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Record W4402507372 · doi:10.2196/52295

Mobile App–Assisted Parent Training Intervention for Behavioral Problems in Children With Autism Spectrum Disorder: Pilot Randomized Controlled Trial

2024· article· en· W4402507372 on OpenAlexvenueno aff
JooHyun Lee, Jaehyun Lim, Soyeon Kang, S. Kim, So Yoon Jung, Sujin Kim, Soon‐Beom Hong, Yu Rang Park

Bibliographic record

VenueJMIR Human Factors · 2024
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialAutism spectrum disorderCBCLRandomizationParent trainingIntervention (counseling)MedicineChecklistAutismDysfunctional familyPhysical therapyChild Behavior ChecklistClinical psychologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: In children with autism spectrum disorder (ASD), problem behaviors play a dysfunctional role, causing as much difficulty with daily living and adjustment as the core symptoms. If such behaviors are not effectively addressed, they can result in physical, economic, and psychological issues not only for the individual but also for family members. OBJECTIVE: We aimed to develop and evaluate the feasibility of a mobile app-assisted parent training program for reducing problem behaviors in children with ASD. METHODS: This open-label, single-center, randomized controlled trial was conducted among parents of children with ASD aged 36-84 months. Participants were recruited from the Department of Psychiatry at Seoul National University Hospital. Participants were randomly assigned (1:1) by a blinded researcher. Randomization was performed using a stratified block randomization (with a block size of 4). Parents in the intervention group completed the mobile app-assisted parent training program at home over a 12-week period. They continued to receive their usual nondrug treatment in addition to the mobile app-assisted parent training program. The control group continued to receive their usual nonpharmaceutical treatment for 12 weeks without receiving the parent training program intervention. The primary outcome measure was the median change in the Korean Child Behavior Checklist (K-CBCL) scores from before to after the intervention. Lower scores on the K-CBCL indicated a decrease in overall problem behavior. RESULTS: Between November 9, 2022, and December 8, 2022, 64 participants were enrolled. Overall, 42 children (intervention group median age: 49, IQR 41-52.5 months; control group median age: 49, IQR 42-58 months) of the participants joined the program. The intervention group included 20 (48%) participants and the control group included 22 (52%) participants. In the intervention group, the K-CBCL total scores showed a decrease after the intervention, with a median difference of -0.5 (95% CI -4.5 to 3). Pervasive developmental disorder scores also showed a decrease, with a median difference of -2.1 (95% CI -8.5 to 2.5). However, there was no significant difference in Clinical Global Impression-Severity of Illness scores after the intervention for both the control and intervention groups. Scores on the Korean version of the Social Communication Questionnaire showed a further decrease after the intervention in the intervention group (median difference -2, 95% CI -4 to 1). Caregivers' stress evaluated using the Korean Parenting Stress Index Fourth Edition-Short Form did not show any significant differences between the control and intervention groups. There were no adverse events related to study participation. CONCLUSIONS: The findings demonstrated the feasibility of using mobile devices for evidence-based parent training to reduce problem behaviors in children with ASD. Mobile devices' accessibility and flexibility may provide a viable alternative for offering early intervention for problem behaviors in children with ASD. TRIAL REGISTRATION: CRIS KCT0007841; https://cris.nih.go.kr/cris/search/detailSearch.do?&seq=23112.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.399
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2024
Admission routes1
Has abstractyes

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