582 resultados para Physical activity and sport


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Background: Evidence on the association between social support and leisure time physical activity (LTPA) is scarce and mostly based on cross-sectional data with different types of social support collapsed into a single index. The aim of this study was to investigate whether social support from the closest person was associated with LTPA.

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In the Public Health White Paper "Healthy Lives, Healthy People" (2010), the UK Government emphasised using incentives and "nudging" to encourage positive, healthy behaviour changes. However, there is little evidence that nudging is effective, in particular for increasing physical activity. We have created a platform to research the effectiveness of health-related behaviour change interventions and incentive schemes. The system consists of an outward-facing website, incorporating tools for incentivizing behaviour change, and a novel physical activity monitoring system. The monitoring system consists of the "Physical Activity Loyalty Card", which contains a passive RFID tag, and a contactless sensor network to detect the cards. This paper describes the application of this novel web-based system to investigate the effectiveness of non-cash incentives to "nudge" adults to undertake more physical activity. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

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BACKGROUND: Low physical activity (PA) levels which increase the risk of chronic disease are reported by two-thirds of the general UK population. Promotion of PA by primary healthcare professionals is advocated but more evidence is needed regarding effective ways of integrating this within everyday practice. This study aims to explore the feasibility of a randomised trial of a pedometer-based intervention, using step-count goals, recruiting patients from primary care. METHOD: Patients, aged 35-75, attending four practices in socioeconomically deprived areas, were invited to complete a General Practice PA Questionnaire during routine consultations. Health professionals invited 'inactive' individuals to a pedometer-based intervention and were randomly allocated to group 1 (prescribed a self-determined goal) or group 2 (prescribed a specific goal of 2500 steps/day above baseline). Both groups kept step-count diaries and received telephone follow-up at 1, 2, 6 and 11 weeks. Step counts were reassessed after 12 weeks. RESULTS: Of the 2154 patients attending, 192 questionnaires were completed (8.9%). Of these, 83 were classified as 'inactive'; 41(10 men; 31 women) completed baseline assessments, with the mean age of participants being 51 years. Mean baseline step counts were similar in group 1 (5685, SD 2945) and group 2 (6513, SD 3350). The mean increase in steps/day was greater in groups 1 than 2 ((2602, SD 1957) vs (748, SD 1997) p=0.005). CONCLUSIONS: A trial of a pedometer-based intervention using self-determined step counts appears feasible in primary care. Pedometers appear acceptable to women, particularly at a perimenopausal age, when it is important to engage in impact loading activities such as walking to maintain bone mineral density. An increase of 2500 steps/day is achievable for inactive patients, but the effectiveness of different approaches to realistic goal-setting warrants further study.

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Background: Accurate assessment tools are required for the surveillance of physical activity (PA) levels and the assessment of the effect of interventions. In addition, increasing awareness of PA is often used as the first step in pragmatic behavioural interventions, as discrepancies between the amount of activity an individual perceives they do and the amount actually undertaken may act as a barrier to change. Previous research has demonstrated differences in the amount of activity individuals report doing, compared to their level of physical activity when measured with an accelerometer. Understanding the characteristics of those whose PA level is ranked differently when measured with either self-report or accelerometry is important as it may inform the choice of instrument for future research. The aim of this project was to determine which individual characteristics are associated with differences between self-reported and accelerometer measured physical activity.

Methods: Participant data from the 2009 wave of the Commuting and Health in Cambridge study were used. Quartiles of self-reported and accelerometer measured PA were derived by ranking each measure from lowest to highest. These quartiles were compared to determine whether individuals’ physical activity was ranked higher by either method. Multinomial logistic regression models were used to investigate the individual characteristics associated with different categories of mismatch.

Results: Data from 486 participants (70% female) were included in the analysis. In adjusted analyses, the physical activity of overweight or obese individuals was significantly more likely to be ranked higher by self-report than by accelerometer than that of normal-weight individuals (OR = 2.07, 95%CI = 1.28–3.34), particularly among women (OR = 3.97, 95%CI = 2.11–7.47).

Conclusions: There was a greater likelihood of mismatch between self-reported and accelerometer measured physical activity levels in overweight or obese adults. Future studies in overweight or obese adults should consider employing both methods of measurement.

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Primary objective: Assess the effects of interventions targeting increased use of the built environment for overall PA in both adults and children.
Secondary objectives

Compare the effects of interventions encouraging the use of existing built environments with interventions that, with or without involving informational approaches, involve building or regenerating environments.
Describe other health benefits (e.g. mental health, risk factors for cardiovascular and other diseases) where outcomes are available.
Explore whether the effects of interventions differ between adults and children and between advantaged and disadvantaged populations.
Identify gaps in the evidence and highlight future research needs in the area.

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Background

Chronic kidney disease is now regarded as a risk factor for cardiovascular disease. The impact of occupational or non-occupational physical activity (PA) on moderate decreases of renal function is uncertain.

Objectives

We aimed to identify the potential association of PA (occupational and leisure-time) on early decline of estimated glomerular filtration rate (eGFR) and to determine the potential mediating effect of PA on the relationship between eGFR and heart disease.

Methods

From the PRIME study analyses were conducted in 1058 employed men. Energy expended during leisure, work and commuting was calculated. Linear regression analyses were used to determine the link between types of PA and moderate decrements of eGFR determined with the KDIGO guideline at the baseline assessment. Cox proportional hazards analyses were used to explore the potential effect of PA on the relationship between eGFR and heart disease, ascertained during follow-up over 10 years.

Results

For these employed men, and after adjustment for known confounders of GFR change, more time spent sitting at work was associated with increased risk of moderate decline in kidney function, while carrying objects or being active at work was associated with decreased risk. In contrast, no significant link with leisure PA was apparent. No potential mediating effect of occupational PA was found for the relationship between eGFR and coronary heart disease.

Conclusion

Occupational PA (potential modifiable factors) could provide a dual role on early impairment of renal function, without influence on the relationship between early decrease of e-GFR and CHD risk.

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Evidence is mounting on the association between the built environment and physical activity (PA) with a call for intervention research. A broader approach which recognizes the role of supportive environments that can make healthy choices easier is required. A systematic review was undertaken to assess the effectiveness of interventions to encourage PA in urban green space. Five databases were searched independently by two reviewers using search terms relating to 'physical activity', 'urban green space' and 'intervention' in July 2014. Eligibility criteria included: (i) intervention to encourage PA in urban green space which involved either a physical change to the urban green space or a PA intervention to promote use of urban green space or a combination of both; and (ii) primary outcome of PA. Of the 2405 studies identified, 12 were included. There was some evidence (4/9 studies showed positive effect) to support built environment only interventions for encouraging use and increasing PA in urban green space. There was more promising evidence (3/3 studies showed positive effect) to support PAprograms or PA programs combined with a physical change to the built environment, for increasing urban green space use and PAof users. Recommendations for future research include the need for longer term follow-up post-intervention, adequate control groups, sufficiently powered studies, and consideration of the social environment, which was identified as a significantly under-utilized resource in this area. Interventions that involve the use of PA programs combined with a physical change to the built environment are likely to have a positive effect on PA. Robust evaluations of such interventions are urgently required. The findings provide a platform to inform the design, implementation and evaluation of future urban green space and PAintervention research.

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There is now a strong body of research that suggests that the form of the built environment can influence levels of physical activity, leading to an increasing interest in incorporating health objectives into spatial planning and regeneration policies and projects. There have been a number of strands to this research, one of which has sought to develop “objective” measurements of the built environment using Geographic Information Science (GIS) involving measures of connectivity and proximity to compare the relative “walkability” of different neighbourhoods. The development of the “walkability index” (e.g. Leslie et al 2007, Frank et al 2010) has become a popular indicator of spatial distribution of those features of the built environment that are considered to have the greatest positive influence on levels of physical activity. The success of this measure is built on its ability to succinctly capture built environment correlates of physical activity using routinely available spatial data, which includes using road centre lines as a basis of a proxy for connectivity.

This paper discusses two key aspects of the walkability index. First, it follows the suggestion of Chin et al (2008) that the use of a footpath network (where available), rather than road centre lines, may be far more effective in evaluating walkability. This may be particularly important for assessing changes in walkability arising from pedestrian-focused infrastructure projects, such as greenways. Second, the paper explores the implication of this for how connectivity can be measured. The paper takes six different measures of connectivity and first analyses the relationships between them and then tests their correlation with actual levels of physical activity of local residents in Belfast, Northern Ireland. The analysis finds that the best measurements appear to be intersection density and metric reach and uses this finding to discuss the implications of this for developing tools that may better support decision-making in spatial planning.

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OBJECTIVE: To investigate the characteristics of those doing no moderate-vigorous physical activity (MVPA) (0days/week), some MVPA (1-4days/week) and sufficient MVPA (≥5days/week) to meet the guidelines in order to effectively develop and target PA interventions to address inequalities in participation.

METHOD: A population survey (2010/2011) of 4653 UK adults provided data on PA and socio-demographic characteristics. An ordered logit model investigated the covariates of 1) participating in no PA, 2) participating in some PA, and 3) meeting the PA guidelines. Model predictions were derived for stereotypical subgroups to highlight important policy and practice implications.

RESULTS: Mean age of participants was 45years old (95% CI 44.51, 45.58) and 42% were male. Probability forecasting showed that males older than 55years of age (probability=0.20; 95% CI 0.11, 0.28), and both males (probability=0.31; 95% CI 0.17, 0.45) and females (probability=0.38; 95% CI 0.27, 0.50) who report poor health are significantly more likely to do no PA.

CONCLUSIONS: Understanding the characteristics of those doing no MVPA and some MVPA could help develop population-level interventions targeting those most in need. Findings suggest that interventions are needed to target older adults, particularly males, and those who report poor health.