背景:老年人通常易患抑郁症,可能与自然衰老或其他疾病重叠的症状,因此错过了常规筛查问卷。尽管在老年人中使用的证据仍然有限,但被动感测数据已被推广为抑郁症状检测的工具。因此,本研究旨在回顾通过智能手机和智能手表使用被动感知数据在老年人抑郁症状筛查中的最新知识。
方法:在PubMed,IEEEXplore数字图书馆,和PsycINFO。研究使用被动感测数据进行筛选的文献,监视器,和/或通过智能手机和/或腕部穿戴式可穿戴设备预测老年人(60岁及以上)的抑郁症状被纳入初始筛查.包括2012年1月至2022年9月发表的国际期刊的英文研究。通过叙事分析进一步分析了综述的研究。
结果:21项纳入的研究大部分是在西方国家进行的,少数在亚洲和澳大利亚。大多数研究采用队列研究设计(n=12),其次是横截面设计(n=7)和病例对照设计(n=2)。最受欢迎的被动感测数据与使用活动描记术的睡眠和身体活动有关。睡眠特征,例如睡眠发作后长时间的觉醒,随着较低水平的体力活动,表现出与抑郁症的显著关联。然而,队列研究对来自不完整随访和潜在混杂效应的数据质量表示担忧.
结论:被动传感数据,比如睡眠,和身体活动参数应促进抑郁症状的检测。然而,有效性,可靠性,可行性,和隐私问题仍需进一步探索。
BACKGROUND: The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine screening questionnaires. Passive sensing data have been promoted as a tool for depressive symptoms detection though there is still limited evidence on its usage in the elderly. Therefore, this study aims to review current knowledge on the use of passive sensing data via smartphones and smartwatches in depressive symptom screening for the elderly.
METHODS: The search of literature was performed in PubMed, IEEE Xplore digital library, and PsycINFO. Literature investigating the use of passive sensing data to screen, monitor, and/or predict depressive symptoms in the elderly (aged 60 and above) via smartphones and/or wrist-worn wearables was included for initial screening. Studies in English from international journals published between January 2012 to September 2022 were included. The reviewed studies were further analyzed by a narrative analysis.
RESULTS: The majority of 21 included studies were conducted in Western countries with a few in Asia and Australia. Most studies adopted a cohort study design (n = 12), followed by cross-sectional design (n = 7) and a case-control design (n = 2). The most popular passive sensing data was related to sleep and physical activity using an actigraphy. Sleep characteristics, such as prolonged wakefulness after sleep onset, along with lower levels of physical activity, exhibited a significant association with depression. However, cohort studies expressed concerns regarding data quality stemming from incomplete follow-up and potential confounding effects.
CONCLUSIONS: Passive sensing data, such as sleep, and physical activity parameters should be promoted for depressive symptoms detection. However, the validity, reliability, feasibility, and privacy concerns still need further exploration.