Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Departments of Psychiatry and Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Sci Rep. 2022 Jun 2;12(1):9162. doi: 10.1038/s41598-022-12792-3.
The use of digital phenotyping methods in clinical care has allowed for improved investigation of spatiotemporal behaviors of patients. Moreover, detecting abnormalities in mobile sensor data patterns can be instrumental in identifying potential changes in symptomology. We propose a method that temporally aligns sensor data in order to achieve interpretable measures of similarity between time points. These computed measures can then be used for anomaly detection, baseline routine computation, and trajectory clustering. In addition, we apply this method on a study of 695 college participants, as well as on a patient with worsening anxiety and depression. With varying temporal constraints, we find mild correlations between changes in routine and clinical scores. Furthermore, in our experiment on an individual with elevated depression and anxiety, we are able to cluster GPS trajectories, allowing for improved understanding and visualization of routines with respect to symptomology. In the future, we aim to apply this method on individuals that undergo data collection for longer periods of time, thus allowing for a better understanding of long-term routines and signals for clinical intervention.
数字表型方法在临床护理中的应用，使得对患者的时空行为进行更深入的研究成为可能。此外，通过检测移动传感器数据模式中的异常情况，有助于识别症状的潜在变化。我们提出了一种方法，对传感器数据进行时间对齐，以实现对时间点之间相似性的可解释度量。然后可以使用这些计算得到的度量来进行异常检测、基线常规计算和轨迹聚类。此外，我们将该方法应用于一项针对 695 名大学生参与者的研究，以及一位焦虑和抑郁症状加重的患者。通过改变时间约束，我们发现常规和临床评分的变化之间存在轻度相关性。此外，在对一位抑郁和焦虑症状加重的个体进行的实验中，我们能够对 GPS 轨迹进行聚类，从而更好地理解和可视化与症状相关的日常生活。未来，我们的目标是将该方法应用于那些进行更长时间数据收集的个体，从而更好地了解长期日常生活和临床干预的信号。
