Effectiveness of Adaptive Digital Interventions Triggered by Passive Sensing for Sleep Improvement in Adults: A Systematic Review and Meta-Analysis

Abstract

Background/Objectives Adaptive digital interventions that respond to real-time physiological data from passive sensors are emerging as personalized tools for sleep improvement. This systematic review and meta-analysis aimed to evaluate the effectiveness of such interventions in improving sleep outcomes and broader health indicators among adults. Data Sources A comprehensive literature search was conducted across PubMed, Embase, Cochrane CENTRAL and ScienceDirect for studies published from January 2015 to July 2025. models. Effect sizes were reported as standardized mean differences (SMDs) with 95% confidence intervals (CI). Heterogeneity was assessed using I². Results Twelve RCTs (n = 798 participants) were included. Adaptive interventions significantly improved wake after sleep onset (WASO: SMD = 3.22; 95% CI: 3.02 to 3.41), with moderate heterogeneity (I² = 70.7%). Effects on PSQI, sleep efficiency, and latency were small and non-significant. However, secondary outcomes showed favorable results, including improvements in quality of life (SMD = 1.36), depressive symptoms (SMD = 0.53), sleep duration (SMD = 0.41), and neuropsychiatric inventory scores (SMD =-1.21). Subgroup analyses revealed greater benefits in populations with cognitive impairments and interventions using advanced sensing tools (MotionWatch8). Conclusions Adaptive digital interventions triggered by passive sensing show promise in reducing night-time awakenings and enhancing mood and quality of life. Their utility may be greatest in cognitively vulnerable populations. Further research is needed to optimize adaptivity algorithms, ensure sustained engagement, and assess long-term outcomes in real-world settings.