bioRxiv ScienceSearch

bioRxiv · 10.1101/205856

Long-Term Leisure-Time Physical Activity and Other Health Habits as Predictors of Objectively Monitored Late-Life Physical Activity: A 40-Year Twin Study

Abstract

IMPORTANCEModerate-to-vigorous physical activity (MVPA) in old age is an important indicator of good health and functional capacity enabling independent living.\n\nOBJECTIVETo investigate whether physical activity and other health habits at ages 31-48 years predict objectively measured MVPA decades later.\n\nDESIGN, SETTING, AND PARTICIPANTSThis prospective twin cohort study in Finland comprised 616 individuals (197 complete twin pairs, including 91 monozygotic pairs, born 1940-1944), who responded to baseline questionnaires in 1975, 1981, and 1990, and participated in accelerometer monitoring at follow-up (mean age, 73 years).\n\nEXPOSURESPrimary exposure was long-term leisure-time physical activity, 1975-1990 (LT-mMET index). Covariates were body mass index (BMI), work-related physical activity, smoking, heavy alcohol use and health status in 1990, and socioeconomic status.\n\nMAIN OUTCOMES AND MEASURESPhysical activity was measured with a waist-worn triaxial accelerometer (at least 10 hours per day for at least 4 days) to obtain daily mean MVPA values.\n\nRESULTSHigh baseline LT-mMET index predicted higher amounts of MVPA (increase in R2 of 6.9% after age and sex adjustment, P<.001) at follow-up. After addition of BMI to the regression model, the R2 value of the whole multivariate model was 17.2%, and with further addition of baseline smoking, socioeconomic status, and health status, the R2 increased to 20.3%. In pairwise analyses, differences in MVPA amount were seen only among twin pairs who were discordant at baseline for smoking (n=40 pairs, median follow-up MVPA 25 vs. 35 min, P=.037) or for health status (n=69 pairs, 30 vs. 44 min, P=.014). For smoking, the difference in MVPA also was seen for monozygotic pairs, but for health status, it was seen only for dizygotic pairs. Mediation analysis showed that shared genetic factors explained 82% of the correlation between LT-mMET and MVPA.\n\nCONCLUSIONS AND RELEVANCELow leisure-time physical activity at younger age, overweight, smoking, low socioeconomic status, and health problems predicted low MVPA in old age in individual-based analyses. However, based on the pairwise analyses and quantitative trait modeling, genetic factors and smoking seem to be important determinants of later-life MVPA.

Source connections

Explore related subjects

Keep this discovery

BibTeXRIS

Waller, K., Vaha-Ypya, H., Tormakangas, T., Hautasaari, P., Lindgren, N., Iso-Markku, P., Heikkila, K., Rinne, J., Kaprio, J., Sievanen, H., Kujala, U. M.. 2017-10-19. Long-Term Leisure-Time Physical Activity and Other Health Habits as Predictors of Objectively Monitored Late-Life Physical Activity: A 40-Year Twin Study. https://doi.org/10.1101/205856

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Translating surveillance data into incidence estimates

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which to estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence is not changing between monitoring rounds, resulting in underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds sampling varying incidence. We also show how to accommodate the asymptomatic period characteristic to most diseases. For practical use, we produce an approximation of the model, which is subsequently shown accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations.

epidemiology

The Swiss Primary Ciliary Dyskinesia registry: objectives, methods and first results

Primary Ciliary Dyskinesia (PCD) is a rare hereditary, multi-organ disease caused by defects in ciliary structure and function. It results in a wide range of clinical manifestations, most commonly in the upper and lower airways. Central data collection in national and international registries is essential to studying the epidemiology of rare diseases and filling in gaps in knowledge of diseases such as PCD. For this reason, the Swiss Primary Ciliary Dyskinesia Registry (CH-PCD) was founded in 2013 as a collaborative project between epidemiologists and adult and paediatric pulmonologists.\n\nThe registry records patients of any age, suffering from PCD, who are treated and resident in Switzerland. It collects information from patients identified through physicians, diagnostic facilities, and patient organisations. The registry dataset contains data on diagnostic evaluations, lung function, microbiology and imaging, symptoms, treatments, and hospitalizations.\n\nBy May 2018, CH-PCD has contacted 566 physicians of different specialties and identified 134 patients with PCD. At present this number represents an overall 1 in 63,000 prevalence of people diagnosed with PCD in Switzerland. Prevalence differs by age and region; it is highest in children and adults younger than 30 years, and in Espace Mittelland. The median age of patients in the registry is 25 years (range 5-73), and 49 patients have a definite PCD diagnosis based on recent international guidelines. Data from CH-PCD are contributed to international collaborative studies and the registry facilitates patient identification for nested studies.\n\nCH-PCD has proven to be a valuable research tool that already has highlighted weaknesses in PCD clinical practice in Switzerland. Development of centralised diagnostic and management centres and adherence to international guidelines are needed to improve diagnosis and management--particularly for adult PCD patients.

epidemiology

Perfect Counterfactuals for Epidemic Simulations

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervetnion, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a single world approach to matching simulations of controlled epidemics to their exact uncontrolled counterfac-tual. Our method borrows concepts from percolation approaches prune out possible epidemic histories and create potential epidemic graph that can be realized to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models, and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids non-sensical inferences.

epidemiology