Day-to-day variability in sleep and activity predict the onset of a hypomanic episode in patients with bipolar disorder
Language English Country Netherlands Media print-electronic
Document type Journal Article, Observational Study
PubMed
39793618
DOI
10.1016/j.jad.2025.01.026
PII: S0165-0327(25)00032-1
Knihovny.cz E-resources
- MeSH
- Bipolar Disorder * diagnosis psychology physiopathology MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Mania * diagnosis psychology MeSH
- Wearable Electronic Devices MeSH
- Prospective Studies MeSH
- Psychiatric Status Rating Scales MeSH
- Sleep * physiology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Observational Study MeSH
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.
Campbell Family Research Institute Centre for Addiction and Mental Health Toronto Ontario Canada
Department of MicroData Analytics Dalarna University Sweden
Department of Psychiatry Dalhousie University Halifax Nova Scotia Canada
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