Literature DB >> 33372896

Comparison of the Physical Activity Measured by a Consumer Wearable Activity Tracker and That Measured by Self-Report: Cross-Sectional Analysis of the Health eHeart Study.

Alexander J Beagle1, Geoffrey H Tison2, Kirstin Aschbacher2, Jeffrey E Olgin2, Gregory M Marcus2, Mark J Pletcher3.   

Abstract

BACKGROUND: Commercially acquired wearable activity trackers such as the Fitbit provide objective, accurate measurements of physically active time and step counts, but it is unclear whether these measurements are more clinically meaningful than self-reported physical activity.
OBJECTIVE: The aim of this study was to compare self-reported physical activity to Fitbit-measured step counts and then determine which is a stronger predictor of BMI by using data collected over the same period reflecting comparable physical activities.
METHODS: We performed a cross-sectional analysis of data collected by the Health eHeart Study, a large mobile health study of cardiovascular health and disease. Adults who linked commercially acquired Fitbits used in free-living conditions with the Health eHeart Study and completed an International Physical Activity Questionnaire (IPAQ) between 2013 and 2019 were enrolled (N=1498). Fitbit step counts were used to quantify time by activity intensity in a manner comparable to the IPAQ classifications of total active time and time spent being sedentary, walking, or doing moderate activities or vigorous activities. Fitbit steps per day were computed as a measure of the overall activity for exploratory comparisons with IPAQ-measured overall activity (metabolic equivalent of task [MET]-h/wk). Measurements of physical activity were directly compared by Spearman rank correlation. Strengths of associations with BMI for Fitbit versus IPAQ measurements were compared using multivariable robust regression in the subset of participants with BMI and covariates measured.
RESULTS: Correlations between synchronous paired measurements from Fitbits and the IPAQ ranged in strength from weak to moderate (0.09-0.48). In the subset with BMI and covariates measured (n=586), Fitbit-derived predictors were generally stronger predictors of BMI than self-reported predictors. For example, an additional hour of Fitbit-measured vigorous activity per week was associated with nearly a full point reduction in BMI (-0.84 kg/m2, 95% CI -1.35 to -0.32) in adjusted analyses, whereas the association between self-reported vigorous activity measured by IPAQ and BMI was substantially smaller in magnitude (-0.17 kg/m2, 95% CI -0.34 to -0.00; P<.001 versus Fitbit) and was dominated by the Fitbit-derived predictor when compared head-to-head in a single adjusted multivariable model. Similar patterns of associations with BMI, with Fitbit dominating self-report, were seen for moderate activity and total active time and in comparisons between overall Fitbit steps per day and IPAQ MET-h/wk on standardized scales.
CONCLUSIONS: Fitbit-measured physical activity was more strongly associated with BMI than self-reported physical activity, particularly for moderate activity, vigorous activity, and summary measures of total activity. Consumer-marketed wearable activity trackers such as the Fitbit may be useful for measuring health-relevant physical activity in clinical practice and research. ©Alexander J Beagle, Geoffrey H Tison, Kirstin Aschbacher, Jeffrey E Olgin, Gregory M Marcus, Mark J Pletcher. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 29.12.2020.

Entities:  

Keywords:  adult; body mass index; cardiovascular diseases; exercise; fitness trackers; mHealth; obesity; overweight; public health; self-report

Mesh:

Year:  2020        PMID: 33372896      PMCID: PMC7803477          DOI: 10.2196/22090

Source DB:  PubMed          Journal:  JMIR Mhealth Uhealth        ISSN: 2291-5222            Impact factor:   4.773


  48 in total

1.  Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association.

Authors:  Miriam E Nelson; W Jack Rejeski; Steven N Blair; Pamela W Duncan; James O Judge; Abby C King; Carol A Macera; Carmen Castaneda-Sceppa
Journal:  Circulation       Date:  2007-08-01       Impact factor: 29.690

2.  Patterns of adult stepping cadence in the 2005-2006 NHANES.

Authors:  Catrine Tudor-Locke; Sarah M Camhi; Claudia Leonardi; William D Johnson; Peter T Katzmarzyk; Conrad P Earnest; Timothy S Church
Journal:  Prev Med       Date:  2011-06-25       Impact factor: 4.018

3.  Wearable devices as facilitators, not drivers, of health behavior change.

Authors:  Mitesh S Patel; David A Asch; Kevin G Volpp
Journal:  JAMA       Date:  2015-02-03       Impact factor: 56.272

Review 4.  Global Overview of the Epidemiology of Atherosclerotic Cardiovascular Disease.

Authors:  Simon Barquera; Andrea Pedroza-Tobías; Catalina Medina; Lucía Hernández-Barrera; Kirsten Bibbins-Domingo; Rafael Lozano; Andrew E Moran
Journal:  Arch Med Res       Date:  2015-06-29       Impact factor: 2.235

5.  2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines.

Authors:  Donna K Arnett; Roger S Blumenthal; Michelle A Albert; Andrew B Buroker; Zachary D Goldberger; Ellen J Hahn; Cheryl Dennison Himmelfarb; Amit Khera; Donald Lloyd-Jones; J William McEvoy; Erin D Michos; Michael D Miedema; Daniel Muñoz; Sidney C Smith; Salim S Virani; Kim A Williams; Joseph Yeboah; Boback Ziaeian
Journal:  J Am Coll Cardiol       Date:  2019-03-17       Impact factor: 24.094

6.  Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers.

Authors:  Carlos A Celis-Morales; Francisco Perez-Bravo; Luis Ibañez; Carlos Salas; Mark E S Bailey; Jason M R Gill
Journal:  PLoS One       Date:  2012-05-09       Impact factor: 3.240

7.  Physical activity and all-cause mortality across levels of overall and abdominal adiposity in European men and women: the European Prospective Investigation into Cancer and Nutrition Study (EPIC).

Authors:  Ulf Ekelund; Heather A Ward; Teresa Norat; Jian'an Luan; Anne M May; Elisabete Weiderpass; Stephen J Sharp; Kim Overvad; Jane Nautrup Østergaard; Anne Tjønneland; Nina Føns Johnsen; Sylvie Mesrine; Agnès Fournier; Guy Fagherazzi; Antonia Trichopoulou; Pagona Lagiou; Dimitrios Trichopoulos; Kuanrong Li; Rudolf Kaaks; Pietro Ferrari; Idlir Licaj; Mazda Jenab; Manuela Bergmann; Heiner Boeing; Domenico Palli; Sabina Sieri; Salvatore Panico; Rosario Tumino; Paolo Vineis; Petra H Peeters; Evelyn Monnikhof; H Bas Bueno-de-Mesquita; J Ramón Quirós; Antonio Agudo; María-José Sánchez; José María Huerta; Eva Ardanaz; Larraitz Arriola; Bo Hedblad; Elisabet Wirfält; Malin Sund; Mattias Johansson; Timothy J Key; Ruth C Travis; Kay-Tee Khaw; Søren Brage; Nicholas J Wareham; Elio Riboli
Journal:  Am J Clin Nutr       Date:  2015-01-14       Impact factor: 7.045

8.  Association between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors.

Authors:  Séverine Sabia; Vincent T van Hees; Martin J Shipley; Michael I Trenell; Gareth Hagger-Johnson; Alexis Elbaz; Mika Kivimaki; Archana Singh-Manoux
Journal:  Am J Epidemiol       Date:  2014-02-04       Impact factor: 4.897

Review 9.  How fast is fast enough? Walking cadence (steps/min) as a practical estimate of intensity in adults: a narrative review.

Authors:  Catrine Tudor-Locke; Ho Han; Elroy J Aguiar; Tiago V Barreira; John M Schuna; Minsoo Kang; David A Rowe
Journal:  Br J Sports Med       Date:  2018-06       Impact factor: 13.800

10.  Accelerometer compared with questionnaire measures of physical activity in relation to body size and composition: a large cross-sectional analysis of UK Biobank.

Authors:  Wenji Guo; Timothy J Key; Gillian K Reeves
Journal:  BMJ Open       Date:  2019-01-29       Impact factor: 2.692

View more
  2 in total

1.  Physical activity changes among office workers during the COVID-19 pandemic lockdown and the agreement between objective and subjective physical activity metrics.

Authors:  Alec Gonzales; Jia-Hua Lin; Jackie S Cha
Journal:  Appl Ergon       Date:  2022-07-20       Impact factor: 3.940

2.  Global Impact of COVID-19 Pandemic on Physical Activity Habits of Competitive Runners: An Analysis of Wearable Device Data.

Authors:  Julia Lee Romero; Qin Lv
Journal:  Int J Environ Res Public Health       Date:  2022-10-10       Impact factor: 4.614

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.