TY  - JOUR
AU  - Borelli, Jessica L
AU  - Wang, Yuning
AU  - Li, Frances Haofei
AU  - Russo, Lyric N
AU  - Tironi, Marta
AU  - Yamashita, Ken
AU  - Zhou, Elayne
AU  - Lai, Jocelyn
AU  - Nguyen, Brenda
AU  - Azimi, Iman
AU  - Marcotullio, Christopher
AU  - Labbaf, Sina
AU  - Jafarlou, Salar
AU  - Dutt, Nikil
AU  - Rahmani, Amir
PY  - 2025
DA  - 2025/6/3
TI  - Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study
JO  - JMIR Form Res
SP  - e67964
VL  - 9
KW  - depression
KW  - college students
KW  - emerging adulthood
KW  - machine learning
KW  - passive sensing
AB  - Background: Depression is the top contributor to global disability. Early detection of depression and depressive symptoms enables timely intervention and reduces their physical and social consequences. Prevalence estimates of depression approach 30% among college students. Passive, device-based sensing further enables detection of depressive symptoms at a low burden to the individual. Objective: We leveraged an ensemble machine learning method (light gradient boosting machine) to detect depressive symptoms entirely through passive sensing. Methods: A diverse sample of undergraduate students (N=28; mean age 19.96, SD 1.23 y; 15/28, 54% women; 13/28, 46% Latine; 10/28, 36% Asian; 4/28, 14% non-Latine White; 11/28, 4% other) participated in an intensive longitudinal study. Participants wore 2 devices (an Oura ring for sleep and physiology data, and a Samsung smartwatch for physiology and movement data) and installed the AWARE software on their mobile devices, which collects passive sensing data such as screen time. Participants were derived from a randomized controlled trial of a positive psychology mobile health intervention. They completed a self-report measure of depressive symptoms administered weekly over a 19- to 22-week period. Results: The light gradient boosting machine model achieved an F1-score of 0.744 and a Cohen κ coefficient of 0.474, indicating moderate agreement between the predicted labels and the ground truth. The most predictive features of depressive symptoms were sleep quality and missed mobile interactions. Conclusions: Findings suggest that data collected from passive sensing devices may provide real-time, low-cost insight into the detection of depressive symptoms in college students and may present an opportunity for future prevention and perhaps intervention. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2025/1/e67964
UR  - https://doi.org/10.2196/67964
DO  - 10.2196/67964
ID  - info:doi/10.2196/67964
ER  -