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Validity of Cardiorespiratory Fitness Measured with Fitbit Compared to VO2max

Klepin, Katharine1; Wing, David2; Higgins, Michael2; Nichols, Jeanne1,2; Godino, Job G.2,3

Medicine & Science in Sports & Exercise: May 17, 2019 - Volume Publish Ahead of Print - Issue - p
doi: 10.1249/MSS.0000000000002041
Original Investigation: PDF Only

Purpose Cardiorespiratory fitness (CRF), broadly defined as the body’s ability to utilize oxygen, is a well-established prognostic marker of health, but it is not routinely measured. This may be due to the difficulty of acquiring high-quality CRF measures. The purpose of this study was to independently determine the validity of the Fitbit Charge 2’s measure of CRF (Fitbit CRF).

Methods 65 healthy adults between the ages of 18 and 45 (55% female, 45% male) were recruited to undergo gold standard 2 max">VO2 max testing and wear a Fitbit Charge 2 continuously for one week during which they were instructed to complete a qualifying outdoor run to derive the Fitbit CRF (units: mL•Kg-1•min-1). This measure was compared with 2 max">VO2 max measures (units: mL•Kg-1•min-1) epoched at 15 and 60 seconds.

Results Bland Altman analyses revealed that Fitbit CRF had a positive bias of 1.59 mL•Kg-1•min-1 compared to laboratory data epoched at 15 seconds and 0.30 mL•Kg-1•min-1 compared to data epoched at 60 seconds (N=60). F statistics (2.09; 0.08) and p-values (0.133; 0.926) from Bradley-Blackwood tests for the concordance of Fitbit CRF with 15 and 60 second laboratory data, respectively, supports the null hypothesis of equal means and variances indicating there is concordance between the two measures. Mean absolute percentage error was less than 10% for each comparison.

Conclusions The Fitbit Charge 2 provides an acceptable level of validity when measuring CRF in young, healthy, and fit adults who are able to run. Further research is required to determine if it is a potentially useful tool in clinical practice and epidemiological research to quantify, categorize, and longitudinally track risk for adverse outcomes.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

1School of Exercise and Nutritional Sciences, San Diego State University, San Diego, CA;

2Exercise and Physical Activity Resource Center, University of California, San Diego, San Diego, CA;

3Center for Wireless and Population Health Systems, University of California, San Diego, San Diego, CA

Corresponding Author: Job G. Godino, PhD, 858-822-3749,, 9500 Gilman Drive #0811, La Jolla, CA 92093

The authors acknowledge funding support for the publication of this work from the Mobilize Center, a National Institutes of Health (NIH) Big Data to Knowledge Center of Excellence supported by NIH grant U54 EB020405. The authors have no conflicts of interest to report. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by ACSM.

Accepted for publication: 3 May 2019.

© 2019 American College of Sports Medicine