Leisure-time Inactivity Research Articles

Physical activity in leisure time and at work and risk of dementia: A prospective cohort study of 117,616 individuals

Background and aimsUp to 40% of all dementia cases may be preventable, primarily by treating or acting on well-established cardiovascular risk factors such as diabetes, hypertension, smoking, and physical inactivity. Whether physical inactivity is associated with risk of non-Alzheimer's dementia – a disease influenced by cardiovascular risk factors – and whether a given association differs for physical activity in leisure time and at work remains unknown. MethodsWe conducted a prospective cohort study including 117,616 individuals from the Copenhagen General Population Study and the Copenhagen City Heart Study with up to 43 years of follow-up. ResultsMultifactorially adjusted hazard ratios for low versus high physical activity at leisure time was 1.60 (95% confidence interval 1.40–1.83) for non-Alzheimer's dementia and 0.94 (0.80–1.11) for Alzheimer's disease. Corresponding values for non-Alzheimer's dementia after additional adjustment for physical activity at work or apolipoprotein E (APOE) genotype were 1.60 (1.40–1.83) and 1.82 (1.34–2.15). Multifactorially and APOE adjusted hazard ratios for high versus low physical activity at work were 1.50 (1.10–2.05) for non-Alzheimer's dementia and 1.62 (1.14–2.31) for Alzheimer's disease. When combining the two types of physical activity, physical activity in leisure time had the strongest relationship with risk of non-Alzheimer's dementia. ConclusionsPhysical inactivity in leisure time was associated with increased risk of non-Alzheimer's dementia, independent of modifiable risk factors and physical activity at work. The present study thus provides evidence for public health advice on physical activity in leisure time for the vascular part of dementia.

Association between added sugar intake and overall diet quality in the Finnish adult population.

Added sugar intake has been associated with several health issues, but few studies have examined its association with overall diet quality. We aimed at examining the association between added sugar intake and overall diet quality in Finnish adults. Associations between added sugar intake and sociodemographic factors, lifestyle factors, and BMI were also explored. Our data comprised 5094 adults residing in Finland who participated in the National FinHealth 2017 Study. Dietary intake was assessed by a validated FFQ. Food consumption and nutrient intakes were calculated using the Finnish national food composition database. Added sugar intake was estimated based on food categorisation and identifying naturally occurring sugar sources. Overall diet quality was assessed by the modified Baltic Sea Diet Score. The average added sugar intake was 7·6 E % in women and 8·3 E % in men in this study population. Added sugar intake was inversely associated with education (P = 0·03 women; P = 0·001 men), physical activity (P < 0·0001), and BMI in men (P = 0·003), and directly with smoking (P = 0·002 women; P < 0·0001 men). Added sugar intake was inversely associated with overall diet quality in both sexes (P < 0·0001). No interactions were found except for men's physical activity subgroups, the inverse association being stronger among active men than moderately active or inactive men (Pfor interaction = 0·005). Our findings suggest that high added sugar intake is associated with several unhealthy dietary and lifestyle habits, including poor-quality diets, smoking and leisure-time inactivity in Finnish adults. Efforts to improve diet quality should consider added sugar intake equally in the whole population.

Mediation of the Relationship of Acculturation With Glycemic Control in Asian Americans With Diabetes.

To assess the role of physical activity and healthcare access in mediating the association of acculturation with cardiometabolic health in Asian American adults with diabetes. Cross-sectional study. National Health and Nutrition Examination Survey (NHANES) 2011-2018. Non-pregnant Asian American adults with doctor diagnosed diabetes. Participants were classified into low, moderate, and high acculturation status. Self-reported leisure-time, work, and transportation-based physical activity were summed for overall physical activity. Health insurance, frequency of healthcare receipt, and glycohemoglobin (HbA1c) check in the past year indicated healthcare access. Cardiometabolic health indicators included HbA1c, total and high density lipoprotein-cholesterol, and systolic and diastolic blood pressure. Linear and logistic regression estimates, standardized using y-standardization, and assessed mediation of acculturation with cardiometabolic health. Low acculturation was significantly related to greater leisure-time inactivity odds [OR (95% CI) = 2.25 (1.05, 4.82)], overall insufficient activity [OR (95% CI) = 2.30 (1.12, 4.74)], and uninsured status [OR (95% CI) = 5.62 (1.55, 20.41)]. Asian Americans with low acculturation had significantly higher log HbA1c than those with high acculturation (β ± SE = .078 ± .038); however, this association was not significant after adjusting for leisure-time activity. Leisure-time activity mediated 48.9% of acculturation and HbA1c association, and the indirect effect was statistically significant [estimate (95% CI) = .021 (.002, 0.047)]. Results suggest that promoting sufficient leisure-time activity could improve glycemic control in least acculturated Asian Americans with diabetes.

Cross-sectional and prospective relationship between occupational and leisure-time inactivity and cognitive function in an ageing population: the European Prospective Investigation into Cancer and Nutrition in Norfolk (EPIC-Norfolk) study.

BackgroundThe current evidence for higher physical activity and better cognitive function and lower risk of dementia is strong but not conclusive. More robust evidence is needed to inform public-health policy. We provide further insight into discrepancies observed across studies, reporting on habitual inactivity including that during work.MethodsWe examined cross-sectional and prospective relationships of physical inactivity during leisure and occupation time, with cognitive performance using a validated physical-activity index in a cohort of 8585 men and women aged 40–79 years at baseline (1993–1997) for different domains using a range of cognitive measures. Cognitive testing was conducted between 2006 and 2011 (including a pilot phase 2004–2006). Associations were examined using multinomial logistic-regression adjusting for socio-demographic and health variables as well total habitual physical activity.ResultsInactivity during work was inversely associated with poor cognitive performance (bottom 10th percentile of a composite cognition score): odds ratio (OR) = 0.68 [95% confidence interval (CI) 0.54, 0.86], P = 0.001. Results were similar cross-sectionally: OR = 0.65 (95% CI 0.45, 0.93), P = 0.02. Manual workers had increased risk of poor performance compared with those with an occupation classified as inactive. Inactivity during leisure time was associated with increased risk of poor performance in the cross-sectional analyses only.ConclusionsThe relationship between inactivity and cognition is strongly confounded by education, social class and occupation. Physical activity during leisure may be protective for cognition, but work-related physical activity is not protective. A greater understanding of the mechanisms and confounding underlying these paradoxical findings is needed.

Socio-economic and demographic characteristics associated with risk behaviour patterns for chronic non-communicable diseases in Brazil: data from the National Health Survey, 2013.

To identify risk behaviour patterns for chronic non-communicable diseases (NCD) in the Brazilian population and to investigate associated socio-economic and demographic factors. Factor analysis was used to identify patterns considering the following risk behaviours: consumption of soft drinks/artificial juice, sweet foods, red meat with apparent fat, chicken skin; inadequate consumption of fruits and vegetables; alcohol abuse; smoking; absence of physical activity during leisure time; and time spent watching television. The χ 2 test was used to compare ratios. All analyses accounted for weighting factors and the study's complex sampling design effect. The socio-economic and demographic variables evaluated were gender, age, schooling level and macro region of residence. National Health Survey, a household survey with national representation, conducted in 2013 in Brazil.ParticipantsIndividuals (n 60202) aged 18 years or over. Four risk behaviour patterns were identified: 'Physical inactivity in leisure time and Inadequate consumption of fruits and vegetables', 'Saturated fat', 'Alcohol and Smoking' and 'Sedentary behaviour and Sugar', explaining 52·01 % of the total variance. Overall, greater adherence to 'Saturated fat' and 'Alcohol and Smoking' patterns was observed among men and those with lower education level. The 'Sedentary behaviour and Sugar' and 'Physical inactivity in leisure time and Inadequate consumption of fruits and vegetables' patterns had greater adherence among younger individuals, and the first was associated with higher education whereas the second with less education among individuals residing in the North and Northeast regions. Risk behaviour patterns for NCD were heterogeneous, reflecting the socio-economic and demographic differences in Brazil.

Change in health and social factors in mid-adulthood and corresponding changes in leisure-time physical inactivity in a prospective cohort

BackgroundTo identify whether changes in adult health and social factors are associated with simultaneous changes in inactivity.MethodsHealth, social factors and leisure-time inactivity (activity frequency < 1/week) were self-reported at 33y and 50y in the 1958 British birth cohort (N = 12,271). Baseline (33y) health and social factors and also patterns of change in factors 33y-to-50y were related to inactivity 33y-to-50y (never inactive, persistently inactive, deteriorating to inactivity, or improving from inactivity) using multinomial logistic regression.ResultsApproximately 31% were inactive at 33y and 50y; 35% changed status 33y-to-50y (17% deteriorating to inactivity, 18% improving from inactivity). Baseline poor health and obesity were associated with subsequent (33y-to-50y) inactivity; e.g. for poor health, relative risk ratios (RRRs) for deteriorating to inactivity (vs never inactive) and improving from inactivity (vs persistently inactive) were 1.38(1.16,1.64) and 0.77(0.63,0.94) respectively. Adverse changes in health and weight were associated with simultaneous adverse changes in inactivity; e.g. worsening health (vs always good/excellent health) was associated with higher risk of deteriorating to inactivity (RRR:2.20(1.85,2.62)) and lower risk of improving from inactivity (RRR:0.61(0.49,0.77)). However, improving health and weight loss were not associated with improving from inactivity. Worsening self-efficacy 33y-to-50y was associated with lower risk of improving from inactivity; there was no association between improving self-efficacy and inactivity change. Downward social mobility was not associated with deteriorating to or improving from inactivity. Changes in depression symptom level, marriage/co-habitation or parenthood 33y-to-50y were not associated with inactivity changes. No associations were observed for employment.ConclusionsAssociated changes in mid-life health factors with deleterious inactivity changes, highlight the importance of maintaining health, weight and self-efficacy across adulthood to deter inactivity.

Lifetime risk factors for leisure-time physical inactivity in mid-adulthood

We aimed to identify factors from different life-stages that were associated with inactivity at two adult ages and stability and change between these ages. Leisure-time inactivity (activity frequency <1/wk) was assessed at 33y and 50y in the 1958 British Birth cohort (N = 12,271). We created scores representing several domains, i.e. physical health, mental function, social, family and neighbourhood circumstances at different life-stages, and examined associations with adult inactivity. 31% were inactive at 33y and 50y with 17% deteriorating to, and 18% improving from, inactivity. Adjusting for all domains and life-stages, most concurrent factors were associated with inactivity: e.g. per 1-unit worsening physical status (score range:0–2) ORadjusted for 50y inactivity was 1.56(1.44,1.70). Physical status at 33y was associated with inactivity patterns 33y-to-50y (Relative risk ratios (RRRs) of inactivity persistence and deterioration (vs never inactive) per 1-unit worsening status (score:0–1) were 1.73(1.51,1.99) and 1.28(1.10,1.49) respectively; RRR for improvement (vs persistently inactive) was 0.75(0.63,0.88). Some early-life domain scores were associated with inactivity independent of concurrent factors: e.g. per 1-unit worsening early-life social score (range:0–3) ORadjusted for 50y inactivity was 1.12(1.05,1.19). Highly urbanised neighbourhood in early adulthood was associated with inactivity (e.g. RRRs for persistent inactivity and deterioration were 1.42(1.22,1.65) and 1.15(1.01,1.31) respectively; 0.82(0.68,0.98) for improvement). Concurrent physical and mental function were associated with adult inactivity at two ages; poorer physical status was associated with greater risk of inactivity persistence and deterioration and lower risk of improvement 33y-to-50y. Young adult neighbourhood and early-life social and family circumstances were independently associated with mid-life inactivity.

The role of financial strain and self-control in explaining health behaviours: the GLOBE study.

BackgroundWhy lower socioeconomic groups behave less healthily can only partly be explained by direct costs of behaving healthily. We hypothesize that low income increases the risk of facing financial strain. Experiencing financial strain takes up cognitive ‘bandwidth’ and leads to less self-control, and subsequently results in more unhealthy behaviour. We therefore aim to investigate (i) whether a low income increases the likelihood of experiencing financial strain and of unhealthy behaviours, (ii) to what extent more financial strain is associated with less self-control and, subsequently, (iii) whether less self-control is related to more unhealthy behaviour.MethodsCross-sectional survey data were obtained from participants (25–75 years) in the fifth wave of the Dutch GLOBE study (N = 2812) in 2014. The associations between income, financial strain, self-control and health-behaviour-related outcomes (physical inactivity in leisure-time, obesity, smoking, excessive alcohol intake, and weekly fruit and vegetable intake) were analysed with linear regression and generalized linear regression models (log link).ResultsExperiencing great compared with no financial strain increased the risk of all health-behaviour-related outcomes, independent of income. Low self-control, as compared with high self-control, also increased the risk of an unhealthy lifestyle. Taking self-control into account slightly attenuated the associations between financial strain and the outcomes.ConclusionGreat financial strain and low self-control are consistently associated with unhealthy behaviours. Self-control may partly mediate between financial strain and unhealthy behaviour. Interventions that relieve financial strain may free up cognitive bandwidth and improve health behaviour.

Stability and change in leisure-time physical inactivity and its predictors in mid-adulthood: findings from a prospective British birth cohort study

BackgroundChanges in inactivity occur within individuals as they age, but knowledge of influences on these variations, is lacking. Most studies ignore changes in potential influences, instead focusing on one timepoint. We aimed to identify whether changes in health and social factors are associated with concurrent changes in inactivity. MethodsHealth, social factors, and leisure-time inactivity (activity frequency less than once a week) were self-reported at 33 (baseline) and 50 years in the 1958 British birth cohort (n=12 271). Using multinomial logistic regression, we assessed the association of baseline health and social factors, and stability and change in these factors from age 33 to 50 years, with inactivity patterns from ages 33 to 50 years (never inactive, persistently inactive, deteriorating, or improving). Missing values were imputed via multiple imputation; results were broadly similar to those using observed values. FindingsApproximately 3804 individuals (31%) were inactive at both ages; 4295 (35%) had changed inactivity from age 33 to 50 (2086 [17%] deteriorating, 2209 [18%] improving). Baseline poor-rated health and obesity were associated with subsequent (33–50 years) inactivity; for example, for poor-rated health, relative risk ratios (RRRs) for persistent inactivity and deterioration versus never inactive were 2·01 (95% CI 1·70–2·38) and 1·38 (1·16–1·64), respectively; and for improvement versus persistently inactive was 0·77 (0·63–0·94). Stability and change in health factors were associated with concurrent inactivity patterns; for example, compared with always good or excellent health, worsening and always poor health were associated with lower RRRs for inactivity improvement (0·64 [0·50–0·80] and 0·58 [0·44–0·76], respectively). Low self-efficacy at baseline was associated with persistent inactivity and deterioration; worsening and always low self-efficacy from 33 to 50 years were associated with detrimental inactivity patterns. Although manual socioeconomic position at baseline was associated with persistent inactivity and deterioration, associations for changes in socioeconomic position were less consistent. Baseline depression was associated with persistent inactivity; depression at 33–50 years was not associated with inactivity patterns. No associations were observed for employment, marital or cohabitation status, or parenthood status. InterpretationSelf-reported data could potentially bias results because of misclassification, whereas the prospective repeated data spanning decades is a study strength. Associated changes in mid-life health factors with deleterious changes in inactivity shown in our study, highlight the maintenance of health, weight, and self-efficacy across adulthood to deter inactivity. Several of these factors are relatively common, underscoring their potential importance for population levels of inactivity in mid-adulthood. FundingThis work was supported by the Department of Health Policy Research Programme through the Public Health Research Consortium. The views expressed in this abstract are those of the authors and not necessarily those of the Department of Health.

INATIVIDADE FÍSICA NO LAZER EM ESTUDANTES DE EDUCAÇÃO FÍSICA, BAHIA

Introduction: Physical inactivity is one of the main risk factors related to worldwide mortality, with great implications for chronic diseases. In relation to university students, physical inactivity represents a prevalent behavior. Objective: To estimate the prevalence and factors associated with leisure-time physical inactivity in university students of a Physical Education course. Methodology: It is a cross-sectional study carried out in a public higher education institution from state of Bahia. The physical inactivity investigated by Prevalence Ratio (PR). Significance level was 5%. Results: 111 university students participated in it. The prevalence of physical inactivity in leisure-time was 14.4%. Female students were associated with leisure-time physical inactivity (PR: 6.22; CI95%: 1.62 – 23.90), university students of higher age groups were less inactive in leisure-time when compared to younger ones (PR: 0.18; 95%CI: 0.04 – 0.92), and married students were more inactive in leisure-time. Conclusion: The prevalence of physical inactivity in the leisure-time was lower in Physical Education students. Married female students aged between 18 to 20 years old had higher prevalence of leisure-time physical inactivity.

Determinants in early adulthood of leisure-time physical inactivity stability and change in mid-life: findings from a prospective birth cohort

BackgroundLeisure-time physical inactivity has a high prevalence and associated disease burden. Information about factors that affect inactivity stability and change is needed. We aimed to establish whether early adult factors predict inactivity patterns over subsequent decades in mid-life. MethodsThe 1958 British birth cohort is a nationwide follow-up study of all births during 1 week in March, 1958. Leisure-time inactivity in 12 271 participants was self-reported as activity (eg, walking, swimming) of less than once a week at ages 33 and 50 years. Associations of early adult (23–33 years) factors with four patterns between the ages of 33 and 50 years (never inactive, persistently inactive, deteriorating, or improving) were assessed by multinomial logistic regression, with and without adjustment for childhood factors. Missing values were imputed with multiple imputation chained equations. Findings3863 adults (32%) were inactive at 33 years; 3791 (31%) were inactive at 50 years. 4257 (35%) had changed inactivity status between 33 and 50 years (2093 [17%] deteriorating, 2164 [18%] improving). Relative risk ratios for factors associated with persistent versus never inactive were: physically limiting illness 1·21 per number of ages exposed—ie, per increase in scale [0, 1, 2] for each age [23 years and 33 years] exposed—(95% CI 1·04–1·42); obesity per number of ages exposed 1·30 (1·11–1·51), perceived underweight versus perceived right weight 1·31 (1·06–1·62), attained height per 5 cm 0·93 (0·89–0·97), depression per number of ages exposed 1·32 (1·18–1·47), education per decrease on a 5-point scale 1·30 (1·21–1·40), and neighbourhood (0·68 [0·54–0·86], 0·62 [0·49–0·78], and 0·84 [0·72–0·99] in suburbs and service centres, rural areas and seaside resorts, and growth areas vs stable industrial areas, respectively). No associations were observed for social class, cohabitation, employment, and parenthood. Associations for inactivity deterioration (vs never) were similar to those for persistently inactive. Only two factors were associated with inactivity improvement—parenthood (although associations varied for number of children at 23 years and 33 years) and neighbourhood. Associations remained, albeit attenuated, after adjustment for childhood factors. InterpretationAlthough our study of leisure-time inactivity is based on self-report, repeat, prospective data spanning decades provides a rare opportunity to investigate an important health determinant that is amenable to modification. Adult inactivity status changes are not uncommon (35% changed between the ages of 33 and 50 years), providing opportunities for behaviour interventions. Early adult factors predicted persistence of inactivity and deterioration; fewer factors predicted improvement. The neighbourhood of young adults had long-lasting associations with inactivity patterns. FundingThis work was supported by the Department of Health Policy Research Programme through the Public Health Research Consortium. The views expressed in this abstract are those of the authors and not necessarily those of the Department of Health.

Early Life Factors and Adult Leisure Time Physical Inactivity Stability and Change.

Physical inactivity has a high prevalence and associated disease burden. A better understanding of influences on sustaining and changing inactive lifestyles is needed. We aimed to establish whether leisure time inactivity was stable in midadulthood and whether early life factors were associated with inactivity patterns. In the 1958 British birth cohort (n = 12,271), leisure time inactivity (frequency, less than once a week) assessed at 33 and 50 yr was categorized as "never inactive," "persistently inactive," "deteriorating," or "improving." Early life factors (birth to 16 yr) were categorized into three (physical, social, and behavioral) domains. Using multinomial logistic regression, we assessed associations with inactivity persistence and change of factors within each early life domain and the three domains combined with and without adjustment for adult factors. Inactivity prevalence was similar at 33 and 50 yr (approximately 31%), but 17% deteriorated and 18% improved with age. In models adjusted for all domains simultaneously, factors associated with inactivity persistence versus never inactive were prepubertal stature (8% lower risk/height SD), poor hand control/coordination (17% higher risk/increase on four-point scale), cognition (16% lower/SD in ability) (physical); parental divorce (25% higher), class at birth (7% higher/reduction on four-point scale), minimal parental education (16% higher), household amenities (2% higher/increase in 19-point score (high = poor)) (social); and inactivity (22% higher/reduction in activity on four-point scale), low sports aptitude (47% higher), smoking (30% higher) (behavioral). All except stature, parental education, sports aptitude, and smoking were associated also with inactivity deterioration. Poor hand control/coordination was the only factor associated with improved status (13% lower/increase on four-point scale) versus persistently inactive. Adult leisure time inactivity is moderately stable. Early life factors are associated with persistent and deteriorating inactivity over decades in midadulthood but rarely with improvement.

Accumulation of Domain-Specific Physical Inactivity and Presence of Hypertension in Brazilian Public Healthcare System.

Hypertension is one of the most common noncommunicable diseases worldwide, and physical inactivity is a risk factor predisposing to its occurrence and complications. However, it is still unclear the association between physical inactivity domains and hypertension, especially in public healthcare systems. Thus, this study aimed to investigate the association between physical inactivity aggregation in different domains and prevalence of hypertension among users of Brazilian public health system. 963 participants composed the sample. Subjects were divided into quartiles groups according to 3 different domains of physical activity (occupational; physical exercises; and leisure-time and transportation). Hypertension was based on physician diagnosis. Physical inactivity in occupational domain was significantly associated with higher prevalence of hypertension (OR = 1.52 [1.05 to 2.21]). The same pattern occurred for physical inactivity in leisure-time (OR = 1.63 [1.11 to 2.39]) and aggregation of physical inactivity in 3 domains (OR = 2.46 [1.14 to 5.32]). However, the multivariate-adjusted model showed significant association between hypertension and physical inactivity in 3 domains (OR = 2.57 [1.14 to 5.79]). The results suggest an unequal prevalence of hypertension according to physical inactivity across different domains and increasing the promotion of physical activity in the healthcare system is needed.

Early life predictors of adult leisure-time physical inactivity: prospective findings from the 1958 British Birth Cohort

BackgroundLeisure-time physical inactivity has a high prevalence and associated disease burden. Adult inactivity research ignores earlier life factors from which later life influences can originate. We aimed to establish whether early life factors influence adult inactivity. MethodsThe 1958 British Birth Cohort is a nationwide follow-up study of all births during 1 week in March, 1958. The outcome of the present study was leisure-time inactivity, defined as activity frequency of less than once a week, assessed at ages 33 years, 42 years, and 50 years (n=12 776). Early life factors (birth to 16 years) were categorised into three domains (physical, social, behavioural). We assessed stability of inactivity from 33 years to 50 years and associations with adult inactivity using logistic regression of: factors within domains, the three domains combined (ie, multivariable associations), and allowing for adult factors. Missing values were imputed with multiple imputation chained equations. FindingsAt each adult age, about 32% of participants were inactive (31% at 33 years, 34% at 42 years, and 30% at 50 years). 1189 (9%) were inactive at all three ages. In analysis of the three domains simultaneously, factors related to adult inactivity were: short prepubertal stature, poor hand control or physical coordination, and poor cognition (physical); low class at birth, minimal parental education, poor household amenities, parental divorce, and institutional care (social); and inactivity, average or lower sports aptitude, smoking, and externalising and unsociable behaviours (behavioural). Odds ratios for inactivity at age 33 years ranged from 0·86 per SD increase in cognition (95% CI 0·82–0·91) to 1·41 (1·23–1·61) for average or lower sports aptitude. Associations weakened slightly but were mostly maintained after adjustment for adult covariates. After allowing for adult covariates participants with unskilled manual backgrounds had 23% higher odds of inactivity at 50 years than those from professional or managerial backgrounds. InterpretationOur study, based on self-report, focuses only on leisure-time inactivity. However, to have repeat, prospective data on inactivity spanning several decades in adulthood is rare, and leisure-time inactivity is likely to be amenable to modification. Adult inactivity is only moderately stable, providing opportunities for behaviour change. Factors from early life are associated with adult inactivity, allowing for early identification of groups vulnerable to later inactivity. FundingThis work was supported by the Department of Health Policy Research Programme through the Public Health Research Consortium. The Great Ormond Street and University College London Institute of Child Health was supported in part by the Department of Health's National Institute for Health Biomedical Research Centre.

Early-life predictors of leisure-time physical inactivity in midadulthood: findings from a prospective British birth cohort.

Much adult physical inactivity research ignores early-life factors from which later influences may originate. In the 1958 British birth cohort (followed from 1958 to 2008), leisure-time inactivity, defined as activity frequency of less than once a week, was assessed at ages 33, 42, and 50 years (n = 12,776). Early-life factors (at ages 0-16 years) were categorized into 3 domains (i.e., physical, social, and behavioral). We assessed associations of adult inactivity 1) with factors within domains, 2) with the 3 domains combined, and 3) allowing for adult factors. At each age, approximately 32% of subjects were inactive. When domains were combined, factors associated with inactivity (e.g., at age 50 years) were prepubertal stature (5% lower odds per 1-standard deviation higher height), hand control/coordination problems (14% higher odds per 1-point increase on a 4-point scale), cognition (10% lower odds per 1-standard deviation greater ability), parental divorce (21% higher odds), institutional care (29% higher odds), parental social class at child's birth (9% higher odds per 1-point reduction on a 4-point scale), minimal parental education (13% higher odds), household amenities (2% higher odds per increase (representing poorer amenities) on a 19-point scale), inactivity (8% higher odds per 1-point reduction in activity on a 4-point scale), low sports aptitude (13% higher odds), and externalizing behaviors (i.e., conduct problems) (5% higher odds per 1-standard deviation higher score). Adjustment for adult covariates weakened associations slightly. Factors from early life were associated with adult leisure-time inactivity, allowing for early identification of groups vulnerable to inactivity.

Long-Term Physical Activity and Risk of Age-Related Cataract: A Population-Based Prospective Study of Male and Female Cohorts

To examine the association of total and specific types of physical activity, including walking or bicycling, exercising, work or occupational activity, home or housework, and leisure time inactivity with the risk of age-related cataract in women and men. Population-based prospective cohort study. A total of 52 660 participants (23 853 women and 28 807 men) 45 to 83 years of age from the Swedish Mammography Cohort and the Cohort of Swedish Men. Physical activity was assessed using a self-administered questionnaire at baseline. Cataract diagnosis and extraction were identified through linkage to registers. Incident age-related cataract diagnosis and cataract extraction. During a mean 12.1 years of follow-up (between January 1, 1998, and December 31, 2011; 634 631 person-years), 11 580 incident age-related cataract cases were identified. After adjusting for potential confounders, the highest quartile of total physical activity was statistically significantly associated with 13% decreased risk of cataract compared with the lowest (hazard ratio [HR], 0.87; 95% confidence interval [CI], 0.82-0.92). Walking or bicycling (>60 minutes/day vs. hardly ever; HR, 0.88; 95% CI, 0.82-0.95) and work or occupational activity (heavy manual labor vs. mostly sitting; HR, 0.84; 95% CI, 0.78-0.91) also were associated with decreased risk of cataract. Exercise training and home or housework were not associated with cataract risk. Leisure time inactivity was associated with increased risk of cataract (>6 vs. <1 hours/day; HR, 1.27; 95% CI, 1.07-1.50). The HR for high long-term total physical activity compared with low levels both at 30 years of age and at baseline was 0.76 (95% CI, 0.69-0.85). Our findings indicate that high total physical activity, especially in the long term, and such specific types of physical activity as walking or bicycling and work or occupational activity, may be associated with decreased risk of age-related cataract. Conversely, high inactivity levels may be associated with increased risk of cataract.

Associations of Unhealthy Lifestyle Factors with Sexual Inactivity and Sexual Dysfunctions in Denmark

Studies have linked obesity, a sedentary lifestyle, and tobacco smoking to erectile dysfunction, but the evidence linking unhealthy lifestyle factors to other sexual dysfunctions or to sexual inactivity is conflicting. To examine associations between unhealthy lifestyle factors and sexual inactivity with a partner and four specific sexual dysfunctions in each sex. We used nationally representative survey data from 5,552 Danish men and women aged 16-97 years in 2005. Cross-sectional associations of lifestyle factors with sexual inactivity and sexual dysfunctions were estimated by logistic regression-derived, confounder-adjusted odds ratios (ORs). We calculated ORs for sexual inactivity with a partner and for sexual dysfunction and sexual difficulties overall in both sexes, for erectile dysfunction, anorgasmia, premature ejaculation, and dyspareunia in men, and for lubrication insufficiency, anorgasmia, dyspareunia, and vaginismus in women. Obesity (body mass index [BMI]≥30 kg/m(2) ) or a substantially increased waist circumference (men ≥102 cm; women ≥88 cm), physical inactivity, and, among women, tobacco smoking were each significantly associated with sexual inactivity in the last year. Among sexually active men, both underweight (BMI <20 kg/m(2) ) and obesity, a substantially increased waist circumference, physical inactivity in leisure time, high alcohol consumption (>21 alcoholic beverages/week), tobacco smoking, and use of hard drugs were each significantly positively associated with one or more sexual dysfunctions (ORs between 1.71 and 22.0). Among sexually active women, the only significant positive association between an unhealthy lifestyle factor and sexual dysfunction was between hashish use and anorgasmia (OR 2.85). In both sexes, several unhealthy lifestyle factors were associated with sexual inactivity with a partner in the last year. Additionally, among sexually active participants, men with unhealthy lifestyles were significantly more likely to experience sexual dysfunctions. Considering the importance of a good sex life, our findings may be useful in attempts to promote healthier lifestyles.

Factors associated with leisure-time physical activity among residents of urban areas from a municipality.

The objective of this study was to analyze factors associated with leisure-time physical activity among residents of urban areas. A cross-sectional epidemiological study was conducted on a representative sample of the urban population of Feira de Santana, Bahia, Brazil, aged 15 years or older. The sample consisted of 3,597 subjects, including 71.4% females and 28.6% males. A questionnaire containing sociodemographic data, self-reported diseases, and leisure-time physical activity was applied. Prevalence ratios and 95% confidence intervals were calculated, adopting a level of significance of p ≤ 0.05. The frequency of subjects considered to be active during leisure time was 27.7%. Sociodemographic characteristics (gender, income, and educational level) were associated with a higher frequency of physical activity. In conclusion, the frequency of leisure-time inactivity was high in the population from the municipality of Feira de Santana, especially among women, low-income subjects, and subjects with low educational level. These findings contribute to the discussion about the need to implement public health policies and to create spaces for active leisure-time activity.

Markers of Sedentarism: The Joint Canada/U.S. Survey of Health

The Joint Canada/United States Survey of Health (JCUSH) was a one-time collaborative survey undertaken by Statistics Canada and the National Center for Health Statistics. This analysis provides country-, sex-, and age-specific comparative markers of adult obesity and sedentarism, defined as independent and collective groupings of self-reported leisure-time inactivity (<1.5 MET-hours/day), usual occupational sitting, and no/low active transportation (<1 hour/week). Logistic regression assessed the likelihood of sedentarism in U.S. vs. Canada, with and without adjusting for BMI-defined obesity categories: healthy weight (18.5 ≤ BMI <25 kg/m2; n = 3542), overweight (25 ≤ BMI < 30 kg/m2; n = 2,651), and obesity (BMI ≥ 30 kg/m2; n = 1470). Compared with Canadians, U.S. adults are 24% more likely to be overweight/ obese, 59% more likely to be inactive in leisure-time, 19% more likely to report no/low active transportation, and 39% more likely to collectively report all sedentarism markers, adjusting for sex and age. Focusing on obese individuals in both countries, obese U.S. residents were 90% more likely to be inactive during leisure-time, 41% more likely to report no/low active transportation, and 73% more likely to report all sedentarism markers. This ecological analysis sheds light on differential risks of obesity and sedentarism in these neighboring countries.

Health-related factors correlate with behavior trends in physical activity level in old age: longitudinal results from a population in São Paulo, Brazil.

BackgroundPhysical inactivity in leisure time is common among elderly in Brazil and this finding is particularly alarming considering that this population is greatly affected by chronic diseases. The identification of health factors that influence physical activity level (PAL) will help in the development of strategies for increasing PAL older adults. The current research aimed to identify variables that independently affect behavior trends in PAL over the course of two years among elderly.MethodsA survey entitled the Epidoso Project ("Epidemiology of aging") studied 1,667 community-based older individuals in São Paulo city, Brazil over the course of two years. Physical activity level was determined through questions about frequency and duration of physical activities. Body Mass Index was calculated; functional capacity was assessed through the ADL (activities of daily living) scale; cognition was assessed by Mini-Mental State Examination; and mental health was assessed through the Dysthymia Screening. Experiences of falls and fractures were also assessed. Subjects were divided into three groups according to their self-report of Physical Activity Level: a - Regularly Active; b - Insufficiently Active and c - Physically Inactive. Behavior trends in PAL were also measured after two years. Multivariate regression model methodology was used to test associations longitudinally.ResultsResults from the final model demonstrated that the risk of a not favorable behavior trend in PAL, which included the group who remained physically inactive and the group that displayed decreased PAL, in this cohort of older adults was significantly increased if the individual was female (OR = 2.50; 95% CI = 1.60-3.89; P < 0.01), older (80 y vs. 65 y, OR = 6.29, 95% CI = 2.69-14.67; P < 0.01), dependent on help from others for activities in the ADL scale (moderate-severe = 4-7+ vs. 0 ADLs) (OR = 2.25, 95% CI = 1.20-4.21; P < 0.011) or had experienced a history of falls with consequences (OR = 6.88, 95% CI = 0.91-52.01; P < 0.062).ConclusionsAge, gender, ADL scores and falls were associated with a not favorable behavior trend in PAL. Promotion programs should target these factors, reducing barriers to achieve desired changes in PAL.