and (g) data extraction and analysis.

Definitions

In this review, we examined the impact of technology interventions on the grocery shopping performance of school-age individuals with IDD. Grocery shopping is a multi-step activity and involves the performance of one or more of the following six component skills (adapted from Mechling & Gast, 2003): (a) make a shopping list, (b) read the aisle or item words, (c) navigate the store and select the item, (d) read prices and calculate cost, (e) pay for the items, and (f) communicate with staff. See Table 1 for the definitions of the six components of grocery shopping.

Table 1.

Component Skills for Grocery Shopping Skill and Definitions.

Component Skill	Definition
(a) Make a shopping list	Creating a written memo, audio file, or picture document that provide the records of target purchase items
(b) Read the aisle or item words	Speaking or identifying (e.g., by pointing) symbols, texts that appeal on aisle-signs, labels, etc.
(c) Navigate the store and select the item	Walking to the location for a target item. Picking up or pointing to the target item and placing item in cart or basket (as appropriate). Placing the shortest line
(d) Read prices or calculate cost	Comparing the price or selecting the lowest-priced item (e.g., number line). Counting the total value to pay (e.g., counting on strategy, one-more-than strategy)
(e) Pay for the items	Purchasing items through paying process. Giving money to the clerk or paying money through the machine (i.e., self-checkout)
(f) Communicate with staff	Communicating with staff members at store (e.g., asking for assistance, greeting staff).

Search Procedures

We performed an electronic search during November 2019 in four electronic databases—ProQuest Educational Journals, ERIC, PsycINFO, and Academic Search Complete— to identify articles for this review. All combinations of the following keywords were used: (1) “developmental disabilit*” OR “intellectual disabilit*” OR “autis*” OR “mental* retard*” OR “down syndrome” OR “handicap*”; AND (2) “purchas*” OR “shop*” OR “grocer*” OR “store*.” There were no restrictions regarding the year in which the study was published. The search was limited to peer-reviewed articles published in English. The initial database search yielded 639 articles. After 185 duplicate articles were removed, 454 articles moved forward to the screening process.

Screening and reliability of inclusion criteria

Next, both the first and second authors conducted a screening by reading the title and abstract (and, if necessary, the methods section) to remove articles that did not address at least one component of grocery shopping, and did not include at least one individual with IDD. This resulted in the removal of 425 articles. Figure 1 shows the search and screening process.

Figure 1.

PRISMA chart.

The initial review of articles resulted in 29 articles for further evaluation. At this time, the first author performed an ancestral search by reviewing the reference lists of the 29 articles, and conducted a forward search by locating papers that cited one of the 29 articles. This resulted in an additional 15 papers (for a total of 44) that received a full review using the inclusion and exclusion criteria.

A study had to meet five criteria to be included in this meta-analytic review: (a) included school-age students (i.e., ages 5–24) with developmental disabilities (i.e., intellectual disabilities, ASD); (b) used single-case research experimental methods; (c) provided information on at least one component of grocery shopping skills as a dependent variable (see Table 1); (d) used technology (including low-tech and/or high-tech options) to improve shopping skills; (e) collected at least one data point in a community-based setting (e.g., a grocery store, a group home) before the intervention phase and after the intervention phase; and (f) presented data in graphical displays.

Application of Design Standards

After screening with inclusion and exclusion criteria, we evaluated the design quality for each study by using the design standards developed by the What Works Clearinghouse (WWC; Kratochwill et al., 2013). The authors examined the methodology of the remaining 29 articles using the WWC standards (Version 4.0), inclusive of the additional criteria recommended to analyze studies. Studies meeting the design standards with or without reservation were included in this review, while those not meeting the standards were excluded. We applied the following six criteria for each study: (a) included a systematic manipulation of the independent variable; (b) provided information on the dependent variable as collected by more than one assessor; (c) presented inter-assessor agreement for at least 20% of data points in each phase and for each condition and (d) at least 80% of these collected data points were judged to be in agreement; (e) provided at least three demonstrations of intervention effect; and (f) included at least three data points at each phase in order to demonstrate effect over time (What Works Clearinghouse, 2017, version 4.0, p. A-4). For the 29 articles, 19 studies from 15 articles met the WWC design quality standards, while 14 articles were removed because they did not meet the standards.

Inter-rater Reliability for Inclusion

The second author randomly selected over one-third (n = 10) of the 29 articles to calculate inter-rater reliability for inclusion. To calculate the percentage of agreement, the authors divided agreements by agreements plus disagreements for each evaluation of the six design standards criteria for each individual study. Inter-rater reliability was calculated as 91%.

Extraction of Descriptive Information and Coding of Potential Moderators

Studies identified as Meets Design Standards or Meets Design Standards with Reservations were summarized based on the following potential moderators: (a) participant age; (b) participant diagnosis; (c) instructional settings; (d) technology type (low-tech, high-tech); and (e) technology output (auditory, visual/picture, text, combination). If a study variable was not indicated in the articles, it was coded as “not provided.”

The participant age data was separated into three categories: (a) 5 to 11 (elementary school), (b) 12 to 15 (middle school), and (c) 16 to 24 (high school and post-secondary). For participant diagnosis, there were three categories: (a) intellectual disability, (b) autism or ASD, and (c) multiple disability. The instructional settings for the studies were coded as either (a) classroom (e.g., a school setting), (b) community-based (e.g., grocery stores, convenience stores, group homes), or (c) both. Technology interventions were categorized as low-tech and/or high-tech interventions. Low-tech interventions made use of non-electronic technology supports such as paper visual schedules, flash cards with the name of a targeted food item, and number lines to assist with calculating costs (Colyer & Collins, 1996). High-tech interventions made use of electronic technology supports such as audio-recorders, calculators, and apps on tablet computers (Bouck et al., 2014; Hoffmann et al., 2017). Summaries of information for each study are presented in Table 2.

Table 2.

Summary of Studies.

Study	Design	Participant (s)		Instructional Setting (s)	Independent Variable (s)		Sensory Modality	ES
Study	Design	Number, Age	Diagnosis	Instructional Setting (s)	Technology in School	Technology in Store	Sensory Modality	ES
Alcantara (1994)	MBD-P	2, 8–9 y	ASD	Both	Videotape (VBI)	Photograph	V + A	0.99
Baumgart & Van Walleghem (1987)—Study 1	MPD-W	1, 18 y	Moderate ID	Classroom	Computer (CAI)	Flashcard	V + A	0.81
Baumgart & Van Walleghem (1987)—Study 2	MPD-W	1, 18 y	Moderate ID	Classroom	Flashcard	Flashcard	V + A	1
Bouck et al. (2012)	ABAB	3, 18–20 y	Moderate ID/ Multiple disability	Classroom	Audio recorder	Audio recorder	A	P1 = -0.06
								P2 = -0.11
								P3 = -0.33
Bouck et al. (2013)—Study 1^a	ATD	3, 17–19 y	Multiple disability	Both	Audio recorder	Audio recorder	A	P1 s = 1, P1p = 1
								P2 s = 1, P2p =1
								P3 s = 0.67, P3p = 0.67
Bouck et al. (2013)—Study 2^a	ATD	3, 17–19 y	Multiple disability	Both	Audio recorder	Audio recorder	A	P1 s = -0.67, P1p = -0.75
								P2 s = -0.67, P2p = -1
								P3 s = 0, P3p = 0.17
Cakmak & Cakmak (2015)	MPD-P	3, 17–19 y	ASD / Mild—Moderate ID	Classroom	Tablets (VBI)	N/A	V + A	1
Cihak et al. (2004)	MPD-P	5, 17–19 y	Moderate ID	Both	Visual schedule	Visual schedule	V	1
Colyer & Collins (1996)	MPD-P	3, 12–15 y	Mild—Moderate ID	Classroom	Flashcard	N/A	V	0.99
Douglas et al. (2015)—Study 2a	MPD-W	3, 17–20 y	Moderate ID	Classroom	Computer (CAI)	Tablets	V + A	P1 = 1
								P2 = 1
								P3 = 1
Douglas et al. (2015)—Study 2b	MPD-W	3, 17–20 y	Moderate ID	Classroom	Computer (CAI)	Tablets	V + A	P1 = 1
								P2 = 1
								P3 = 1
Giere et al. (1989)—Study 1	MBD-P	3, 19–21 y	Moderate—Severe ID	Community^b	Visual schedule^b	Visual schedules^b	V	0.92
Giere et al. (1989)—Study 2	MBD-P	3, 19–21 y	Moderate—Severe ID	Community	Visual schedule^b	Visual schedule	V	1
Gil et al. (2019)	MPD-P	3,19–21 y	Moderate ID / ASD	Community	N/A	Tablets	V	0.94
Goo et al. (2016)	MPD-P	4, 17–18 y	Moderate ID	Classroom	Computer	Card with text	V + A	0.90
Hansen & Morgan (2008)	MBD-P	3, 16–17 y	Mild—Moderate ID	Classroom	Computer	N/A	V + A	1
Hsu et al. (2016)	MTD-PR&S	4, 14–15 y	Moderate ID	Classroom	Tablets	Tablets	V	1
Hsu et al. (2014)	MPD-P	3, 13–14 y	ID	Classroom	Tablets	Tablets	V	1
Yakubova & Taber-Doughty (2013)	MBD-P	3, 12–15 y	ASD	Classroom	Computer (video modeling)	N/A	V + A	1

Note. ASD = autism spectrum disability, ID = intellectual disability, (B) = baseline, (I) = intervention, MBD-S = multiple baseline design across settings ATD = alternating treatment design, MPD-ST = multiple probe design across subtest, MPD-P = multiple probe design across participants, MPD-W = multiple probe design across words, MBD-P = multiple baseline design across participants, MTD-PR&S = multiple treatment design across price ranges and settings, MPD-S&P = multiple probe design across settings and participants, VBI = video based instruction, CAI = computer assisted instruction, V = visual, A = auditory, P1 = participant 1, P2 = participant 2, P3 = participant 3.

a. P#s is the effect size for self-recorded audio, P#p is the effect size for pre-recorded audio.

b. Instruction and assessment conducted in a group home setting.

Data Extraction and Analysis

We used the Tau-U effect size to analyze the data. Tau-U is a nonparametric effect size that measures non-overlapping data between phases (Parker, Vannest, & Davis, 2011). We selected Tau-U because it demonstrates strong statistical power compared to other nonoverlap indexes, and Tau-U also provides a conservative analysis by measuring A–B phase nonoverlap data with the option of controlling for positive baseline trends in any pattern (Parker, Vannest, & Davis, 2011). To obtain the data needed for the calculation of Tau-U, the first author used WebPlotDigitizer (Rohatgi, 2017), which is an open-source web-based software tool to extract numerical data from existing graph images. Using this tool, data values were extracted from adjacent baseline and intervention phases by clicking on the graph. This data was then used in Tau-U Calculator (http://www.singlecaseresearch.org/calculators/tau-u) to calculate Tau-U and confidence interval (CI) values for each contrast (Vannest et al., 2016).

The results of Tau analyses are reported as a range from an effect of -1.0 to 1.0, where outcomes in the negative range represent deteriorating effects (Parker, Vannest, Davis, & Sauber, 2011). Tau effect sizes can be interpreted as follows: .20 and below, small improvement; 0.20-0.60, moderate improvement; 0.60-0.80, large improvement; and over 0.80, a very large improvement (Vannest & Ninci, 2015).

Results

A total of 19 studies from 15 articles met the criteria and WWC standards and are reviewed in this paper. Table 2 presents a summary of each article according to the participant age, diagnosis, settings, and independent variable(s). Table 3 contains a forest plot with potential moderators, group Tau-U values, and CI.

Table 3.

Forest Plot and Effect Size Data by Potential Moderators.

Study Variables	Number of Participants	Percentage	Tau-U (90% CI)
Participants
Age
5-11	2	3.6%	0.98 [0.79, 1]
12-15	13	23.2%	0.99 [0.90, 1]
16-24	41	73.2%	0.73 [0.64, 0.82]
Disability
ID	41	73.2%	0.91 [0.84, 0.98]
ASD	8	14.3%	1 [0.83, 1]
Multiple disability	7	12.5%	0.03 [0.25, 0.31]
Settings for training
School (i.e., classroom)	31	55.4%	0.93 [0.85, 1]
Stores	6	10.7%	0.97 [0.73, 1]
Combination (schools + stores)	16	28.6%	0.62 [0.48, 0.76]
Community	3	5.4%	1 [0.59, 1]
Types of technology in school
Low-tech
Visual schedule^a	11	19.6%	0.98 [0.83, 1]
Flashcard (paper with number)	4	7.1%	1 [0.79, 1]
High-tech
Audio recorder	9	16.1%	-0.01 [-0.25, 0.24]
Video	2	3.6%	0.99 [0. 79, 1]
Computer	17	30.4%	0.88 [0.77, 0.99]
Tablets (iPad, mobile phone)^b	13	23.2%	0.95 [0.85, 1]
Types of technology in store
Picture (photograph)	2	3.6%	0.99 [0.79, 1]
Visual schedule	11	19.6%	0.98 [0.83, 1]
Flashcard	2	3.6%	0.90 [0.71, 1]
Card with text	4	7.1%	0.90 [0.55, 1]
Audio recorder	9	16.1%	-0.01 [-0.25, 0.24]
Tablets	16	28.6%	0.94 [0.85, 1]
N/A	12	21.4%	–

a. Giere et al. (Studies 1 and 2, 1989) provided instruction in a group home setting.

b. Gil et al. (2019) provided instruction in a community store.

Participant and Settings

A total of 56 participants between the ages of 8 and 21 were reported for the 19 studies included in this review. For analysis, the age variable was categorized into three ranges as elementary school students (CA = 5 to 11), middle school students (CA = 12 to 15), and high school and post-secondary students (CA = 16 to 24). Only two students (3.6%) fell within the youngest age group, while a majority of the students (n = 41, 73.2%) were between the ages of 16 and 24. Thirteen students (23.2%) fell within the ages of 12-15. The Tau-U value of the elementary group was within the range of a very large improvement (ES = 0.98, 90% CI [0.79, 1]). When looking at the middle school group, the Tau-U value was also in the range of a very large improvement (ES = 0.99, 90% CI [0.90, 1]). Finally, the Tau-U value of the high school age group was in the range of a large improvement (ES = 0.73, 90% CI [0.64, 0.82]). The Tau-U values for two disability groups were in the range of a very large improvement, including for those students with intellectual disabilities (n = 41, ES = 0.91, 90% CI [0.84, 0.98]) and ASD (n = 8, ES = 1, 90% CI [0.83, 1]), while the Tau-U value of multiple disabilities group was in the range of a small improvement (n = 7, ES = 0.03, 90% CI [0.25, 0.31]).

The data reported in these studies was collected in a wide variety of settings. To be included in this review, the study needed to include data on at least one probe conducted in a community setting before intervention, as well as one probe after intervention (i.e., a minimum of two probes at two different times in a community setting). The instructional settings for these studies varied widely, including academic settings (e.g., school classrooms), community settings (e.g., community stores, group home kitchens), and mixtures (i.e., a combination of instructional settings). The Tau-U value for instruction in school settings only (n = 31) was within the range of a very large improvement (ES = 0.93, 90% CI [0.85, 1]). Three studies used community group home settings, resulting in a Tau-U value in the range of a very large improvement (ES = 1, 90% CI [0.59, 1]. In the case of instruction in the community store settings only (n = 6), the Tau-U value was in the range of a large improvement (ES = 0.97, 90% CI [0.73, 1]). The Tau-U value for a mix of school and community store settings (n = 16) was in the range of a large improvement (ES = 0.62, 90% CI [0.48, 0.76]).

Although training in a variety of settings has been demonstrated to have a positive impact, Cihak et al. (2004) provided an interesting systematic investigation of the impact of instructional setting on the community shopping performance of five young adults with moderate ID. The authors reported that interventions that made combined use of simulation training (in an academic setting) and community-based training produced more efficient and effective outcomes for generalization than did interventions that provided either simulation or community-based training alone.

Shopping Skills

Each of the six component shopping skills were addressed in at least one study (see Table 4). None of the studies addressed all six component skills. One study addressed five component skills, while 12 addressed only one of the six skills. The most frequently addressed component skill was paying for the items (n = 10). The least frequently addressed skill was making the shopping list (n = 1). Eight of the studies provided information on both maintenance and generalization (Baugmart & Van Walleghem, 1987, Studies 1 and 2; Cihak et al., 2004; Colyer & Collins, 1996; Giere et al., 1989; Hansen & Morgan, 2008; Hsu et al., 2014, 2016), seven examined maintenance only (Alcantara, 1994; Bouck et al., 2013, Studies 1 and 2; Cakmak & Cakmak, 2015; Douglas et al., 2015, Studies 2a and 2b; Giere et al., 1989), and three examined only generalization (Gil et al., 2019; Goo et al., 2016; Yakubova & Taber-Doughty, 2013).

Table 4.

Shopping Component Skills.

StudyAuthors (Year)	Shopping Component Skills
StudyAuthors (Year)	Make a Shopping List	Read the Aisle or Item Words	Navigate the Store and Select the Item	Read Prices and Calculate Cost	Pay for the Items	Communicate With Staff
Alcantara (1994)		•	•	•	•	•
Baugmart & Van Walleghem (1987)—Study 1		•
Baugmart & Van Walleghem (1987)—Study 2		•
Bouck et al. (2012)			•
Bouck et al. (2013)—Study 1		•
Bouck et al. (2013)—Study 2			•
Cakmak & Cakmak (2015)		•	•	•	•
Cihak et al. (2004)					•
Colyer & Collins (1996)					•
Douglas et al. (2015)—Study 2a		•
Douglas et al. (2015)—Study 2b			•
Giere et al. (1989)—Study 1	•
Giere et al. (1989)—Study 2		•	•	•	•
Gil et al. (2019)		•	•	•	•
Goo et al. (2016)		•	•	•
Hansen & Morgan (2008)			•		•
Hsu et al. (2016)					•
Hsu et al. (2014)					•
Yakubova & Taber-Doughty (2013)					•	•

Technology Supports

A wide variety of technologies were used in the 19 studies (see Table 2) including both low-tech and high-tech supports. Specifically, the effect size for low-tech approaches was within the range of a very large improvement (n = 15, ES = 0.99, 90% CI [0.86, 1]), and for high-tech approaches, also a very large improvement (n = 41, ES = 0.83, 90% CI [0.76, 0.90]). The technologies used addressed the auditory modality (e.g., audio recorder), visual modality (e.g., visual schedule), or a combination of both auditory and visual (e.g., computer with voice output).

Low-tech supports

All low-tech supports were made of paper, and overall resulted in a very large improvement (n = 15, ES = 0.99, 90% CI [0.86, 1]). Specifically, visual schedules (i.e., an organized series of images to support performance in a task) were within the range of a very large improvement (n = 11, ES = 0.98, 90% CI [0.83, 1]). For example, Giere et al. (1989) used a visual schedule approach to teach three young adults to make a shopping list (Study 1), and then to navigate the store and purchase desired items (Study 2). The use of flashcards (i.e., small pieces of paper containing a single image, number, or word) also were within the range of a very large improvement (n = 4, ES = 1, 90% CI [0.79, 1]). Colyer and Collins (1996), for example, described a classroom-based intervention in which students were taught a “next dollar” strategy (e.g., to pay $4 for an item costing $3.69). After instruction using flashcards that each provided the price for a variety of items, three of the four students made appropriate generalized use of the strategy while shopping in a community store. Flashcards can also be used to support learning to read single words, as in Baumgart and Van Walleghem (1987, Study 2). The authors reported that the use of flashcards as part of an instructional procedure with a young adult with moderate ID assisted him in learning to read the words that appear in aisle signs in a store.

High-tech supports

Most high-tech interventions involved the use of computer-assisted instruction (CAI) in the classroom, and the effect size was within the range of a very large improvement (n = 17, ES = 0.88, 90% CI [0.77, 0.99]). For example, Douglas et al. (2015, Study 2a) taught students to read the aisle or item words through a CAI approach in which the student spoke aloud the words that were flashed on a computer screen.

Audio recorders provide audio support for the completion of a task. For example, Bouck et al. (2012) taught three young adults with disabilities to listen to a shopping list spoken aloud by audio recorders as a cognitive support during grocery shopping. The total effect size for all studies using audio recorders is within the range of a minimal effect (n = 9, ES = -0.01, 90% CI [-0.25, 0.24]).

The effect size for the use of video was within the range of a very large improvement (n = 2, ES = 0.99, 90% CI [0.79, 1]). For example, Alcantara (1994) created a video model (played in VHS videocassette player) which portrayed the 32 steps necessary to complete a shopping activity. After receiving a training package that included viewing the video model, the participants demonstrated high levels of performance with the component shopping skills, including walking to the store, selecting and paying for the target items, and exiting the store.

Handheld technologies, including tablet technology (e.g., iPad), were also within the range of a very large improvement (n = 13, ES = 0.95, 90% CI [0.85, 1]). For example, Hsu et al. (2016) reported on the benefits of the use of an app that served as a mobile purchasing assistant system to support students in calculating the assumed total value of items. Four secondary school students with moderate ID performed better with the use of the app rather than with a traditional approach which did not include the use of the app.

Modalities

A majority of students (n = 25) used technology that exclusively targeted the visual modality. Specifically, 15 students used low-tech visuals (e.g., visual schedules, flash cards), while 10 students used high-tech approaches (e.g., tablets). It should be noted that in many of these situations, the use of the technology (e.g., a visual schedule) may have initially included verbal instruction from a teacher; however, the technology itself provided supports addressing only the visual modality.

Nine students used technology that exclusively focused on the auditory modality (e.g., an audio recorder). A small number of these studies systematically investigated the impact of different types of auditory cues. For example, Bouck et al. (2013) reported that shopping lists recorded using the participant’s voice resulted in more positive outcomes than shopping lists which made use of the researcher’s voice.

Twenty-two students made use of technology that addressed both visual and auditory modalities (e.g., computer with voice output, video clips with narrative). Yakubova and Taber-Doughty (2013), for example, investigated the use of video models (which provided both visual and auditory supports) to assist students in learning to purchase items and to perform appropriate social skills. Three students with disabilities learned to pay for the target items (e.g., stand in line, purchase the items with cash) as well as make use of appropriate social behaviors (e.g., say “hello” and “thank you” to the cashier).

Social Validity

Only nine studies (Bouck et al., 2013, Studies 1 and 2; Cakmak & Cakmak, 2015; Douglas et al., 2015, Studies 2a and 2b; Gil et al., 2019; Hsu et al., 2016; Yakubova & Taber-Doughty, 2013) provided information on social validity—consumer satisfaction with the goals, procedures, and outcomes of the intervention (Wolf, 1978). In these nine studies, information on social validity was collected from the person with IDD (n = 6), as well as the students’ parents (n = 4), teachers (n = 8), or store cashiers (n = 2). Positive feedback was observed for all four groups. For example, the students in Bouck et al. (2013) indicated that they enjoyed using audio recorders to grocery shop. Both parents (Cakmak & Cakmak, 2015; Douglas et al., 2015; Hsu et al., 2016) and teachers (Hsu et al., 2016) reported that the shopping skills their children had learned could make them more independent, and support positive social relations. Finally, there was important evidence that the AT interventions would be well-received in the community: grocery clerks (e.g., cashiers) described their belief that the technology interventions would make the students more independent at the store (Hsu et al., 2016), and increase opportunities for social interaction (Yakubova & Taber-Doughty, 2013).

Discussion

The purpose of this review was to evaluate the effectiveness of technology interventions on grocery shopping skills for students with IDD. Our findings suggest that individuals with IDD can be supported in demonstrating grocery shopping skills through the appropriate use of technology. Students across a wide range of ages and disability types demonstrated improvement when provided with technology interventions. There is also evidence that technology can be effectively used both as an IT in the classroom (i.e., to help a student learn a skill), and as an AT in stores (i.e., to help a student perform a skill). For the studies reported within the current review, successful outcomes were seen both for studies in which instruction took place both in the classroom and in community settings such as grocery stores, as well as in studies in which instruction was provided exclusively in the community. This suggests that while performance in the criterion environment (i.e., the community) is a critical element of functional life skills, there may also be an important role for classroom instruction in basic skills (e.g., reading a shopping list) that is then practiced and performed in a community setting (Cihak et al., 2004).

A wide range of technology, including both low-tech and high-tech approaches, were observed to support grocery shopping skills. In fact, both low-tech and high-tech approaches resulted in very large improvements (as measured using Tau-U), especially for those technologies that provided support using a visual modality. One frequently identified advantage for low-tech is its relatively low cost and durability (Sauer et al., 2010). It is important to note that while low-tech approaches can be of benefit, they often involve a need for expert instruction (Cihak et al., 2004). While high-tech approaches may initially appear more expensive, there is a growing body of research demonstrating that techniques such as video modeling can lead to rapid independent performance and may ultimately be seen as more efficient and cost-effective in some situations (Cihak et al., 2006; Taber-Doughty et al., 2008; Yakubova & Taber-Doughty, 2013). Consideration also should be given to the potential for high-tech AT to provide access to a variety of modalities (e.g., images, text, audio, video) as task appropriate supports, as well as the opportunity for access to a variety of tools or apps (e.g., phone, text messaging, calendar) in a single device (Brandt et al., 2020).

It is also of interest to note the shift over time in the types of technologies used to support community participation, as new technologies have become available (and less expensive). For example, for those studies which made use of technology in stores, all six articles published before 2011 made use of low-tech supports, while eight articles of the nine published after 2011 made use of high-tech supports (see Table 2). This is not to imply that high-tech should be used simply because it is newly available or seen as more current—as noted earlier, positive outcomes have been observed for both low-tech and high-tech approaches.

Interventionists should consider the skills and needs of the individual student, and the characteristics of the target store environment when choosing technology to support grocery shopping. Students may demonstrate personal preferences for particular types of technology (Scherer et al., 2011), and some shopping environments (e.g., a noisy store) may necessitate adaptations to the technology (e.g., the provision of visual supports to complement speech output; Babb et al., in press). Interventionists should consider a feature-matching process to review both low-tech and high-tech options (Satsangi et al., 2019), and to identify technology that will provide an effective, efficient, and socially acceptable support for community participation activities such as grocery shopping (Babb et al., 2019).

The findings of this review provide evidence that technology can support students with IDD in increased participation in a valued and necessary community skill: grocery shopping. The technology supports described here may not result in independent performance of the full range of component skills in grocery shopping; as noted earlier, none of the 19 studies addressed all six components of grocery shopping. The studies do, however, provide evidence that technology interventions can increase participation in a necessary and socially valued community activity, providing both desired outcomes (i.e., the purchase of groceries), as well as an opportunity to develop greater independence and a sense of self-determination (Shogren & Wehmeyer, 2017).

Limitations and Future Research

There are several limitations to this review’s findings that should be considered. First, similar to other review papers, there might be some studies which contain relevant research but were not included. For this meta-analytic review, the authors only included peer-reviewed articles which were written in English. Book chapters and dissertations were excluded, potentially creating a publication bias in this review. Second, the authors used the WWC standards and only included the articles which met the WWC criteria. Therefore, there are published studies which examined the use of technology to support shopping skills which were excluded from this review because they did not meet the WWC standards. Finally, this review only includes those studies for which Tau-U could be calculated. Therefore, studies which cannot be measured for an A-B contrast (e.g., studies which used an alternating treatments design without baseline) were excluded from this review.

This meta-analysis is the first to investigate the impact of technology intervention on grocery shopping for individuals with IDD. Future research should examine technology interventions that provide support for the performance of the full sequence of shopping behaviors, and the generalized use of these skills in a wide variety of shopping settings (e.g., convenience stores, supermarkets). In addition, attention should be given to aspects of grocery shopping that have been minimally investigated to date. For example, even though communication with others is a regular part of the grocery shopping experience (e.g., placing an order at the deli counter, thanking store staff), only two articles (Alcantara, 1994; Yakubova & Taber-Doughty, 2013) considered the process of communication with others at the store. Communication is a vital part of community participation and the shopping experience (McNaughton et al., 2019; Rydeman, 2010), and should be addressed in future investigations of shopping interventions.

The results of this meta-analytic review both contribute to the literature and also identify several areas for future research. Shopping is just one of many desirable community activities, and the studies in this review may provide important direction as to how other needed skills, such as community transportation, restaurant dining, and workplace participation, might be addressed for learners with IDD. The ability to purchase food, however, is an important life skill and may be a key component of other valued outcomes (e.g., independent living, community participation, self-determination). There is clear evidence that appropriate technology intervention can support individuals with a wide variety of disabilities, at a wide variety of ages, in shopping in the community. Continued research is needed to develop effective, efficient, and socially appropriate shopping interventions for persons with IDD, and to support wide-spread adoption of evidence-based practices.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Sojung Jung

David McNaughton

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