JMIR mHealth and uHealth · 2026
High accuracy of passive monitoring for detecting hypomanic symptoms in bipolar disorder: an explainable machine learning approach.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Halabi Ramzi, Alda Martin, Tolend Mirkamal, O'Donovan Claire, Hintze Arend, Mulsant Benoit H, Ortiz Abigail
Аннотация
BACKGROUND: When trying to intervene early to prevent manic episodes, clinicians are hindered by patients underreporting their symptoms as they are becoming hypomanic or manic (i.e., emerging symptoms). Continuous passive monitoring using wearable technology offers a potential solution to address this problem by capturing behavioral signals that do not depend on self-awareness. However, its clinical utility requires validation before implementation.
OBJECTIVE: This study aimed to evaluate whether passively collected wearable data can detect hypomanic symptoms using an explainable machine learning framework.
METHODS: We applied explainable machine learning to densely sampled multimodal data from a two-year observational study of digital monitoring in 178 patients with a diagnosis of bipolar disorder (BD) (mean follow-up 540 days; range 120-706 days). Data on activity and sleep were collected continuously using wearable technology. Self-reported data was collected daily on their mood, energy, and anxiety; and weekly using the Altman Self-Rating Mania (ASRM) scale. To identify robust digital markers of hypomanic symptoms, we analyzed 252 time- and frequency-domain features from 43 participants who experienced at least one ASRM-defined symptomatic interval.
RESULTS: Passively collected data achieved high accuracy, comparable to daily self-reported data in detecting symptomatic intervals (AU-ROC 0.93 vs 0.91). Combined daily passive wearable data features achieved higher sensitivity (92.0% vs 85.4%) and balanced accuracy (0.90 vs 0.89), and stronger rule-out utility (NPV 97.8% vs 96.1%) than self-report data, with daily sleep features alone driving most of this signal (AU-ROC 0.91; sensitivity 91.8%). The high classification performance using wearable data was explained by reductions in spectral entropy and flux across domains, consistent with decreased complexity and variability in mood, activity, and sleep patterns, consistent with a shift toward pathologically stable states.
CONCLUSIONS: Passive monitoring using wearable technology can detect hypomanic symptoms with high accuracy, supporting its potential as a clinically meaningful tool for continuous, objective monitoring in patients who underreport hypomanic symptoms. These findings are associative and concurrent in nature; they do not establish predictive validity. Prospective studies are needed to evaluate predictive validity for early detection of ASRM-defined symptomatic intervals.
OBJECTIVE: This study aimed to evaluate whether passively collected wearable data can detect hypomanic symptoms using an explainable machine learning framework.
METHODS: We applied explainable machine learning to densely sampled multimodal data from a two-year observational study of digital monitoring in 178 patients with a diagnosis of bipolar disorder (BD) (mean follow-up 540 days; range 120-706 days). Data on activity and sleep were collected continuously using wearable technology. Self-reported data was collected daily on their mood, energy, and anxiety; and weekly using the Altman Self-Rating Mania (ASRM) scale. To identify robust digital markers of hypomanic symptoms, we analyzed 252 time- and frequency-domain features from 43 participants who experienced at least one ASRM-defined symptomatic interval.
RESULTS: Passively collected data achieved high accuracy, comparable to daily self-reported data in detecting symptomatic intervals (AU-ROC 0.93 vs 0.91). Combined daily passive wearable data features achieved higher sensitivity (92.0% vs 85.4%) and balanced accuracy (0.90 vs 0.89), and stronger rule-out utility (NPV 97.8% vs 96.1%) than self-report data, with daily sleep features alone driving most of this signal (AU-ROC 0.91; sensitivity 91.8%). The high classification performance using wearable data was explained by reductions in spectral entropy and flux across domains, consistent with decreased complexity and variability in mood, activity, and sleep patterns, consistent with a shift toward pathologically stable states.
CONCLUSIONS: Passive monitoring using wearable technology can detect hypomanic symptoms with high accuracy, supporting its potential as a clinically meaningful tool for continuous, objective monitoring in patients who underreport hypomanic symptoms. These findings are associative and concurrent in nature; they do not establish predictive validity. Prospective studies are needed to evaluate predictive validity for early detection of ASRM-defined symptomatic intervals.