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.

• Klíčová slova
• Activity, Bipolar disorder, Densely-sampled, Mood variability, Onset, Sleep, Wearable technology,
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Several studies have reported on the feasibility of electronic (e-)monitoring using computers or smartphones in patients with mental disorders, including bipolar disorder (BD). While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioeconomic status and use of health apps, to our knowledge, no study has examined clinical characteristics that might impact adherence with e-monitoring in patients with BD. We analyzed adherence to e-monitoring in patients with BD who participated in an ongoing e-monitoring study and evaluated whether demographic and clinical factors would predict adherence. METHODS: Eighty-seven participants with BD in different phases of the illness were included. Patterns of adherence for wearable use, daily and weekly self-rating scales over 15 months were analyzed to identify adherence trajectories using growth mixture models (GMM). Multinomial logistic regression models were fitted to compute the effects of predictors on GMM classes. RESULTS: Overall adherence rates were 79.5% for the wearable; 78.5% for weekly self-ratings; and 74.6% for daily self-ratings. GMM identified three latent class subgroups: participants with (i) perfect; (ii) good; and (iii) poor adherence. On average, 34.4% of participants showed "perfect" adherence; 37.1% showed "good" adherence; and 28.2% showed poor adherence to all three measures. Women, participants with a history of suicide attempt, and those with a history of inpatient admission were more likely to belong to the group with perfect adherence. CONCLUSIONS: Participants with higher illness burden (e.g., history of admission to hospital, history of suicide attempts) have higher adherence rates to e-monitoring. They might see e-monitoring as a tool for better documenting symptom change and better managing their illness, thus motivating their engagement.

• Klíčová slova
• Adherence, Bipolar disorder, Electronic monitoring,
• Publikační typ
• časopisecké články MeSH