Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood
Abstract Anticipating clinical transitions in bipolar disorder (BD) is essential for the development of clinically actionable predictions. Our aim was to determine what is the earliest indicator of the onset of depressive symptoms in BD. We hypothesized that changes in activity would be the earliest...
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| Format: | Article |
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SpringerOpen
2025-04-01
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| Series: | International Journal of Bipolar Disorders |
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| Online Access: | https://doi.org/10.1186/s40345-025-00379-6 |
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| author | Abigail Ortiz Ramzi Halabi Martin Alda Alexandra DeShaw Muhammad I. Husain Abraham Nunes Claire O’Donovan Rachel Patterson Benoit H. Mulsant Arend Hintze |
| author_facet | Abigail Ortiz Ramzi Halabi Martin Alda Alexandra DeShaw Muhammad I. Husain Abraham Nunes Claire O’Donovan Rachel Patterson Benoit H. Mulsant Arend Hintze |
| author_sort | Abigail Ortiz |
| collection | DOAJ |
| description | Abstract Anticipating clinical transitions in bipolar disorder (BD) is essential for the development of clinically actionable predictions. Our aim was to determine what is the earliest indicator of the onset of depressive symptoms in BD. We hypothesized that changes in activity would be the earliest indicator of future depressive symptoms. The study was a prospective, observational, contactless study. Participants were 127 outpatients with a primary diagnosis of BD, followed up for 12.6 (5.7) [(mean (SD)] months. They wore a smart ring continuously, which monitored their daily activity and sleep parameters. Participants were also asked to complete weekly self-ratings using the Patient Health Questionnaire (PHQ-9) and Altman Self-Rating Mania Scale (ASRS) scales. Primary outcome measures were depressive symptom onset detection metrics (i.e., accuracy, sensitivity, and specificity); and detection delay (in days), compared between self-rating scales and wearable data. Depressive symptoms were labeled as two or more consecutive weeks of total PHQ-9 > 10, and data-driven symptom onsets were detected using time-frequency spectral derivative spike detection (TF-SD2). Our results showed that day-to-day variability in the number of steps anticipated the onset of depressive symptoms 7.0 (9.0) (median (IQR)) days before they occurred, significantly earlier than the early prediction window provided by deep sleep duration (median (IQR), 4.0 (5.0) days; p <.05). Taken together, our results demonstrate that changes in activity were the earliest indicator of depressive symptoms in participants with BD. Transition to dynamic representations of behavioral phenomena in psychiatry may facilitate episode forecasting and individualized preventive interventions. |
| format | Article |
| id | doaj-art-e897b766469242c9aff096bb285ffa3d |
| institution | Kabale University |
| issn | 2194-7511 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Bipolar Disorders |
| spelling | doaj-art-e897b766469242c9aff096bb285ffa3d2025-08-20T03:42:40ZengSpringerOpenInternational Journal of Bipolar Disorders2194-75112025-04-0113111210.1186/s40345-025-00379-6Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and moodAbigail Ortiz0Ramzi Halabi1Martin Alda2Alexandra DeShaw3Muhammad I. Husain4Abraham Nunes5Claire O’Donovan6Rachel Patterson7Benoit H. Mulsant8Arend Hintze9Department of Psychiatry, Temerty Faculty of Medicine, University of TorontoCampbell Family Research Institute, Centre for Addiction and Mental Health (CAMH)Department of Psychiatry, Dalhousie UniversityDepartment of Psychiatry, Dalhousie UniversityDepartment of Psychiatry, Temerty Faculty of Medicine, University of TorontoDepartment of Psychiatry, Dalhousie UniversityDepartment of Psychiatry, Dalhousie UniversityCampbell Family Research Institute, Centre for Addiction and Mental Health (CAMH)Department of Psychiatry, Temerty Faculty of Medicine, University of TorontoDepartment of MicroData Analytics, Dalarna UniversityAbstract Anticipating clinical transitions in bipolar disorder (BD) is essential for the development of clinically actionable predictions. Our aim was to determine what is the earliest indicator of the onset of depressive symptoms in BD. We hypothesized that changes in activity would be the earliest indicator of future depressive symptoms. The study was a prospective, observational, contactless study. Participants were 127 outpatients with a primary diagnosis of BD, followed up for 12.6 (5.7) [(mean (SD)] months. They wore a smart ring continuously, which monitored their daily activity and sleep parameters. Participants were also asked to complete weekly self-ratings using the Patient Health Questionnaire (PHQ-9) and Altman Self-Rating Mania Scale (ASRS) scales. Primary outcome measures were depressive symptom onset detection metrics (i.e., accuracy, sensitivity, and specificity); and detection delay (in days), compared between self-rating scales and wearable data. Depressive symptoms were labeled as two or more consecutive weeks of total PHQ-9 > 10, and data-driven symptom onsets were detected using time-frequency spectral derivative spike detection (TF-SD2). Our results showed that day-to-day variability in the number of steps anticipated the onset of depressive symptoms 7.0 (9.0) (median (IQR)) days before they occurred, significantly earlier than the early prediction window provided by deep sleep duration (median (IQR), 4.0 (5.0) days; p <.05). Taken together, our results demonstrate that changes in activity were the earliest indicator of depressive symptoms in participants with BD. Transition to dynamic representations of behavioral phenomena in psychiatry may facilitate episode forecasting and individualized preventive interventions.https://doi.org/10.1186/s40345-025-00379-6Bipolar disorderWearable technologyDensely-sampledMood variabilityActivitySleep |
| spellingShingle | Abigail Ortiz Ramzi Halabi Martin Alda Alexandra DeShaw Muhammad I. Husain Abraham Nunes Claire O’Donovan Rachel Patterson Benoit H. Mulsant Arend Hintze Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood International Journal of Bipolar Disorders Bipolar disorder Wearable technology Densely-sampled Mood variability Activity Sleep |
| title | Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood |
| title_full | Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood |
| title_fullStr | Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood |
| title_full_unstemmed | Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood |
| title_short | Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood |
| title_sort | day to day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood |
| topic | Bipolar disorder Wearable technology Densely-sampled Mood variability Activity Sleep |
| url | https://doi.org/10.1186/s40345-025-00379-6 |
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