Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry

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.

A Validation Study of the Fitbit One in Daily Life Using Different Time Intervals

“…The Fitbit substantially overestimates daily step activity and daily minutes spent in moderate, vigorous, and moderate-vigorous physical activity activities compared with the ActiGraph, which is in line with previous research (8,10). However, Gomersall et al (14) reported that the Fitbit One underestimated daily minutes of daily minutes of moderate-vigorous physical activity with 19.2 minIday . On the basis of these findings-when being compared with the ActiGraph, the Fitbit is not suitable to assess intensity levels of physical activity in daily life.…”

“…Moreover, the Fitbit underestimated the energy expenditure compared with indirect calorimetry. So far, only three studies examined the validity of the Fitbit One in daily life (10,14,20). High Pearson"s r and intraclass correlations were reported for steps taken per day compared with the ActiGraph (10).…”

Wearable sensors: can they benefit patients with chronic kidney disease?

“…Satellite navigation on cell-phones allows precise walking/running track recording, etc. There are numerous (commercial) activity trackers available which are validated by multiple studies [82][83][84][85][86] such as the SenseWear armband ® (BodyMedia Inc., Pittsburgh, PA, USA), Fitbit ® (Fitbit Inc., San Francisco, CA, USA), Garmin activity trackers (Garmin, Olathe, KS, USA), Jawbone (San Francisco, CA, USA), and many more. Although recent research showed that all these monitors did have error ranges in free-living conditions [82][83][84], it is also suggested that these activity trackers can offer an opportunity to stimulate health, fitness, and quality of life [82,87], particularly when used for consumer-based self-monitoring to encourage behavioral changes [85].…”

“…There are numerous (commercial) activity trackers available which are validated by multiple studies [82][83][84][85][86] such as the SenseWear armband ® (BodyMedia Inc., Pittsburgh, PA, USA), Fitbit ® (Fitbit Inc., San Francisco, CA, USA), Garmin activity trackers (Garmin, Olathe, KS, USA), Jawbone (San Francisco, CA, USA), and many more. Although recent research showed that all these monitors did have error ranges in free-living conditions [82][83][84], it is also suggested that these activity trackers can offer an opportunity to stimulate health, fitness, and quality of life [82,87], particularly when used for consumer-based self-monitoring to encourage behavioral changes [85]. Various studies have used and validated activity trackers to measure PA in both healthy controls [88,89] and CKD patients [90][91][92] and showed that this objective measure is more accurate than self-reported PA by questionnaires, which often over-or underestimates a patients' PA level [93].…”

Comparing the Data Quality of GPS Devices and Smartphones for Assessing Relationships between Place, Mobility, and Health: A Field Study (Preprint)

“…While this research generally shows reasonable acceptance among varied user groups, there remain gaps in understanding the spatial accuracy of each of these devices -particularly those that are available on the consumer market and thus could facilitate population-level research. Therefore, building on prior health informatics research examining the accuracy of other types devices used in research [14,15], we compare the accuracy of two widely-available location-tracking devices which determine location using two different technical approaches (described further below).…”