SMARTer Approach to Personalizing Intervention for Children With Autism Spectrum Disorder
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Purpose: This review article introduces research methods for personalization of intervention. Our goals are to review evidence-based practices for improving social communication impairment in children with autism spectrum disorder generally and then how these practices can be systematized in ways that personalize intervention, especially for children who respond slowly to an initial evidence-based practice. Method: The narrative reflects on the current status of modular and targeted interventions on social communication outcomes in the field of autism research. Questions are introduced regarding personalization of interventions that can be addressed through research methods. These research methods include adaptive treatment designs and the Sequential Multiple Assignment Randomized Trial. Examples of empirical studies using research designs are presented to answer questions of personalization. Conclusion: Bridging the gap between research studies and clinical practice can be advanced by research that attempts to answer questions pertinent to the broad heterogeneity in children with autism spectrum disorder, their response to interventions, and the fact that a single intervention is not effective for all children. Presentation Video: https://doi.org/10.23641/asha.7298021
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Recent intervention research models, such as the SMART, hold promise for systematizing a personalized approach to NDBIs.
“recent intervention research models, such as the Sequential Multiple Assignment Randomized Trial (SMART; Collins et al., 2007), hold promise for systematizing a personalized approach to NDBIs (Kasari et al., 2018)”
From Neurodiversity and Autism Intervention: Reconciling Perspectives Through a Naturalistic Developmental Behavioral Intervention Framework (2022) · cites this paper as Kasari et al., 2018
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- Authors
- Kasari, C., Sturm, A., & Shih, W.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- Journal of Speech, Language, and Hearing Research
- Published
- 2018-11-08
- Volume
- 61
- Issue
- 11
- Pages
- 2629-2640
- ISSN
- 1092-4388 (ISSN-L)
- 1558-9102
- PMC
- PMC6693574
- OpenAlex
- W2899666912
- Cited by
- 46
- References
- 74
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 7 · 2023: 4 · 2022: 6 · 2021: 4 · 2020: 4 · 2019: 1 · 2018: 1
- FWCI
- 0.34
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Speech Language and Hearing Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute on Deafness and Other Communication Disorders (R13DC003383)
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD073975)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Social Sciences › Psychology › Developmental and Educational Psychology › Behavioral and Psychological Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Child Nutrition and Feeding Issues
- Keywords
- autism spectrum disorder, personalized interventions, social communication deficits, evidence-based practice, sequential multiple assignment randomized trial, treatment response heterogeneity, children
- MeSH terms
- Autism Spectrum Disorder, Child, Communication, Humans, Early Intervention, Educational, Evidence-Based Practice, Precision Medicine, Social Skills
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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