5 results on '"Barrett, D. Jonah"'
Search Results
2. Examining the latent structure and correlates of sensory reactivity in autism: a multi-site integrative data analysis by the autism sensory research consortium
- Author
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Williams, Zachary J., Schaaf, Roseann, Ausderau, Karla K., Baranek, Grace T., Barrett, D. Jonah, Cascio, Carissa J., Dumont, Rachel L., Eyoh, Ekomobong E., Failla, Michelle D., Feldman, Jacob I., Foss-Feig, Jennifer H., Green, Heather L., Green, Shulamite A., He, Jason L., Kaplan-Kahn, Elizabeth A., Keçeli-Kaysılı, Bahar, MacLennan, Keren, Mailloux, Zoe, Marco, Elysa J., Mash, Lisa E., McKernan, Elizabeth P., Molholm, Sophie, Mostofsky, Stewart H., Puts, Nicolaas A. J., Robertson, Caroline E., Russo, Natalie, Shea, Nicole, Sideris, John, Sutcliffe, James S., Tavassoli, Teresa, Wallace, Mark T., Wodka, Ericka L., and Woynaroski, Tiffany G.
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- 2023
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3. Autism intervention meta-analysis of early childhood studies (Project AIM): updated systematic review and secondary analysis.
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Sandbank, Micheal, Bottema-Beutel, Kristen, Crowley LaPoint, Shannon, Feldman, Jacob I., Barrett, D. Jonah, Caldwell, Nicolette, Dunham, Kacie, Crank, Jenna, Albarran, Suzanne, and Woynaroski, Tiffany
- Subjects
CINAHL database ,PSYCHOLOGY information storage & retrieval systems ,MEDICAL databases ,CONFIDENCE intervals ,META-analysis ,SYSTEMATIC reviews ,DEVELOPMENTAL disabilities ,TREATMENT effectiveness ,AUTISM ,EARLY intervention (Education) ,RESEARCH funding ,DESCRIPTIVE statistics ,COMMUNICATION ,MEDLINE ,SOCIAL skills ,EMOTIONAL intelligence ,SECONDARY analysis ,ERIC (Information retrieval system) ,EVALUATION ,CHILDREN - Published
- 2023
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4. Autism intervention meta-analysis of early childhood studies (Project AIM): updated systematic review and secondary analysis.
- Author
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Sandbank M, Bottema-Beutel K, Crowley LaPoint S, Feldman JI, Barrett DJ, Caldwell N, Dunham K, Crank J, Albarran S, and Woynaroski T
- Subjects
- Child, Humans, Child, Preschool, Behavior Therapy, Early Intervention, Educational, Social Skills, Adaptation, Psychological, Autistic Disorder therapy
- Abstract
Objective: To summarize the breadth and quality of evidence supporting commonly recommended early childhood autism interventions and their estimated effects on developmental outcomes., Design: Updated systematic review and meta-analysis (autism intervention meta-analysis; Project AIM)., Data Sources: A search was conducted in November 2021 (updating a search done in November 2017) of the following databases and registers: Academic Search Complete, CINAHL Plus with full text, Education Source, Educational Administration Abstracts, ERIC, Medline, ProQuest Dissertations and Theses, PsycINFO, Psychology and Behavioral Sciences Collection, and SocINDEX with full text, Trials , and ClinicalTrials.gov., Eligibility Criteria for Selecting Studies: Any controlled group study testing the effects of any non-pharmacological intervention on any outcome in young autistic children younger than 8 years., Review Methods: Newly identified studies were integrated into the previous dataset and were coded for participant, intervention, and outcome characteristics. Interventions were categorized by type of approach (such as behavioral, developmental, naturalistic developmental behavioral intervention, and technology based), and outcomes were categorized by domain (such as social communication, adaptive behavior, play, and language). Risks of bias were evaluated following guidance from Cochrane. Effects were estimated for all intervention and outcome types with sufficient contributing data, stratified by risk of bias, using robust variance estimation to account for intercorrelation of effects within studies and subgroups., Results: The search yielded 289 reports of 252 studies, representing 13 304 participants and effects for 3291 outcomes. When contributing effects were restricted to those from randomized controlled trials, significant summary effects were estimated for behavioral interventions on social emotional or challenging behavior outcomes (Hedges' g=0.58, 95% confidence interval 0.11 to 1.06; P=0.02), developmental interventions on social communication (0.28, 0.12 to 0.44; P=0.003); naturalistic developmental behavioral interventions on adaptive behavior (0.23, 0.02 to 0.43; P=0.03), language (0.16, 0.01 to 0.31; P=0.04), play (0.19, 0.02 to 0.36; P=0.03), social communication (0.35, 0.23 to 0.47; P<0.001), and measures of diagnostic characteristics of autism (0.38, 0.17 to 0.59; P=0.002); and technology based interventions on social communication (0.33, 0.02 to 0.64; P=0.04) and social emotional or challenging behavior outcomes (0.57, 0.04 to 1.09; P=0.04). When effects were further restricted to exclude caregiver or teacher report outcomes, significant effects were estimated only for developmental interventions on social communication (0.31, 0.13 to 0.49; P=0.003) and naturalistic developmental behavioral interventions on social communication (0.36, 0.23 to 0.49; P<0.001) and measures of diagnostic characteristics of autism (0.44, 0.20 to 0.68; P=0.002). When effects were then restricted to exclude those at high risk of detection bias, only one significant summary effect was estimated-naturalistic developmental behavioral interventions on measures of diagnostic characteristics of autism (0.30, 0.03 to 0.57; P=0.03). Adverse events were poorly monitored, but possibly common., Conclusion: The available evidence on interventions to support young autistic children has approximately doubled in four years. Some evidence from randomized controlled trials shows that behavioral interventions improve caregiver perception of challenging behavior and child social emotional functioning, and that technology based interventions support proximal improvements in specific social communication and social emotional skills. Evidence also shows that developmental interventions improve social communication in interactions with caregivers, and naturalistic developmental behavioral interventions improve core challenges associated with autism, particularly difficulties with social communication. However, potential benefits of these interventions cannot be weighed against the potential for adverse effects owing to inadequate monitoring and reporting., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the National Center for Advancing Translational Sciences of the National Institutes of Health, and the National Institute on Deafness and other Communication Disorders of the National Institutes of Health for the submitted work. MS has received fees for presenting research findings in invited talks from Children’s Healthcare of Atlanta and the New Jersey Autism Center of Excellence, and from law firms representing the National Disability Insurance Scheme of Australia for providing expert evidence on the efficacy of early childhood interventions in court hearings. In the past three years, she taught courses in a program that was accredited by the Behavior Analyst Certification Board on both behavioral and NDBI early childhood interventions. KB-B has previously received fees for consulting with school districts on intervention practices for autistic children and teaches courses on autism interventions in her role as an associate professor of special education. She has also accepted speaker fees to discuss her work on research quality, adverse events, and researcher conflicts of interest as they pertain to autism intervention research. She also receives royalties for a coedited book titled Clinical Guide to Early Interventions for Children with Autism published by Springer. SCLP was formerly affiliated with an entity that trained students to become board certified behavior analysts and provided early intensive behavioral intervention. She is currently employed by the TEACCH Autism Program and served as an interventionist on an intervention developed at TEACCH for autistic transition age youth. JIF has been paid to provide adaptive horseback riding lessons (an animal assisted therapy). He is employed in a department that teaches students to provide early communication therapies. NC is a board certified behavior analyst at the doctoral level (BCBA-D) and is the current president elect of the Arkansas Association for Behavior Analysis. She teaches courses in a university program accredited by the behavior analyst certification board and formerly provided quality assurance and consultation services for the Arkansas Medicaid waiver program which provides behavioral based services for children with autism aged 0-8. SA is a board certified behavior analyst who directly provides services to autistic children, adolescents, and adults. She is co-owner of a clinical practice that receives direct payment for behavior analytic services through contracts with local school districts, private and public insurance payors, and Texas Medicaid waiver programs. Susanne is an instructor for coursework that is approved by the behavior analyst certification board, and she serves as a practicum and field supervisor for master’s level students in pursuit of advanced degrees in the field of behavior analysis. KD is a PhD candidate in a department that teaches students to provide early communication therapies, including some evaluated as part of this meta-analysis. JC was previously employed as an early intervention therapist, and was paid to provide behavioral and NDBI type therapies to children. TW is the parent of an autistic child; has previously been paid to provide traditional behavioral, naturalistic developmental behavioral, and developmental interventions to young children on the autism spectrum; has received grant funding from internal and external agencies, including the National Institutes of Health and the Vanderbilt Institute for Clinical and Translational Research, to study the efficacy of various interventions geared toward young children with autism (though not to support this specific work); and is employed by the Department of Hearing and Speech Sciences at Vanderbilt University Medical Center, which offers intervention services (which include the types of interventions evaluated in this meta-analysis) for autistic children through their outpatient clinics and trains clinical students in the provision of treatments delivered over the course of early childhood. All other authors have no conflicts of interest to declare., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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- 2023
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5. Examining the Latent Structure and Correlates of Sensory Reactivity in Autism: A Multi-site Integrative Data Analysis by the Autism Sensory Research Consortium.
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Williams ZJ, Schaaf R, Ausderau KK, Baranek GT, Barrett DJ, Cascio CJ, Dumont RL, Eyoh EE, Failla MD, Feldman JI, Foss-Feig JH, Green HL, Green SA, He JL, Kaplan-Kahn EA, Keçeli-Kaysılı B, MacLennan K, Mailloux Z, Marco EJ, Mash LE, McKernan EP, Molholm S, Mostofsky SH, Puts NAJ, Robertson CE, Russo N, Shea N, Sideris J, Sutcliffe JS, Tavassoli T, Wallace MT, Wodka EL, and Woynaroski TG
- Abstract
Background Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these "supra-modal" traits in the autistic population. Methods Leveraging a combined sample of 3,868 autistic youth drawn from 12 distinct data sources (ages 3-18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a "general response pattern" factor for each supra-modal construct and determine the added value of "modality-specific response pattern" scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO and SEEK (sub)constructs. Results All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses unambiguously supported the validity of a supra-modal HYPER construct (ω
H = .800), whereas a coherent supra-modal HYPO construct was not supported (ωH = .611), and supra-modal SEEK models suggested a more limited version of the construct that excluded some sensory modalities (ωH = .799; 4/7 modalities). Within each sensory construct, modality-specific subscales demonstrated substantial added value beyond the supra-modal score. Meta-analytic correlations varied by construct, although sensory features tended to correlate most strongly with other domains of core autism features and co-occurring psychiatric symptoms. Certain subconstructs within the HYPO and SEEK domains were also associated with lower adaptive behavior scores. Limitations: Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to parent-report of observable behaviors and excluded multisensory items that reflect many "real-world" sensory experiences. Conclusion Psychometric issues may limit the degree to which some measures of supra-modal HYPO/SEEK can be interpreted. Depending on the research question at hand, modality-specific response pattern scores may represent a valid alternative method of characterizing sensory reactivity in autism.- Published
- 2023
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