Randomized Trial of a Computer-Assisted Intervention for Children With Autism in Schools

Pellecchia, Melanie; Marcus, Steven C.; Spaulding, Christine J; Seidman, Max; Xie, Ming; Rump, Keiran; Reisinger, Erica M.; Mandell, David S.

doi:10.1016/j.jaac.2019.03.029

Cited by 21 publications

(27 citation statements)

References 21 publications

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“…Because needed resources are not always available, teachers may find it difficult or impossible to act on intentions to use an EBP [ 40 ]. The classrooms studied are often under-staffed and chaotic, which can make it difficult to initiate, or even remember to use some EBPs despite relatively strong intentions to use that EBP [ 5 , 44 , 45 ]. The setting therefore may have biased our observed association between intentions and EBP use towards the null [ 40 , 54 ].…”

Section: Discussionmentioning

confidence: 99%

“…Perhaps, chief among these is our reliance on self-reported EBP if study participants consistently reported more EBP use than was accurate. The high variability in this reporting, however, combined with our prior validation of this method, minimizes concerns regarding this limitation [ 43 – 45 ]. Social desirability bias could have also inflated teachers’ reported intentions, in addition to their reported EBP use, which would not inflate their association.…”

Section: Discussionmentioning

confidence: 99%

“…It was approved by the Institutional Review Boards of the School District and the University of Pennsylvania. Based on our prior studies, we estimated that we could recruit 75% of eligible teachers [ 43 – 45 ]. We therefore proposed a sample of 70 teachers from a total of 91 teachers of the district’s kindergarten-through-second-grade autism support classrooms.…”

Section: Methodsmentioning

confidence: 99%

“…To avoid burdening teachers earlier in the school year when they are especially busy, the questionnaire was administered about 2 months after the training. We also expected that data collected early in the year would be less generalizable because teacher behavior (and presumably their intention) does not reflect their usual behavior [ 43 – 45 ].…”

Section: Methodsmentioning

confidence: 99%

“…The current study focuses on teachers' use of visual schedules and discrete trial training (DT) in classrooms serving students with autism. When these EBPs are used by teachers, children show improved emotional, social, and academic outcomes [41][42][43][44][45]. These two EBPs are used as examples of EBPs that improve student outcomes but are not implemented frequently [41][42][43][44][45].…”

Section: Specific Study Goalsmentioning

confidence: 99%

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Predicting implementation: comparing validated measures of intention and assessing the role of motivation when designing behavioral interventions

Fishman

Лушин

Mandell

2020

Implement Sci Commun

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Background Behavioral intention (which captures one’s level of motivation to perform a behavior) is considered a causal and proximal mechanism influencing the use of evidence-based practice (EBP). Implementation studies have measured intention differently, and it is unclear which is most predictive. Some use items referring to “evidence-based practice” in general, whereas others refer to a specific EBP. There are also unresolved debates about whether item stems should be worded “I intend to,” “I will,” or “How likely are you to” and if a single-item measure can suffice. Using each stem to refer to either a specific EBP or to “evidence-based practice,” this study compares the ability of these commonly used measures to predict future EBP implementation. The predictive validity is important for causal model testing and the development of effective implementation strategies. Methods A longitudinal study enrolled 70 teachers to track their use of two EBPs and compare the predictive validity of six different items measuring teachers’ intention. The measures differ by whether an item refers to a specific EBP, or to “evidence-based practices” in general, and whether the stem is worded in one of the three ways: “I intend to,” “I will,” or “How likely are you to.” For each item, linear regressions estimated the variance in future behavior explained. We also compared the predictive validity of a single item versus an aggregate of items by inter-correlating the items using different stems and estimating the explained variance in EBP implementation. Results Depending on the EBP and how intention was measured, the explained variance in implementation ranged from 3.5 to 29.0%. Measures that referred to a specific EBP, rather than “evidence-based practices” in general, accounted for more variance in implementation (e.g., 29.0% vs. 8.6%, and 11.3% vs. 3.5%). The predictive validity varied depending on whether stems were worded “I intend to,” “I will,” or “How likely are you to.” Conclusions The observed strength of the association between intentions and EBP use will depend on how intention is measured. The association was much stronger if an item referred to a specific EBP, rather than EBP in general. To predict implementation, the results support using an aggregate of two or three intention items that refer to the specific EBP. An even more pragmatic measure of intention consisting of a single item can also predict implementation. As discussed, the relationship will also vary depending on the EBP, which has direct implications for causal model testing and the design of implementation strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Specific Study Goalsmentioning

confidence: 99%

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Predicting implementation: comparing validated measures of intention and assessing the role of motivation when designing behavioral interventions

Fishman

Лушин

Mandell

2020

Implement Sci Commun

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What does it mean for an autism intervention to be evidence‐based?

Vivanti

2022

Autism Research

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Although there is consensus in the field that individuals on the autism spectrum should receive interventions that are evidence-based, the concept of "evidencebased" is multifaceted and subject to ongoing development and debate. In this commentary, we review historical developments, methodological approaches, as well as areas of controversies and research directions in the establishment of an evidence base for autism intervention.

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Digital interventions for autism spectrum disorders: A systematic review and meta‐analysis

Wang,

Ma,

et al. 2024

Pediatric Investigation

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ImportanceDigital technology is now widely available for the interventions of autism, but its validity and feasibility remain to be proved.ObjectiveThis study aimed to investigate the effectiveness of digital health interventions (DHIs) in improving core symptoms or intelligence quotient in patients with autism spectrum disorder (ASD).MethodsThree databases including PubMed, Cochrane, and Scopus, were searched on November 15, 2022. Randomized clinical trials that enrolled patients with ASD who received DHIs and a control group without DHI treatment were included. Cochrane risk of bias tool (RoB 2) was applied to assess the risk of bias.ResultsA total of 33 studies, involving 1285 participants (658 [51.2%] in DHI groups and 627 [48.8%] in control groups), were analyzed to investigate the differences between DHI groups and control groups. Significantly greater improvements in the overall performance of ASD were observed in the DHI groups compared to the control groups (including active, waitlist, treatment‐as‐usual, and no treatment) with an effect size of 1.89 (Cohen's d 95% confidence interval [CI]: 1.26–2.52). Studies with treatment‐as‐usual, waitlist, and no treatment control demonstrated large effect sizes of Cohen's d 3.41 (95% CI: 0.84–5.97), Cohen's d 4.27 (95% CI: 1.95–6.59), and Cohen's d 4.52 (95% CI: 2.98–6.06) respectively. In contrast, studies with active control revealed insignificant effect sizes (Cohen's d 0.73, 95% CI: 0.12–1.33).InterpretationThis meta‐analysis found significantly greater improvements in core symptoms or intelligence quotient in ASD patients receiving DHIs compared to those in control conditions. ASD patients may benefit from the DHIs and reduce the economic burden.

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Randomized Trial of a Computer-Assisted Intervention for Children With Autism in Schools

Cited by 21 publications

References 21 publications

Predicting implementation: comparing validated measures of intention and assessing the role of motivation when designing behavioral interventions

Predicting implementation: comparing validated measures of intention and assessing the role of motivation when designing behavioral interventions

What does it mean for an autism intervention to be evidence‐based?

Digital interventions for autism spectrum disorders: A systematic review and meta‐analysis

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