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      Association Between Questionnaire- and Accelerometer-Assessed Physical Activity: The Role of Sociodemographic Factors

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          Abstract

          The correlation between objective and self-reported measures of physical activity varies between studies. We examined this association and whether it differed by demographic factors or socioeconomic status (SES). Data were from 3,975 Whitehall II (United Kingdom, 2012–2013) participants aged 60–83 years, who completed a physical activity questionnaire and wore an accelerometer on their wrist for 9 days. There was a moderate correlation between questionnaire- and accelerometer-assessed physical activity (Spearman's r = 0.33, 95% confidence interval: 0.30, 0.36). The correlations were higher in high-SES groups than in low-SES groups ( P 's = 0.02), as defined by education ( r = 0.38 vs. r = 0.30) or occupational position ( r = 0.37 vs. r = 0.29), but did not differ by age, sex, or marital status. Of the self-reported physical activity, 68.3% came from mild activities, 25% from moderate activities, and only 6.7% from vigorous activities, but their correlations with accelerometer-assessed total physical activity were comparable (range of r 's, 0.21–0.25). Self-reported physical activity from more energetic activities was more strongly associated with accelerometer data (for sports, r = 0.22; for gardening, r = 0.16; for housework, r = 0.09). High-SES persons reported more energetic activities, producing stronger accelerometer associations in these groups. Future studies should identify the aspects of physical activity that are most critical for health; this involves better understanding of the instruments being used.

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          International physical activity questionnaire: 12-country reliability and validity.

          Physical inactivity is a global concern, but diverse physical activity measures in use prevent international comparisons. The International Physical Activity Questionnaire (IPAQ) was developed as an instrument for cross-national monitoring of physical activity and inactivity. Between 1997 and 1998, an International Consensus Group developed four long and four short forms of the IPAQ instruments (administered by telephone interview or self-administration, with two alternate reference periods, either the "last 7 d" or a "usual week" of recalled physical activity). During 2000, 14 centers from 12 countries collected reliability and/or validity data on at least two of the eight IPAQ instruments. Test-retest repeatability was assessed within the same week. Concurrent (inter-method) validity was assessed at the same administration, and criterion IPAQ validity was assessed against the CSA (now MTI) accelerometer. Spearman's correlation coefficients are reported, based on the total reported physical activity. Overall, the IPAQ questionnaires produced repeatable data (Spearman's rho clustered around 0.8), with comparable data from short and long forms. Criterion validity had a median rho of about 0.30, which was comparable to most other self-report validation studies. The "usual week" and "last 7 d" reference periods performed similarly, and the reliability of telephone administration was similar to the self-administered mode. The IPAQ instruments have acceptable measurement properties, at least as good as other established self-reports. Considering the diverse samples in this study, IPAQ has reasonable measurement properties for monitoring population levels of physical activity among 18- to 65-yr-old adults in diverse settings. The short IPAQ form "last 7 d recall" is recommended for national monitoring and the long form for research requiring more detailed assessment.
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            Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy.

            Strong evidence shows that physical inactivity increases the risk of many adverse health conditions, including major non-communicable diseases such as coronary heart disease, type 2 diabetes, and breast and colon cancers, and shortens life expectancy. Because much of the world's population is inactive, this link presents a major public health issue. We aimed to quantify the eff ect of physical inactivity on these major non-communicable diseases by estimating how much disease could be averted if inactive people were to become active and to estimate gain in life expectancy at the population level. For our analysis of burden of disease, we calculated population attributable fractions (PAFs) associated with physical inactivity using conservative assumptions for each of the major non-communicable diseases, by country, to estimate how much disease could be averted if physical inactivity were eliminated. We used life-table analysis to estimate gains in life expectancy of the population. Worldwide, we estimate that physical inactivity causes 6% (ranging from 3·2% in southeast Asia to 7·8% in the eastern Mediterranean region) of the burden of disease from coronary heart disease, 7% (3·9-9·6) of type 2 diabetes, 10% (5·6-14·1) of breast cancer, and 10% (5·7-13·8) of colon cancer. Inactivity causes 9% (range 5·1-12·5) of premature mortality, or more than 5·3 million of the 57 million deaths that occurred worldwide in 2008. If inactivity were not eliminated, but decreased instead by 10% or 25%, more than 533 000 and more than 1·3 million deaths, respectively, could be averted every year. We estimated that elimination of physical inactivity would increase the life expectancy of the world's population by 0·68 (range 0·41-0·95) years. Physical inactivity has a major health eff ect worldwide. Decrease in or removal of this unhealthy behaviour could improve health substantially. None.
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              2011 Compendium of Physical Activities: a second update of codes and MET values.

              The Compendium of Physical Activities was developed to enhance the comparability of results across studies using self-report physical activity (PA) and is used to quantify the energy cost of a wide variety of PA. We provide the second update of the Compendium, called the 2011 Compendium. The 2011 Compendium retains the previous coding scheme to identify the major category headings and specific PA by their rate of energy expenditure in MET. Modifications in the 2011 Compendium include cataloging measured MET values and their source references, when available; addition of new codes and specific activities; an update of the Compendium tracking guide that links information in the 1993, 2000, and 2011 compendia versions; and the creation of a Web site to facilitate easy access and downloading of Compendium documents. Measured MET values were obtained from a systematic search of databases using defined key words. The 2011 Compendium contains 821 codes for specific activities. Two hundred seventeen new codes were added, 68% (561/821) of which have measured MET values. Approximately half (317/604) of the codes from the 2000 Compendium were modified to improve the definitions and/or to consolidate specific activities and to update estimated MET values where measured values did not exist. Updated MET values accounted for 73% of all code changes. The Compendium is used globally to quantify the energy cost of PA in adults for surveillance activities, research studies, and, in clinical settings, to write PA recommendations and to assess energy expenditure in individuals. The 2011 Compendium is an update of a system for quantifying the energy cost of adult human PA and is a living document that is moving in the direction of being 100% evidence based.
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                Author and article information

                Journal
                Am J Epidemiol
                Am. J. Epidemiol
                aje
                amjepid
                American Journal of Epidemiology
                Oxford University Press
                0002-9262
                1476-6256
                15 March 2014
                4 February 2014
                4 February 2014
                : 179
                : 6
                : 781-790
                Author notes
                [* ]Correspondence to Dr. Séverine Sabia, Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 6BT, United Kingdom (e-mail: s.sabia@ 123456ucl.ac.uk ); or Dr. Vincent T. van Hees, MoveLab—Physical Activity and Exercise Research, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE2 4HH, United Kingdom (e-mail: vincent.van-hees@ 123456newcastle.ac.uk ).

                Abbreviations: CI, confidence interval; MET, metabolic equivalent; SES, socioeconomic status.

                Article
                kwt330
                10.1093/aje/kwt330
                3939851
                24500862
                41566b79-fb83-430f-afd1-93a200104712
                © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted reuse, distribution, and reproduction in anymedium, provided the original work is properly cited.

                History
                : 27 September 2013
                : 4 December 2013
                Page count
                Pages: 10
                Categories
                Practice of Epidemiology

                Public health
                accelerometry,cohort studies,elderly,epidemiologic methods,physical activity,questionnaires

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