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      Individual Barriers to an Active Lifestyle at Older Ages Among Whitehall II Study Participants After 20 Years of Follow-up

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          Abstract

          This cohort study examines sociodemographic, behavioral, and health-related factors associated with sedentary behavior and different intensities of physical activity in older adults after 20 years of follow-up.

          Key Points

          Question

          What are the long-term individual-level barriers of an active lifestyle in older age?

          Findings

          In this cohort study of 3896 older adults with accelerometer data, barriers associated with decreased physical activity and increased sedentary behavior in later life, already evident in midlife, were older age, living alone, obesity, morbidities, and poor physical functioning. In older age, there was also evidence of clustering of behavioral factors, given that no current smoking, eating more fruits and vegetables, and drinking more alcohol were associated with decreased sedentary time and more physical activity.

          Meaning

          These findings suggest that midlife implementation of targeted policies integrating all physical activity components and other healthy behaviors may be an effective approach to promote an active lifestyle in older age.

          Abstract

          Importance

          Identification of individual-level barriers associated with decreased activity in older age is essential to inform effective strategies for preventing the health outcomes associated with high sedentary behavior and lack of physical activity during aging.

          Objective

          To assess cross-sectional and prospective associations of a large set of factors with objectively assessed sedentary time and physical activity at older age.

          Design, Setting, and Participants

          This population-based cohort study was conducted among participants in the Whitehall II accelerometer substudy with accelerometer data assessed in 2012 to 2013. Among 4880 participants invited to the accelerometer substudy, 4006 individuals had valid accelerometer data. Among them, 3808 participants also had factors assessed in 1991 to 1993 (mean [SD] follow-up time, 20.3 [0.5] years), 3782 participants had factors assessed in 2002 to 2004 (mean [SD] follow-up time, 9.1 [0.3] years), and 3896 participants had factors assessed in 2012 to 2013 (mean follow up time, 0 years). Data were analyzed from May 2020 through July 2021.

          Exposures

          Sociodemographic factors (ie, age, sex, race and ethnicity, occupational position, and marital status), behavioral factors (ie, smoking, alcohol intake, and fruit and vegetable intake), and health-related factors (ie, body mass index, 36-Item Short Form Health Survey (SF-36) physical and mental component summary scores [PCS and MCS], and number of chronic conditions) were assessed among 3808 individuals in 1991 to 1993; 3782 individuals in 2002 to 2004; and 3896 individuals in 2012 to 2013. High alcohol intake was defined as more than 14 units of alcohol per week, and high fruit and vegetable intake was defined as twice daily or more.

          Main Outcomes and Measures

          Accelerometer-assessed time spent in sedentary behavior, light-intensity physical activity (LIPA), and moderate to vigorous physical activity (MVPA) in 2012 to 2013 were analyzed in 2021 using multivariate linear regressions.

          Results

          A total of 3896 participants (986 [25.3%] women; age range, 60-83 years; mean [SD] age, 69.4 [5.7] years) had accelerometer data and exposure factors available in 2012 to 2013. Older age, not being married or cohabiting, having overweight, having obesity, more chronic conditions, and poorer SF-36 PCS, assessed in midlife or later life, were associated with increased sedentary time at the expense of time in physical activity. Mean time differences ranged from 9.8 min/d (95% CI, 4.1 to 15.6 min/d) of sedentary behavior per 10-point decrease in SF-36 PCS to 51.4 min/d (95% CI, 37.2 to65.7 min/d) of sedentary behavior for obesity vs reference range weight, from −6.2 min/d (95% CI, −8.4 to −4.1 min/d) of LIPA per 5 years of age to −28.0 min/d (95% CI, −38.6 to −17.4 min/d) of LIPA for obesity vs reference range weight, and from −5.3 min/d (95% CI, −8.2 to −2.4 min/d) of MVPA per new chronic condition to −23.4 min/d (95% CI, −29.2 to −17.6 min/d) of MVPA for obesity vs reference range weight in 20-year prospective analyses for men. There was also evidence of clustering of behavioral factors: high alcohol intake, high fruit and vegetable consumption, and no current smoking were associated with decreased sedentary time (mean time difference in cross-sectional analysis in men: −12.7 min/d [95% CI, −19.8 to −5.5 min/d]; −6.0 min/d [95% CI, −12.3 to −0.2]; and −37.4 min/d [95% CI, − 56.0 to −18.8 min/d], respectively) and more physical activity.

          Conclusions and Relevance

          This study found a large range of individual-level barriers associated with a less active lifestyle in older age, including sociodemographic, behavioral, and health-related factors. These barriers were already evident in midlife, suggesting the importance of early implementation of targeted interventions to promote physical activity and reduce sedentary time.

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          Most cited references36

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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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            Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis

            Abstract Objective To examine the dose-response associations between accelerometer assessed total physical activity, different intensities of physical activity, and sedentary time and all cause mortality. Design Systematic review and harmonised meta-analysis. Data sources PubMed, PsycINFO, Embase, Web of Science, Sport Discus from inception to 31 July 2018. Eligibility criteria Prospective cohort studies assessing physical activity and sedentary time by accelerometry and associations with all cause mortality and reported effect estimates as hazard ratios, odds ratios, or relative risks with 95% confidence intervals. Data extraction and analysis Guidelines for meta-analyses and systematic reviews for observational studies and PRISMA guidelines were followed. Two authors independently screened the titles and abstracts. One author performed a full text review and another extracted the data. Two authors independently assessed the risk of bias. Individual level participant data were harmonised and analysed at study level. Data on physical activity were categorised by quarters at study level, and study specific associations with all cause mortality were analysed using Cox proportional hazards regression analyses. Study specific results were summarised using random effects meta-analysis. Main outcome measure All cause mortality. Results 39 studies were retrieved for full text review; 10 were eligible for inclusion, three were excluded owing to harmonisation challenges (eg, wrist placement of the accelerometer), and one study did not participate. Two additional studies with unpublished mortality data were also included. Thus, individual level data from eight studies (n=36 383; mean age 62.6 years; 72.8% women), with median follow-up of 5.8 years (range 3.0-14.5 years) and 2149 (5.9%) deaths were analysed. Any physical activity, regardless of intensity, was associated with lower risk of mortality, with a non-linear dose-response. Hazards ratios for mortality were 1.00 (referent) in the first quarter (least active), 0.48 (95% confidence interval 0.43 to 0.54) in the second quarter, 0.34 (0.26 to 0.45) in the third quarter, and 0.27 (0.23 to 0.32) in the fourth quarter (most active). Corresponding hazards ratios for light physical activity were 1.00, 0.60 (0.54 to 0.68), 0.44 (0.38 to 0.51), and 0.38 (0.28 to 0.51), and for moderate-to-vigorous physical activity were 1.00, 0.64 (0.55 to 0.74), 0.55 (0.40 to 0.74), and 0.52 (0.43 to 0.61). For sedentary time, hazards ratios were 1.00 (referent; least sedentary), 1.28 (1.09 to 1.51), 1.71 (1.36 to 2.15), and 2.63 (1.94 to 3.56). Conclusion Higher levels of total physical activity, at any intensity, and less time spent sedentary, are associated with substantially reduced risk for premature mortality, with evidence of a non-linear dose-response pattern in middle aged and older adults. Systematic review registration PROSPERO CRD42018091808.
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              Cohort Profile: the Whitehall II study.

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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                7 April 2022
                April 2022
                7 April 2022
                : 5
                : 4
                : e226379
                Affiliations
                [1 ]Centre of Research in Epidemiology and Statistics, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Université de Paris, Paris, France
                [2 ]Accelting, Almere, the Netherlands
                [3 ]Department of Epidemiology and Public Health, University College London, United Kingdom
                [4 ]Department of Radiobiology and Regenerative Medicine, Institute for Radiological Protection and Nuclear Safety, Fontenay-Aux-Roses, France
                Author notes
                Article Information
                Accepted for Publication: February 19, 2022.
                Published: April 7, 2022. doi:10.1001/jamanetworkopen.2022.6379
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Chen M et al. JAMA Network Open.
                Corresponding Author: Mathilde Chen, PhD, Centre of Research in Epidemiology and Statistics, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Université de Paris, 10 avenue de Verdun, 75010 Paris, France ( mathilde.chen@ 123456inserm.fr ).
                Author Contributions: Dr Chen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Bloomberg, Sabia.
                Acquisition, analysis, or interpretation of data: Chen, Yerramalla, van Hees, Landré, Fayosse, Benadjaoud, Sabia.
                Drafting of the manuscript: Chen, Benadjaoud, Sabia.
                Critical revision of the manuscript for important intellectual content: Chen, Yerramalla, van Hees, Bloomberg, Landré, Fayosse, Benadjaoud.
                Statistical analysis: Chen, Yerramalla, van Hees, Landré, Benadjaoud.
                Obtained funding: Sabia.
                Administrative, technical, or material support: van Hees, Sabia.
                Supervision: Fayosse, Sabia.
                Conflict of Interest Disclosures: None reported.
                Funding/Support: The Whitehall II study was supported by grants R01AG056477 and RF1AG062553 from the US National Institutes of Health National Institute on Aging, R024227 and S011676, K013351 from the UK Medical Research Council, RG/16/11/32334 from the British Heart Foundation, and 221854/Z/20/Z from the Wellcome Trust. Dr Chen, Ms Yerramalla, and Dr Sabia were supported by grant ANR-19-CE36-0004-01 from the French National Research Agency. Ms Bloomberg was supported by grant ES/P000592/1 from the UK Economic and Social Research.
                Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Additional Contributions: The authors thank all participating civil service departments and their welfare, personnel, and establishment officers; the British Occupational Health and Safety Agency; the British Council of Civil Service Unions; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team. The Whitehall II Study team comprised research scientists, statisticians, study coordinators, nurses, data managers, administrative assistants, and data entry staff who made the study possible. These individuals and organizations were not compensated for this work.
                Article
                zoi220199
                10.1001/jamanetworkopen.2022.6379
                8990327
                35389501
                f1358c2c-eb6b-4fc9-8597-2e3b7fcd6e82
                Copyright 2022 Chen M et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 26 October 2021
                : 19 February 2022
                Categories
                Research
                Original Investigation
                Online Only
                Public Health

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