Stein Institute for Research on Aging, Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States.
JMIR Ment Health. 2016 Sep 7;3(3):e42. doi: 10.2196/mental.5798.
Recognition and timely action around "warning signs" of illness exacerbation is central to the self-management of bipolar disorder. Due to its heterogeneity and fluctuating course, passive and active mobile technologies have been increasingly evaluated as adjunctive or standalone tools to predict and prevent risk of worsening of course in bipolar disorder. As predictive analytics approaches to big data from mobile health (mHealth) applications and ancillary sensors advance, it is likely that early warning systems will increasingly become available to patients. Such systems could reduce the amount of time spent experiencing symptoms and diminish the immense disability experienced by people with bipolar disorder. However, in addition to the challenges in validating such systems, we argue that early warning systems may not be without harms. Probabilistic warnings may be delivered to individuals who may not be able to interpret the warning, have limited information about what behaviors to change, or are unprepared to or cannot feasibly act due to time or logistic constraints. We propose five essential elements for early warning systems and provide a conceptual framework for designing, incorporating stakeholder input, and validating early warning systems for bipolar disorder with a focus on pragmatic considerations.
识别和及时采取行动应对疾病恶化的“预警信号”是双相情感障碍自我管理的核心。由于其异质性和波动的病程，被动和主动移动技术已越来越多地被评估为辅助或独立工具，以预测和预防双相情感障碍病程恶化的风险。随着移动健康（mHealth）应用程序和辅助传感器的大数据预测分析方法的进步，早期预警系统可能会越来越多地提供给患者。这些系统可以减少患者经历症状的时间，并减轻双相情感障碍患者所经历的巨大残疾。然而，除了验证此类系统的挑战之外，我们还认为早期预警系统可能并非没有危害。可能会向那些可能无法解释警告、对要改变的行为知之甚少、或由于时间或逻辑限制而准备不足或无法实际采取行动的个人发出概率性警告。我们提出了早期预警系统的五个基本要素，并提供了一个概念框架，用于设计、纳入利益相关者的投入，并验证双相情感障碍的早期预警系统，重点是务实的考虑。
