Digital and telehealth behavioral sleep interventions for improving sleep outcomes in children and adolescents with autism spectrum disorder: a systematic review and meta-analysis.

Researchers reviewed 12 studies testing online and telehealth sleep programs for autistic children aged 2-18. These technology-based programs showed small-to-moderate improvements in children's sleep problems, especially helping children fall asleep faster. The programs were delivered through apps, websites, or video calls rather than face-to-face visits. While promising, more research is needed with larger groups of children and better ways to measure sleep improvements.

This systematic review and meta-analysis examined digital and telehealth behavioral sleep interventions for autistic children and adolescents aged 2-18 years. The review included 12 studies: 6 randomized controlled trials (N=349) and 6 non-randomized studies (N=57). Results from RCTs showed that digital/telehealth interventions produced small-to-moderate improvements in parent-reported sleep problems compared to controls (SMD=-0.57). Benefits were most pronounced for sleep initiation, particularly reducing sleep-onset delay.

Non-randomized studies suggested larger improvements but had methodological limitations. The findings support technology-delivered behavioral interventions as a viable approach to address sleep difficulties in autistic youth, though future research requires larger samples, objective measures, and longer follow-up periods.

Technology-delivered behavioral sleep interventions offer a promising, accessible approach for addressing sleep difficulties in autistic youth. These interventions may be particularly valuable where face-to-face services are limited. However, implementation should focus on sleep initiation problems, and clinicians should consider combining with objective sleep monitoring for comprehensive assessment.

High heterogeneity between studies (I²=62%), reliance on parent-reported outcomes rather than objective sleep measures, small sample sizes in non-randomized studies, and lack of long-term follow-up data. Methodological concerns limit interpretation of non-randomized study results.

Sleep problems are highly prevalent in children with Autism Spectrum Disorder (ASD), negatively impacting core symptoms, daytime functioning, and family well-being. While behavioral interventions are the first-line treatment, access is often limited. Digital and telehealth delivery models offer a promising approach to increase accessibility. This review systematically evaluates the effectiveness of these technology-based behavioral sleep interventions for youth with ASD.

Following PRISMA guidelines, we systematically searched five databases for randomized controlled trials (RCTs) and non-randomized studies (NRCTs) of digital or telehealth behavioral sleep interventions for youth (2-18 years) with ASD. The primary outcome was child sleep. Two reviewers independently screened studies, extracted data, and assessed the risk of bias. We conducted random-effects meta-analyses and sensitivity analyses. 12 studies met inclusion criteria: 6 RCTs (N = 349) and 6 NRCTs (N = 57).

Across RCTs, digital and telehealth interventions significantly improved parent-reported sleep problems compared with controls (pooled SMD = -0.57, 95 % CI -1.10 to -0.03; I = 62 %), reflecting a small-to-moderate benefit. Improvements were most evident for sleep initiation outcomes such as sleep-onset delay. In contrast, pooled NRCT results indicated large apparent gains (SMC = -1.03) but with wide confidence intervals and substantial heterogeneity, limiting interpretability. Evidence from randomized trials supports that digital and telehealth-delivered behavioral sleep interventions can yield small-to-moderate improvements in caregiver-reported sleep outcomes among autistic youth-particularly in reducing time to sleep onset.

Non-randomized findings are encouraging but methodologically uncertain. Future research should prioritize adequately powered RCTs using objective sleep measures, standardized intervention frameworks, and longer follow-up to strengthen causal inference and inform scalable best-practice models.