论文标题
使用智能手机加速度计监测躁郁症的抑郁症
Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers
论文作者
论文摘要
当前对躁郁症的管理依赖于自我报告的问卷和临床医生的访谈。发展情绪恶化的客观度量也可能使早期干预措施避免过渡到抑郁状态。这项研究的目的是使用从智能手机记录的加速数据来预测被诊断患有双相情感障碍的参与者的抑郁水平。从52名参与者中收集数据，平均为37周的加速数据，每个参与者记录了相应的抑郁评分。时间变化的隐藏马尔可夫模型用于提取活动，睡眠和昼夜节律的每周特征。个性化回归达到的平均绝对误差为1.00（0.57）的可能量表为0到27，并且能够以0.84的精度对抑郁症进行分类（0.16）。结果表明，智能手机加速度计得出的特征能够提供抑郁症的客观标记。由于广泛使用智能手机，因此存在低障碍物的吸收障碍，其个性化模型能够解释个人行为的差异并提供对抑郁症的准确预测。
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.
