Accessibility settings

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57418, first published .
Woman uses smartphone with mental health data overlay, showing depression graph

Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review

Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review

Journals

  1. Priestley K, Laijawala R, Hazelgrove K, Bind R, Rebecchini L, Mariani N, Alford S, Kirkpatrick M, Mancino F, Kim S, Pushpakanthan S, Biaggi A, Cavaliere L, Di Benedetto M, Matijaš M, Žutić M, Brekalo M, Nakić Radoš S, Żukowska K, Braniecka A, Jackowska M, Bessi M, Agnoletto E, Melloni E, Benedetti F, Bulgheroni M, La Gamba M, Martín Isla C, Izquierdo Morcillo C, Lekadir K, Salo V, Seikku T, Räikkönen K, Godara M, Schneider-Schmid U, Entringer S, Buß C, de Barra D, Woods A, Dazzan P, Cattaneo A, Pariante C. HappyMums mobile application study protocol: use of a smartphone application to gather data predictive of antenatal depression. BMJ Open 2026;16(2):e106978 View
  2. Schat E, Schreuder M, Ceulemans E. Statistical process control for real-time monitoring in clinical psychology: State of the art and future research agenda. Neuroscience Applied 2026;5:106991 View
  3. Xu Y, Zhao Z, Zhu H, Lai S, Jia Y, Liu G. Machine Learning-Empowered Depression Detection With Wearable Skin Electronics: A Review. IEEE Sensors Journal 2026;26(6):7949 View
  4. Vander Zwalmen Y, Maerevoet M, Coenen T, Hoorelbeke K, Chen S, De Brouwer M, Vanderhasselt M, Van Hoecke S, Bombeke K, De Raedt R, Koster E. Mobile technology for just-in-time prediction of depression: a scoping review. Nature Mental Health 2026;4(5):851 View
  5. Chang Y, Hsieh M, Ju P, Chang C. Early change in daily step counts after antidepressant initiation among adults with depression: A within-person interrupted time series study. Journal of Affective Disorders 2026;406:121696 View
  6. Xu H, Zhou Y, You W, Duan J, Alhassan A, Almahmood M, Sun Z. Mapping hot routes and perceived landscape values of “City Walk” in Beijing’s historical neighbourhoods in the social media era. Humanities and Social Sciences Communications 2026 View
  7. Hammelrath L, Rane R, Gijsen S, Jüres F, Brose A, Ritter K, Hilbert K, Jacobi F, Renneberg B, Fehm L, Kathmann N, Lueken U, Knaevelsrud C. Comparing personalized and population-based models for predicting momentary negative affect in internalizing disorders: A digital phenotyping study. Neuroscience Applied 2026;5:107006 View
  8. Cui J, Wang K, Liu H, Zhu S, Li X, Chen J, Zheng Z, Wang H, Zheng Y, Xue C. Microsphere-enhanced flexible triboelectric nanogenerator with PVDF/CNT–PDMS composite for micro-expression recognition and mental health monitoring. Sensors and Actuators B: Chemical 2026;463:140115 View
  9. Paulus M. Current Themes of AI in Mental Health: Actionable Evidence and Guardrails for Mood and Anxiety Care. Journal of Mood & Anxiety Disorders 2026:100181 View