Dataset Information

Association between physical activity levels in mid-life with physical activity in old age: a 20-year tracking study in a prospective cohort.

ABSTRACT:

Objectives

This study aims to examine the tracking and predictability of physical activity in old age from overall physical activity and participation in sport, recreational activity and walking in mid-life.

Design

Prospective population-based cohort study.

Setting

British Regional Heart Study participants recruited from primary care centres in the UK in 1978-1980.

Participants and outcome measures

Men (n=3413) self-reported their physical activity at baseline, 12, 16 and 20-year follow-ups and were categorised as inactive or active and having high or low participation in sport, walking and recreational activities. Tracking was assessed using kappa statistics and random effects models. Logistic regression estimated the odds of being active at 20-year follow-up according to physical activity participation in mid-life.

Results

Among 3413 men (mean age at baseline 48.6±5.4 years) with complete data, tracking of overall physical activity was moderate (kappa: 0.23-0.26). Tracking was higher for sports participation (kappa: 0.35-0.38) compared with recreational activity (kappa: 0.16-0.24) and walking (kappa: 0.11-0.15). Intraclass correlation coefficients demonstrated similar levels of stability and only marginally weakened after controlling for covariates. Compared with inactive men, being active at baseline was associated with greater odds of being active at 20-year follow-up (OR 2.7, 95% CI 2.4 to 3.2) after adjusting for sociodemographic, health and lifestyle variables. Playing sport in mid-life was more strongly associated with being active at 20-year follow-up than other domains, particularly when sport participation began earlier in life.

Conclusion

Being physically active in mid-life increases the odds of being active in old age. Promoting physical activity in later life might be best achieved by promoting sport participation earlier in the life course.

SUBMITTER: Aggio D

PROVIDER: S-EPMC5724234 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association between physical activity levels in mid-life with physical activity in old age: a 20-year tracking study in a prospective cohort.

Aggio Daniel D Papacosta Olia O Lennon Lucy L Whincup Peter P Wannamethee Goya G Jefferis Barbara J BJ

BMJ open 20170921 8

<h4>Objectives</h4>This study aims to examine the tracking and predictability of physical activity in old age from overall physical activity and participation in sport, recreational activity and walking in mid-life.<h4>Design</h4>Prospective population-based cohort study.<h4>Setting</h4>British Regional Heart Study participants recruited from primary care centres in the UK in 1978-1980.<h4>Participants and outcome measures</h4>Men (n=3413) self-reported their physical activity at baseline, 12, 1 ...[more]

PMID: 28935690

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Project description:BackgroundMaintenance of physical activity (PA) during later life is associated with optimal health; however, the long-term trajectories of PA into old age and their predictors have not been extensively researched using latent class methods. This study aimed to identify trajectories of self-reported PA and their predictors in men transitioning from midlife to old age.Methods7735 men (aged 40-59 years) recruited in 1978-80 were followed up after 12, 16 and 20 years, reporting PA, health status, lifestyle behaviours and socio-demographic characteristics. Group-based trajectory modelling identified the trajectories of PA and associations with time-stable and time-varying covariates. We considered a range of sociodemographic and health and lifestyle factors as potential covariates.Results4952 men (mean baseline age 49.1 ± 5.6 years) providing PA data at ≥3 time points were included in analyses. Three distinct 20-year trajectories were identified: low decreasing (24.6%, n = 1218), light stable (51.1%, n = 2530) and moderate increasing (24.3%, n = 1204). Being older, having a manual occupation, having never married or had children, residing in the midlands or North of England, suffering from a range of health conditions, being a smoker/ex-smoker and never consuming breakfast cereal or alcohol were independently associated with reduced odds of belonging to the moderate increasing trajectory group compared to the low decreasing group. Of the time-varying covariates considered, leaving employment was associated with a decrease in PA in the low decreasing group (β -0.306, p < 0.001) but an increase in the light stable (β 0.324, p < 0.001) and moderate increasing groups (β 0.847, p < 0.001). Developing cardiovascular-related conditions was associated with a decrease in PA in the low decreasing (β -0.408, p < 0.001) and light stable groups (β -0.118, p < 0.001) but no association was observed in the moderate increasing group (β -0.060, p = 0.313).ConclusionsThree distinct trajectories of PA were identified in men transitioning from midlife to old age, of which nearly a quarter had persistently low levels of PA. Promotion efforts may need to focus attention prior to middle age and consider a number of sociodemographic, health and lifestyle factors to sustain PA into old age. The effects of retirement and development of cardiovascular disease may vary according to PA trajectories.

Project description:Background:Previous physical activity (PA) tracking studies have examined the stability of overall PA and/or PA types, but few have investigated how specific types of sport/exercise track over the life course. The aim of this study was to determine how specific sports/exercises in midlife track and predict future sport/exercise and PA in men transitioning to old age. Methods:Seven thousand seven hundred thirty-five men (aged 40-59?years) recruited in 1978-80 were followed up after 12, 16 and 20?years. At each wave men self-reported participation in sport/exercise. Frequent sport/exercise participants (>?1/month) reported the types of sport/exercise they engaged in. Men also reported total PA, health status, lifestyle behaviours and socio-demographic characteristics. Stability of each sport/exercise was assessed using kappa statistics and intraclass correlation coefficients. Logistic regression estimated the odds of participating in sport/exercise and being active at 20-year follow up according to specific types of sport/exercise in midlife. Results:Three thousand three hundred eighty-four men with complete data at all waves were included in analyses. Tracking of specific sports/exercises ranged from fair to substantial, with golf being the most common and most stable. Bowls was the most frequently adopted. Odds of participating in sport/exercise and being active in old age varied according to sport/exercise types in midlife. Golf and bowls in midlife were the strongest predictors of sport/exercise participation in old age. Golf, cricket and running/jogging in midlife were among the strongest predictors of being active in old age. Compared to participating in just one sport/exercise in midlife, sampling multiple sports/exercises was more strongly associated with sport/exercise participation and being active in old age. Conclusion:The stability of sport/exercise participation from midlife to old age varies by type. Specific sports/exercises in midlife may be more likely to predict future PA than others. However, participating in a range of sports/exercises may be optimal for preserving PA into old age.

Project description:ObjectivesTo examine cumulative associations between midlife health behaviors and walking speed and upper-limb strength in early old age.DesignProspective cohort study.SettingWhitehall II Study.ParticipantsIndividuals (mean age 49.1 ± 5.9 in 1991-93) with health behavior data for at least two of the three assessments (1991-93, 1997-99, 2002-04) and physical functioning measures in 2007-09 (mean age 65.9 ± 5.9) (N = 5,671).MeasurementsA trained nurse assessed walking speed and upper-limb strength. Unhealthy behaviors were defined as current or recent smoking, nonmoderate alcohol consumption (abstinence or heavy drinking), fruit and vegetable consumption less than twice per day, and physical inactivity (<1 h/wk of moderate and <1 h/wk of vigorous physical activity). For each unhealthy behavior, a cumulative score was calculated as the number of times a person reported the behavior over the three assessments divided by 3. The score ranged between 0 (never) and 1 (all three times).ResultsIn linear regression models adjusted for age, sex, education, marital status, and height, all unhealthy behaviors in 1991-93 were associated with slower walking speed in 2007-09, with differences ranging from 0.10 (nonmoderate alcohol consumption) to 0.25 (physical inactivity) of a standard deviation between participants with and without the unhealthy behavior (Pt-test <.001). For walking speed, the accumulation-of-risk model provided the best fit for unhealthy diet (β for a 1-point increment in the low fruit and vegetable consumption score = -0.29, 95% confidence interval (CI) = -0.36 to -0.22) and physical inactivity (β = -0.37, 95% CI = -0.45 to -0.29). For smoking and nonmoderate alcohol consumption, a cumulative effect was also observed, but partial F-tests did not suggest that it provided a better fit than models with behaviors in 1991-93, 1997-99, or 2002-04. All behavioral scores except smoking were associated with grip strength, but F-tests supported the accumulation-of-risk hypothesis only for physical inactivity.ConclusionThese findings highlight the importance of duration of unhealthy behaviors, particularly for diet and physical activity, when examining associations with physical functioning.

Dataset Information

Association between physical activity levels in mid-life with physical activity in old age: a 20-year tracking study in a prospective cohort.

Objectives

Design

Setting

Participants and outcome measures

Results

Conclusion

Publications

Association between physical activity levels in mid-life with physical activity in old age: a 20-year tracking study in a prospective cohort.

Similar Datasets

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