Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Real World Evidence & Epidemiology, Department of Value Evidence, H. Lundbeck A/S, Copenhagen, Denmark.
Sleep. 2022 Jun 13;45(6). doi: 10.1093/sleep/zsac067.
The early detection of mental disorders is crucial. Patterns of smartphone behavior have been suggested to predict mental disorders. The aim of this study was to develop and compare prediction models using a novel combination of smartphone and sleep behavior to predict early indicators of mental health problems, specifically high perceived stress and depressive symptoms.
The data material included two separate population samples nested within the SmartSleep Study. Prediction models were trained using information from 4522 Danish adults and tested in an independent test set comprising of 1885 adults. The prediction models utilized comprehensive information on subjective smartphone behavior, objective night-time smartphone behavior, and self-reported sleep behavior. Receiver operating characteristics area-under-the-curve (ROC AUC) values obtained in the test set were recorded as the performance metrics for each prediction model.
Neither subjective nor objective smartphone behavior was found to add additional predictive information compared to basic sociodemographic factors when forecasting perceived stress or depressive symptoms. Instead, the best performance for predicting poor mental health was found in the sleep prediction model (AUC = 0.75, 95% CI: 0.72-0.78) for perceived stress and (AUC = 0.83, 95%CI: 0.80-0.85) for depressive symptoms, which included self-reported information on sleep quantity, sleep quality and the use of sleep medication.
Sleep behavior is an important predictor when forecasting mental health symptoms and it outperforms novel approaches using objective and subjective smartphone behavior.
早期发现精神障碍至关重要。有研究表明，智能手机行为模式可用于预测精神障碍。本研究旨在开发并比较使用智能手机和睡眠行为的新组合来预测心理健康问题早期指标（即高感知压力和抑郁症状）的预测模型。
本研究的数据材料包括嵌套在 SmartSleep 研究中的两个独立人群样本。使用来自 4522 名丹麦成年人的信息来训练预测模型，并在由 1885 名成年人组成的独立测试集中进行测试。预测模型利用了有关主观智能手机行为、客观夜间智能手机行为和自我报告睡眠行为的综合信息。在测试集中记录获得的接收器操作特征曲线下面积（ROC AUC）值作为每个预测模型的性能指标。
无论是主观的还是客观的智能手机行为，在预测感知压力或抑郁症状时，与基本的社会人口统计学因素相比，都没有发现额外的预测信息。相反，睡眠预测模型在预测不良心理健康方面表现最佳（感知压力的 AUC = 0.75，95%CI：0.72-0.78；抑郁症状的 AUC = 0.83，95%CI：0.80-0.85），其中包括自我报告的睡眠量、睡眠质量和睡眠药物使用信息。
睡眠行为是预测心理健康症状的重要指标，它优于使用客观和主观智能手机行为的新方法。
