Currently, the golden standard for assessing the severity of depressive and manic symptoms in patients with bipolar disorder (BD) is clinical evaluations using validated rating scales such as the Hamilton Depression Rating Scale 17-items (HDRS) and the Young Mania Rating Scale (YMRS). Frequent automatic estimation of symptom severity could potentially help support monitoring of illness activity and allow for early treatment intervention between outpatient visits. The present study aimed (1) to assess the feasibility of producing daily estimates of clinical rating scores based on smartphone-based self-assessments of symptoms collected from a group of patients with BD; (2) to demonstrate how these estimates can be utilized to compute individual daily risk of relapse scores. Based on a total of 280 clinical ratings collected from 84 patients with BD along with daily smartphone-based self-assessments, we applied a hierarchical Bayesian modelling approach capable of providing individual estimates while learning characteristics of the patient population. The proposed method was compared to common baseline methods. The model concerning depression severity achieved a mean predicted R² of 0.57 (SD = 0.10) and RMSE of 3.85 (SD = 0.47) on the HDRS, while the model concerning mania severity achieved a mean predicted R² of 0.16 (SD = 0.25) and RMSE of 3.68 (SD = 0.54) on the YMRS. In both cases, smartphone-based self-reported mood was the most important predictor variable. The present study shows that daily smartphone-based self-assessments can be utilized to automatically estimate clinical ratings of severity of depression and mania in patients with BD and assist in identifying individuals with high risk of relapse.

目前，评估双相情感障碍 (BD) 患者抑郁和躁狂症状严重程度的黄金标准是使用经过验证的评定量表进行临床评估，例如汉密尔顿抑郁评定量表 17 项 (HDRS) 和年轻躁狂评定量表 (YMRS) ）。频繁自动估计症状严重程度可能有助于支持疾病活动的监测，并允许在门诊就诊之间进行早期治疗干预。本研究的目的是 (1) 评估基于智能手机对从一组 BD 患者收集的症状进行自我评估来生成临床评分每日估计值的可行性； (2) 演示如何利用这些估计来计算个人每日复发风险评分。基于从 84 名 BD 患者收集的总共 280 个临床评级以及基于智能手机的日常自我评估，我们应用了分层贝叶斯建模方法，该方法能够提供个体估计，同时了解患者群体的特征。将所提出的方法与常见的基线方法进行了比较。有关抑郁严重程度的模型在 HDRS 上的平均预测R ²为 0.57 (SD = 0.10)，RMSE 为 3.85 (SD = 0.47)，而有关躁狂严重程度的模型的平均预测R ²为 0.16 (SD = 0.25) YMRS 上的 RMSE 为 3.68 (SD = 0.54)。在这两种情况下，基于智能手机的自我报告情绪都是最重要的预测变量。本研究表明，每日基于智能手机的自我评估可用于自动评估双相情感障碍患者抑郁和躁狂严重程度的临床评级，并帮助识别复发风险高的个体。