PASSIVE SMARTPHONE SENSING FOR MENTAL HEALTH MONITORING: A STRUCTURED REVIEW OF BEHAVIORAL BIOMARKERS, PREDICTIVE VALIDITY, AND ETHICAL RISKS

Abstract

Methods: This structured review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Systematic searches were carried out in PubMed/MEDLINE and Google Scholar to identify peer-reviewed articles published between January 2018 and June 2026. Studies met inclusion criteria if they examined passive sensing frameworks (e.g., AWARE, Beiwe, RADAR-Base) collecting GPS, accelerometry, or communication metadata for psychiatric monitoring, resulting in a final sample of 15 core empirical studies. Findings: The evidence shows that changes in spatial mobility, sleep architecture, and communication latency display strong longitudinal correlations with symptom severity scores for major depression (PHQ-9) and generalized anxiety (GAD-7). Machine learning models incorporating multi-modal data streams reached high predictive accuracy (AUC 0.72–0.88) when forecasting depressive shifts and spotting prodromal relapse signatures in bipolar disorder. Even with these advancements, notable translation barriers persist, especially concerning algorithmic generalizability across diverse socioeconomic groups and the ethical threat of "surveillance creep." Conclusion: Passive smartphone sensing offers incredible potential for proactive, continuous psychiatric care. However, actual clinical translation will require strict cross-cultural validation of digital biomarkers alongside the creation of solid data governance frameworks to protect patient autonomy and privacy.