8 resultados para activity intensity

em DigitalCommons@The Texas Medical Center


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Research has shown that physical activity serves a preventive function against the development of several major chronic diseases. However, studying physical activity and its health benefits is difficult due to the complexity of measuring physical activity. The overall aim of this research is to contribute to the knowledge of both correlates and measurement of physical activity. Data from the Women On The Move study were used for this study (n = 260), and the results are presented in three papers. The first paper focuses on the measurement of physical activity and compares an alternate coding method with the standard coding method for calculating energy expenditure from a 7-day activity diary. Results indicate that the alternative coding scheme could produce similar results to the standard coding in terms of total activity expenditure. Even though agreement could not be achieved by dimension, the study lays the groundwork for a coding system that saves considerable amount of time in coding activity and has the ability to estimate expenditure more accurately for activities that can be performed at varying intensity levels. The second paper investigates intra-day variability in physical activity by estimating the variation in energy expenditure for workers and non-workers and identifying the number of days of diary self-report necessary to reliably estimate activity. The results indicate that 8 days of activity are needed to reliably estimate total activity for individuals who don't work and 12 days of activity are needed to reliably estimate total activity for those who work. Days of diary self-report required by dimension for those who don't work range from 6 to 16 and for those who work from 6 to 113. The final paper presents findings on the relationship between daily living activity and Type A behavior pattern. Significant findings are observed for total activity and leisure activity with the Temperament Scale summary score. Significant findings are also observed for total activity, household chores, work, leisure activity, exercise, and inactivity with one or more of the individual items on the Temperament Scale. However, even though some significant findings were observed, the overall models did not reveal meaningful associations. ^

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Introduction. There is a need for physical activity interventions based in primary care clinics that take advantage of community resources. The purpose of this randomized controlled trial was to compare the effects of two physical activity interventions: (1) physical activity prescription by a primary care provider plus referral to community physical activity resources and (2) physical activity prescription only. ^ Methods. Sedentary adult patients recruited from a general medicine clinic were randomized to receive a physical activity prescription, delivered by the primary care provider, plus referral to community physical activity resources (n=38) or physical activity prescription only (n=32). Outcomes were use of community resources (exercise facility and personal trainers), physical activity levels (self-report questionnaire and pedometer), and attitudes regarding physical activity assessed at 8 weeks. ^ Results. Three of 38 (7.9%) subjects referred to the community resources and none of the 32 subjects in the prescription only group used the community resources during the 8 week trial. Sixteen of 32 subjects in the prescription plus referral group and 19 of 38 in the prescription group completed the self-report follow-up forms at 8 weeks. For minutes of moderate- or vigorous-intensity physical activity per week, there were no between-group differences at baseline, follow-up, or change from baseline to follow-up. However, for moderate- and vigorous-intensity physical activity, there were significant improvements from baseline to follow-up within each group. For attitudes related to physical activity, there were no between-group differences at baseline, follow-up, or change from baseline to follow-up; neither were there any within-group changes. ^ Discussion. Physical activity prescription delivered by a healthcare provider in the context of a routine primary care visit can improve physical activity levels, with no additional improvement gained by referring to community resources. The intervention was feasible for primary care providers to deliver, but only 50% of subjects returned the self-report physical activity questionnaire at the 8 week assessment. ^

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Obesity and physical inactivity are modifiable risk factors that are associated with several health issues; they are major factors in up to 30% of major cancers. Elevated levels of circulating insulin-like growth factor-I (IGF-I) have been associated with high body composition measurements and high cancer risk; exogenous estrogen use is associated with low circulating IGF-I levels and high cancer risk. The relationship between physical activity and circulating IGF levels is complex and findings of previous studies of their relationship remain inconsistent; however, these studies included vague definitions of physical activity. In this study, we used cross-sectional data from the Women's Health Initiative to determine the relationship between specific measures of physical activity (e.g., intensity, duration, and frequency) and circulating IGF-I levels, accounting for exogenous estrogen use and body composition. These data were collected from women enrolled at Women's Health Initiative clinical centers at Baylor College of Medicine and Wake Forest University School of Medicine. Multivariate linear regression analysis showed that circulating IGF-I and IGF-binding protein (BP) 3 levels were positively associated with frequency, duration, and intensity of physical activity. Circulating IGF-I levels and the molar IGF-I:IGF-BP3 ratio were significantly associated with frequency of walking, whereas circulating IGF-BP3 levels were significantly associated with strenuous physical activity, suggesting that different aspects of physical activity and their effects on fitness affect members of the IGF family differently. The results from our study support the recommendation of a regular exercise routine, particularly that of strenuous intensity, for postmenopausal women as a means to prevention of cancer.^

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Research studies on the association between exposures to air contaminants and disease frequently use worn dosimeters to measure the concentration of the contaminant of interest. But investigation of exposure determinants requires additional knowledge beyond concentration, i.e., knowledge about personal activity such as whether the exposure occurred in a building or outdoors. Current studies frequently depend upon manual activity logging to record location. This study's purpose was to evaluate the use of a worn data logger recording three environmental parameters—temperature, humidity, and light intensity—as well as time of day, to determine indoor or outdoor location, with an ultimate aim of eliminating the need to manually log location or at least providing a method to verify such logs. For this study, data collection was limited to a single geographical area (Houston, Texas metropolitan area) during a single season (winter) using a HOBO H8 four-channel data logger. Data for development of a Location Model were collected using the logger for deliberate sampling of programmed activities in outdoor, building, and vehicle locations at various times of day. The Model was developed by analyzing the distributions of environmental parameters by location and time to establish a prioritized set of cut points for assessing locations. The final Model consisted of four "processors" that varied these priorities and cut points. Data to evaluate the Model were collected by wearing the logger during "typical days" while maintaining a location log. The Model was tested by feeding the typical day data into each processor and generating assessed locations for each record. These assessed locations were then compared with true locations recorded in the manual log to determine accurate versus erroneous assessments. The utility of each processor was evaluated by calculating overall error rates across all times of day, and calculating individual error rates by time of day. Unfortunately, the error rates were large, such that there would be no benefit in using the Model. Another analysis in which assessed locations were classified as either indoor (including both building and vehicle) or outdoor yielded slightly lower error rates that still precluded any benefit of the Model's use.^

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Study Aims. The neighborhood environment has been shown to influence physical activity levels, but little is known about how far-reaching these effects are. This study sought to determine whether or not the development of a New Urbanist community in Austin, Texas affected the physical activity levels of residents in surrounding neighborhoods. The results were stratified by demographic characteristics, residential distance from the New Urbanist community, and type of physical activity to determine if any observed changes varied by these factors.^ Methods. Self-report questionnaires were mailed to a random sample of households located within one mile of the New Urbanist development. The questionnaire included questions about physical activity behaviors before and after the community was constructed in 2006. Changes in reported levels of physical activity between the two time points were examined.^ Results. The prevalence and average minutes per week of total physical activity did not change. Significant increases in the frequency and quantity of moderate-intensity leisure-time physical activity were observed. The amount of time spent walking and biking for recreation outside of the neighborhood increased significantly among those living close to Mueller. The weekly time spent in vigorous-intensity activity increased significantly, especially among those living closer to Mueller. There were significant decreases in the prevalence of and time spent walking for transport. The use of Mueller parks and trails was highest among participants living close to Mueller.^ Conclusions. The nearby Mueller development does not appear to have encouraged sedentary members of the surrounding population to become physically active, but might be associated with increased weekly physical activity among those who were already active. The increase in vigorous-intensity physical activity and recreational walking/biking outside of the neighborhood among those subjects living closer to Mueller may be attributed to the use of Mueller parks and trails, which was also considerably higher among this group. People may be substituting the time they spent walking for transport before the Mueller development with moderate-intensity leisure-time physical activity. Future research should seek to identify barriers that may be preventing more nearby residents from using Mueller facilities for physical activity. ^

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The built environment is recognized as having an impact on health and physical activity. Ecological theories of physical activity suggest that enhancing access to places to be physically active may increase activity levels. Studies show that users of fitness facilities are more likely to be active than inactive and active people are more likely to report access to fitness facilities. The purpose of this study was to examine the ecologic relationship between density of fitness facilities and self-reported levels of physical activity in adults in selected Metropolitan Statistical Areas (MSAs) in the United States.^ The 2007 MSA Business Patterns and the 2007 Behavioral Risk Factor Surveillance System (BRFSS) were used to gather fitness facility and physical activity data for 141 MSAs in the United States. Pearson correlations were performed between fitness facility density (number of facilities/100,000 people) and six summary measures of physical activity prevalence. Regional analysis was done using the nine U.S. Standard Regions for Temperature and Precipitation. ^ Direct correlations between fitness facility density and the percent of those physically active (r=0.27, 95% CI 0.11, 0.42, p=0.0012), those meeting moderate-intensity activity guidelines, (r=0.23, 95% CI 0.07, 0.38, p=0.006), and those meeting vigorous-intensity activity guidelines (r=0.30, 95% CI 0.14, 0.44, p=0.003) were found. An inverse correlation was found between fitness facility density and the percent of people physically inactive (r=-0.45, 95% CI -0.57, -0.31), p<0.0001). Regional analysis showed the same trends across most regions.^ Access to fitness facilities, defined here as fitness facility density, is related to physical activity levels. Results suggest the potential importance of the influence of the built environment on physical activity behaviors. Public health officials and city planners should consider the possible positive effect that increasing the number of fitness facilities in communities would have on activity levels.^

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This study examines variations in physical activity by season, and within seasons by age and gender among park users living in the Cameron Park Colonia, a low-income Hispanic community along the Texas-Mexico border. This is the first study of its kind to evaluate seasonal variations by physical activity among a Hispanic population. We hypothesized that (1) there are no differences in overall physical activity by season; (2) youth engage in more sport-related physical activity compared to adults, (3) males engage in more physical activity than females, and (4) there are differences in physical activity between walk-trail users compared to non walk-trail users in the park.^ Physical activity behavioral data was collected (males n=2,093; females n=1,014) at two time periods (winter 2007; summer 2007) via direct observations and assessed park use, walking trail use, and physical activity (moderate-to-vigorous physical activity (MVPA) by seasons. Frequencies for physical activities were calculated for gender, age groups, and season. Separate Pearson's chi-square analyses were used to address variations in physical activity, age, gender, intensity level of physical activity by season, between walk-trails users and non walk-trail users.^ People visiting the park engaged in more sedentary behavior in winter than summer and a higher percentage engaged in MVPA in the summer than winter (p<.05). More females engaged in light activity compared to males (p<.05). Walk-trail users consisted mostly of females and engaged in more light activity than non walk-trail users (p<.05) who participated in more MVPA.^ Increasing access to parks and walk-trails may be an intervention strategy to increase physical activity among Hispanics. More research is needed to assess promoting trail use and determining long-term effects on physical activity among minority/ethnic groups at greater risk of a sedentary lifestyle and reasons for trail use and non-use. Future studies should focus on the types of activities Hispanics engage in at different parks particularly between men and women. As a result of this study city officials and planners may use this information to build and design parks that cater to the types of activities that Hispanics engage in and may use to meet physical activity guidelines.^

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Background. This study was designed to evaluate the effects of the Young Leaders for Healthy Change program, an internet-delivered program in the school setting that emphasized health advocacy skills-development, on nutrition and physical activity behaviors among older adolescents (13–18 years). The program consisted of online curricular modules, training modules, social media, peer and parental support, and a community service project. Module content was developed based on Social Cognitive Theory and known determinants of behavior for older adolescents. ^ Methods. Of the 283 students who participated in the fall 2011 YL program, 38 students participated in at least ten of the 12 weeks and were eligible for this study. This study used a single group-only pretest/posttest evaluation design. Participants were 68% female, 58% white/Caucasian, 74% 10th or 11th graders, and 89% mostly A and/or B students. The primary behavioral outcomes for this analysis were participation in 60-minutes of physical activity per day, 20-minutes of vigorous- or moderate- intensity physical activity (MVPA) participation per day, television and computer time, fruit and vegetable (FV) intake, sugar-sweetened beverage intake, and consumption of breakfast, home-cooked meals, and fast food. Other outcomes included knowledge, beliefs, and attitudes related to healthy eating, physical activity, and advocacy skills. ^ Findings. Among the 38 participants, no significant changes in any variables were observed. However, among those who did not previously meet behavioral goals there was an 89% increase in students who participated in more than 20 minutes of MVPA per day and a 58% increase in students who ate home-cooked meals 5–7 days per week. The majority of participants met program goals related to knowledge, beliefs, and attitudes prior to the start of the program. Participants reported either maintaining or improving to the goal at posttest for all items except FV intake knowledge, taste and affordability of healthy foods, interest in teaching others about being healthy, and ease of finding ways to advocate in the community. ^ Conclusions. The results of this evaluation indicated that promoting healthy behaviors requires different strategies than maintaining healthy behaviors among high school students. In the school setting, programs need to target the promotion and maintenance of health behaviors to engage all students who participate in the program as part of a class or club activity. Tailoring the program using screening and modifying strategies to meet the needs of all students may increase the potential reach of the program. The Transtheoretical Model may provide information on how to develop a tailored program. Additional research on how to utilize the constructs of TTM effectively among high school students needs to be conducted. Further evaluation studies should employ a more expansive evaluation to assess the long-term effectiveness of health advocacy programming.^