Abstract
PurposeThis study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children.Methods59 healthy boys and girls aged 9–11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis.ResultsFitbit Charge HR underestimated steps (~11.8 steps per minute), heart rate (~3.58 bpm), and metabolic equivalents (~0.55 METs per minute) and overestimated energy expenditure (~0.34 kcal per minute) relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4%; specificity SLPA: 83.1%). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs.ConclusionsFitbit Charge HR had adequate sensitivity in classifying moderate and vigorous intensity physical activity and sleep, but had limitations in detecting wake, and was more accurate in detecting heart rate during sleep than during exercise, in healthy children. Further research is needed to understand potential challenges and limitations of these consumer devices.
Highlights
The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA)
It is widely recognized that physical activity and sleep are important determinants of health. [1,2] the observed associations of physical activity and sleep vary considerably with a variety of health outcomes. [3,4] some of this heterogeneity may result from true biological mechanisms, the challenge of precisely measuring these complex habitual behaviors is likely a major contributor
Tools for the objective assessment of the frequency, intensity, and duration of physical activity and sleep in adults and children have largely been developed for short-term use within a research and clinical environment. [7,8] recent advances in microtechnology, data processing, wireless communication, and battery capacity have resulted in the proliferation of low-cost, non-invasive, devices within the consumer space
Summary
It is widely recognized that physical activity and sleep are important determinants of health. [1,2] the observed associations of physical activity and sleep vary considerably with a variety of health outcomes. [3,4] some of this heterogeneity may result from true biological mechanisms, the challenge of precisely measuring these complex habitual behaviors is likely a major contributor. [7,8] recent advances in microtechnology, data processing, wireless communication, and battery capacity have resulted in the proliferation of low-cost, non-invasive, devices within the consumer space. These devices have appealing designs and can and passively be used by consumers to track their physical activity and sleep over long periods of time, generating an unprecedented amount of data. [13] The combination of these data streams through branched equation modeling and/or machine learned algorithms may result in more accurate measurement of physical activity and sleep, [13,14] and allows for the assessment of newly developed
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