Bian Jiang, Guo Yi, Xie Mengjun, Parish Alice E, Wardlaw Isaac, Brown Rita, Modave François, Zheng Dong, Perry Tamara T
Department of Health Outcomes and Policy, University of Florida, Gainesville, FL, United States.
Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR, United States.
JMIR Mhealth Uhealth. 2017 Jul 25;5(7):e105. doi: 10.2196/mhealth.7346.
Smart wearables such as the Fitbit wristband provide the opportunity to monitor patients more comprehensively, to track patients in a fashion that more closely follows the contours of their lives, and to derive a more complete dataset that enables precision medicine. However, the utility and efficacy of using wearable devices to monitor adolescent patients' asthma outcomes have not been established.
The objective of this study was to explore the association between self‑reported sleep data, Fitbit sleep and physical activity data, and pediatric asthma impact (PAI).
We conducted an 8‑week pilot study with 22 adolescent asthma patients to collect: (1) weekly or biweekly patient‑reported data using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of PAI, sleep disturbance (SD), and sleep‑related impairment (SRI) and (2) real-time Fitbit (ie, Fitbit Charge HR) data on physical activity (F-AM) and sleep quality (F‑SQ). To explore the relationship among the self-reported and Fitbit measures, we computed weekly Pearson correlations among these variables of interest.
We have shown that the Fitbit-derived sleep quality F-SQ measure has a moderate correlation with the PROMIS SD score (average r=-.31, P=.01) and a weak but significant correlation with the PROMIS PAI score (average r=-.18, P=.02). The Fitbit physical activity measure has a negligible correlation with PAI (average r=.04, P=.62).
Our findings support the potential of using wrist-worn devices to continuously monitor two important factors-physical activity and sleep-associated with patients' asthma outcomes and to develop a personalized asthma management platform.
诸如Fitbit手环之类的智能可穿戴设备提供了更全面监测患者的机会,以一种更紧密贴合患者生活轨迹的方式追踪患者,并获得更完整的数据集以实现精准医疗。然而,使用可穿戴设备监测青少年患者哮喘结局的效用和有效性尚未得到证实。
本研究的目的是探讨自我报告的睡眠数据、Fitbit睡眠和身体活动数据与儿童哮喘影响(PAI)之间的关联。
我们对22名青少年哮喘患者进行了一项为期8周的试点研究,以收集:(1)使用患者报告结局测量信息系统(PROMIS)对PAI、睡眠障碍(SD)和睡眠相关损害(SRI)进行的每周或每两周一次的患者报告数据,以及(2)关于身体活动(F-AM)和睡眠质量(F-SQ)的实时Fitbit(即Fitbit Charge HR)数据。为了探讨自我报告和Fitbit测量之间的关系,我们计算了这些感兴趣变量之间的每周皮尔逊相关性。
我们已经表明,Fitbit得出的睡眠质量F-SQ测量值与PROMIS SD评分具有中等相关性(平均r = -0.31,P = 0.01),与PROMIS PAI评分具有弱但显著的相关性(平均r = -0.18,P = 0.02)。Fitbit身体活动测量值与PAI的相关性可忽略不计(平均r = 0.04,P = 0.62)。
我们的研究结果支持使用腕戴设备持续监测与患者哮喘结局相关的两个重要因素——身体活动和睡眠,并开发个性化哮喘管理平台的潜力。