Effects of Digital Sleep Interventions on Sleep Among College Students and Young Adults: Systematic Review and Meta-Analysis.

BACKGROUND

College students and young adults (18-25 years) frequently experience poor sleep quality, with insomnia being particularly prevalent among this population. Given the widespread use of digital devices in the modern world, electronic device-based sleep interventions present a promising solution for improving sleep outcomes. However, their effects in this population remain underexplored.

OBJECTIVE

We aimed to synthesize current evidence on the effectiveness of electronic device-based sleep interventions in enhancing sleep outcomes among college students and young adults.

METHODS

In total, 5 electronic databases (PubMed, CINAHL, Cochrane Library, Embase, and Web of Science) were searched to identify randomized controlled trials on digital sleep interventions. Sleep interventions, including cognitive behavioral therapy for insomnia, mindfulness, and sleep education programs delivered via web-based platforms or mobile apps, were evaluated for their effects on sleep quality, sleep parameters, and insomnia severity. Pooled estimates of postintervention and follow-up effects were calculated using Hedges g and 95% CIs under a random-effects model. Heterogeneity was assessed with I statistics, and moderator and meta-regression analyses were performed to explore sources of heterogeneity. Evidence quality was evaluated using the Grading of Recommendations Assessment, Development, and Evaluations framework.

RESULTS

This study included 13 studies involving 5251 participants. Digital sleep interventions significantly improved sleep quality (Hedges g=-1.25, 95% CI -1.83 to -0.66; I=97%), sleep efficiency (Hedges g=0.62, 95% CI 0.18-1.05; I=60%), insomnia severity (Hedges g=-4.08, 95% CI -5.14 to -3.02; I=99%), dysfunctional beliefs and attitudes about sleep (Hedges g=-1.54, 95% CI -3.33 to -0.99; I=85%), sleep hygiene (Hedges g=-0.19, 95% CI -0.34 to -0.03; I=0%), and sleep knowledge (Hedges g=-0.27, 95% CI 0.09-0.45; I=0%). The follow-up effects were significant for sleep quality (Hedges g=-0.53, 95% CI -0.96 to -0.11; I=78%) and insomnia severity (Hedges g=-2.65, 95% CI -3.89 to -1.41; I=99%). Moderator analyses revealed several significant sources of heterogeneity in the meta-analysis examining the effects of digital sleep interventions on sleep outcomes. Variability in sleep quality was influenced by the sleep assessment tool (P<.001), intervention type and duration (P=.001), therapist guidance (P<.001), delivery mode (P=.002), history of insomnia (P<.001), and the use of intention-to-treat analysis (P=.001). Heterogeneity in insomnia severity was primarily attributed to differences in the sleep assessment tool (P<.001), while the effect size on sleep efficiency varied based on intervention duration (P=.02). The evidence quality ranged from moderate to very low certainty across measured outcomes.

CONCLUSIONS

Digital sleep interventions are effective in improving sleep quality and reducing insomnia severity, with moderate effects on dysfunctional beliefs and attitudes about sleep, sleep hygiene, and sleep knowledge. These interventions offer a viable approach to managing sleep problems in college students and young adults.

TRIAL REGISTRATION

PROSPERO CRD42024595126; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024595126.