Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review

Bipolar disorder (BD) features episodic shifts among mania, hypomania, depression, mixed states, and euthymia. Timely detection of mood transitions is difficult due to infrequent clinical touchpoints. Digital health technologies, including wearables and smartphones, offer a unique opportunity to passively and continuously monitor behavior and physiology that could reflect underlying mood dynamics in real-world settings.

This study aimed to systematically review passively collected digital markers for BD mood states, characterize devices/modalities and analytic approaches, appraise risk of bias, and identify design gaps and priorities for clinical translation.

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched MEDLINE, PsycINFO, Scopus, IEEE Xplore, and ACM Digital Library (February 16, 2026). We included peer-reviewed studies of adults with bipolar I disorder or bipolar II disorder (BDI or BDII) that measured passively collected markers and related them to depressive, manic, hypomanic, mixed, or euthymic states. Studies that relied exclusively on active measures (eg, lab tests and ecological momentary assessment) were excluded. Two independent reviewers screened studies, extracted study characteristics and results, conducted narrative synthesis, and assessed risk of bias.

Of 23,727 records, 57 studies met criteria. Most enrolled ≤50 participants (n=34, 60%) and monitored ≤365 days (n=46, 81%); 11 out of 57 studies (19%) collected data only in the clinic. Eight digital marker domains emerged: physical activity, heart rate (HR), electrodermal activity (EDA), geolocation, smartphone use, light exposure, sleep, and speech. Consistent patterns linked depression to reduced mobility and social interaction, later/variable sleep, and lower daytime light; mania and hypomania were associated with higher and more variable activity, shorter/advanced sleep, and increased communication. Circadian features derived from sleep/activity repeatedly aided prediction. EDA tended to be lower in depression; HR variability findings were mixed across settings and methods. Keyboard and speech features (eg, timing and prosody) showed associations and performed well in classification models. Twenty-one studies used machine learning; several reported strong performance for episode prediction/classification. However, external validation was usually absent, samples were small, monitoring windows were often short relative to episode timescales, clinical labels were infrequent/misaligned, and missingness was rarely modeled despite likely informativeness.

Passive digital markers for BD show promise, with the most robust signals aligning with DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) diagnostic features (sleep-wake patterns, activity, socialization, geolocation, and speech). To move from promise to practice, future studies should adopt longer within-subject monitoring, align label cadence with sensing granularity, standardize features/reporting, preregister analyses, externally validate models, minimize data collection to protect privacy, and expand physiological measurement beyond HR and EDA. These steps are essential to develop reliable, actionable tools for earlier detection and management of BD mood episodes.