Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States.
Beth Israel Deaconess Medical Center, Boston, MA, United States.
J Med Internet Res. 2023 Dec 29;25:e47006. doi: 10.2196/47006.
In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams.
This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study.
We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders.
We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions.
Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.
在临床数字表型研究这一新兴领域，缺乏详细介绍方法学的文献，包括为技术基础设施、患者参与、纵向研究参与以及成功报告和分析多样化的被动和主动数字数据流构建成功架构方面所面临的关键挑战和困境。
本文从现有证据基础和最佳实践的角度，提供了我们研究设计的叙述性理由，并重点介绍了我们从该数字表型研究实施挑战和成功中获得的初步经验教训。
我们描述了一项数字表型试点可行性研究的设计和实施方法，重点介绍了综合关键文献的情况，以及在实施一项包含不同地理位置和人群的多站点研究时对实用适应的推理，该研究使用临床医生验证的单相抑郁、双相 I 型障碍或双相 II 型障碍或健康对照的诊断病史招募患者，以进行心境障碍的纵向数字表型研究。
我们根据被动和主动收集的表型数据质量以及患者入组情况，描述了多站点数字表型试点研究的可行性。与相关研究相比，总体数据质量（评估为获得的传感器数据量与预期数据量之比）较高。报告了研究参与者的相关人口统计学特征的结果，揭示了研究参与者的招募特征，包括年龄（健康对照组亚组的平均年龄范围为 31 岁，双相 II 型障碍亚组为 38 岁）、性别（女性参与者居多，双相 II 型障碍亚组 7/11，64%为女性，健康对照组亚组 29/32，91%为女性）和智能手机操作系统（iOS 与 Android；双相 II 型障碍亚组中，iOS 为 7/11，64%，健康对照组亚组为 29/32，91%）。我们还描述了心境障碍和其他精神疾病的数字表型研究的实施考虑因素。
在 Android 和 iOS 智能手机上进行情感障碍的数字表型研究是可行的，使用开源平台获得的数据质量高于类似研究。虽然数字表型数据质量与性别和种族无关，但研究参与者的报告人口统计学特征揭示了可能由于该领域的自然主义研究而导致的选择偏差的重要信息。我们相信，鉴于我们在两个独特站点上的部署数据，所描述的方法在其他研究环境和患者人群中是可以复制和推广的。
