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Population attributable fraction of leading non-communicable cardiovascular diseases due to leisure-time physical inactivity: a systematic review

Journal

BMJ OPEN SPORT & EXERCISE MEDICINE
Volume 5, Issue 1, Pages -

Publisher

BMJ PUBLISHING GROUP
DOI: 10.1136/bmjsem-2019-000512

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Funding

  1. Canadian Institutes of Health Research (CIHR) Emerging Teams grant - Mobility in Aging (Institute of Aging)
  2. CIHR
  3. Michael Smith Foundation for Health Research (MSFHR)

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Objective The aim of this systematic review was to investigate the methods used for estimating the population attributable fraction (PAF) to leisure-time physical inactivity (PI) of coronary artery diseases, hypertension and stroke in order to provide the best available estimate for PAF. Design Systematic review. Data sources Four electronic databases (MEDLINE/PubMed, EMBASE, SPORTDiscus, and Cumulative Index to Nursing and Allied Health Literature) were searched from inception to August 2018. Eligibility criteria for selecting studies This review included prospective cohort studies, with men and women aged >= 18 years old, investigating the PAF attributable to leisure-time PI related to coronary artery diseases, hypertension and stroke. Results The PAF estimates of the three studies included were 13% (3%-22%) for 'stage-1 hypertension' subtype incidence due to 'non-regular exercise'; 25% (10.4%-35.8%) for 'stage-2 hypertension' subtype incidence due to 'activity of daily living' and 'vigorous-intensity sports'; and 8.5% (1.7%-16.7%) for 'total: fatal and non-fatal' cardiovascular events of 'incidence and mortality' endpoints due to non-accumulation of 550 kcal/week (subsets not specified). Conclusions The PAF estimate exhibited a protective dose-response relationship between hypertension and an increased amount of energy expenditure of leisure-time PI. In order to enhance accuracy of PAF estimates, the following steps are recommended: (1) to clearly define and state the working definition of leisure-time PI and dose using a reliable and valid objective measurement tool; (2) use a clear definition of outcome subtypes and endpoints using reliable and valid objective measures; and (3) estimate PAF using modelling techniques based on prospective data and ensuring to report 95% CI.

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