Victory Amanda, Letkiewicz Allison, Cochran Amy L
Department of Psychiatry, University of Michigan, Ann Arbor, MI, US.
Department of Psychiatry, University of Wisconsin, Madison, WI, US.
Curr Opin Syst Biol. 2020 Jun;21:25-31. doi: 10.1016/j.coisb.2020.07.008. Epub 2020 Jul 23.
Mood disorders present on-going challenges to the medical field, with difficulties ranging from establishing effective treatments to understanding complexities of one's mood. One solution is the use of mobile apps and wearables for measuring physiological symptoms and real-time mood in order to shape mood and behavior. Current digital research is focused on increasing engagement in monitoring mood, uncovering mood dynamics, predicting mood, and providing digital microinterventions. This review discusses the importance and risks of user engagement, as well as barriers to improving it. Research on mood dynamics highlights the possibility to reveal data-driven computational phenotypes that could guide treatment. Mobile apps are being used to track voice patterns, GPS, and phone usage for predicting mood and treatment response. Future directions include utilizing mobile apps to deliver and evaluate microinterventions. To continue these advances, standardized reporting and study designs should be considered to improve digital solutions for mood disorders.
情绪障碍给医学领域带来了持续的挑战,困难涵盖从确立有效治疗方法到理解个人情绪的复杂性等各个方面。一种解决方案是使用移动应用程序和可穿戴设备来测量生理症状和实时情绪,以便塑造情绪和行为。当前的数字研究专注于提高对情绪监测的参与度、揭示情绪动态、预测情绪以及提供数字微干预措施。本综述讨论了用户参与的重要性和风险,以及改善用户参与度的障碍。对情绪动态的研究凸显了揭示数据驱动的计算表型以指导治疗的可能性。移动应用程序正被用于追踪语音模式、全球定位系统(GPS)和手机使用情况,以预测情绪和治疗反应。未来的方向包括利用移动应用程序来提供和评估微干预措施。为了延续这些进展,应考虑采用标准化报告和研究设计,以改进针对情绪障碍的数字解决方案。