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Day-to-day variability in sleep and activity predict the onset of a hypomanic episode in patients with bipolar disorder

A. Ortiz, R. Halabi, M. Alda, A. Burgos, A. DeShaw, C. Gonzalez-Torres, MI. Husain, C. O'Donovan, M. Tolend, A. Hintze, BH. Mulsant

. 2025 ; 374 (-) : 75-83. [pub] 20250108

Language English Country Netherlands

Document type Journal Article, Observational Study

Persistent link   https://www.medvik.cz/link/bmc25009374

Detecting transitions in bipolar disorder (BD) is essential for implementing early interventions. Our aim was to identify the earliest indicator(s) of the onset of a hypomanic episode in BD. We hypothesized that objective changes in sleep would be the earliest indicator of a new hypomanic or manic episode. In this prospective, observational, contactless study, participants used wearable technology continuously to monitor their daily activity and sleep parameters. They also completed weekly self-ratings using the Altman Self-Rating Mania Scale (ASRM). Using time-frequency spectral derivative spike detection, we assessed the sensitivity, specificity, and balanced accuracy of wearable data to identify a hypomanic episode, defined as at least one or more weeks with consecutive ASRM scores ≥10. Of 164 participants followed for a median (IQR) of 495.0 (410.0) days, 50 experienced one or more hypomanic episodes. Within-night variability in sleep stages was the earliest indicator identifying the onset of a hypomanic episode (mean ± SD): sensitivity: 0.94 ± 0.19; specificity: 0.80 ± 0.19; balanced accuracy: 0.87 ± 0.13; followed by within-day variability in activity levels: sensitivity: 0.93 ± 0.18; specificity: 0.84 ± 0.13; balanced accuracy: 0.89 ± 0.11. Limitations of our study includes a small sample size. Strengths include the use of densely sampled data in a well-characterized cohort followed for over a year, as well as the use of a novel approach using time-frequency analysis to dynamically assess behavioral features at a granular level. Detecting and predicting the onset of hypomanic (or manic) episodes in BD is paramount to implement individualized early interventions.

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$a Detecting transitions in bipolar disorder (BD) is essential for implementing early interventions. Our aim was to identify the earliest indicator(s) of the onset of a hypomanic episode in BD. We hypothesized that objective changes in sleep would be the earliest indicator of a new hypomanic or manic episode. In this prospective, observational, contactless study, participants used wearable technology continuously to monitor their daily activity and sleep parameters. They also completed weekly self-ratings using the Altman Self-Rating Mania Scale (ASRM). Using time-frequency spectral derivative spike detection, we assessed the sensitivity, specificity, and balanced accuracy of wearable data to identify a hypomanic episode, defined as at least one or more weeks with consecutive ASRM scores ≥10. Of 164 participants followed for a median (IQR) of 495.0 (410.0) days, 50 experienced one or more hypomanic episodes. Within-night variability in sleep stages was the earliest indicator identifying the onset of a hypomanic episode (mean ± SD): sensitivity: 0.94 ± 0.19; specificity: 0.80 ± 0.19; balanced accuracy: 0.87 ± 0.13; followed by within-day variability in activity levels: sensitivity: 0.93 ± 0.18; specificity: 0.84 ± 0.13; balanced accuracy: 0.89 ± 0.11. Limitations of our study includes a small sample size. Strengths include the use of densely sampled data in a well-characterized cohort followed for over a year, as well as the use of a novel approach using time-frequency analysis to dynamically assess behavioral features at a granular level. Detecting and predicting the onset of hypomanic (or manic) episodes in BD is paramount to implement individualized early interventions.
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$a Halabi, Ramzi $u Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
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$a Alda, Martin $u Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada; National Institute of Mental Health, Klecany, Czech Republic
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$a DeShaw, Alexandra $u Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
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$a Gonzalez-Torres, Christina $u Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
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$a Husain, Muhammad I $u Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
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$a Hintze, Arend $u Department of MicroData Analytics, Dalarna University, Sweden
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