ArticlePsychiatry research2026
Agreement between smartphone-based mobile sensing and actigraphy sleep metrics in young people with bipolar disorder.
Article in Psychiatry research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundSleep disturbance is a core feature of bipolar disorder (BD) and often precedes mood recurrence, particularly in youth. Smartphone-based mobile sensing offers a scalable alternative to objective sleep measurement via actigraphy, but its validity in youths with BD is unclear.
methodsAnalyses included adolescents and young adults (ages 14-25) with BD-I/II from the PROMPT-BD study with at least four days of concurrent actigraphy and mobile sensing. Actigraphy-derived sleep metrics were compared with smartphone-derived proxies. Agreement was evaluated using root mean squared error (RMSE) and mixed-effects models. Zero-inflated negative binomial models examined associations between actigraphy-derived and mobile-sensing derived wake after sleep onset (WASO). Sensitivity analyses tested robustness to missing data, smartphone use patterns, sleep window definitions, operating system, presence of mood symptoms and anxiety, and weekend effects.
resultsMobile sensing showed strong convergence with actigraphy for sleep timing and duration. RMSEs were <21 minutes for onset, offset, midsleep, and TST, with strongest agreement for midsleep (RMSE = 14.8 minutes). Mobile sensing slightly overestimated sleep duration and estimated earlier timing, and underestimated WASO. Greater WASO significantly increased the odds of detecting any via mobile sensing; mobile sensing proxies were sensitive to the presence of nocturnal wakefulness but did not provide an accurate estimate of the extent of sleep fragmentation. Findings were robust across sensitivity analyses.
conclusionsPassive smartphone-derived sleep metrics approximated actigraphy-based estimates of sleep timing and duration in youth with BD. Given the widespread availability of smartphones in this population, this supports their potential as scalable tools for monitoring circadian disruption and informing early intervention.
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