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      Objectively-Assessed Patterns and Reported Domains of Sedentary Behavior Among Japanese Older Adults

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          Abstract

          Background

          Prolonged periods of sitting time can be adversely associated with older adults’ well-being and functional capacities. Understanding patterns and contexts of sedentary behaviors (SB) can inform approaches to prevention. This study examined Japanese older adults’ objectively-assessed patterns and reported domains of SB and their interrelationships.

          Methods

          Participants ( n = 297; aged 65–84 years) of this cross-sectional study wore an accelerometer for 7 days and completed a survey. Five measures related to SB patterns were identified from the accelerometer data. SB from six domains, socio-demographics, and chronic conditions were identified from the survey data. Relative contributions of six domains to objectively-measured prolonged sedentary time (≥30 minutes) and the number of breaks were examined in a series of multivariate linear regressions. Covariates were socio-demographics, chronic conditions, and accelerometer wear time.

          Results

          On average, participants spent 8.8 hours a day sedentary (58% of accelerometer wear time), with 7.6 breaks per sedentary hour, and 3.7 hours a day through prolonged sedentary bouts (4.4 time/day). The proportions of time in the SB domains were 9.4% for car, 4.0% for public transport, 6.1% for work, 45.5% for television (TV) viewing, 9.8% for computer use, and 25.1% for other leisure. Domains of SB that contributed significantly to longer sedentary time through prolonged bouts were TV viewing and computer use. TV viewing was also associated with a lesser number of breaks.

          Conclusions

          For Japanese older adults, initiatives to address SB could focus on breaking-up prolonged periods of SB by encouraging more frequent breaks, especially during TV viewing.

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          Most cited references14

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          Measurement of adults' sedentary time in population-based studies.

          Sedentary time (too much sitting) increasingly is being recognized as a distinct health risk behavior. This paper reviews the reliability and validity of self-reported and device-based sedentary time measures and provides recommendations for their use in population-based studies. The focus is on instruments that have been used in free-living, population-based research in adults. Data from the 2003-2006 National Health and Nutrition Examination Survey are utilized to compare the descriptive epidemiology of sedentary time that arises from the use of different sedentary time measures. A key recommendation from this review is that, wherever possible, population-based monitoring of sedentary time should incorporate both self-reported measures (to capture important domain- and behavior-specific sedentary time information) and device-based measures (to measure both total sedentary time and patterns of sedentary time accumulation). Copyright © 2011 American Journal of Preventive Medicine. All rights reserved.
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            The descriptive epidemiology of sitting. A 20-country comparison using the International Physical Activity Questionnaire (IPAQ).

            Recent epidemiologic evidence points to the health risks of prolonged sitting, that are independent of physical activity, but few papers have reported the descriptive epidemiology of sitting in population studies with adults. This paper reports the prevalence of "high sitting time" and its correlates in an international study in 20 countries. Representative population samples from 20 countries were collected 2002-2004, and a question was asked on usual weekday hours spent sitting. This question was part of the International Prevalence Study, using the International Physical Activity Questionnaire (IPAQ). The sitting measure has acceptable reliability and validity. Daily sitting time was compared among countries, and by age group, gender, educational attainment, and physical activity. Data were available for 49,493 adults aged 18-65 years from 20 countries. The median reported sitting time was 300 minutes/day, with an interquartile range of 180-480 minutes. Countries reporting the lowest amount of sitting included Portugal, Brazil, and Colombia (medians ≤180 min/day), whereas adults in Taiwan, Norway, Hong Kong, Saudi Arabia, and Japan reported the highest sitting times (medians ≥360 min/day). In adjusted analyses, adults aged 40-65 years were significantly less likely to be in the highest quintile for sitting than adults aged 18-39 years (AOR=0.796), and those with postschool education had higher sitting times compared with those with high school or less education (OR=1.349). Physical activity showed an inverse relationship, with those reporting low activity on the IPAQ three times more likely to be in the highest-sitting quintile compared to those reporting high physical activity. Median sitting time varied widely across countries. Assessing sitting time is an important new area for preventive medicine, in addition to assessing physical activity and sedentary behaviors. Population surveys that monitor lifestyle behaviors should add measures of sitting time to physical activity surveillance. Moreover, the use of objective measures to capture the spectrum of sedentary (sitting) and physical activity behaviors is encouraged, particularly in low- and middle-income countries commencing new surveillance activities. Copyright © 2011 American Journal of Preventive Medicine. All rights reserved.
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              Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm.

              We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P < 0·001) and locomotive (r 0·961, P < 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cut-off classification models. This method may be useful for field investigations as well as for self-monitoring by general users.
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                Author and article information

                Journal
                J Epidemiol
                J Epidemiol
                JE
                Journal of Epidemiology
                Japan Epidemiological Association
                0917-5040
                1349-9092
                5 September 2019
                27 October 2018
                2019
                : 29
                : 9
                : 334-339
                Affiliations
                [1 ]Faculty of Health and Sport Sciences, University of Tsukuba, Ibaraki, Japan
                [2 ]Faculty of Sport Sciences, Waseda University, Saitama, Japan
                [3 ]School of Arts and Letters, Meiji University, Tokyo, Japan
                [4 ]Department of Preventive Medicine and Public Health, Tokyo Medical University, Tokyo, Japan
                [5 ]Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
                [6 ]Behavioural Epidemiology Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
                [7 ]Swinburne University of Technology, Hawthorn, VIC, Australia
                Author notes
                Address for correspondence. Ai Shibata, PhD, Faculty of Health and Sport Sciences, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo 112-0012, Japan (e-mail: shibata.ai.ga@ 123456u.tsukuba.ac.jp ).
                Article
                JE20180041
                10.2188/jea.JE20180041
                6680057
                30369510
                fc26636c-4b09-4a28-a228-3c27d55c3108
                © 2018 Ai Shibata et al.

                This is an open access article distributed under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 26 March 2018
                : 27 July 2018
                Funding
                Funded by: Japan Society for the Promotion of Science, open-funder-registry 10.13039/501100001691;
                Award ID: 15K01647
                Funded by: Ministry of Education, Culture, Sports, Science and Technology, open-funder-registry 10.13039/501100001700;
                Award ID: S1511017
                Categories
                Original Article
                Social Epidemiology

                prolonged sitting,breaks,accelerometer,elderly
                prolonged sitting, breaks, accelerometer, elderly

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