Literature DB >> 28483930

Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure.

Mary T Imboden1, Michael B Nelson1, Leonard A Kaminsky2, Alexander Hk Montoye1.   

Abstract

BACKGROUND/AIM: Consumer-based physical activity (PA) monitors have become popular tools to track PA behaviours. Currently, little is known about the validity of the measurements provided by consumer monitors. We aimed to compare measures of steps, energy expenditure (EE) and active minutes of four consumer monitors with one research-grade accelerometer within a semistructured protocol.
METHODS: Thirty men and women (18-80 years old) wore Fitbit One (worn at the waist), Fitbit Zip (waist), Fitbit Flex (wrist), Jawbone UP24 (wrist) and one waist-worn research-grade accelerometer (ActiGraph) while participating in an 80 min protocol. A validated EE prediction equation and active minute cut-points were applied to ActiGraph data. Criterion measures were assessed using direct observation (step count) and portable metabolic analyser (EE, active minutes). A repeated measures analysis of variance (ANOVA) was used to compare differences between consumer monitors, ActiGraph, and criterion measures. Similarly, a repeated measures ANOVA was applied to a subgroup of subjects who didn't cycle.
RESULTS: Participants took 3321±571 steps, had 28±6 active min and expended 294±56 kcal based on criterion measures. Comparatively, all monitors underestimated steps and EE by 13%-32% (p<0.01); additionally the Fitbit Flex, UP24, and ActiGraph underestimated active minutes by 35%-65% (p<0.05). Underestimations of PA and EE variables were found to be similar in the subgroup analysis.
CONCLUSION: Consumer monitors had similar accuracy for PA assessment as the ActiGraph, which suggests that consumer monitors may serve to track personal PA behaviours and EE. However, due to discrepancies among monitors, individuals should be cautious when comparing relative and absolute differences in PA values obtained using different monitors. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  Physical activity measurement; accelerometry; active minutes; energy expenditure; simulated free-living protocol; steps/day

Mesh:

Year:  2017        PMID: 28483930     DOI: 10.1136/bjsports-2016-096990

Source DB:  PubMed          Journal:  Br J Sports Med        ISSN: 0306-3674            Impact factor:   13.800


  39 in total

1.  Investigating the within-person relationships between activity levels and sleep duration using Fitbit data.

Authors:  Yue Liao; Michael C Robertson; Andrea Winne; Ivan H C Wu; Thuan A Le; Diwakar D Balachandran; Karen M Basen-Engquist
Journal:  Transl Behav Med       Date:  2021-03-16       Impact factor: 3.046

Review 2.  Use of Physical Activity Monitors in Rheumatic Populations.

Authors:  Christine A Pellegrini; Sara M Powell; Nicholas Mook; Katherine DeVivo; Linda Ehrlich-Jones
Journal:  Curr Rheumatol Rep       Date:  2018-10-06       Impact factor: 4.592

3.  Effects of lorcaserin (Belviq®) on nicotine- and food-maintained responding in non-human primates.

Authors:  David S Jacobs; Claire E Barkin; Michelle R Kohut; Jack Bergman; Stephen J Kohut
Journal:  Drug Alcohol Depend       Date:  2017-10-10       Impact factor: 4.492

4.  Physical Activity, Nutrition, and Obesity among Pacific Islander Youth and Young Adults in Southern California: An Exploratory Study.

Authors:  Sora P Tanjasiri; Lenny D Wiersma; Karen L Moy; Archana McEligot
Journal:  Hawaii J Med Public Health       Date:  2018-10

5.  Change in Objectively Measured Activity Levels Resulting from the EMPOWER Study Lifestyle Intervention.

Authors:  B Rockette-Wagner; J Cheng; Z Bizhanova; A M Kriska; S M Sereika; C E Kline; C C Imes; J K Kariuki; D D Mendez; L E Burke
Journal:  Transl J Am Coll Sports Med       Date:  2022

6.  Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method.

Authors:  Vlad Manea; Katarzyna Wac
Journal:  J Pers Med       Date:  2020-10-31

Review 7.  Toward Harmonized Treadmill-Based Validation of Step-Counting Wearable Technologies: A Scoping Review.

Authors:  Christopher C Moore; Aston K McCullough; Elroy J Aguiar; Scott W Ducharme; Catrine Tudor-Locke
Journal:  J Phys Act Health       Date:  2020-07-11

8.  Physical activity patterns, adherence to using a wearable activity tracker during a 12-week period and correlation between self-reported function and physical activity in working age individuals with hip and/or knee osteoarthritis.

Authors:  Elin Östlind; Anita Sant'Anna; Frida Eek; Kjerstin Stigmar; Eva Ekvall Hansson
Journal:  BMC Musculoskelet Disord       Date:  2021-05-15       Impact factor: 2.362

9.  Discrimination of simultaneous psychological and physical stressors using wristband biosignals.

Authors:  Mert Sevil; Mudassir Rashid; Iman Hajizadeh; Mohammad Reza Askari; Nicole Hobbs; Rachel Brandt; Minsun Park; Laurie Quinn; Ali Cinar
Journal:  Comput Methods Programs Biomed       Date:  2020-12-17       Impact factor: 5.428

Review 10.  Use of Fitbit Devices in Physical Activity Intervention Studies Across the Life Course: Narrative Review.

Authors:  Ruth Gaelle St Fleur; Sara Mijares St George; Rafael Leite; Marissa Kobayashi; Yaray Agosto; Danielle E Jake-Schoffman
Journal:  JMIR Mhealth Uhealth       Date:  2021-05-28       Impact factor: 4.773

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