Lipschitz Jessica M, Lin Sidian, Saghafian Soroush, Pike Chelsea K, Burdick Katherine E
Department of Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.
Acta Psychiatr Scand. 2025 Mar;151(3):434-447. doi: 10.1111/acps.13765. Epub 2024 Oct 13.
Effective treatment of bipolar disorder (BD) requires prompt response to mood episodes. Preliminary studies suggest that predictions based on passive sensor data from personal digital devices can accurately detect mood episodes (e.g., between routine care appointments), but studies to date do not use methods designed for broad application. This study evaluated whether a novel, personalized machine learning approach, trained entirely on passive Fitbit data, with limited data filtering could accurately detect mood symptomatology in BD patients.
We analyzed data from 54 adults with BD, who wore Fitbits and completed bi-weekly self-report measures for 9 months. We applied machine learning (ML) models to Fitbit data aggregated over two-week observation windows to detect occurrences of depressive and (hypo)manic symptomatology, which were defined as two-week windows with scores above established clinical cutoffs for the Patient Health Questionnaire-8 (PHQ-8) and Altman Self-Rating Mania Scale (ASRM) respectively.
As hypothesized, among several ML algorithms, Binary Mixed Model (BiMM) forest achieved the highest area under the receiver operating curve (ROC-AUC) in the validation process. In the testing set, the ROC-AUC was 86.0% for depression and 85.2% for (hypo)mania. Using optimized thresholds calculated with Youden's J statistic, predictive accuracy was 80.1% for depression (sensitivity of 71.2% and specificity of 85.6%) and 89.1% for (hypo)mania (sensitivity of 80.0% and specificity of 90.1%).
We achieved sound performance in detecting mood symptomatology in BD patients using methods designed for broad application. Findings expand upon evidence that Fitbit data can produce accurate mood symptomatology predictions. Additionally, to the best of our knowledge, this represents the first application of BiMM forest for mood symptomatology prediction. Overall, results move the field a step toward personalized algorithms suitable for the full population of patients, rather than only those with high compliance, access to specialized devices, or willingness to share invasive data.
双相情感障碍（BD）的有效治疗需要对情绪发作做出迅速反应。初步研究表明，基于个人数字设备的被动传感器数据进行的预测能够准确检测情绪发作（例如，在常规护理预约之间），但迄今为止的研究并未使用适用于广泛应用的方法。本研究评估了一种全新的、个性化的机器学习方法，该方法完全基于被动的Fitbit数据进行训练，且数据过滤有限，能否准确检测BD患者的情绪症状。
我们分析了54名成年BD患者的数据，这些患者佩戴Fitbit，并在9个月内每两周完成一次自我报告测量。我们将机器学习（ML）模型应用于在两周观察窗口内汇总的Fitbit数据，以检测抑郁和（轻）躁狂症状的发生情况，这些症状分别被定义为两周窗口内患者健康问卷-8（PHQ-8）和奥特曼自我评定躁狂量表（ASRM）得分高于既定临床临界值的情况。
正如所假设的，在几种ML算法中，二元混合模型（BiMM）森林在验证过程中获得了最高的受试者工作特征曲线下面积（ROC-AUC）。在测试集中，抑郁的ROC-AUC为86.0%，（轻）躁狂的ROC-AUC为85.2%。使用通过约登指数计算的优化阈值，抑郁的预测准确率为80.1%（敏感性为71.2%，特异性为85.6%），（轻）躁狂的预测准确率为89.1%（敏感性为80.0%，特异性为90.1%）。
我们使用适用于广泛应用的方法在检测BD患者的情绪症状方面取得了良好的性能。研究结果进一步证明了Fitbit数据能够产生准确的情绪症状预测。此外，据我们所知，这是BiMM森林首次应用于情绪症状预测。总体而言，研究结果使该领域朝着适用于全体患者的个性化算法迈进了一步，而不仅仅适用于那些依从性高、能够使用专业设备或愿意分享侵入性数据的患者。
