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      Associations of sedentary behavior and physical activity with older adults’ physical function: an isotemporal substitution approach

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          Abstract

          Backgrounds

          The purpose of this study was to examine, in a sample of Japanese older adults, the associations of objectively-assessed sedentary behavior (SB) and physical activity (PA) with performance-based physical function. The isotemporal substitution (IS) approach was used to model simultaneously the effects of the specific activity being performed and the activity being displaced, in an equal time-exchange manner.

          Methods

          Among 287 older adults (65–84 years), we used accelerometers to identify the daily average time spent on SB (≤1.5 METs); light-intensity PA (LIPA) (>1.5 to <3.0 METs); and moderate- to vigorous-intensity PA (MVPA) (≥3.0 METs). Physical function was assessed using five performance-based measures: hand grip strength, usual and maximum gait speeds, timed up and go, and one-legged stance with eyes open. We employed three linear regression models – a single-activity model, a partition model, and an IS model – to assess the associations of SB, LIPA, and MVPA with each of the five measures of physical function.

          Results

          There were significant positive associations in the single-activity and partition models between MVPA and the measures of physical function (with the exception of hand grip strength). The IS models found that replacing SB or LIPA with MVPA was significantly and favorably associated with physical function measures.

          Conclusions

          These findings indicate that replacing small amounts of SB and LIPA with MVPA (such as 10 min) may contribute to improvements in older adults’ physical function.

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          Most cited references25

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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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            Reallocating time to sleep, sedentary behaviors, or active behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005-2006.

            Sleep and sedentary and active behaviors are linked to cardiovascular disease risk biomarkers, and across a 24-hour day, increasing time in 1 behavior requires decreasing time in another. We explored associations of reallocating time to sleep, sedentary behavior, or active behaviors with biomarkers. Data (n = 2,185 full sample; n = 923 fasting subanalyses) from the cross-sectional 2005-2006 US National Health and Nutrition Examination Survey were analyzed. The amounts of time spent in sedentary behavior, light-intensity activity, and moderate-to-vigorous physical activity (MVPA) were derived from ActiGraph accelerometry (ActiGraph LLC, Pensacola, Florida), and respondents reported their sleep duration. Isotemporal substitution modeling indicated that, independent of potential confounders and time spent in other activities, beneficial associations (P < 0.05) with cardiovascular disease risk biomarkers were associated with the reallocation of 30 minutes/day of sedentary time with equal time of either sleep (2.2% lower insulin and 2.0% lower homeostasis model assessment of β-cell function), light-intensity activity (1.9% lower triglycerides, 2.4% lower insulin, and 2.2% lower homeostasis model assessment of β-cell function), or MVPA (2.4% smaller waist circumference, 4.4% higher high-density lipoprotein cholesterol, 8.5% lower triglycerides, 1.7% lower glucose, 10.7% lower insulin, and 9.7% higher homeostasis model assessment of insulin sensitivity. These findings provide evidence that MVPA may be the most potent health-enhancing, time-dependent behavior, with additional benefit conferred from light-intensity activities and sleep duration when reallocated from sedentary time.
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              Classifying household and locomotive activities using a triaxial accelerometer.

              The purpose of this study was to develop a new algorithm for classifying physical activity into either locomotive or household activities using a triaxial accelerometer. Sixty-six volunteers (31 men and 35 women) participated in this study and were separated randomly into validation and cross-validation groups. All subjects performed 12 physical activities (personal computer work, laundry, dishwashing, moving a small load, vacuuming, slow walking, normal walking, brisk walking, normal walking while carrying a bag, jogging, ascending stairs and descending stairs) while wearing a triaxial accelerometer in a controlled laboratory setting. Each of the three signals from the triaxial accelerometer was passed through a second-order Butterworth high-pass filter to remove the gravitational acceleration component from the signal. The cut-off frequency was set at 0.7 Hz based on frequency analysis of the movements conducted. The ratios of unfiltered to filtered total acceleration (TAU/TAF) and filtered vertical to horizontal acceleration (VAF/HAF) were calculated to determine the cut-off value for classification of household and locomotive activities. When the TAU/TAF discrimination cut-off value derived from the validation group was applied to the cross-validation group, the average percentage of correct discrimination was 98.7%. When the VAF/HAF value similarly derived was applied to the cross-validation group, there was relatively high accuracy but the lowest percentage of correct discrimination was 63.6% (moving a small load). These findings suggest that our new algorithm using the TAU/TAF cut-off value can accurately classify household and locomotive activities. Copyright 2010 Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                +81-3-3299-2361 , yasunaga@bunka.ac.jp
                shibata.ai.ga@u.tsukuba.ac.jp
                ishiikaori@aoni.waseda.jp
                Javad.Koohsari@baker.edu.au
                inoue@tokyo-med.ac.jp
                takemi.sugiyama@acu.edu.au
                Neville.Owen@bakeridi.edu.au
                koka@waseda.jp
                Journal
                BMC Geriatr
                BMC Geriatr
                BMC Geriatrics
                BioMed Central (London )
                1471-2318
                6 December 2017
                6 December 2017
                2017
                : 17
                : 280
                Affiliations
                [1 ]GRID grid.443789.5, Faculty of Liberal Arts and Sciences, , Bunka Gakuen University, ; 3-22-1 Yoyogi, Shibuya-ku, Tokyo, 151-8523 Japan
                [2 ]ISNI 0000 0001 2369 4728, GRID grid.20515.33, Faculty Health and Sport Sciences, , University of Tsukuba, ; Tsukuba, Japan
                [3 ]ISNI 0000 0004 1936 9975, GRID grid.5290.e, Faculty of Sport Sciences, , Waseda University, ; Tokorozawa, Japan
                [4 ]Behavioural Epidemiology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
                [5 ]ISNI 0000 0001 2194 1270, GRID grid.411958.0, Institute for Health & Aging, , Australian Catholic University, ; Melbourne, Australia
                [6 ]ISNI 0000 0001 0663 3325, GRID grid.410793.8, Department of Preventive Medicine and Public Health, , Tokyo Medical University, ; Tokyo, Japan
                [7 ]ISNI 0000 0004 0409 2862, GRID grid.1027.4, Swinburne University of Technology, ; Melbourne, Australia
                Article
                675
                10.1186/s12877-017-0675-1
                5719750
                29212458
                6325f945-8556-4008-b399-b43766442b63
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 22 August 2017
                : 23 November 2017
                Funding
                Funded by: JSPS KAKENHI Grant
                Award ID: 15K01534
                Award Recipient :
                Funded by: MEXT-Supported Program for the Strategic Research Foundation at Private Universities
                Award ID: S1511017
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Geriatric medicine
                accelerometer,active behaviors,functional test,mobility,sitting
                Geriatric medicine
                accelerometer, active behaviors, functional test, mobility, sitting

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