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A Scoping Review of Smartphone Passive Sensing Methods for Sleep Monitoring: Approaches, Limitations, and Future Research
ABSTRACT
Background:
Given widespread smartphone adoption, passive sensing has emerged as a promising, scalable method for monitoring various health behaviors, including sleep. Yet, how these methods are being used to measure sleep and their validity remains unclear.
Objective:
This scoping review aimed to summarize the literature on smartphone-based passive sensing methods for sleep monitoring with respect to: 1) the methodological approaches used to estimate sleep duration and timing using smartphone sensors and 2) the convergence of these mobile sensing estimates with established sleep assessment measures.
Methods:
Following Arksey and O’Malley’s framework for conducting scoping reviews and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, we searched MEDLINE, Embase, PsycINFO, Web of Science, ACM, and IEEE Xplore databases through February 28, 2026 for articles using smartphone passive sensing to monitor sleep. Eligible studies were peer-reviewed journal articles or conference papers published in English between January 2010 and February 2026 that reported original human subject research which 1) utilized smartphone passive sensing methods to measure sleep duration or timing, and 2) assessed convergence with one or more alternative sleep measurement methods, including self-reports, wearables or actigraphy, and polysomnography. Exclusions were sensing methods wholly or partially reliant on wearables, external environmental sensors, or active user input, sample sizes fewer than five, pediatric populations younger than 12, nonempirical research (e.g., protocols, reviews), incomplete reports, and non-English studies.
Results:
Of 8422 identified records, 38 met inclusion criteria. We narratively synthesized 1) methodological approaches to estimating sleep duration and timing using smartphone sensors, including information about the sensors, features, and algorithms employed across studies, and 2) validity of these estimates compared to sleep diaries (n=21 studies), actigraphy and wearables (n=11), polysomnography (n=5), and other self-report measures (n=10). Comparator findings are further organized to reflect three convergent validity indicators: estimation accuracy, systematic bias (e.g., over/underestimation), and strength of associations or consistency. Overall, findings suggest that smartphones can estimate sleep patterns with moderate resolution, though convergence varied across studies, sleep outcomes, and comparator methods
Conclusions:
Smartphone passive sensing shows promise as a scalable and unobtrusive method for sleep monitoring, although mixed validity findings and substantial methodological heterogeneity preclude drawing definitive conclusions. Key areas of consideration for future research are highlighted. Clinical Trial: None
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