648 resultados para Level of physical activity


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The 10,000 Steps Rockhampton project is a multi-strategy community-wide, physical activity intervention based on the simultaneous implementation of five strategies, each identified as 'best practice' for the promotion of physical activity. Several community partners were engaged to develop and implement the strategies during the first eighteen months of the project. These included: the local media (TV, newspaper and radio); the local Division of General Practice and other health professional groups; the Heart Foundation and ‘Just Walk It’; the local council; and several large worksites. A local physical activity task force was also formed to administer a 'micro-grants' scheme, and to guide the development of community based strategies. The presentation will focus on the critical elements involved in developing and maintaining relationships with community partners. These include identification and ‘courting’ of potential partners, strategies for keeping them engaged, and the challenges of maintaining the balance between ‘top-down’ (evidence-based) and ‘bottom-up’ (community-driven) strategies. Data on implementation and uptake of the key strategies will also be presented. These include: 1) process data on the number of health

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The purpose of this research was to explore the influence of physical activity on depressive symptomatology and adolescent alcohol use during an underexplored transition from middle school to high school. The study initiative is supported by the fact that research has shown a unique and simultaneous decrease in physical activity (CDC, 2010), increase in depressive symptomatology (SAMHSA, 2010) and increase in alcohol use (USDHHS, 2011) during middle adolescence. A risk and resilience framework was used in efforts to conceptualize how these variables may be inter-related. Data from waves I and II of the National Longitudinal Study of Adolescent Health (Add Health, Bearman et al., 1997; Udry, 1997) was used (N = 2,054; aged 13–15 years). The sample was ethnically and racially diverse (58.2% White, 24% African American, 11.7% Hispanic, and 6.1% other). Structural equation models were developed to test the potential influence physical activity has on adolescent alcohol use (e.g., frequency of alcohol use and binge alcohol use) and whether any of the relationship was mediated by depressive symptomatology or varied as a function of gender. Results demonstrated that there was a significant influence of structured physical activity (e.g., sports) on adolescent alcohol use. However, contrary to the proposed hypothesis, engaging in structured physical activity appeared to contribute to greater binge drinking among adolescents. Instead of demonstrating a protective feature, the findings suggest that engaging in structured physical activity places adolescents at risk for binge drinking. Furthermore, no significant relationships, positive or negative, were found for the influence of physical activity (structured and unstructured) on frequency of alcohol use. The findings regarding mediation revealed binge drinking as a mediator between physical activity (structured) and depressive symptomatology. These findings provide support for research, practice, and policy initiatives focused on developing a more comprehensive understanding of alcohol use drinking behaviors, physical activity involvement, and depressive symptomatology among adolescents, which this study demonstrates are all associated with one another. Results represent an initial step toward evaluating these relationships at a much younger age.

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Tradicionalmente, los biomarcadores han sido de interés en las ciencias del deporte para medir el rendimiento, el progreso en el entrenamiento y para identificar el sobreentrenamiento. Durante los últimos años, cada vez hay mayor interés en evaluar los efectos relacionados con la salud que se producen en el organismo debidos a una actividad física regular y al deporte. El valor o la concentración de un biomarcador depende de muchos factores, como el grado de entrenamiento, el grado de fatiga y del tipo, la intensidad y la duración del ejercicio, aparte de la edad y del sexo. La mayor parte de los biomarcadores se miden en sangre, orina y saliva. Una de las principales limitaciones que presentan los biomarcadores bioquímicos es la falta de valores de referencia adaptados específicamente para deportistas y personas físicamente activas. Las concentraciones pueden variar considerablemente de los valores de referencia normales. Por lo tanto, es importante adaptar los valores de referencia siempre y cuando sea posible y controlar a cada sujeto regularmente, con el fin de establecer su propia escala de referencia. Otros biomarcadores útiles son la composición corporal (específicamente masa muscular, masa grasa, peso), la condición física (capacidad cardiorrespiratoria, fuerza, agilidad, flexibilidad), frecuencia cardíaca y presión arterial. Dependiendo de la finalidad, será conveniente analizar uno o varios biomarcadores. Para esta revisión, profundizaremos en los biomarcadores que se emplean para evaluar condición física, fatiga crónica, sobreentrenamiento, riesgo cardiovascular, estrés oxidativo e inflamación.

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Background and Study Rationale Being physically active is a major contributor to both physical and mental health. More specifically, being physically active lowers risk of coronary heart disease, high blood pressure, stroke, metabolic syndrome (MetS), diabetes, certain cancers and depression, and increases cognitive function and wellbeing. The physiological mechanisms that occur in response to physical activity and the impact of total physical activity and sedentary behaviour on cardiometabolic health have been extensively studied. In contrast, limited data evaluating the specific effects of daily and weekly patterns of physical behaviour on cardiometabolic health exist. Additionally, no other study has examined interrelated patterns and minute-by-minute accumulation of physical behaviour throughout the day across week days in middle-aged adults. Study Aims The overarching aims of this thesis are firstly to describe patterns of behaviour throughout the day and week, and secondly to explore associations between these patterns and cardiometabolic health in a middle-aged population. The specific objectives are to: 1 Compare agreement between the International Physical Activity Questionnaire-Short Form (IPAQ-SF) and GENEActiv accelerometer-derived moderate-to-vigorous (MVPA) activity and secondly to compare their associations with a range of cardiometabolic and inflammatory markers in middle-aged adults. 2 Determine a suitable monitoring frame needed to reliably capture weekly, accelerometer-measured, activity in our population. 3 Identify groups of participants who have similar weekly patterns of physical behaviour, and determine if underlying patterns of cardiometabolic profiles exist among these groups. 4 Explore the variation of physical behaviour throughout the day to identify whether daily patterns of physical behaviour vary by cardiometabolic health. Methods All results in this thesis are based on data from a subsample of the Mitchelstown Cohort; 475 (46.1% males; mean aged 59.7±5.5 years) middle-aged Irish adults. Subjective physical activity levels were assessed using the IPAQ-SF. Participants wore the wrist GENEActiv accelerometer for 7 consecutive days. Data was collected at 100Hz and summarised into a signal magnitude vector using 60s epochs. Each time interval was categorised based on validated cut-offs. Data on cardiometabolic and inflammatory markers was collected according to standard protocol. Cardiometabolic outcomes (obesity, diabetes, hypertension and MetS) were defined according to internationally recognised definitions by World Health Organisation (WHO) and Irish Diabetes Federation (IDF). Results The results of the first chapter suggest that the IPAQ-SF lacks the sensitivity to assess patterning of activity and guideline adherence and assessing the relationship with cardiometabolic and inflammatory markers. Furthermore, GENEActiv accelerometer-derived MVPA appears to be better at detecting relationships with cardiometabolic and inflammatory markers. The second chapter examined variations in day-to-day physical behaviour levels between- and within-subjects. The main findings were that Sunday differed from all other days in the week for sedentary behaviour and light activity and that a large within-subject variation across days of the week for vigorous activity exists. Our data indicate that six days of monitoring, four weekdays plus Saturday and Sunday, are required to reliably estimate weekly habitual activity in all activity intensities. In the next chapter, latent profile analysis of weekly, interrelated patterns of physical behaviour identified four distinct physical behaviour patterns; Sedentary Group (15.9%), Sedentary; Lower Activity Group (28%), Sedentary; Higher Activity Group (44.2%) and a Physically Active Group (11.9%). Overall the Sedentary Group had poorer outcomes, characterised by unfavourable cardiometabolic and inflammatory profiles. The remaining classes were characterised by healthier cardiometabolic profiles with lower sedentary behaviour levels. The final chapter, which aimed to compare daily cumulative patterns of minute-by-minute physical behaviour intensities across those with and without MetS, revealed significant differences in weekday and weekend day MVPA. In particular, those with MetS start accumulating MVPA later in the day and for a shorted day period. Conclusion In conclusion, the results of this thesis add to the evidence base regards an optimal monitoring period for physical behaviour measurement to accurately capture weekly physical behaviour patterns. In addition, the results highlight whether weekly and daily distribution of activity is associated with cardiometabolic health and inflammatory profiles. The key findings of this thesis demonstrate the importance of daily and weekly physical behaviour patterning of activity intensity in the context of cardiometabolic health risk. In addition, these findings highlight the importance of using physical behaviour patterns of free-living adults observed in a population-based study to inform and aid health promotion activity programmes and primary care prevention and treatment strategies and development of future tailored physical activity based interventions.

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These bookmarks state: African-Americans are less likely to exercise than Caucasians. In S.C., 1 in 3 African-Americans has high blood pressure and 1 in 3 has high cholesterol. This causes an increase in death rates from heart disease and stroke. Regular physical activity can help to: prevent heart disease and prevent high blood pressure.

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Appropriate measures of physical activity are essential for determining the population prevalence of physical activity, for tracking trends over time, and for guiding intervention efforts. Physical activity measurement is characterised by the synthesis of information on the type, frequency, intensity, and duration of activity over a specified period. To date, emphasis in physical activity assessment has been on the measurement of leisure time physical activities. However, some domestic and transport related activities entail energy expenditures equivalent to moderate intensity of 3.0–6.0 METS1 considered to be of sufficient intensity to achieve a health benefit are yet to be included in routine population level physical activity surveillance. This leads to population estimates based only on measures of leisure time physical activities.

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Objective To test a conceptual model linking parental physical activity orientations, parental support for physical activity, and children's self-efficacy perceptions with physical activity participation. Participants and setting The sample consisted of 380 students in grades 7 through 12 (mean age, 14.0±1.6 years) and their parents. Data collection took place during the fall of 1996. Main outcome measures Parents completed a questionnaire assessing their physical activity habits, enjoyment of physical activity, beliefs regarding the importance of physical activity, and supportive behaviors for their child's physical activity. Students completed a 46-item inventory assessing physical activity during the previous 7 days and a 5-item physical activity self-efficacy scale. The model was tested via observed variable path analysis using structural equation modeling techniques (AMOS 4.0). Results An initial model, in which parent physical activity orientations predicted child physical activity via parental support and child self-efficacy, did not provide an acceptable fit to the data. Inclusion of a direct path from parental support to child physical activity and deletion of a nonsignificant path from parental physical activity to child physical activity significantly improved model fit. Standardized path coefficients for the revised model ranged from 0.17 to 0.24, and all were significant at the p<0.0001 level. Conclusions Parental support was an important correlate of youth physical activity, acting directly or indirectly through its influence on self-efficacy. Physical activity interventions targeted at youth should include and evaluate the efficacy of individual-level and community-level strategies to increase parents’ capacity to provide instrumental and motivational support for their children's physical activity.

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Child care centers differ systematically with respect to the quality and quantity of physical activity they provide, suggesting that center-level policies and practices, as well as the center's physical environment, are important influences on children's physical activity behavior. Purpose To summarize and critically evaluate the extant peer-reviewed literature on the influence of child care policy and environment on physical activity in preschool-aged children. Methods A computer database search identified seven relevant studies that were categorized into three broad areas: cross-sectional studies investigating the impact of selected center-level policies and practices on moderate-to-vigorous physical activity (MVPA), studies correlating specific attributes of the outdoor play environment with the level and intensity of MVPA, and studies in which a specific center-level policy or environmental attribute was experimentally manipulated and evaluated for changes in MVPA. Results Staff education and training, as well as staff behavior on the playground, seem to be salient influences on MVPA in preschoolers. Lower playground density (less children per square meter) and the presence of vegetation and open play areas also seem to be positive influences on MVPA. However, not all studies found these attributes to be significant. The availability and quality of portable play equipment, not the amount or type of fixed play equipment, significantly influenced MVPA levels. Conclusions Emerging evidence suggests that several policy and environmental factors contribute to the marked between-center variability in physical activity and sedentary behavior. Intervention studies targeting these factors are thus warranted.

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Aims and objectives To investigate whether physical activity is a protective factor against metabolic syndrome in middle-aged and older women. Background Socio-demographic and lifestyle behaviour factors contribute to metabolic syndrome. To minimise the risk of metabolic syndrome, several global guidelines recommend increasing physical activity level. However, only limited research has investigated the relationship between physical activity levels and metabolic syndrome in middle-aged and older women after adjusting for socio-demographic and lifestyle behaviour factors. Design Cross-sectional design. Methods A convenience sample of 326 middle-aged and older women was recruited. Metabolic syndrome was confirmed according to the National Cholesterol Education Program, Adult Treatment Panel III guidelines, and physical activity levels were measured by the International Physical Activity Questionnaire. Results The sample had a mean age of 60•9 years, and the prevalence of metabolic syndrome was 43•3%. Postmenopausal women and women with low socioeconomic status (low-education background, without personal income and currently unemployed) had a significantly higher risk of developing metabolic syndrome. After adjusting for significant socio-demographic and lifestyle behaviour factors, the women with moderate or high physical activity levels had a significantly lower (OR = 0•10; OR = 0•11, p < 0•001) risk of metabolic syndrome and a lower risk for each specific component of metabolic syndrome, including elevated fasting plasma glucose (OR = 0•29; OR = 0•26, p = 0•009), elevated blood pressure (OR = 0•18; OR = 0•32, p = 0•029), elevated triglycerides (OR = 0•41; OR = 0•15, p = 0•001), reduced high-density lipoprotein (OR = 0•28; OR = 0•27, p = 0•004) and central obesity (OR = 0•31; OR = 0•22, p = 0•027). Conclusions After adjusting for socio-demographic and lifestyle behaviour factors, physical activity level was a significant protective factor against metabolic syndrome in middle-aged and older women. Higher physical activity levels (moderate or high physical activity level) reduced the risk of metabolic syndrome in middle-aged and older women. Relevance to clinical practice Appropriate strategies should be developed to encourage middle-aged and older women across different socio-demographic backgrounds to engage in moderate or high levels of physical activity to reduce the risk of metabolic syndrome.

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Objective To assess the impact of exercise referral schemes on physical activity and health outcomes. Design Systematic review and meta-analysis. Data sources Medline, Embase, PsycINFO, Cochrane Library, ISI Web of Science, SPORTDiscus, and ongoing trial registries up to October 2009. We also checked study references. Study selection - Design: randomised controlled trials or non-randomised controlled (cluster or individual) studies published in peer review journals. - Population: sedentary individuals with or without medical diagnosis. - Exercise referral schemes defined as: clear referrals by primary care professionals to third party service providers to increase physical activity or exercise, physical activity or exercise programmes tailored to individuals, and initial assessment and monitoring throughout programmes. - Comparators: usual care, no intervention, or alternative exercise referral schemes. Results Eight randomised controlled trials met the inclusion criteria, comparing exercise referral schemes with usual care (six trials), alternative physical activity intervention (two), and an exercise referral scheme plus a self determination theory intervention (one). Compared with usual care, follow-up data for exercise referral schemes showed an increased number of participants who achieved 90-150 minutes of physical activity of at least moderate intensity per week (pooled relative risk 1.16, 95% confidence intervals 1.03 to 1.30) and a reduced level of depression (pooled standardised mean difference −0.82, −1.28 to −0.35). Evidence of a between group difference in physical activity of moderate or vigorous intensity or in other health outcomes was inconsistent at follow-up. We did not find any difference in outcomes between exercise referral schemes and the other two comparator groups. None of the included trials separately reported outcomes in individuals with specific medical diagnoses. Substantial heterogeneity in the quality and nature of the exercise referral schemes across studies might have contributed to the inconsistency in outcome findings. Conclusions Considerable uncertainty remains as to the effectiveness of exercise referral schemes for increasing physical activity, fitness, or health indicators, or whether they are an efficient use of resources for sedentary people with or without a medical diagnosis.