Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study
BackgroundDepression 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. Pass...
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JMIR Publications
2025-06-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2025/1/e67964 |
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| author | Jessica L Borelli Yuning Wang Frances Haofei Li Lyric N Russo Marta Tironi Ken Yamashita Elayne Zhou Jocelyn Lai Brenda Nguyen Iman Azimi Christopher Marcotullio Sina Labbaf Salar Jafarlou Nikil Dutt Amir Rahmani |
| author_facet | Jessica L Borelli Yuning Wang Frances Haofei Li Lyric N Russo Marta Tironi Ken Yamashita Elayne Zhou Jocelyn Lai Brenda Nguyen Iman Azimi Christopher Marcotullio Sina Labbaf Salar Jafarlou Nikil Dutt Amir Rahmani |
| author_sort | Jessica L Borelli |
| collection | DOAJ |
| description |
BackgroundDepression 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.
ObjectiveWe leveraged an ensemble machine learning method (light gradient boosting machine) to detect depressive symptoms entirely through passive sensing.
MethodsA 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.
ResultsThe 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.
ConclusionsFindings 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. |
| format | Article |
| id | doaj-art-077ec3b6735a43cd9b6b34c723f1a1bb |
| institution | DOAJ |
| issn | 2561-326X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj-art-077ec3b6735a43cd9b6b34c723f1a1bb2025-08-20T03:19:20ZengJMIR PublicationsJMIR Formative Research2561-326X2025-06-019e6796410.2196/67964Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot StudyJessica L Borellihttps://orcid.org/0000-0001-8471-6732Yuning Wanghttps://orcid.org/0000-0001-7351-6866Frances Haofei Lihttps://orcid.org/0000-0002-2612-6336Lyric N Russohttps://orcid.org/0000-0002-3815-3622Marta Tironihttps://orcid.org/0000-0003-0610-6650Ken Yamashitahttps://orcid.org/0009-0009-0509-058XElayne Zhouhttps://orcid.org/0000-0001-9604-9404Jocelyn Laihttps://orcid.org/0000-0002-6457-3313Brenda Nguyenhttps://orcid.org/0009-0004-6480-8292Iman Azimihttps://orcid.org/0000-0001-5003-299XChristopher Marcotulliohttps://orcid.org/0009-0000-0736-5166Sina Labbafhttps://orcid.org/0000-0002-9478-2546Salar Jafarlouhttps://orcid.org/0000-0002-9706-0901Nikil Dutthttps://orcid.org/0000-0002-3060-8119Amir Rahmanihttps://orcid.org/0000-0003-0725-1155 BackgroundDepression 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. ObjectiveWe leveraged an ensemble machine learning method (light gradient boosting machine) to detect depressive symptoms entirely through passive sensing. MethodsA 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. ResultsThe 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. ConclusionsFindings 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.https://formative.jmir.org/2025/1/e67964 |
| spellingShingle | Jessica L Borelli Yuning Wang Frances Haofei Li Lyric N Russo Marta Tironi Ken Yamashita Elayne Zhou Jocelyn Lai Brenda Nguyen Iman Azimi Christopher Marcotullio Sina Labbaf Salar Jafarlou Nikil Dutt Amir Rahmani Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study JMIR Formative Research |
| title | Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study |
| title_full | Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study |
| title_fullStr | Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study |
| title_full_unstemmed | Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study |
| title_short | Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study |
| title_sort | detection of depressive symptoms in college students using multimodal passive sensing data and light gradient boosting machine longitudinal pilot study |
| url | https://formative.jmir.org/2025/1/e67964 |
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