The purpose of this study was to identify factors of the physical environment that may influence time spent on walking and bicycling.
Demographic factors and time spent on walking and bicycling ...(during leisure time and for commuting purposes) were assessed with a self-administered questionnaire. GIS databases were used to objectively measure the total square area of green space and recreational space (woods, parks, sport grounds, allotments for vegetable gardens, and grounds for day trips) in a circle around the postal code of a respondent with a radius of 300 and 500 m. Multilevel regression analysis was used to study the association between walking and bicycling on the one hand, and green and recreational space on the other hand. Analyses were adjusted for gender, age, and educational level.
In a neighborhood defined as a circle with a 300-m radius, the square area of sport grounds was associated with bicycling in general and the square area of parks was associated with bicycling for commuting purposes. It is, however, very likely that these results reflect the association of living in the outskirts of town and time spent on bicycling.
The present study showed green and recreational space, specifically sport grounds and parks, to be associated with time spent on bicycling.
Leisure time physical activity in compliance with recommended levels is associated with improved health and lower mortality, but little is known on whether these physical activity habits are stable ...among adults and what characteristics predict physical activity changes. Our objective was to determine change in the levels of leisure time physical activity among adults during a period of 10 yr.
Detailed information on time spent on cycling, gardening, doing odd jobs, and sports from three measurement periods (1993-1997, 1998-2002, and 2003-2007) of the population-based Doetinchem Cohort Study was used to define being active: spending at least 3.5 h·wk(-1) on moderate to vigorous physical activities, an approximation of the Dutch recommended level.
Almost one-third (31.4%) of the population were active at all three points in time, 3.6% were inactive, and 45.0% of the participants changed their level of physical activity, almost equally distributed over decreasers, increasers, and varying. Not smoking (odds ratio (OR) = 1.47, 95% confidence limits (CL) = 1.14-1.89) and high socioeconomic status (OR = 1.43, 95% CL = 1.07-1.92) were associated with staying active. Inactive men (OR = 0.73, 95% CL = 0.57-0.94) had the highest risk of staying inactive, whereas good perceived health was associated with becoming active (OR = 1.49, 95% CL = 1.09-2.03).
The finding that, in a decade, almost half of the population changed from active to inactive or vice versa affects the interpretation of the long-term health effects of physical activity measured only once, and it stresses the importance of interventions not only in increasing physical activity levels but also in maintaining a physically active lifestyle.
ObjectivesLack of physical activity (PA) has been hypothesised as an underlying mechanism in the adverse health effects of shift work. Therefore, our aim was to compare non-occupational PA levels ...between shift workers and non-shift workers. Furthermore, exposure–response relationships for frequency of night shifts and years of shift work regarding non-occupational PA levels were studied.MethodsData of 5980 non-shift workers and 532 shift workers from the European Prospective Investigation into Cancer and Nutrition-Netherlands (EPIC-NL) were used in these cross-sectional analyses. Time spent (hours/week) in different PA types (walking/cycling/exercise/chores) and intensities (moderate/vigorous) were calculated based on self-reported PA. Furthermore, sports were operationalised as: playing sports (no/yes), individual versus non-individual sports, and non-vigorous-intensity versus vigorous-intensity sports. PA levels were compared between shift workers and non-shift workers using Generalized Estimating Equations and logistic regression.ResultsShift workers reported spending more time walking than non-shift workers (B=2.3 (95% CI 1.2 to 3.4)), but shift work was not associated with other PA types and any of the sports activities. Shift workers who worked 1–4 night shifts/month (B=2.4 (95% CI 0.6 to 4.3)) and ≥5 night shifts/month (B=3.7 (95% CI 1.8 to 5.6)) spent more time walking than non-shift workers. No exposure–response relationships were found between years of shift work and PA levels.ConclusionsShift workers spent more time walking than non-shift workers, but we observed no differences in other non-occupational PA levels. To better understand if and how PA plays a role in the negative health consequences of shift work, our findings need to be confirmed in future studies.
Purpose It is hypothesized that the intensity of physical activity habits, rather than the time spent on those activities, might mediate cognitive function. This study tested a possible association ...between changes in the time spent on or the average intensity of weekly physical activities and changes in cognitive function in healthy men and women. Methods This longitudinal cohort study with 1,904 healthy men and women (45–75 years of age) assessed physical activity by a questionnaire and cognitive function with a neuropsychological test battery twice with an interval of 5 years. Results Multiple linear regression analyses showed that changes in the time spent on physical activities were not associated with changes in cognitive function over a 5-year period. By contrast, changes in average intensity of weekly activities were significantly and positively associated with processing speed (beta = 0.063; p < 0.05). Conclusions In this longitudinal cohort study, an increase or smaller decline in average intensity was associated with a smaller age-related decline in processing speed, estimated at 6 years of aging.
This study aimed to evaluate the association between physical activity and the incidence of coronary heart disease (CHD) in individuals with and without CHD risk factors.
EPIC-CVD is a case-cohort ...study of 29 333 participants that included 13 582 incident CHD cases and a randomly selected sub-cohort nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Self-reported physical activity was summarized using the Cambridge physical activity index (inactive, moderately inactive, moderately active, and active). Participants were categorized into sub-groups based on the presence or the absence of the following risk factors: obesity (body mass index ≥30 kg/m2), hypercholesterolaemia (total cholesterol ≥6.2 mmol/L), history of diabetes, hypertension (self-reported or ≥140/90 mmHg), and current smoking. Prentice-weighted Cox regression was used to assess the association between physical activity and incident CHD events (non-fatal and fatal).Compared to inactive participants without the respective CHD risk factor (referent), excess CHD risk was highest in physically inactive and lowest in moderately active participants with CHD risk factors. Corresponding excess CHD risk estimates amongst those with obesity were 47% 95% confidence interval (CI) 32-64% and 21% (95%CI 2-44%), with hypercholesterolaemia were 80% (95%CI 55-108%) and 48% (95%CI 22-81%), with hypertension were 80% (95%CI 65-96%) and 49% (95%CI 28-74%), with diabetes were 142% (95%CI 63-260%), and 100% (95%CI 32-204%), and amongst smokers were 152% (95%CI 122-186%) and 109% (95%CI 74-150%).
In people with CHD risk factors, moderate physical activity, equivalent to 40 mins of walking per day, attenuates but does not completely offset CHD risk.
Physical activity is inversely related to cardiovascular diseases. However, the type of activities that contribute most to these beneficial effects remain unclear. For this reason, we investigated ...self-reported leisure time physical activities in relation to fatal/nonfatal cardiovascular disease incidence.
The Dutch Monitoring Project on Risk Factors for Chronic Diseases Study, carried out between 1993 and 1997, is a prospective cohort study of over 23000 men and women aged 20–65 years from the general Dutch population.
From 1994 till 1997 physical activity was assessed with a questionnaire in 7451 men and 8991 women who were followed for an average of 9.8 years. Cox proportional hazards models were used adjusting for age, sex, other physical activities, smoking, alcohol consumption, and educational level.
Almost the entire study population (97%) was engaged in walking, about 75% in regular cycling, and about half the population in sports or gardening. Cycling hazard ratio (HR): 0.82, 95% confidence interval (CI): 0.71–0.95 and sports (HR: 0.74, 95% CI: 0.64–0.87) were both inversely related to cardiovascular disease incidence, whereas walking and gardening were not. For sports (P < 0.001), but not for cycling (P = 0.06), we found a dose - response relationship with respect to cardiovascular disease incidence. Engaging in both cycling and sports resulted in an even greater risk reduction (HR: 0.64, 95% CI: 0.52–0.77).
In this relatively active population, types of activities of at least moderate intensity, such as cycling and sports were associated with lower CVD incidence, whereas activities of lower intensity, such as walking and gardening, were not.
Community-based health promotion is a widely advocated strategy in public health to favorably alter lifestyle. The aim of this study was to investigate the net effect of a cardiovascular ...disease-prevention program (Hartslag Limburg) on lifestyle factors after 5 years of intervention (1998-2003).
In a cohort study, 5-year mean changes in lifestyle factors (energy intake; fat intake; time spent on leisure-time physical activity; walking, bicycling, and sports; and smoking behavior) between subjects from the intervention area (n=2356) and the control area (n=758) were compared for men and women and for those with a low (less than intermediate secondary education) and a moderate (intermediate vocational or higher secondary education) or high (higher vocational education or university) educational level. Adjustments were made for age and the mean of the individual pre- and post-intervention measurement of the variable under study. When stratifying for gender, adjustments were made for educational level, and vice versa.
In general, lifestyle factors changed unfavorably in the control group, whereas changes were less pronounced or absent in the intervention group. The adjusted difference in mean change in lifestyle factors between the intervention group and the control group was significant (p<or=0.05) for energy intake (-0.2 megajoule per day among both women and those with a low educational level); fat intake (-2.5 grams per day g/d among women and -3 g/d among those with a low educational level); time spent walking (+2.2 hours per week hrs/wk among women and +2.3 hrs/wk among those with a low educational level); time spent on total leisure-time physical activity (+2.1 hrs/wk among women); and time spent bicycling (+0.6 hrs/wk among those with a low educational level).
The community intervention Hartslag Limburg succeeded in preventing age- and time-related unfavorable changes in energy intake, fat consumption, walking, and bicycling, particularly among women and those with low SES.
We examined health-related quality of life in adults who became physically active at recommended levels over a 10-year period compared with adults with different physical activity patterns. Methods. ...We examined men and women aged 26 to 70 years (mean SD = 47.4 10.1) in the Doetinchem Cohort Study 3 times between 1995 and 2009. We distinguished participants who became physically active (n = 618), remained active (n = 1286), remained inactive (n = 727), became physically inactive (n = 535), or with varying activity levels (n = 455) over 10 years. We used multivariable linear regression analyses to determine differences in health-related quality of life (survey similar to the 36-Item Short-Form Health Survey) at 10-year follow-up. Results. Adults who became physically active reported better physical functioning, vitality, and general health after 10 years than did persistently inactive adults and adults who became inactive. They also reported less bodily pain and better social functioning than adults who became inactive. No differences were observed with adults who remained active or with varying activity levels. Conclusions. Adopting a physically active lifestyle may result in a better health-related quality of life, comparable to remaining physically active over 10 years.
Objective. To explore the associations between sitting time in various domains and mental health for workers and nonworkers and the role of weight status. Design. Cross-sectional analyses were ...performed for 1064 respondents (47% men, mean age 59 years) from the Doetinchem Cohort Study 2008-2009. Sedentary behavior was measured by self-reported time spent sitting during transport, leisure time, and at work. Mental health was assessed by the Mental Health Inventory (MHI-5). BMI was calculated based on measured body height and weight. Results. Neither sitting time during transport nor at work was associated with mental health. In the working population, sitting during leisure time, and particularly TV viewing, was associated with poorer mental health. BMI was an effect modifier in this association with significant positive associations for healthy-weight non-workers and obese workers. Conclusion. Both BMI and working status were effect modifiers in the relation between TV viewing and mental health. More longitudinal research is needed to confirm the results and to gain insight into the causality and the underlying mechanisms for the complex relationships among sedentary behaviors, BMI, working status, and mental health.
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