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Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents.

ABSTRACT: BACKGROUND: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. OBJECTIVE: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). METHODS: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. RESULTS: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). CONCLUSIONS: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.

SUBMITTER: Bian J

PROVIDER: S-EPMC5548986 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents.

Bian Jiang J Guo Yi Y Xie Mengjun M Parish Alice E AE Wardlaw Isaac I Brown Rita R Modave François F Zheng Dong D Perry Tamara T TT

JMIR mHealth and uHealth 20170725 7

<h4>Background</h4>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.<h4>Objective</h4>The objective of this study was to explore the association be ...[more]

PMID: 28743679

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Project description:Study objectivesTo investigate the association between self-reported and objective measures of sleep and wind turbine noise (WTN) exposure.MethodsThe Community Noise and Health Study, a cross-sectional epidemiological study, included an in-house computer-assisted interview and sleep pattern monitoring over a 7 d period. Outdoor WTN levels were calculated following international standards for conditions that typically approximate the highest long-term average levels at each dwelling. Study data were collected between May and September 2013 from adults, aged 18-79 y (606 males, 632 females) randomly selected from each household and living between 0.25 and 11.22 kilometers from operational wind turbines in two Canadian provinces. Self-reported sleep quality over the past 30 d was assessed using the Pittsburgh Sleep Quality Index. Additional questions assessed the prevalence of diagnosed sleep disorders and the magnitude of sleep disturbance over the previous year. Objective measures for sleep latency, sleep efficiency, total sleep time, rate of awakening bouts, and wake duration after sleep onset were recorded using the wrist worn Actiwatch2® from a subsample of 654 participants (289 males, 365 females) for a total of 3,772 sleep nights.ResultsParticipant response rate for the interview was 78.9%. Outdoor WTN levels reached 46 dB(A) with an arithmetic mean of 35.6 and a standard deviation of 7.4. Self-reported and objectively measured sleep outcomes consistently revealed no apparent pattern or statistically significant relationship to WTN levels. However, sleep was significantly influenced by other factors, including, but not limited to, the use of sleep medication, other health conditions (including sleep disorders), caffeine consumption, and annoyance with blinking lights on wind turbines.ConclusionsStudy results do not support an association between exposure to outdoor WTN up to 46 dB(A) and an increase in the prevalence of disturbed sleep. Conclusions are based on WTN levels averaged over 1 y and, in some cases, may be strengthened with an analysis that examines sleep quality in relation to WTN levels calculated during the precise sleep period time.

Dataset Information

Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents.

Publications

Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents.

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