Digital Biomarkers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review
Notice bibliographique
Résumé
Background: Bipolar disorder (BD) features episodic shifts among (hypo)mania, 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. Objective: We aim to systematically review passively collected digital biomarkers for BD mood states, characterize devices/modalities and analytic approaches, appraise risk of bias, and identify design gaps and priorities for clinical translation. Methods: Following PRISMA guidelines (PROSPERO CRD42024607765), we searched MEDLINE, PsycINFO, Scopus, IEEE Xplore, and ACM Digital Library (February 7, 2025). We included peer-reviewed studies of adults with BD I/II that measured passively collected digital biomarkers and related them to depressive, (hypo)manic, mixed, or euthymic states. Active-only measures (e.g. lab tests, ecological-momentary assessment) and studies entangling BD with other diagnoses were excluded. Two independent reviewers screened studies and extracted study characteristics and results. We grouped digital biomarkers into categories and conducted narrative synthesis. Risk of bias was assessed with PROBAST (predictive models) and the Newcastle-Ottawa Scale (observational studies). Results: Of 8,355 records, 45 studies met criteria. Most enrolled ≤50 participants (64%) and monitored ≤100 days (49%); 29% collected data only in-clinic. Nine biomarker domains emerged: physical activity, heart rate (HR), electrodermal activity (EDA), geolocation, keyboard use, light exposure, sleep, socialization, and speech. Consistent patterns linked depression to reduced mobility and social interaction, later/variable sleep, and lower daytime light; (hypo)mania was 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; HRV findings were mixed across settings and methods. Keyboard and speech features (e.g., timing, prosody) showed associations and performed well in classifiers. Fifteen studies used ML; several reported strong performance for episode prediction/classification (AUROC ≈0.80-0.98 in larger cohorts), yet external validation was 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. Conclusions: Passive digital biomarkers for BD show promise, with the most robust signals aligning with DSM-5 behavioral and circadian features (sleep-wake patterns, activity/mobility, 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, pre-register analyses, externally validate models, minimize data to protect privacy, and expand physiological measurement beyond heart rate and electrodermal activity. These steps are essential to develop reliable, actionable tools for earlier detection and management of BD mood episodes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,006 |
| Bibliométrie | 0,012 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».