Digital Health Interventions for Sexual Health Education Among Adolescents With Autism Spectrum Disorder: Scoping Review
Bibliographic record
Abstract
Background: Adolescents with autism spectrum disorder (ASD) experience persistent barriers to accessing comprehensive and developmentally appropriate sexual health education. Conventional curricula often fail to reflect their cognitive, social, and communication needs, increasing vulnerability to misinformation and sexual exploitation. Digital health interventions offer a promising avenue to deliver tailored, interactive, and accessible learning experiences for adolescents with ASD. Objective: This scoping review aimed to map and synthesize the evidence on digital health interventions designed to provide sexual health education to adolescents with ASD. Methods: A scoping review was conducted using Arksey and O'Malley's framework, refined with Joanna Briggs Institute guidance and reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) standards. In total, 6 databases (ie, PubMed, Scopus, CINAHL, ERIC, PsycINFO, and Web of Science) were searched from 2010 to June 2025. Eligible studies involved adolescents aged 10 to 19 years with ASD, used digital platforms to deliver sexual or reproductive health education, and were published in English. Two reviewers independently screened, extracted, and synthesized data using descriptive and thematic approaches. Results: A total of 16 studies met the inclusion criteria. Most studies were conducted in high-income countries and delivered content through video-based, web-based, or mobile modalities. Key features associated with positive learning outcomes included personalization, strong visual interactivity, and caregiver involvement. Reported improvements focused on sexual knowledge, behavioral understanding, and user acceptability. However, methodological limitations were common, including small and nonrepresentative samples, a lack of standardized outcome measures, and minimal gender-specific or culturally adapted content. Notably, co-design with autistic adolescents and implementation in low- and middle-income countries was scarce. Conclusions: Digital health interventions demonstrate promising early effectiveness for delivering inclusive, developmentally appropriate sexual health education to adolescents with ASD. To advance this field, future research must strengthen methodological rigor, include diverse and gender-balanced populations, use participatory design, and ensure cultural adaptability to support equitable access globally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".