Body fatness and physical activity at young ages and the risk of breast cancer in premenopausal women

We examined the relationship between body fatness, sports participation and breast cancer risk in 1560 premenopausal cases and 1548 controls, from three related population-based case–control studies in the UK. Half of the women with breast cancer were aged less than 36 years at diagnosis. Women who perceived themselves as plump at age 10 years had a relative risk of 0.83 (95% confidence interval 0.69–0.99, P=0.03) as compared with those who perceived themselves as thin. Self-reported obesity compared with leanness at diagnosis was associated with a relative risk of 0.78 (95% confidence interval 0.56–1.06, P=0.11). Women who reported having been plump at age 10 years and overweight or obese at diagnosis had a relative risk of 0.75 (95% confidence interval 0.56–1.01, P=0.06) as compared with those who reported being thin at age 10 years and at diagnosis. Findings for three related measures of body fatness suggested that obesity is associated with a reduced risk of premenopausal breast cancer. There was no association between sports participation and breast cancer risk in these premenopausal women. The relative risk for spending an average of more than 1 h per week in sports compared with less from ages 12 to 30 years was 1.00 (95% CI 0.86–1.16, P=0.98)

Objective vs. Self-Reported Physical Activity and Sedentary Time: Effects of Measurement Method on Relationships with Risk Biomarkers

<p><b>Purpose:</b> Imprecise measurement of physical activity variables might attenuate estimates of the beneficial effects of activity on health-related outcomes. We aimed to compare the cardiometabolic risk factor dose-response relationships for physical activity and sedentary behaviour between accelerometer- and questionnaire-based activity measures.</p> <p><b>Methods:</b> Physical activity and sedentary behaviour were assessed in 317 adults by 7-day accelerometry and International Physical Activity Questionnaire (IPAQ). Fasting blood was taken to determine insulin, glucose, triglyceride and total, LDL and HDL cholesterol concentrations and homeostasis model-estimated insulin resistance (HOMAIR). Waist circumference, BMI, body fat percentage and blood pressure were also measured.</p> <p><b>Results:</b> For both accelerometer-derived sedentary time (<100 counts.min−1) and IPAQ-reported sitting time significant positive (negative for HDL cholesterol) relationships were observed with all measured risk factors – i.e. increased sedentary behaviour was associated with increased risk (all p≤0.01). However, for HOMAIR and insulin the regression coefficients were >50% lower for the IPAQ-reported compared to the accelerometer-derived measure (p<0.0001 for both interactions). The relationships for moderate-to-vigorous physical activity (MVPA) and risk factors were less strong than those observed for sedentary behaviours, but significant negative relationships were observed for both accelerometer and IPAQ MVPA measures with glucose, and insulin and HOMAIR values (all p<0.05). For accelerometer-derived MVPA only, additional negative relationships were seen with triglyceride, total cholesterol and LDL cholesterol concentrations, BMI, waist circumference and percentage body fat, and a positive relationship was evident with HDL cholesterol (p = 0.0002). Regression coefficients for HOMAIR, insulin and triglyceride were 43–50% lower for the IPAQ-reported compared to the accelerometer-derived MVPA measure (all p≤0.01).</p> <p><b>Conclusion:</b> Using the IPAQ to determine sitting time and MVPA reveals some, but not all, relationships between these activity measures and metabolic and vascular disease risk factors. Using this self-report method to quantify activity can therefore underestimate the strength of some relationships with risk factors.</p&gt

Confusion and Conflict in Assessing the Physical Activity Status of Middle-Aged Men

BACKGROUND: Physical activity (including exercise) is prescribed for health and there are various recommendations that can be used to gauge physical activity status. The objective of the current study was to determine whether twelve commonly-used physical activity recommendations similarly classified middle-aged men as sufficiently active for general health. METHODS AND FINDINGS: We examined the commonality in the classification of physical activity status between twelve variations of physical activity recommendations for general health in ninety men aged 45-64 years. Physical activity was assessed using synchronised accelerometry and heart rate. Using different guidelines but the same raw data, the proportion of men defined as active ranged from to 11% to 98% for individual recommendations (median 73%, IQR 30% to 87%). There was very poor absolute agreement between the recommendations, with an intraclass correlation coefficient (A,1) of 0.24 (95% CI, 0.15 to 0.34). Only 8% of men met all 12 recommendations and would therefore be unanimously classified as active and only one man failed to meet every recommendation and would therefore be unanimously classified as not sufficiently active. The wide variability in physical activity classification was explained by ostensibly subtle differences between the 12 recommendations for thresholds related to activity volume (time or energy), distribution (e.g., number of days of the week), moderate intensity cut-point (e.g., 3 vs. 4 metabolic equivalents or METs), and duration (including bout length). CONCLUSIONS: Physical activity status varies enormously depending on the physical activity recommendation that is applied and even ostensibly small differences have a major impact. Approximately nine out of every ten men in the present study could be variably described as either active or not sufficiently active. Either the effective dose or prescription that underlies each physical activity recommendation is different or each recommendation is seeking the same prescriptive outcome but with variable success

Effectiveness of YouRAction, an Intervention to Promote Adolescent Physical Activity Using Personal and Environmental Feedback: A Cluster RCT

Background: In this study the one and six months effects of the computer-tailored YouRAction (targeting individual level determinants) and YouRAction+e (targeting in addition perceived environmental determinants) on compliance with the moderate-to-vigorous physical activity (MVPA) guideline and weight status are examined. In addition the use and appreciation of both interventions are studied. Methods: A three-armed cluster randomized trial was conducted in 2009-2010 with measurements at baseline, one and six months post intervention. School classes were assigned to one of the study arms (YouRaction, YouRAction+e and Generic Information (GI) control group). MVPA was derived from self-reports at baseline, one and six months post intervention. Body Mass Index and waist circumference were measured at baseline and six months post intervention in a random sub-sample of the population. Use of the interventions was measured by webserver logs and appreciation by self-reports. Multilevel regression analyses were conducted to study the effects of the intervention against the GI control group. ANOVA's and chi-square tests were used to describe differences in use and appreciation between study arms. Results: There were no statistically significant intervention effects on compliance with the MVPA guideline, overweight or WC. Access to the full intervention was significantly lower for YouRAction (24.0%) and YouRAction+e (21.7%) compared to the GI (54.4%). Conclusion: This study could not demonstrate that the YouRAction and YouRAction+e interventions were effective in promoting MVPA or improve anthropometric outcomes among adolescents, compared to generic information. Insufficient use and exposure to the intervention content may be an explanation for the lack of effects

Systematic Development of the YouRAction program, a computer-tailored Physical Activity promotion intervention for Dutch adolescents, targeting personal motivations and environmental opportunities

Background. Increasing physical activity (PA) among adolescents is an important health promotion goal. PA has numerous positive health effects, but the majority of Dutch adolescents do not meet PA requirements. The present paper describes the systematic development of a theory-based computer-tailored intervention, YouRAction, which targets individual and environmental factors determining PA among adolescents. Design. The intervention development was guided by the Intervention Mapping protocol, in order to define clear program objectives, theoretical methods and practical strategies, ensure systematic program planning and pilot-testing, and anticipate on implementation and evaluation. Two versions of YouRAction were developed: one that targets individual determinants and an extended version that also provides feedback on opportunities to be active in the neighbourhood. Key determinants that were targeted included: knowledge and awareness, attitudes, self-efficacy and subjective norms. The extended version also addressed perceived availability of neighbourhood PA facilities. Both versions aimed to increase levels of moderate-to-vigorous PA among adolescents. The intervention structure was based on self-regulation theory, comprising of five steps in the process of successful goal pursuit. Monitoring of PA behaviour and behavioural and normative feedback were used to increase awareness of PA behaviour; motivation was enhanced by targeting self-efficacy and attitudes, by means of various interactive strategies, such as web movies; the perceived environment was targeted by visualizing opportunities to be active in an interactive geographical map of the home environment; in the goal setting phase, the adolescents were guided in setting a goal and developing an action plan to achieve this goal; in the phase of active goal pursuit adolescents try to achieve their goal and in the evaluation phase the achievements are evaluated. Based on the results of the evaluation adolescents could revise their goal or choose another behaviour to focus on. The intervention is delivered in a classroom setting in three lessons. YouRAction will be evaluated in a cluster-randomized trial, with classes as unit of randomization. Evaluation will focus on PA outcomes, cognitive mediators/moderators and process measures. Discussion. The planned development of YouRAction resulted in two computer-tailored interventions aimed at the promotion of PA in a Dutch secondary school setting. Trial registration. NTR1923

Patterns and correlates of physical activity: a cross-sectional study in urban Chinese women

<p>Abstract</p> <p>Background</p> <p>Inactivity is a modifiable risk factor for many diseases. Rapid economic development in China has been associated with changes in lifestyle, including physical activity. The purpose of this study was to investigate the patterns and correlates of physical activity in middle-aged and elderly women from urban Shanghai.</p> <p>Methods</p> <p>Study population consisted of 74,942 Chinese women, 40–70 years of age, participating in the baseline survey of the Shanghai Women's Health Study (1997–2000), an ongoing population-based cohort study. A validated, interviewer-administered physical activity questionnaire was used to collect information about several physical activity domains (exercise/sports, walking and cycling for transportation, housework). Correlations between physical activity domains were evaluated by Spearman rank-correlation coefficients. Associations between physical activity and socio-demographic and lifestyle factors were evaluated by odds ratios derived from logistic regression.</p> <p>Results</p> <p>While more than a third of study participants engaged in regular exercise, this form of activity contributed only about 10% to daily non-occupational energy expenditure. About two-thirds of women met current recommendations for lifestyle activity. Age was positively associated with participation in exercise/sports and housework. Dietary energy intake was positively associated with all physical activity domains. High socioeconomic status, unemployment (including retirement), history of chronic disease, small household, non-smoking status, alcohol and tea consumption, and ginseng intake were all positively associated with exercise participation. High socioeconomic status and small household were inversely associated with non-exercise activities.</p> <p>Conclusion</p> <p>This study demonstrates that physical activity domains other than sports and exercise are important contributors to total energy expenditure in women. Correlates of physical activity are domain-specific. These findings provide important information for research on the health benefits of physical activity and have public health implications for designing interventions to promote participation in physical activity.</p