Tazawa Yuuki, Liang Kuo-Ching, Yoshimura Michitaka, Kitazawa Momoko, Kaise Yuriko, Takamiya Akihiro, Kishi Aiko, Horigome Toshiro, Mitsukura Yasue, Mimura Masaru, Kishimoto Taishiro
Keio University School of Medicine, Tokyo, Japan.
Faculty of Science and Technology, Keio University, Kanagawa, Japan.
Heliyon. 2020 Feb 4;6(2):e03274. doi: 10.1016/j.heliyon.2020.e03274. eCollection 2020 Feb.
We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices.
We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation.
Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning.
The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted.
The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.
我们旨在开发一种机器学习算法，以基于可穿戴设备的数据筛查抑郁症并评估其严重程度。
我们使用了一种可穿戴设备，该设备可计算步数、能量消耗、身体活动、睡眠时间、心率、皮肤温度和紫外线暴露量。抑郁症患者和健康志愿者在研究期间持续佩戴该设备。每小时对患者和健康志愿者之间的各项指标进行比较。使用XGBoost构建机器学习模型，并应用10折交叉验证进行验证。
45名抑郁症患者和41名健康对照者参与了研究，共产生了相当于5250天的数据。在某些比较中，患者和健康志愿者之间的心率、步数和睡眠存在显著差异。当患者症状改善时，纵向观察也发现了类似差异。基于七天的数据，该模型识别有症状患者的准确率为0.76，预测汉密尔顿抑郁量表-17得分的相关系数为0.61。皮肤温度、与睡眠时间相关的特征以及这些指标之间的相关性是机器学习中最显著的特征。
参与本研究的受试者数量较少，可能削弱了研究的统计学意义。尽管我们对多重比较进行了校正，但各组之间的人口统计学数据仍存在差异。虽然对内部数据进行了10折交叉验证，但未在独立数据集中进行验证。
结果表明，利用可穿戴设备和机器学习可能有助于识别抑郁症以及评估其严重程度。
