Currey Danielle, Torous John
Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Massachusetts, USA.
BJPsych Open. 2022 Jun 3;8(4):e106. doi: 10.1192/bjo.2022.507.
Smartphones can facilitate patients completing surveys and collecting sensor data to gain insight into their mental health conditions. However, the utility of sensor data is still being explored. Prior studies have reported a wide range of correlations between passive data and survey scores.
To explore correlations in a large data-set collected with the mindLAMP app. Additionally, we explored whether passive data features could be used in models to predict survey results.
Participants were asked to complete daily and weekly mental health surveys. After screening for data quality, our sample included 147 college student participants and 270 weeks of data. We examined correlations between six weekly surveys and 13 metrics derived from passive data features. Finally, we trained logistic regression models to predict survey scores from passive data with and without daily surveys.
Similar to other large studies, our correlations were lower than prior reports from smaller studies. We found that the most useful features came from GPS, call, and sleep duration data. Logistic regression models performed poorly with only passive data, but when daily survey scores were included, performance greatly increased.
Although passive data alone may not provide enough information to predict survey scores, augmenting this data with short daily surveys can improve performance. Therefore, it may be that passive data can be used to refine survey score predictions and clinical utility may be derived from the combination of active and passive data.
智能手机有助于患者完成调查问卷并收集传感器数据，从而深入了解自己的心理健康状况。然而，传感器数据的效用仍在探索之中。先前的研究报告了被动数据与调查得分之间存在广泛的相关性。
探讨通过mindLAMP应用程序收集的大数据集中的相关性。此外，我们还探讨了被动数据特征是否可用于模型中以预测调查结果。
要求参与者完成每日和每周的心理健康调查。在筛选数据质量后，我们的样本包括147名大学生参与者和270周的数据。我们检查了六项每周调查与从被动数据特征得出的13项指标之间的相关性。最后，我们训练了逻辑回归模型，以根据有无每日调查的被动数据来预测调查得分。
与其他大型研究类似，我们的相关性低于较小研究的先前报告。我们发现最有用的特征来自全球定位系统（GPS）、通话和睡眠时间数据。仅使用被动数据时，逻辑回归模型的表现较差，但纳入每日调查得分后，表现大幅提高。
尽管仅被动数据可能无法提供足够信息来预测调查得分，但通过简短的每日调查来扩充这些数据可以提高性能。因此，被动数据可能可用于完善调查得分预测，并且临床效用可能源自主动数据与被动数据的结合。
