Inter-Device Agreement between Fitbit Flex 1 and 2 for Assessing Sedentary Behavior and Physical Activity.

Sunku Kwon,Youngwon Kim,Yang Bai,Ryan D Burns,Wonwoo Byun

doi:10.3390/ijerph18052716

Abstract

This study examined the inter-model agreement between the Fitbit Flex (FF) and FF2 in estimating sedentary behavior (SED) and physical activity (PA) during a free-living condition. 33 healthy adults wore the FF and FF2 on non-dominant wrist for 14 consecutive days. After excluding sleep and non-wear time, data from the FF and FF2 was converted to the time spent (min/day) in SED and PA using a proprietary algorithm. Pearson’s correlation was used to evaluate the association between the estimates from FF and FF2. Mean absolute percent errors (MAPE) were used to examine differences and measurement agreement in SED and PA estimates between FF and FF2. Bland-Altman (BA) plots were used to examine systematic bias between two devices. Equivalence testing was conducted to examine the equivalence between the FF and FF2. The FF2 had strong correlations with the FF in estimating SED and PA times. Compared to the FF, the FF2 yielded similar SED and PA estimates along with relatively low measurement discords and did not have significant systematic biases for SED and Moderate-to-vigorous PA estimates. Our findings suggest that researchers may choose FF2 as a measurement of SED and PA when FF is not available in the market during the longitudinal PA research.

Highlights

It is no secret that regular physical activity (PA) provides many health benefits
We excluded any participants who were under 18 years-old, physically disabled, pregnant, or unable to participate in regular PA as recommended by a physician associated with this study
Our results indicated that the Fitbit FlexTM 2 (FF2) could provide similar estimates of sedentary behavior (SED) and PA compared to the Fitbit Flex (FF) during a free-living condition

Summary

Introduction

It is no secret that regular physical activity (PA) provides many health benefits. Benefits include, but are not limited to, reduced hypertension, obesity, type-2 diabetes, as well as lower medical costs across the lifespan [1,2,3]. Built-in accelerometers in consumer activity monitors can estimate the frequency and intensity of the user’s PA by calculating the acceleration of the user’s bodily movement primarily based on the proprietary algorithm [9,10] most consumer activity monitors are typically worn on the wrist and connected to a mobile application, which increase users’ adherence to wearing time. With their popularity, capability, and cost-efficiency, consumer-based activity monitors have great potential for being effective tools that assess SED and PA in large cohorts within both clinical and research settings

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