Physical Activity Levels, Correlates, and All-Cause Mortality Risk in People Living With Different Health Conditions.

BACKGROUND: To better understand physical activity behavior and its health benefits in people living with health conditions, we studied people with and without 20 different self-reported health conditions with regard to (1) their physical activity levels, (2) factors correlated with these physical activity levels, and (3) the association between physical activity and all-cause mortality. METHODS: We used a subsample (n = 88,659) of the Lifelines cohort study from the Netherlands. For people living with and without 20 different self-reported health conditions, we studied the aforementioned factors in relation to physical activity. Physical activity was assessed with the Short Questionnaire to Assess Health-Enhancing Physical Activity Questionnaire, and mortality data were obtained from the Dutch death register. RESULTS: People with a reported health condition were less likely to meet physical activity guidelines than people without a reported health condition (odds ratios ranging from 0.55 to 0.89). Higher body mass index and sitting time, and lower self-rated health, physical functioning, and education levels were associated with lower odds of meeting physical activity guidelines across most health conditions. Finally, we found a protective association between physical activity and all-cause mortality in both people living with and without different health conditions. CONCLUSION: People living with different health conditions are generally less physically active compared with people living without a health condition. Both people living with and without self-reported health conditions share a number of key factors associated with physical activity levels. We also observed the expected protective association between physical activity and all-cause mortality.

Stability of clustering of lifestyle risk factors in the Dutch adult population and the association with mental health.

BACKGROUND: Lifestyle factors often co-occur in clusters. This study examines whether clusters of lifestyle risk factors, such as smoking, alcohol use, physical inactivity, poor diet, sexual risk behaviour, cannabis and other drug use, change over time in a representative sample of Dutch adults. Additionally, the association between mental health and self-reported depression of lifestyle clusters was examined. METHODS: Each year cross-sectional data of approximately 7500 individuals of 18 years and older from the annual Dutch Health Survey of 2014-2019 were used. Clusters were determined by a two-step cluster analysis. Furthermore, regression analyses determined the association between clusters of lifestyle risk factors and mental health. RESULTS: Results show six clusters composed of one, multiple or no lifestyle risk factors. The clusters remained relatively stable over time: in some clusters, the number of people slightly changed between 2014 and 2019. More specifically, clusters that increased in size were the cluster with no lifestyle risk factors and the cluster with multiple lifestyle risk factors. Furthermore, results show that clusters with none to a few lifestyle risk factors were associated with better mental health and a lower prevalence of self-reported depression compared with clusters with multiple lifestyle risk factors. CONCLUSIONS: The clustering of lifestyle risk factors remained stable over time. People with multiple lifestyle risk factors had poorer mental health than those without risk factors. These findings may emphasize the need for intervention strategies targeting this subgroup with multiple lifestyle risk factors.

[The Lifestyle Monitor: prevalence rates for health policy]. / De Leefstijlmonitor: Cijfers voor gezondheidsbeleid.

The Lifestyle Monitor (LSM) was launched in 2013 on behalf of the Ministry of Health, Welfare and Sports to reorganize the multiple data collections in the field of lifestyle and health in the Netherlands. The reorganization should enhance the efficiency and coherence of the lifestyle and health data collections and should provide unambiguous figures for policymakers. This article describes the background and content (including the lifestyle-themes) of the LSM and the tasks and roles of the collaborating parties involved. The measurement methods used and requirements for data requests are described as well. Finally, some examples of figures and trends over the period 2014-2020 to underpin the health policy are described.

An inventory of national surveillance systems assessing physical activity, sedentary behaviour and sport participation of adults in the European Union.

BACKGROUND: Physical inactivity has been recognised as a global public health problem that requires concerted action. This calls for systematic physical activity (PA) surveillance as a mechanism for assessing the problem and evaluating the effectiveness of related policies. Because countries tend to design their policy measures based on national surveillance data, here we present an inventory of existing national surveillance systems on PA, sedentary behaviour (SB) and sport participation (SP) among adult population in all European Union (EU) Member States. METHODS: As a part of the European Physical Activity and Sports Monitoring System (EUPASMOS) project, a questionnaire was constructed in the form of an on-line survey to collect detailed information on existing national surveillance systems on either PA, SB, or SP. National HEPA focal points from all 27 EU Member States were invited to answer the on-line questionnaire and data collection took part in the period May 2018-September 2019. RESULTS: National monitoring of PA or SB or SP for adults has been established in 16/27 EU Member States, that host 33 different PA/SB/SP monitoring systems. Apart from 3 countries that are using accelerometers (Finland, Ireland and Portugal), surveillance is typically based on questionnaires. In most Member States these questionnaires have not been validated in the particular language and cultural setting. Next, specific domains and dimensions of PA, SB and SP assessed vary a lot across countries. Only 3 countries (the Netherlands, Portugal and Slovenia) are monitoring all three behaviours while covering most of the domains and dimensions of PA/SB/SP. Lastly, as half of the existing surveillance systems set an upper age limit, in 9/16 countries that are monitoring PA/SB/SP, no data for people older than 80 years are available. CONCLUSIONS: Systematic surveillance of PA is lacking among 11/27 EU countries, with even few monitoring SB and SP. Besides, existing surveillance systems typically fail to assess all dimensions and domains of PA/SB/SP with only three countries maintaining monitoring systems that encompass all three behaviours while covering most of the domains and dimensions of PA/SB/SP. Hence, additional efforts in advocacy of systematic PA surveillance in the EU are called for.

Physical activity levels of adults with various physical disabilities.

This study examined the physical activity (PA) levels of people with specific disabilities, using health care registration data. Data of 321,656 adults (83%) from the Dutch Public Health Monitor 2012 were used to assess adherence to the World Health Organization (WHO) PA guidelines (%) and the time (min/week) spent on moderate-to-vigorous-intensity and vigorous-intensity PA. Specific physical and sensory (i.e. vision and hearing) disabilities were identified by means of two health claims registries that include reimbursement of functional aids and long-term care. Generalized estimated equations were used to determine the association of PA with disabilities, adjusted for confounders (model 1) and additionally for self-reported activity limitations (model 2). Adults with disabilities had lower levels of WHO PA guidelines adherence (range: -49.8% to -11.9%, p < 0.01) and of moderate-to-vigorous-intensity PA (range: -691 to -200 min/week, p < 0.01) than adults without physical and sensory disabilities. Adults with physical disabilities had the lowest levels. The difference in levels of vigorous-intensity PA between adults with and without physical and sensory disabilities ranged from -12 to 8 min/week Only adults receiving long-term care due to physical disabilities had significantly lower vigorous-intensity PA levels (-12 min/week, p < 0.01). After adjustment for self-reported activity limitations, the difference in PA levels between adults with and without physical and sensory disabilities attenuated, especially among those with physical disabilities, but PA levels were still lower for adults with physical disabilities (-34.5% to -9.8% and -466 to -172 min/week, p < 0.01, respectively). Regardless of self-reported activity limitations, adults with objectively measured disabilities, especially those with physical disabilities, had lower PA levels compared to adults without physical and sensory disabilities.

Physical activity of workers with and without chronic diseases.

OBJECTIVE: To contribute to the development of measures that increase physical activity (PA) levels in workers with and without chronic diseases, insight into workers' PA level is needed. Therefore, this study examined the association between the number of chronic diseases and PA in a Dutch working population. METHODS: Data of 131,032 workers from the Dutch Public Health Monitor 2012 were used in this cross-sectional study conducted in 2015 in the Netherlands. PA was operationalized as adherence (yes/no) to three PA guidelines. One of these was the American College of Sports Medicine (ACSM) guideline (≥ 3 days/week, ≥ 20 min/day of vigorous-intensity activities). Also, the amount of moderate- and vigorous-intensity PA in min/week for those who were physically active for > 0 min/week was calculated. Associations between chronic diseases (0, 1, ≥ 2 chronic diseases) and PA were examined using logistic regression and Generalized Estimating Equations stratified for age (19-54 years/55-64 years). RESULTS: Workers aged 19-54 years with one (OR = 0.90 (99% CI = 0.84-0.95)) and multiple chronic diseases (OR = 0.76 (99% CI = 0.69-0.83)) had lower odds of adhering to the ACSM-guideline than workers without chronic diseases. Similar patterns were found for older workers. Younger workers with one (B = 24.44 (99% CI = 8.59-40.30)) and multiple chronic diseases (B = 49.11 (99% CI = 26.61-71.61)) had a higher amount of moderate PA than workers without chronic diseases. CONCLUSION: Workers with chronic diseases adhered less often to the ACSM-guideline, but among workers aged 19-54 years who were physically active for > 0 min/week, those with chronic diseases spent more time in moderate-intensity PA than those without chronic diseases.

Transport choice when travelling to a sports facility: the role of perceived route features - Results from a cross-sectional study in the Netherlands.

BACKGROUND: Physical activity and sedentary behaviour are independently associated with health outcomes, where physical activity (PA) is associated with health benefits and sedentary behaviour is associated with health risks. One possible strategy to counteract sedentary behaviour is to stimulate active transport use. As monitoring studies in the Netherlands have shown that among sedentary people the proportion of adults who engage in sports (hereafter: sports practitioners) is 62.3%, sports practitioners seem a feasible target group for this strategy. Previous studies have generally reported associations between neighbourhood characteristics and active transport use. However, the neighbourhood covers only part of the route to a certain destination. Therefore, we examined the association between perceived route features and transport choice when travelling up to 7.5 kilometres to a sports facility among sports practitioners. METHODS: For 1118 Dutch sports practitioners - who indicated that they practice a sport and travel to a sports facility - age 18 and older, data on transport choice and perceived features of the route to a sports facility were gathered. Participants were classified into one of three transport groups based on their transport choice: car users, cyclists and walkers. Participants were asked whether perceived route features influenced their transport choice. Logistic regression was used to model the odds of cycling versus car use and walking versus car use in the association with perceived route features, adjusted for potential confounders. RESULTS: Perceived traffic safety was associated with lower odds of cycling (OR: 0.36, 95% CI: 0.15-0.86). Perceived route duration was associated with lower odds of both cycling (OR: 0.54, 95%CI: 0.39-0.75) and walking (OR: 0.60, 95%CI: 0.36-1.00). Perceived distance to a sports facility and having to make a detour when using other transport modes than the chosen transport mode were associated with higher odds of both cycling and walking (ORrange: 1.82-5.21). What and who people encountered during their trip (i.e. visual aspects) was associated with higher odds of both cycling and walking (ORrange: 2.40-3.69). CONCLUSIONS: Perceived traffic safety, duration, distance, detour, and visual aspects, when travelling to a sports facility were associated with transport choice. Therefore, the perception of route features should be considered when stimulating active transport use among sports practitioners.

Changes in weight and health-related quality of life. The Doetinchem Cohort Study.

BACKGROUND: The cross-sectional association between obesity and a lower health-related quality of life (HRQL) is clear. However, less is known about the association between changes in weight and HRQL. We examined the association between weight changes and changes in HRQL in a population-based sample of 2005 men and 2130 women aged 26-70 years. METHODS: Weight was measured two or three times with 5-year intervals between 1995 and 2009, and was categorised as stable (change ≤2 kg, 40%), weight loss (19%), or weight gain 2.1-4.0 kg, 4.1-6.0 kg, or >6 kg (41%). Changes in HRQL (SF36 questionnaire, including physical and mental scales) per weight change category were compared with a stable weight using generalised estimating equations. RESULTS: Weight gain was associated with declines of up to 5 points on five mainly physical scales and holds for different age categories. Especially for women, a dose-response relationship was observed, that is, larger weight gain was associated with larger declines in HRQL. Changes in HRQL for those with weight loss were small, but particularly on the mental scales, changes were in the negative direction compared to a stable weight. CONCLUSIONS: Weight gain and weight loss were associated with unfavourable changes in HRQL compared with a stable weight. For weight gain, this was most pronounced on the physical scales and for weight loss, although less consistent, on the mental scales.

Cross-sectional associations of objectively measured physical activity, cardiorespiratory fitness and anthropometry in European adults.

OBJECTIVE: To quantify the independent associations between objectively measured physical activity (PA), cardiorespiratory fitness (CRF), and anthropometry in European men and women. METHODS: 2,056 volunteers from 12 centers across Europe were fitted with a heart rate and movement sensor at 2 visits 4 months apart for a total of 8 days. CRF (ml/kg/min) was estimated from an 8 minute ramped step test. A cross-sectional analysis of the independent associations between objectively measured PA (m/s(2)/d), moderate and vigorous physical activity (MVPA) (%time/d), sedentary time (%time/d), CRF, and anthropometry using sex stratified multiple linear regression was performed. RESULTS: In mutually adjusted models, CRF, PA, and MVPA were inversely associated with all anthropometric markers in women. In men, CRF, PA, and MVPA were inversely associated with BMI, whereas only CRF was significantly associated with the other anthropometric markers. Sedentary time was positively associated with all anthropometric markers, however, after adjustment for CRF significant in women only. CONCLUSION: CRF, PA, MVPA, and sedentary time are differently associated with anthropometric markers in men and women. CRF appears to attenuate associations between PA, MVPA, and sedentary time. These observations may have implications for prevention of obesity.

The impact of long-term body mass index patterns on health-related quality of life: the Doetinchem Cohort Study.

Overweight is associated with a reduced health-related quality of life (QOL), but less is known about the impact of long-term body mass index (BMI, calculated as weight (kg)/height (m)(2)) patterns on QOL in adults. In the Dutch Doetinchem Cohort Study (1989-2009) that included 1,677 men and 1,731 women aged 20-66 years, 6 BMI patterns were defined by using 4 measurements over a 15-year period: 1) persistent healthy weight (18.5-24.9, reference pattern); 2) persistent overweight (25.0-29.9); 3) persistent obesity (≥30.0); 4) developing overweight; 5) developing obesity; and 6) switching between BMI categories. For each BMI pattern, adjusted QOL (measured on a 0-100 scale) was estimated at the end of this period. The lowest QOL was observed for persistent obesity of all BMI patterns. It was 5.0 points (P = 0.02) lower for 1 mental dimension in men and 6.2-11.6 points (P < 0.05) lower for 5 (mainly physical) dimensions in women. Developing overweight or obesity scored 1.8-6.3 points (P < 0.05) lower on 2-5 (mainly physical) dimensions. Persistent overweight hardly differed from a persistent healthy weight. In women, switching between BMI categories resulted in a lower QOL on the mental dimensions. Studying long-term BMI patterns over a 15-year period showed that persistent obesity, developing overweight, and developing obesity resulted in a lower QOL-particularly on the physical dimensions-compared with a persistent healthy weight.

Influence of calendar period on the association between BMI and coronary heart disease: a meta-analysis of 31 cohorts.

OBJECTIVE: The association between obesity and coronary heart disease (CHD) may have changed over time, for example due to improved pharmacological treatment of CHD risk factors. This meta-analysis of 31 prospective cohort studies explores the influence of calendar period on CHD risk associated with body mass index (BMI). DESIGN AND METHODS: The relative risks (RRs) of CHD for a five-BMI-unit increment and BMI categories were pooled by means of random effects models. Meta-regression analysis was used to examine the influence of calendar period (>1985 v ≤1985) in univariate and multivariate analyses (including mean population age as a covariate). RESULTS: The age, sex, and smoking adjusted RR (95% confidence intervals) of CHD for a five-BMI-unit increment was 1.28(1.22:1.34). For underweight, overweight and obesity, the RRs (compared to normal weight) were 1.11(0.91:1.36), 1.31(1.22:1.41), and 1.78(1.55:2.04), respectively. The univariate analysis indicated 31% (95%CI: -56:0) lower RR of CHD associated with a five-BMI-unit increment and a 51% (95%CI: -78: -14)) lower RR associated with obesity in studies starting after 1985 (n = 15 and 10, respectively) compared to studies starting in or before 1985 (n = 16 and 10). However, in the multivariate analysis, only mean population age was independently associated with the RRs for a five-BMI-unit increment and obesity (-29(95%CI: -55: -5)) and -31(95%CI: -66:3), respectively) per 10-year increment in mean age). CONCLUSION: This study provides no consistent evidence for a difference in the association between BMI and CHD by calendar period. The mean population age seems to be the most important factor that modifies the association between the risk of CHD and BMI, in which the RR decreases with increasing age.

Associations between changes in anthropometric measures and mortality in old age: a role for mid-upper arm circumference?

OBJECTIVES: In elderly individuals, little is known about changes in different anthropometric measures with respect to mortality. We examined the association between changes in eight anthropometric measures and mortality in an elderly population. DESIGN: Longitudinal study including baseline measurements in 1988-1990 and repeated measures in 1993. SETTING: European towns. PARTICIPANTS: A total of 1061 older adults born in 1913-1918 from the Survey in Europe on Nutrition and the Elderly, A Concerted Action study were included in this study. MEASUREMENTS: Weight, body mass index, waist circumference, waist to hip ratio, waist to height ratio, mid-upper arm circumference (MUAC), triceps skinfold thickness, and corrected arm muscle area were taken during both measurements. RESULTS: A Cox regression model was used to examine the association between anthropometric changes (divided into quintiles, smallest change = reference category) and all-cause and cardiovascular disease mortality over approximately 6 years of follow-up, adjusted for baseline measurement of application, age, sex, smoking, education, physical activity, and major chronic diseases. A decrease in weight (≥3.2 kg), waist circumference (≥3.1 cm), and MUAC (≥1.6 cm and 0.6-1.6 cm) were (near) significantly associated with an all-cause mortality risk of 1.48 (95% CI: 0.99-2.20), 1.52 (95% CI: 1.01-2.31), 1.81 (95% CI: 1.17-2.79), and 1.66 (95% CI: 1.10-2.49), respectively. Also for MUAC, an increase (≥1.3 cm) was significantly associated with an increased all-cause and cardiovascular disease mortality risk [hazard ratio, 1.52 (95% CI: 1.00-2.31) and 1.94 (95% CI: 1.00-3.75), respectively]. CONCLUSION: Associations were observed for decreases in only 3 of 8 anthropometric measures and all-cause mortality. Decreases in MUAC had the strongest association with mortality and was the only measure in which an increase also was associated with mortality. This suggests a role for MUAC in the prediction of mortality in elderly individuals.

The association between waist circumference and risk of mortality considering body mass index in 65- to 74-year-olds: a meta-analysis of 29 cohorts involving more than 58 000 elderly persons.

BACKGROUND: For the elderly, the association between waist circumference (WC) and mortality considering body mass index (BMI) remains unclear, and thereby also the evidence base for using these anthropometric measures in clinical practice. This meta-analysis examined the association between WC categories and (cause-specific) mortality within BMI categories. Furthermore, the association of continuous WC with lowest and increased mortality risks was examined. METHODS: Age- and smoking-adjusted relative risks (RRs) of mortality associated with WC-BMI categories and continuous WC (including WC and WC(2)) were calculated by the investigators and pooled by means of random-effects models. RESULTS: During a 5-year-follow-up of 32 678 men and 25 931 women, we ascertained 3318 and 1480 deaths, respectively. A large WC (men: ≥102 cm, women: ≥88 cm) was associated with increased all-cause mortality RRs for those in the 'healthy' weight {1.7 [95% confidence interval (CI): 1.2-2.2], 1.7 (95% CI: 1.3-2.3)}, overweight [1.1(95% CI: 1.0-1.3), 1.4 (95%: 1.1-1.7)] and obese [1.1 (95% CI: 1.0-1.3), 1.6 (95% CI: 1.3-1.9)] BMI category compared with the 'healthy' weight (20-24.9 kg/m(2)) and a small WC (<94 cm, men; <80 cm, women) category. Underweight was associated with highest all-cause mortality RRs in men [2.2 (95% CI: 1.8-2.8)] and women [2.3 (95% CI: 1.8-3.1]. We found a J-shaped association for continuous WC with all-cause, cardiovascular (CVD) and cancer, and a U-shaped association with respiratory disease mortality (P < 0.05). An all-cause (CVD) mortality RR of 2.0 was associated with a WC of 132 cm (123 cm) in men and 116 cm (105 cm) in women. CONCLUSIONS: Our results showed increased mortality risks for elderly people with an increased WC-even across BMI categories- and for those who were classified as 'underweight' using BMI. The results provide a solid basis for re-evaluation of WC cut-points in ageing populations.

The SQUASH was a more valid tool than the OBiN for categorizing adults according to the Dutch physical activity and the combined guideline.

OBJECTIVE: To examine whether the "Short QUestionnaire to ASsess Health-enhancing physical activity" (SQUASH) and the "Injuries and Physical Activity in the Netherlands" questionnaire ("Ongevallen en Bewegen in Nederland," OBiN) were valid in assessing adherence to physical activity (PA) guidelines. STUDY DESIGN AND SETTING: Participants (N=187) aged 20-69 years were categorized as "inactive," "semiactive," or "norm-active" according to the Dutch PA, the American College of Sports Medicine (ACSM), and the combined guideline (adhering to either or both of two other guidelines) by the questionnaires and a combined heart rate monitor and accelerometer (Actiheart). Percentage of exact agreement and maximum disagreement (difference of two categories) for the categorization between questionnaires and Actiheart was calculated. RESULTS: The SQUASH had a significant higher agreement than the OBiN for the Dutch PA (SQUASH: 78%, OBiN: 46%; P<0.01) and combined guideline (SQUASH: 84%, OBiN: 55%; P<0.01). Both questionnaires had a low agreement regarding the ACSM guideline (SQUASH: 37%, OBiN: 34%; P=0.45). The SQUASH had a significant higher maximum disagreement than the OBiN for this guideline (SQUASH: 19.8%, OBiN 8%; P<0.01). CONCLUSION: The SQUASH was a more valid measure than the OBiN for categorizing adults according to the Dutch PA and the combined guideline. Both questionnaires failed to correctly categorize adults according to the ACSM guideline.