Video modeling and children with autism spectrum disorder: a survey of caregiver perspectives.
Video modeling (VM) has shown promise as an effective intervention for individuals with autism spectrum disorder (ASD); however, little is known about what may promote or prevent caregivers' use of this intervention. While VM is an effective tool to support skill development among a wide range of children in research and clinical settings, VM is still not used routinely by caregivers of individuals with ASD. In the current study, we examined the extent to which caregivers of children with ASD have experience with VM and explored their beliefs about using a newly developed scale, the Video Modeling Perceptions Scale (VMPS). In addition, we conducted exploratory psychometric analyses of the scale to determine its feasibility for use in assessing caregivers' perceptions of VM. In general, the VMPS appears to be an informative tool for analyzing caregivers' perceptions of VM. Caregivers held positive perceptions of VM and viewed it as something that could be helpful for their children.
Keywords: autism, video modeling, caregiver perspectives, factor analysis
Currently 1 in 68 children is diagnosed with an autism spectrum disorder (ASD), with some locations in the United States reporting rates as high as 1 in 47 children (Centers for Disease Control and Prevention, 2014). To receive a diagnosis of ASD, children must exhibit deficits in social communication and demonstrate repetitive behavior or interests (American Psychiatric Association, 2013). To address the needs of children with ASD, numerous interventions have been created to develop their cognitive, behavioral, communicative, and interpersonal skills. One such approach, video modeling (VM), appears to be growing in relevance, practicality, and popularity among those (i.e., caregivers and practitioners) who work with individuals with ASD. VM's promise as an effective, efficient, and socially acceptable practice may be impeded, however, if caregivers are unaware of or fail to use this technology. Presently, little is known about caregivers' knowledge or use of VM with children with ASD and/or what factors may promote or impede their application. This article (a) summarizes briefly research on VM with a particular emphasis on caregivers and children with ASD; (b) describes a scale for assessing caregivers' perceptions of VM, the Video Modeling Perceptions Scale (VMPS), and provides some initial technical adequacy data; and (c) discusses implications for future practice and research.
VM is a training technique that has been used for over 40 years with individuals with special needs (e.g., intellectual disability and behavioral disorders), although applications with individuals with ASD have emerged only over the past decade or so (Ayres & Langone, 2005; Bellini & Akullian, 2007; Buggey, Toombs, Gardener, & Cervetti, 1999; Charlop-Christy, Le, & Freeman, 2000). VM procedures typically include (a) recording target actions with some type of recording device (e.g., camcorder, tablet computer, computer, or smartphone), (b) playing back the video models via an electronic medium (e.g., television, computer, portable DVD player, tablet computer, or smartphone), (c) providing social reinforcement for appropriate responses, (d) playing back the video model for inappropriate responses, and (e) after reaching a criterion level, fading the video model with opportunities provided for live imitation (Cardon, 2012). In 2009 the National Autism Council recognized VM as an "established treatment." It is one of many evidence-based practices recognized by the council.
Empirical Support for VM
Research examining the effectiveness of VM has included familiar and unfamiliar peers, siblings, and adults as video models (D'Ateno, Mangiapanello, & Taylor, 2003; Reagon, Higbee, & Endicott, 2006). In general, individuals with ASD demonstrated increased target actions in response to video models regardless of who was performing the target actions. Videos also included self-modeling, footage from a third-person perspective (e.g., the whole person is visible performing the target action), and point-of-view modeling (i.e., only the person's hands are visible). Again, no notable differences emerged from the varied video formats (Ayres & Langone, 2005; Bellini & Akullian, 2007). Additional research suggests that individuals with ASD respond better to videos that target their own individual needs as opposed to commercial models and generalize skills more effectively after being taught via VM than live modeling (Palechka & MacDonald, 2010; Rosenberg, Schwartz, & Davis, 2010). VM is also more time- and cost-effective than using live modeling (Charlop-Christy et al., 2000).
According to Bellini and Akullian (2007) most research on VM was conducted with children between the ages of 5 and 20. No significant differences emerged across three separate age-groups (6 and younger, 7 to 12, and 13 and older) with regard to treatment results, maintenance, or generalization of skills following VM. More recent research (Cardon, 2012, 2013; Cardon & Wilcox, 2011) also provided a downward extension for the use of VM with toddlers and preschoolers with ASD. This research suggested that the intervention was effective in increasing imitation skills among children as young as 24 months. Plavnick, Odom, and Hume (2013) also reported that VM was effective in promoting social skill acquisition among adolescents in group living arrangements. Even in a group setting, rapid social skill gains occurred with VM. Finally, Wilson (2013) suggested that tutorials can be used in school settings to increase the use of this promising instructional practice.
Although VM was applied effectively with a wide range of children with ASD in research and clinical settings, it has not been used routinely by primary caregivers in home-based settings (Cardon, 2012; Cardon, Wilcox, & Campbell, 2011). Therefore, one purpose of this study was to explore factors that may promote or inhibit caregivers' use of VM. The most effective interventions available will be of little assistance for children if their primary caregivers do not use them.
VM and Caregivers
To date, VM has been used to teach imitation, as well as play, self-help, and social skills, to children with ASD (Cardon, 2012; Cardon & Wilcox, 2011; D'Ateno et al., 2003; Nikopoulos & Keenan, 2003; Schwandt et al., 2002). However, caregivers' (e.g., parents, grandparents, and daycare providers) use of VM in the home and community does not appear to be widespread. Sherer and colleagues (2001) did demonstrate, however, that caregivers can use clinician-created video models effectively to improve conversation skills among children with ASD. Little research exists, however, on how caregivers can be taught to create and utilize their own video models. Recently, VM imitation training (VMIT), a subtype of VM that teaches specific imitation skills, was taught successfully to four caregivers of children with ASD (Cardon, 2012). Caregivers used VM via an iPad to increase their children's target actions across a variety of skill domains (e.g., brushing teeth, pushing a car, cleaning up toys, and touching their nose). Caregivers recorded a family member correctly performing the target action and then showed the video model to their child with autism. Children viewed the target behaviors via video model on the iPad three times a week for several weeks before the video model was faded and live modeling was used. All four children made important behavioral gains, and three of four made improvements after only one treatment session. Notably, all caregivers indicated that VM was a helpful and important tool for skill development among children with ASD. This measure of social acceptability prompted an effort to examine caregivers' familiarity with and use of VM.
Caregiver Perceptions of Autism Intervention
Research on caregivers' perceptions of effective treatments for individuals with ASD is just emerging. Hume, Bellini, and Pratt (2005) reported, for example, that caregivers perceived parent training and speech therapy as the two most effective autism treatments. In contrast, Goin-Kochel, Mackintosh, and Myers (2009) found that caregivers perceived most interventions (i.e., developmental, behavioral, and cognitive) to be beneficial for their children. Bowker, D'Angelo, Hicks, and Wells (2011) reported that caregivers believed that their children's behavior and language skills were most improved by traditional autism treatments (e.g., ABA, speech therapy) as opposed to more alternative treatments (e.g., diets, detoxification). Other social validity studies have examined caregivers' perceptions of VM in particular. Callahan, Henson, and Cowan (2008) reported, for example, that caregivers and other school personnel viewed VM as a very important instructional tool. Similarly, Card on et al. (2011) found that caregivers were interested in using VM to support their children's daily routines but lacked knowledge on how to do so. To date, no studies were found that explicitly examined caregivers' perceptions of VM.
While VM has been used to some degree with children with ASD for over a decade (e.g., Ayres & Langone, 2005; Bellini & Akullian, 2007; Charlop-Christy et al., 2000), the rise of personal computers (i.e., the iPod touch, tablet computers, netbooks) as recording and delivery systems has expanded potential applications substantially (e.g., Cardon, 2012; Cihak, Fahrenkrog, Ayres, & Smith, 2010). Initial research suggests, for example, that iPod touches, smartphones, and iPads are viable VM alternatives to televisions, laptops, and portable DVD players (Cihak et al., 2010). Moreover, tablet computers like the iPad have increased the use of VM in homes and educational settings (Cardon, 2012; Dunham, 2011; Gallis, Turner, Fuller, & Cardon, 2012; Sennet & Bowker, 2009). With the increased use of handheld technologies, particularly at home, it is necessary to explore caregivers' perceptions of VM as an intervention strategy for children with ASD. A second purpose of this article, therefore, is to introduce the VMPS and describe initial psychometric analyses to support its utility. Caregivers' perceptions were examined in terms of (a) prior experience with VM, (b) demographic background, and (c) children's characteristics (age and severity).
An e-mail summarizing the study's purposes was created and distributed to caregivers. Caregivers were defined as those who were primarily responsible for child care (e.g., dressing, feeding, instructing) at home. The email was sent to autism professionals (e.g., speech therapists, behaviorists, occupational therapists), autism support groups (e.g., Autism Speaks and the Autism Society of America), and speech and language professionals in all 50 states and Canada with a request to distribute the survey to the caregivers of children with autism.
A 28-item Internet-based Google Doc survey was created to assess caregivers' perceptions of VM (e.g., "Have you seen anyone use VM?" "VM is something I want to learn more about." "The technology required to implement VM would be expensive."). The survey was reviewed institutionally for clarity coherence, feasibility, and potential utility before formal use. The survey was uploaded to Google Docs, where a second round of beta testing was completed to ensure that the e-mail links and navigation were functioning properly. The 28-question survey remained open for 12 weeks, after which it was removed from public access.
The VMPS and Data-Analysis Procedures
The VMPS (Appendix A) examines (a) the degree to which caregivers perceive VM as a positive and potentially effective intervention for individuals with ASD and (b) the barriers that may impede its application in their settings. VM was described as a "child viewing videos demonstrating a particular behavior in order to learn that behavior." The items reflected purported strengths and weaknesses of VM depicted in the professional literature and assessed other potential barriers to more widespread use. Caregivers responded to each item using a 5-point, Likert-type scale to indicate the extent of their agreement and/or disagreement (from 1 = agree completely to 5 disagree completely).
Descriptive information about caregivers' demographic characteristics and experiences with VM was gathered, as well as preliminary analyses of the scale's psychometric properties (e.g., exploratory factor analyses, subsequent scale modifications, and score reliability estimates for a proposed factor structure). Given that the VMPS was designed to assess the perceived benefits and demands of VM, an exploratory factor analysis was conducted to determine whether these constructs emerged as underlying scale factors (Munro, 2001). Factor analyses are typically conducted to reduce a larger set of items or variables into smaller, more meaningful and manageable subsets that can be used to inform and/or address research questions.
A total of 161 caregivers completed the scale (see Table 1 for participant demographics). Most respondents were mothers (92%), and the median child age was 8 years. Over 80% of respondents reported that there were two caregivers in the home and at least two children, including one with ASD. Most caregivers (78%) reported having three children, and a majority (63%) reported that they had heard about the use of VM with individuals with ASD. Sixty percent of respondents, however, had not used it with their own children. Seventy percent reported that they had not observed VM being used with their children by teachers or therapists. On the positive side, most caregivers also said that they would like to use VM with their children (i.e., 48% strongly agreed and 30% agreed).
Caregivers reported that they were most interested in using VM to teach (a) social skills (88%), (b) self-help skills (76%), and (c) daily living skills (76%). Other potential target behaviors included gestures (58%), language (54%), play (53%), and imitation (52%) skills. Only 2% of caregivers indicated that they were not interested in using VM. Caregivers reported that they could learn about VM from multiple sources (see Table 2), with the Internet being the most often selected (71%), followed by speech therapists (60%).
To reduce data into a more manageable and meaningful form, a factor analysis was conducted. This analysis revealed two overarching factors. Factor 1 comprised 7 items related to caregivers' perceptions of their interest in using VM as an intervention tool (e.g., "I feel that VM would enhance the services my child already receives"). Factor 2 consisted of four items associated with caregivers' perceptions of the accessibility (e.g., time, expense, and training required) of VM as an intervention tool (e.g., "Video modeling would place extensive demands on my time"). Table 3 shows mean scores for individual items with higher scores on both factors indicative of more positive perceptions of VM. The internal consistency reliability was .83 and .62, for Factors 1 and 2, respectively, and .73 for the 11-item VMPS, suggesting that scale factors consistently measured their respective concepts.
Zero-order correlations were produced for the association between VMPS factor and scale scores and caregiver characteristics and experiences with VM (see Table 4). With respect to demographic characteristics, higher reported caregiver education was associated with perceptions of greater VM accessibility (r = .21, p = .008). Regarding prior experiences with VM, caregivers who had used or observed someone else using VM perceived greater accessibility (r = .30 and .16, p = .0001 and .048). Furthermore, caregivers who reported that VM had worked well for their child were also more interested in using the technology (r = .48, p = .0001). Finally, caregivers who agreed that "VM is an evidence-based practice" were also more interested in using it as an intervention tool (r = .54, p = .0001). The same association was not found, however, for perceived VM accessibility.
Differences Between Those With and Without VM Experience and Perceptions of VM
One-way ANOVA procedures were employed to examine potential group differences in terms of (a) familiarity or unfamiliarity with VM (i.e., had heard/not heard of; used or not used; or seen or not seen someone else use with their child), and (b) differences in child age (i.e., early, middle, and late childhood). Results suggested that caregivers who had heard of VM reported greater interest in using VM and greater ease of access to VM than caregivers who did not have similar experiences [F (1, 159) = 7.75, p = .006]. Caregivers who had used or seen someone else use VM were also likely to perceive VM as more accessible compared to counterparts without such experiences [F (1, 159) = 15.42 and 3.96, p = .0001 and .048, respectively]. Finally, caregivers of children in early, middle, and late childhood all had similar perceptions of the potential utility and accessibility of VM [F (2,158) = .74 and .26, p = .48 and .77, respectively].
Present findings suggest that VM may be an effective, efficient, and socially acceptable intervention for teaching individuals with ASD a variety of important academic, behavioral, and interpersonal skills. Moreover, advances in instructional technologies and social networking systems have made VM more readily accessible for professionals and caregivers to use with their children. Additional findings suggest that the VMPS may be a useful tool for assessing caregivers' perceptions of VM and formulating more systematic dissemination efforts. In general, caregivers had positive perceptions of VM, and their interest in using it increased with experience. Those who showed interest in VM also believed that their children would benefit from it. Given caregiver interest and optimism, an emerging evidence base, and technological advances that make it more accessible, it makes sense to prepare caregivers more systematically to use VM with their children and to assess subsequent effects on child performance. Caregivers also identified important potential barriers to VM implementation (e.g., unavailability of technology, confusion surrounding technology use, and a lack of explicit training protocols).
Other findings suggested that caregivers' responses were not related to their income levels, their children's severity of autism, and/or the amount of time their children spent in school or day care. Caregivers' level of education and previous experience with VM did, however, have a positive impact on their perceptions of VM accessibility. It is possible that caregivers with higher education levels can access relevant information and tools more readily. Higher educational levels also correlate with earning capability (Mincer, 1974) and may be related to increased ownership of requisite technologies (i.e., video recorders, iPads).
It was also interesting to note that regardless of children's ages, caregivers perceived VM as a beneficial intervention tool. Perhaps greater access to smartphones and tablet computers has influenced caregivers' perceptions of accessibility, particularly given the young age at which children take interest in smart technologies. Until recently, most VM research was conducted with school-age and adolescent children (Bellini & Akullian, 2007; Cardon & Wilcox, 2011); it is important, therefore, that future research and practice focus more extensively on the use of VM with younger children with ASD.
Initial data on the VMPS suggest that it has fairly good technical properties and may be useful in assessing caregivers' perceptions of the potential utility of VM as well as anticipated roadblocks in its implementation. It may be important to increase training opportunities for caregivers to learn more about the potential utility of VM and also increase their use of the technology as an instructional tool for their children with ASD.
Recommendations for Implementation
Those professionals who work with caregivers can help them learn to use VM accurately and successfully with their children. When doing so, it is important to present a range of technological options for capturing and delivering video models. Most smartphones and tablets, for example, have video recording, editing, and storing features available for use. Caregivers should choose target behaviors based on their importance to the child and family. These behaviors should be developmentally appropriate, defined operationally, and easy to observe (Wilson, 2013). A video model is then selected to practice the target behavior several times before recording. The instructional model should use a natural voice and tone, and speak and act at a normal pace.
Other helpful tips for recording include (a) keeping recording areas clutter-free, (b) focusing the camera on individuals and target actions, (c) using appropriate lighting levels, (d) testing the sound quality and levels, and (e) having models remove distracting clothing or jewelry.
An implementation manual was developed by the first author and is available by request. Some specific tips include the following:
1. Place VM devices in front of the child and ensure that objects are close by.
2. Press play, ensure that the child watches the entire clip, and then state, "Time to play," "Let's play," or "Now you." (If an object is required for action, hand to child.)
3. If children imitate the video models within 10 seconds with a purposeful action, then they are reinforced with praise or other validated reinforcers.
4. If they do not imitate the target action, the previous steps are repeated. It is recommended that clips be shown up to three times, as this has proven beneficial in research protocols.
5. If children do not imitate after three attempts, physically prompt them to do the target action being portrayed in the video clips and reinforce as needed.
6. Verbally praise them after physical prompts and move on to another video.
7. Show videos three to five times per week for approximately 20-minute sessions when teaching new skills. If skills don't appear to be maintained, a booster session once a week could be implemented.
8. Fade prompts as necessary and collect formative data to examine effects on children's use of targeted behaviors.
Current findings suggest that VM may be a useful instructional tool for caregivers of children with ASD and other types of disabilities. Obviously, significantly more work must be done in both research and practice. More work must be conducted on how caregivers are using VM across a variety of settings and behaviors. For example, to what extent do caregivers use VM in their daily interactions with children? How accurately do they use VM procedures, and do others use the intervention as well? Does VM reliably produce improvements in children's behavior? Do these intervention gains persist and/or generalize to other settings, individuals, or target behaviors?
Current findings should also be augmented with direct observations of caregiver use of VM. It would be interesting to examine how much and what types of support caregivers need to implement VM with fidelity. Findings from the VMPS must also be viewed as preliminary and interpreted with caution until additional psychometric analyses are conducted. Direct observations of caregivers' use of VM might also provide useful opportunities to validate the VMPS's technical properties.
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Teresa A. Cardon, PhD, CCC-SLP
Utah Valley University
Amy Guimond, PhD
InVision Analytics, LLC
Amanda M. Smith-Treadwell, MA CCC-SLP
Washington State University
Correspondence concerning this article should be addressed to Teresa A. Cardon, PhD, CCC-SLP, 800 W. University Parkway, Orem, UT 84058; e-mail: Teresaemail@example.com
Table 1 Demographic Information of Respondents Participant characteristics n % of caregivers Caregiver responding Mother 144 92.31 Father 6 3.85 Legal guardian 3 1.92 Grandparent 3 1.92 Child's diagnosis Autism 91 58.33 Asperger's 41 26.28 PDD-NOS 22 14.10 Childhood disintegrative disorder 1 0.64 Rhett's disorder 1 0.64 Reported autism severity Mild 58 36.71 Moderate 74 46.84 Severe 23 16.46 Child's age 0-29 months 6 3.73 30-47 months 15 9.32 4-5 years 18 11.18 6-7 years 21 13.04 8-9 years 24 14.91 10-11 years 20 12.42 12-13 years 15 9.32 14 and over 42 26.09 Time child is in school/day care Less than 1 hour 18 11.32 2-10 hours per week 12 7.55 11-20 hours per week 16 10.06 21-30 hours per week 41 25.79 31-40 hours per week 68 42.77 More than 41 hours per week 4 2.25 Note. PDD-NOS--Pervasive Developmental Disorder--Not Otherwise Specified Table 2 Sources Where Caregivers Report They Can Learn About Video Modeling Source n % of caregivers Speech therapist 96 59.63 Pediatrician/MD 33 20.50 Internet 114 70.81 Other parents 86 53.42 Autism magazines 66 40.99 Professional journals 44 27.33 Psychologists 53 32.92 School personnel 64 39.75 Other 86 53.42 Table 3 Means, Standard Deviations, and Factor Analysis Results Using a Promax Rotation: Factor Coefficients for Pattern and Structure Matrices Item M SD Factor 1 1. I would like to learn more about 4.38 0.94 video modeling. 2. Using video modeling is an approach 4.29 1.04 I am interested in trying. 3. I would be interested in using 4.32 0.88 videos developed by a professional with my child. 4. I would be interested in learning 3.78 1.20 to develop my own videos to use with my child. 5. I feel that video modeling would 4.22 0.91 ENHANCE the services my child already receives. 6. I would like to try to implement 4.12 1.04 video modeling with my child. Factor 2 7. The technology required to implement 3.18 1.20 video modeling with my child would be expensive. 8. The technological skills needed 2.09 1.16 to implement video modeling with my child would be beyond my computer and technical abilities. 9. Video modeling would place 2.53 1.15 extensive demands on my time. 10. To effectively implement video 3.29 1.25 modeling with my child, it would require training from a professional. Excluded Item 11. My child would not learn as 3.37 1.14 well from a video because it lacks the personal component of one-on- one therapy. Factor loadings Structure Pattern matrix matrix factor factor Item 1 2 1 2 Factor 1 1. I would like to learn more about .77 -.24 .75 -.18 video modeling. 2. Using video modeling is an approach .86 -.10 .85 -.03 I am interested in trying. 3. I would be interested in using .71 -.06 .70 .00 videos developed by a professional with my child. 4. I would be interested in learning .44 .02 .44 .06 to develop my own videos to use with my child. 5. I feel that video modeling would .67 .12 .68 .18 ENHANCE the services my child already receives. 6. I would like to try to implement .80 .16 .81 .23 video modeling with my child. Factor 2 7. The technology required to implement -.12 .51 -.07 .50 video modeling with my child would be expensive. 8. The technological skills needed .08 .57 .12 .57 to implement video modeling with my child would be beyond my computer and technical abilities. 9. Video modeling would place .08 .59 .13 .60 extensive demands on my time. 10. To effectively implement video -.08 .49 -.04 .48 modeling with my child, it would require training from a professional. Excluded Item 11. My child would not learn as .39 .31 .41 .35 well from a video because it lacks the personal component of one-on- one therapy. Table 4 Bivariate Correlations of Experiences With Video Modeling (VM) and Demographic Characteristics With the Perceptions of VM Scale and Subscale Scores Factor 1: Factor 2: Perceptions Interest Accessibility of VM: in VM of VM Scale score Video modeling is an .54 *** .04 .46 *** evidence-based practice Have you ever tried to use .13 .30 ** .25 ** video modeling with a child? Have you ever observed .12 .16 * .18 * someone else using video modeling to teach specific skills or concepts to a child? How well did video modeling .48 *** -.05 .40 *** work with your child? What is the child's .05 -.01 .04 severity level? Child age -.06 .03 -.03 Number of caregivers in the -.02 .10 .03 home Number of children living -.11 .01 -.08 in the home Hours/week in day care or -.14 .06 -.08 school Education .02 .21 ** .13 Family income .04 .15 .11
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|Author:||Cardon, Teresa A.; Guimond, Amy; Smith-Treadwell, Amanda M.|
|Publication:||Education & Treatment of Children|
|Date:||Aug 1, 2015|
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