Real-time monitoring to predict depressive symptoms: study protocol
IntroductionAccording to the World Health Organization, Depression is the fourth leading cause of global disease burden. However, traditional clinical and self-report assessments of depression have limitations in providing timely diagnosis and intervention. Recently, digital phenotyping studies have...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1465933/full |
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| author | Yu-Rim Lee Jong-Sun Lee |
| author_facet | Yu-Rim Lee Jong-Sun Lee |
| author_sort | Yu-Rim Lee |
| collection | DOAJ |
| description | IntroductionAccording to the World Health Organization, Depression is the fourth leading cause of global disease burden. However, traditional clinical and self-report assessments of depression have limitations in providing timely diagnosis and intervention. Recently, digital phenotyping studies have found the possibility of overcoming these limitations through the use of wearable-devices and smartphones. The present study aims to identify the digital phenotype that significantly predicts depressive symptoms.Methods and analysisThe study will recruit a total of 150 participants in their 20s who have experienced depression for the past two weeks in Korea. The study will collect passive (eg., active energy, exercise minutes, heart rate, heart rate variability, resting energy, resting heart rate, sleep patterns, steps, walking pace) data and Ecological Momentary Assessment (EMA) through smartphone and wearable-device for two weeks. This study will be conducted longitudinally, with two repeated measurements over three months. Passive data will be collected through sensors on the wearable-device, while EMA data will be collected four times a day through a smartphone app. A machine learning algorithm and multilevel model will be used to construct a predictive model for depressive symptoms using the collected data.DiscussionThis study explores the potential of wearable devices and smartphones to improve the understanding and treatment of depression in young adults. By collecting continuous, real-time data on physiological and behavioral patterns, the research uncovers subtle changes in heart rate, activity levels and sleep that correlate with depressive symptoms, providing a deeper understanding of the disorder. The findings provide a foundation for further research and contribute to the advancement of digital mental health. Advances in these areas of research may have implications for the detection and prevention of early warning signs of depression through the use of digital markers. |
| format | Article |
| id | doaj-art-899fd938ebf048fbad7c83a97e99d28e |
| institution | DOAJ |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-899fd938ebf048fbad7c83a97e99d28e2025-08-20T02:47:28ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-03-011510.3389/fpsyt.2024.14659331465933Real-time monitoring to predict depressive symptoms: study protocolYu-Rim LeeJong-Sun LeeIntroductionAccording to the World Health Organization, Depression is the fourth leading cause of global disease burden. However, traditional clinical and self-report assessments of depression have limitations in providing timely diagnosis and intervention. Recently, digital phenotyping studies have found the possibility of overcoming these limitations through the use of wearable-devices and smartphones. The present study aims to identify the digital phenotype that significantly predicts depressive symptoms.Methods and analysisThe study will recruit a total of 150 participants in their 20s who have experienced depression for the past two weeks in Korea. The study will collect passive (eg., active energy, exercise minutes, heart rate, heart rate variability, resting energy, resting heart rate, sleep patterns, steps, walking pace) data and Ecological Momentary Assessment (EMA) through smartphone and wearable-device for two weeks. This study will be conducted longitudinally, with two repeated measurements over three months. Passive data will be collected through sensors on the wearable-device, while EMA data will be collected four times a day through a smartphone app. A machine learning algorithm and multilevel model will be used to construct a predictive model for depressive symptoms using the collected data.DiscussionThis study explores the potential of wearable devices and smartphones to improve the understanding and treatment of depression in young adults. By collecting continuous, real-time data on physiological and behavioral patterns, the research uncovers subtle changes in heart rate, activity levels and sleep that correlate with depressive symptoms, providing a deeper understanding of the disorder. The findings provide a foundation for further research and contribute to the advancement of digital mental health. Advances in these areas of research may have implications for the detection and prevention of early warning signs of depression through the use of digital markers.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1465933/fulldigital phenotypingdepressive disorderecological momentary assessmentwearable devicemultilevel modelingmachine learning |
| spellingShingle | Yu-Rim Lee Jong-Sun Lee Real-time monitoring to predict depressive symptoms: study protocol Frontiers in Psychiatry digital phenotyping depressive disorder ecological momentary assessment wearable device multilevel modeling machine learning |
| title | Real-time monitoring to predict depressive symptoms: study protocol |
| title_full | Real-time monitoring to predict depressive symptoms: study protocol |
| title_fullStr | Real-time monitoring to predict depressive symptoms: study protocol |
| title_full_unstemmed | Real-time monitoring to predict depressive symptoms: study protocol |
| title_short | Real-time monitoring to predict depressive symptoms: study protocol |
| title_sort | real time monitoring to predict depressive symptoms study protocol |
| topic | digital phenotyping depressive disorder ecological momentary assessment wearable device multilevel modeling machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1465933/full |
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