Thota Darshan
Madigan Army Medical Center, Joint Base Lewis-McChord, WA, United States.
JMIR Form Res. 2020 Sep 29;4(9):e18086. doi: 10.2196/18086.
Mental health disorders can disrupt a person's sleep, resulting in lower quality of life. Early identification and referral to mental health services are critical for active duty service members returning from forward-deployed missions. Although technologies like wearable computing devices have the potential to help address this problem, research on the role of technologies like Fitbit in mental health services is in its infancy.
If Fitbit proves to be an appropriate clinical tool in a military setting, it could provide potential cost savings, improve clinician access to patient data, and create real-time treatment options for the greater active duty service member population. The purpose of this study was to determine if the Fitbit device can be used to identify indicators of mental health disorders by measuring the relationship between Fitbit sleep data, self-reported mood, and environmental contextual factors that may disrupt sleep.
This observational cohort study was conducted at the Madigan Army Medical Center. The study included 17 healthy adults who wore a Fitbit Flex for 2 weeks and completed a daily self-reported mood and sleep log. Daily Fitbit data were obtained for each participant. Contextual factors were collected with interim and postintervention surveys. This study had 3 specific aims: (1) Determine the correlation between daily Fitbit sleep data and daily self-reported sleep, (2) Determine the correlation between number of waking events and self-reported mood, and (3) Explore the qualitative relationships between Fitbit waking events and self-reported contextual factors for sleep.
There was no significant difference in the scores for the pre-intevention Pittsburg Sleep Quality Index (PSQI; mean 5.88 points, SD 3.71 points) and postintervention PSQI (mean 5.33 points, SD 2.83 points). The Wilcoxon signed-ranks test showed that the difference between the pre-intervention PSQI and postintervention PSQI survey data was not statistically significant (Z=0.751, P=.05). The Spearman correlation between Fitbit sleep time and self-reported sleep time was moderate (r=0.643, P=.005). The Spearman correlation between number of waking events and self-reported mood was weak (r=0.354, P=.163). Top contextual factors disrupting sleep were "pain," "noises," and "worries." A subanalysis of participants reporting "worries" found evidence of potential stress resilience and outliers in waking events.
Findings contribute valuable evidence on the strength of the Fitbit Flex device as a proxy that is consistent with self-reported sleep data. Mood data alone do not predict number of waking events. Mood and Fitbit data combined with further screening tools may be able to identify markers of underlying mental health disease.
心理健康障碍会扰乱人的睡眠，导致生活质量下降。对于从前线部署任务归来的现役军人而言，早期识别并转介至心理健康服务机构至关重要。尽管可穿戴计算设备等技术有潜力帮助解决这一问题，但关于Fitbit等技术在心理健康服务中的作用的研究尚处于起步阶段。
如果Fitbit在军事环境中被证明是一种合适的临床工具，它可能会节省潜在成本，改善临床医生获取患者数据的途径，并为更多现役军人创造实时治疗方案。本研究的目的是通过测量Fitbit睡眠数据、自我报告的情绪以及可能扰乱睡眠的环境背景因素之间的关系，确定Fitbit设备是否可用于识别心理健康障碍的指标。
这项观察性队列研究在马迪根陆军医疗中心进行。该研究纳入了17名健康成年人，他们佩戴Fitbit Flex两周，并完成每日自我报告的情绪和睡眠日志。为每位参与者获取每日的Fitbit数据。通过干预期间和干预后的调查收集背景因素。本研究有3个具体目标：（1）确定每日Fitbit睡眠数据与每日自我报告睡眠之间的相关性，（2）确定醒来事件数量与自我报告情绪之间的相关性，（3）探索Fitbit醒来事件与自我报告睡眠背景因素之间的定性关系。
干预前匹兹堡睡眠质量指数（PSQI；平均5.88分，标准差3.71分）和干预后PSQI（平均5.33分，标准差2.83分）的得分无显著差异。Wilcoxon符号秩检验表明，干预前PSQI与干预后PSQI调查数据之间的差异无统计学意义（Z = 0.751，P = 0.05）。Fitbit睡眠时间与自我报告睡眠时间之间的Spearman相关性为中等（r = 0.643，P = 0.005）。醒来事件数量与自我报告情绪之间的Spearman相关性较弱（r = 0.354，P = 0.163）。扰乱睡眠的主要背景因素是“疼痛”“噪音”和“担忧”。对报告“担忧”的参与者进行的亚分析发现了潜在压力恢复力和醒来事件中的异常值的证据。
研究结果为Fitbit Flex设备作为与自我报告睡眠数据一致的替代指标的有效性提供了有价值的证据。仅情绪数据无法预测醒来事件的数量。情绪和Fitbit数据与进一步的筛查工具相结合，可能能够识别潜在心理健康疾病的标志物。
