背景:尽管对支持老年心理健康的数字服务的需求不断增加,针对老年人的数字精神卫生保健系统的开发和实施受到阻碍,因为缺乏涉及自然生活环境中社会脆弱的老年人使用者及其照顾者的研究。
目的:本研究旨在确定心率变异性的数字传感数据睡眠质量,身体活动可以预测日常生活环境中社交脆弱的老年人当天或第二天的抑郁症状。此外,这项研究测试了数字心理健康监测平台的可行性,该平台旨在向老年成人用户及其社区护理人员告知老年人健康状况的日常变化.
方法:单臂,非随机生活实验室试点研究是对社会脆弱的老年人(n=25)进行的,他们的社区照顾者(n=16),以及一名管理社会工作者,在COVID-19大流行期间和之后的6周内。每天使用9项患者健康问卷通过与移动聊天机器人的脚本口头对话来评估抑郁症状。抑郁症的数字生物标志物,包括心率变异性,睡眠,和身体活动,使用连续佩戴的可穿戴传感器(FitbitSense)进行测量,除了在充电时间。每日个性化反馈,使用交通信号标志,关于老年成人使用者关于压力的健康状况,睡眠,身体活动,和健康紧急状态显示在移动应用程序上为用户和Web应用程序为他们的社区护理人员。使用多级建模来检查数字生物标志物是否预测当天或第二天的抑郁症状。研究人员亲自在老年成人使用者的家中进行了前后调查,以监测抑郁症状的变化。睡眠质量,和系统可用性。
结果:在31名老年人参与者中,25提供了生活实验室的数据,24提供了测试前的数据。多水平建模结果显示,与平均睡眠相比,每日睡眠碎片(P=.003)和睡眠效率(P=.001)的增加与老年人每日抑郁症状的风险增加有关。pre-post测试结果表明改善抑郁症状(P=0.048)和睡眠质量(P=0.02),但不在系统可用性(P=.18)。
结论:研究结果表明,评估睡眠质量的可穿戴传感器可用于预测社会弱势老年人抑郁症状的每日波动。结果还表明,接受个性化的健康反馈并与社区护理人员分享可能有助于改善老年人的心理健康。然而,额外的当面培训可能是必要的,以提高可用性。
背景:ClinicalTrials.govNCT06270121;https://clinicaltrials.gov/study/NCT06270121。
BACKGROUND: Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments.
OBJECTIVE: This
study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this
study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults.
METHODS: A single-arm, nonrandomized living lab pilot
study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms.
Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability.
RESULTS: Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one\'s average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18).
CONCLUSIONS: The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability.
BACKGROUND: ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/
study/NCT06270121.