使用智能手机加速度计的昼夜节律测量监测双相情感障碍中的抑郁症
Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers
摘要：
目前双相情感障碍的管理依赖于自我报告的问卷调查和与临床医生的访谈。发展恶化情绪的客观措施也可能允许进行早期干预，以避免过渡到抑郁状态。这项研究的目的是使用智能手机记录的加速度数据来预测被诊断为双相情感障碍的参与者人群的抑郁水平。数据收集自52名参与者，平均37周的加速度数据，每个参与者记录相应的抑郁评分。时变隐马尔可夫模型被用来提取每周的活动，睡眠和昼夜节律的功能。个性化回归从0到27的可能尺度实现了1.00（0.57）的平均绝对误差，并且能够以0.84（0.16）的准确度对抑郁症进行分类。结果表明，来自智能手机加速度计的特征能够提供抑郁症的客观标记。由于智能手机的广泛使用，采用的障碍很低，个性化模型能够解释个人行为的差异，并提供准确的抑郁预测。
英文摘要：
Current management of bipolar disorder relies on self-reported questionnaires and interviews with clinicians. The development of objective measures of deteriorating mood may also allow for early interventions to take place to avoid transitions into depressive states. The objective of this study was to use acceleration data recorded from smartphones to predict levels of depression in a population of participants diagnosed with bipolar disorder. Data were collected from 52 participants, with a mean of 37 weeks of acceleration data with a corresponding depression score recorded per participant. Time varying hidden Markov models were used to extract weekly features of activity, sleep and circadian rhythms. Personalised regression achieved mean absolute errors of 1.00(0.57) from a possible scale of 0 to 27 and was able to classify depression with an accuracy of 0.84(0.16). The results demonstrate features derived from smartphone accelerometers are able to provide objective markers of depression. Low barriers for uptake exist due to the widespread use of smartphones, with personalised models able to account for differences in the behaviour of individuals and provide accurate predictions of depression.
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