Abstract
BackgroundActivity trackers are increasingly popular with both consumers and researchers for monitoring activity and for promoting positive behavior change. However, there is a lack of research investigating the performance of these devices in free-living contexts, for which findings are likely to vary from studies conducted in well-controlled laboratory settings.ObjectiveThe aim was to compare Fitbit One and Jawbone UP estimates of steps, moderate-to-vigorous physical activity (MVPA), and sedentary behavior with data from the ActiGraph GT3X+ accelerometer in a free-living context.MethodsThirty-two participants were recruited using convenience sampling; 29 provided valid data for this study (female: 90%, 26/29; age: mean 39.6, SD 11.0 years). On two occasions for 7 days each, participants wore an ActiGraph GT3X+ accelerometer on their right hip and either a hip-worn Fitbit One (n=14) or wrist-worn Jawbone UP (n=15) activity tracker. Daily estimates of steps and very active minutes were derived from the Fitbit One (n=135 days) and steps, active time, and longest idle time from the Jawbone UP (n=154 days). Daily estimates of steps, MVPA, and longest sedentary bout were derived from the corresponding days of ActiGraph data. Correlation coefficients and Bland-Altman plots with examination of systematic bias were used to assess convergent validity and agreement between the devices and the ActiGraph. Cohen’s kappa was used to assess the agreement between each device and the ActiGraph for classification of active versus inactive (≥10,000 steps per day and ≥30 min/day of MVPA) comparable with public health guidelines.ResultsCorrelations with ActiGraph estimates of steps and MVPA ranged between .72 and .90 for Fitbit One and .56 and .75 for Jawbone UP. Compared with ActiGraph estimates, both devices overestimated daily steps by 8% (Fitbit One) and 14% (Jawbone UP). However, mean differences were larger for daily MVPA (Fitbit One: underestimated by 46%; Jawbone UP: overestimated by 50%). There was systematic bias across all outcomes for both devices. Correlations with ActiGraph data for longest idle time (Jawbone UP) ranged from .08 to .19. Agreement for classifying days as active or inactive using the ≥10,000 steps/day criterion was substantial (Fitbit One: κ=.68; Jawbone UP: κ=.52) and slight-fair using the criterion of ≥30 min/day of MVPA (Fitbit One: κ=.40; Jawbone UP: κ=.14).ConclusionsThere was moderate-strong agreement between the ActiGraph and both Fitbit One and Jawbone UP for the estimation of daily steps. However, due to modest accuracy and systematic bias, they are better suited for consumer-based self-monitoring (eg, for the public consumer or in behavior change interventions) rather than to evaluate research outcomes. The outcomes that relate to health-enhancing MVPA (eg, “very active minutes” for Fitbit One or “active time” for Jawbone UP) and sedentary behavior (“idle time” for Jawbone UP) should be used with caution by consumers and researchers alike.
Highlights
Participating in physical activity and minimizing time spent in sedentary behavior are associated with a significantly reduced risk of poor health outcomes, including cardiovascular disease, overweight and obesity, and all-cause mortality [1,2]
Of the 32 participants allocated to the activity tracker groups, 29 provided valid data for the current analyses, with comparable numbers of participants in the Fitbit One group (n=14) and the Jawbone UP group (n=15)
The aim of this study was to compare Fitbit One and Jawbone UP estimates of steps, moderate-to-vigorous physical activity (MVPA), and sedentary behavior to data from the ActiGraph GT3X+ accelerometer in a free-living context. Both the Fitbit One and Jawbone UP demonstrated acceptable accuracy compared with an ActiGraph GT3X+ accelerometer for the estimation of steps per day; there were large over- and underestimates of MVPA
Summary
Participating in physical activity and minimizing time spent in sedentary behavior are associated with a significantly reduced risk of poor health outcomes, including cardiovascular disease, overweight and obesity, and all-cause mortality [1,2]. Common types of activity data from these devices include number of steps, time spent in physical activity by intensity, and time spent “idle.” The devices have additional functions that can be used to support behavior change, such as goal setting (eg, 10,000 steps per day), prompts/cues, and social networking and accountability [7]. The uptake of these devices, both in the consumer market and in research, has been rapid [8-10]. There is a lack of research investigating the performance of these devices in free-living contexts, for which findings are likely to vary from studies conducted in well-controlled laboratory settings
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