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<p>Early detection of depressive symptom changes is vital for timely interventions. Mobile and wearable technologies enable continuous, unobtrusive monitoring of behavioral, psychological and physiological data, offering new possibilities for digital phenotyping and just-in-time prediction of depression. This scoping review synthesized findings from 52 studies to identify commonly used features, evaluate their predictive value and assess methodological approaches. Frequently assessed features included location data, sleep metrics, physical activity, communication patterns, heart rate variability and mood self-reports. Features such as time spent at home, sleep variability and reduced mobility were strongly associated with depressive symptoms. Combining physiological, behavioral and self-report data enhanced predictive performance. Personalized models and anomaly detection approaches outperformed generalized ones in predicting individual symptom changes. Overall, mobile and wearable data show strong potential for just-in-time depression prediction. Future research should emphasize new features, diverse populations and personalized models to improve accuracy and real-world applicability.</p>

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Mobile technology for just-in-time prediction of depression: a scoping review

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Yannick Vander Zwalmen,
  • Matthias Maerevoet,
  • Tina Coenen,
  • Kristof Hoorelbeke,
  • Stephanie Chen,
  • Mathias De Brouwer,
  • Marie-Anne Vanderhasselt,
  • Sofie Van Hoecke,
  • Klaas Bombeke,
  • Rudi De Raedt,
  • Ernst H. W. Koster

摘要

Early detection of depressive symptom changes is vital for timely interventions. Mobile and wearable technologies enable continuous, unobtrusive monitoring of behavioral, psychological and physiological data, offering new possibilities for digital phenotyping and just-in-time prediction of depression. This scoping review synthesized findings from 52 studies to identify commonly used features, evaluate their predictive value and assess methodological approaches. Frequently assessed features included location data, sleep metrics, physical activity, communication patterns, heart rate variability and mood self-reports. Features such as time spent at home, sleep variability and reduced mobility were strongly associated with depressive symptoms. Combining physiological, behavioral and self-report data enhanced predictive performance. Personalized models and anomaly detection approaches outperformed generalized ones in predicting individual symptom changes. Overall, mobile and wearable data show strong potential for just-in-time depression prediction. Future research should emphasize new features, diverse populations and personalized models to improve accuracy and real-world applicability.