Cohen Asher, Naslund John, Lane Erlend, Bhan Anant, Rozatkar Abhijit, Mehta Urvakhsh Meherwan, Vaidyam Aditya, Byun Andrew Jin Soo, Barnett Ian, Torous John
Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA.
Acta Psychiatr Scand. 2025 Mar;151(3):388-400. doi: 10.1111/acps.13712. Epub 2024 May 28.
Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone-based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method.
Participants from two different cohorts, each n = 76, one with diagnoses of depression/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1 month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood/anxiety scores as measured via smartphone surveys.
The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ-9 and anxiety as measured for the GAD-8 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ-9 and GAD-7, these AUCs were maintained when predicting significant symptom change at least 7 days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression/anxiety and schizophrenia populations respectively.
These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.
情绪和焦虑变化的临床评估通常依赖于临床评估或自我报告量表。利用智能手机数字表型数据及由此产生的行为标志物（如睡眠）来增加临床症状评分，为理解患者状态变化提供了一种可扩展且可能更有效的方法。本文探讨了在基于智能手机的数字表型背景下，结合使用主动和被动传感器来评估两组不同患者的情绪和焦虑变化的潜力，以评估这种数字表型方法的初步可靠性和有效性。
来自两个不同队列的参与者，每组n = 76，一组被诊断为抑郁/焦虑，另一组为精神分裂症，使用mindLAMP收集主动数据（如情绪/焦虑调查），以及由智能手机数字表型数据（地理位置、加速度计和屏幕状态）组成的被动数据，为期至少1个月。使用异常检测算法，我们评估了主动和被动数据组合中的统计异常是否能够预测通过智能手机调查测得的情绪/焦虑评分变化。
异常检测模型能够可靠地预测两组患者中由PHQ - 9测量的抑郁症状变化4分或更高，以及由GAD - 8测量的焦虑症状变化，两组的ROC曲线下面积分别为0.65和0.80。对于PHQ - 9和GAD - 7，当提前至少7天预测显著症状变化时，这些AUC得以保持。仅主动数据分别预测了抑郁/焦虑组和精神分裂症组约52%和75%的症状变异性。
这些结果表明异常检测在跨诊断队列中预测症状变化的可行性。这些跨越不同患者群体、不同国家和不同地点（印度和美国）的结果表明，智能手机数字表型数据的异常检测可能为预测症状变化提供一种可靠且有效的方法。未来的工作应强调这些统计方法的前瞻性应用。
