Search

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. [ABSTRACT FROM AUTHOR]

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.

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.

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.

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.

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.

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., (Copyright © 2012 The Obesity Society.)

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., (Copyright © 2013. Published by Elsevier Inc.)

Objectives: To assess the association between Body Mass Index (BMI) and cause-specific mortality in older adults and to assess which BMI was associated with lowest mortality., Design: Prospective study., Setting: European towns., Participants: 1,980 older adults, aged 70-75 years from the SENECA (Survey in Europe on Nutrition and the Elderly: a concerted action) study., Measurements: BMI, examined in 1988/1989, and mortality rates and causes of death during 10 years of follow-up., Results: Cox proportional hazards model including both BMI and BMI², accounting for sex, smoking status, educational level and age at baseline showed that BMI was associated with all-cause mortality (p<0.01), cardiovascular mortality (p<0.01) and mortality from other causes (p<0.01), but not with cancer or respiratory mortality (p>0.3). The lowest all-cause mortality risk was found at 27.1 (95%CI 24.1, 29.3) kg/m², and this risk was increased with statistical significance when higher than 31.4 kg/m² and lower than 21.1 kg/m². The lowest cardiovascular mortality risk was found at 25.6 (95%CI 17.1, 28.4) kg/m², and was increased with statistical significance when higher than 30.9 kg/m²., Conclusion: In this study, BMI was associated with all-cause mortality risk in older people. This risk was mostly driven by an increased cardiovascular mortality risk, as no association was found for mortality risk from cancer or respiratory disease. Our results indicate that the WHO cut-off point of 25 kg/m² for overweight might be too low in old age, but more studies are needed to define specific cut-off points.

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., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Lifestyle interventions in a healthcare setting are effective for weight loss, but it is unclear whether more expensive interventions result in more weight loss. Our objective was to explore the relationship between intervention costs and effectiveness in a systematic review of randomized trials. Intervention studies were selected from 14 reviews and from a systematic MEDLINE-search. Studies had to contain a dietary and a physical activity component and report data on measured weight loss in healthy Caucasian overweight adults. Intervention costs were calculated in a standardized way. The association between costs and percentage weight loss after 1 year was assessed using regression analysis. Nineteen original studies describing 31 interventions were selected. The relationship between weight loss and intervention costs was best described by an asymptotic regression model, which explained 47% of the variance in weight loss. A clinically relevant weight loss of 5% was already observed in interventions of approximately euro110. Results were similar in an intention-to-treat analysis. In conclusion, lifestyle interventions in health care for overweight adults are relatively cheap and higher intervention costs are associated with more weight loss, although the effect of costs on weight loss levels off with growing costs.