Lu Yi-An, Lin Hui-Chen, Tsai Pei-Shan
School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan.
Research Center in Nursing Clinical Practice, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
J Med Internet Res. 2025 May 12;27:e69657. doi: 10.2196/69657.
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.
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.
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.
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.
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.
PROSPERO CRD42024595126; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024595126.
大学生和年轻人（18 - 25岁）经常经历睡眠质量差的问题，失眠在这一人群中尤为普遍。鉴于现代世界中数字设备的广泛使用，基于电子设备的睡眠干预为改善睡眠结果提供了一个有前景的解决方案。然而，它们在这一人群中的效果仍未得到充分探索。
我们旨在综合当前关于基于电子设备的睡眠干预对大学生和年轻人睡眠结果影响的证据。
总共检索了5个电子数据库（PubMed、CINAHL、Cochrane图书馆、Embase和Web of Science），以识别关于数字睡眠干预的随机对照试验。对通过网络平台或移动应用程序提供的睡眠干预措施进行评估，包括失眠的认知行为疗法、正念训练和睡眠教育项目，评估其对睡眠质量、睡眠参数和失眠严重程度的影响。使用随机效应模型下的Hedges g和95%置信区间计算干预后和随访效应的合并估计值。用I统计量评估异质性，并进行调节因素和Meta回归分析以探索异质性来源。使用推荐分级评估、制定和评价框架评估证据质量。
本研究纳入了13项研究，涉及5251名参与者。数字睡眠干预显著改善了睡眠质量（Hedges g = -1.25，95%置信区间 -1.83至 -0.66；I = 97%）、睡眠效率（Hedges g = 0.62，95%置信区间0.18 - 1.05；I = 60%）、失眠严重程度（Hedges g = -4.08，95%置信区间 -5.14至 -3.02；I = 99%）、对睡眠的功能失调信念和态度（Hedges g = -1.54，95%置信区间 -3.33至 -0.99；I = 85%）、睡眠卫生（Hedges g = -0.19，95%置信区间 -0.34至 -0.03；I = 0%）和睡眠知识（Hedges g = -0.27，95%置信区间0.09 - 0.45；I = 0%）。随访效应在睡眠质量（Hedges g = -0.53，95%置信区间 -0.96至 -0.11；I = 78%）和失眠严重程度（Hedges g = -2.65，95%置信区间 -3.89至 -1.41；I = 99%）方面显著。调节因素分析揭示了在Meta分析中，数字睡眠干预对睡眠结果影响的几个显著异质性来源。睡眠质量的变异性受睡眠评估工具（P <.001）、干预类型和持续时间（P =.001）、治疗师指导（P <.001）、交付模式（P =.002）、失眠病史（P <.001）以及意向性分析的使用（P =.001）影响。失眠严重程度的异质性主要归因于睡眠评估工具的差异（P <.001），而对睡眠效率的效应大小因干预持续时间而异（P =.02）。在所测量的结果中，证据质量从中等确定性到非常低确定性不等。
数字睡眠干预在改善睡眠质量和降低失眠严重程度方面是有效的，对睡眠的功能失调信念和态度、睡眠卫生和睡眠知识有中等程度的影响。这些干预措施为管理大学生和年轻人睡眠问题提供了一种可行的方法。
PROSPERO CRD42024595126；https://www.crd.york.ac.uk/PROSPERO/view/CRD42024595126 。
