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 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