77 resultados para Physical-activity Questionnaire


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Background: Concerns of a decrease in physical activity levels (PALs) of children and a concurrent increase in childhood obesity exist worldwide. The exact relation between these two parameters however has as yet to be fully defined in children. Objective: This study examined the relation in 47 children, aged 5–10.5 y (mean age 8.4plusminus0.9 y) between habitual physical activity, minutes spent in moderate, vigorous and hard intensity activity and body composition parameters. Design: Total energy expenditure (TEE) was calculated using the doubly labelled water technique and basal metabolic rate (BMR) was predicted from Schofield's equations. PAL was determined by PAL=TEE/BMR. Time spent in moderate, vigorous and hard intensity activity was determined by accelerometry, using the Tritrac-R3D. Body fatness and body mass index (BMI) were used as the two measures of body composition. Results: Body fat and BMI were significantly inversely correlated with PAL (r=-0.43, P=0.002 and r=-0.45, P=0.001). Times spent in vigorous activity and hard activity were significantly correlated to percentage body fat (r=-0.44, P=0.004 and r=-0.39, P=0.014), but not BMI. Children who were in the top tertiles for both vigorous activity and hard activity had significantly lower body fat percentages than those in the middle and lowest tertiles. Moderate intensity activity was not correlated with measures of body composition. Conclusions: As well as showing a significant relation between PAL and body composition, these data intimate that there may be a threshold of intensity of physical activity that is influential on body fatness. In light of world trends showing increasing childhood obesity, this study supports the need to further investigate the importance of physical activity for children.

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Mediated physical activity interventions can reach large numbers of people at low cost. Programs delivered through the mail that target the stage of motivational readiness have been shown to increase activity. Communication technology (websites and e-mail) might provide a means for delivering similar programs. Randomized trial conducted between August and October 2001. Participants included staff at an Australian university (n=655; mean AGE=43, standard deviation, 10 years). Participants were randomized to either an 8-week, stage-targeted print program (Print) or 8-week, stage-targeted website (Web) program. The main outcome was change in self-reported physical activity.

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The Flow State Scale-2 (FSS-2) and Dispositional Flow Scale-2 (DFS-2) are presented as two self-report instruments designed to assess flow experiences in physical activity. Item modifications were made to the original versions of these scales in order to improve the measurement of some of the flow dimensions. Confirmatory factor analyses of an item identification and a cross-validation sample demonstrated a good fit of the new scales. There was support for both a 9-first-order factor model and a higher order model with a global flow factor. The item identification sample yielded mean item loadings on the first-order factor of .78 for the FSS-2 and .77 for the DFS-2. Reliability estimates ranged from .80 to .90 for the FSS-2, and .81 to .90 for the DFS-2. In the cross-validation sample, mean item loadings on the first-order factor were .80 for the FSS-2, and .73 for the DFS-2. Reliability estimates ranged between .80 to .92 for the FSS-2 and .78 to .86 for the DFS-2. The scales are presented as ways of assessing flow experienced within a particular event (FSS-2) or the frequency of flow experiences in chosen physical activity in general (DFS-2).

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Introduction: This paper reviews studies of physical activity interventions in health care settings to determine effects on physical activity and/or fitness and characteristics of successful interventions. Methods: Studies testing interventions to promote physical activity in health care settings for primary prevention (patients without disease) and secondary prevention (patients with cardiovascular disease [CVD]) were identified by computerized search methods and reference lists of reviews and articles. Inclusion criteria included assignment to intervention and control groups, physical activity or cardiorespiratory fitness outcome measures, and, for the secondary prevention studies, measurement 12 or more months after randomization. The number of studies with statistically significant effects was determined overall as well as for studies testing interventions with various characteristics. Results: Twelve studies of primary prevention were identified, seven of which were randomized. Three of four randomized studies with short-term measurement (4 weeks to 3 months after randomization), and two of five randomized studies with long-term measurement (6 months after randomization) achieved significant effects on physical activity. Twenty-four randomized studies of CVD secondary prevention were identified; 13 achieved significant effects on activity and/or fitness at twelve or more months. Studies with measurement at two time points showed decaying effects over time, particularly if the intervention were discontinued. Successful interventions contained multiple contacts, behavioral approaches, supervised exercise, provision of equipment, and/or continuing intervention. Many studies had methodologic problems such as low follow-up rates. Conclusion: Interventions in health care settings can increase physical activity for both primary and secondary prevention. Long-term effects are more likely with continuing intervention and multiple intervention components such as supervised exercise, provision of equipment, and behavioral approaches. Recommendations for additional research are given.

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Background, Regular physical activity in older adults can facilitate healthy aging, improve functional capacity, and prevent disease. However, factors associated with physical inactivity in older populations are poorly understood. This study attempts to identify social-cognitive and perceived environmental influences associated with physical activity participation in older populations. Methods. In a randomly selected sample of 449 Australian adults age 60 and older, we assessed self-reported physical activity and a range of social-cognitive and perceived environmental factors. Respondents were classified as sufficiently active and inactive based on energy expenditure estimates (kcal/week) derived from self-reported physical activity. Two logistic regression models, with and without self-efficacy included, were conducted to identify modifiable independent predictors of physical activity. Results. Significantly more males than females were physically active. Physical activity participation was related to age with a greater proportion of those age 65-69 being active than those age 60-64 or 70 or older. High self-efficacy, regular participation of friends and family, finding footpaths safe for walking, and access to local facilities were significantly associated with being active. Conclusion. Identifying predictors of physical activity in older populations, particularly social support, facility access, and neighbourhood safety, can inform the development of policy and intervention strategies to promote the health of older people. (C) 2000 American Health Foundation and Academic Press.

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Background. International research indicates that blue-collar employees typically exhibit lower rates of leisure-time physical activity. While lack of time and work demands are commonly reported barriers to activity, the extent to which time-at-work mediates the relationship between occupation and leisure-time physical activity is unclear. This study investigated the association between occupation, time spent in paid employment, and participation in leisure-time physical activity. Methods. This was a secondary analysis of cross-sectional data from the 1995 Australian Health Survey, focusing on employed persons ages 18-64 years (n = 24,454), Occupation was coded as per the Australian Standard Classification of Occupations and collapsed into three categories (professional, white-collar, blue-collar). Hours worked was categorized into eight levels, ranging from 1-14 to more than 50 h per week. Participation in leisure-time physical activity was categorized as either insufficient or sufficient for health, consistent with recommended levels of energy expenditure (1600 METS-min/fortnight). The relationship between occupation, hours worked, and leisure-time physical activity was examined using logistic regression. Analyses were conducted separately for male and female, and the results are presented as a series of models that successively adjust for a range of potential covariates: age, living arrangement, smoking status, body mass index, and self-reported health. Results. Individuals in blue-collar occupations were approximately 50% more likely to be classified as insufficiently active. This occupational variability in leisure-time physical activity was not explained by hours worked. There was a suggested relationship between hours worked and leisure-time physical activity; however, this differed between men and women, and was difficult to interpret. Conclusions. Occupational variability in leisure-time physical activity cannot be explained by hours worked. Therefore, reports that work constitutes a barrier to participation should be explored further. Identification of the factors contributing to occupational variability in leisure-time physical activity will add to our understanding of why population subgroups differ in their health risk profiles, and assist in the development of health promotion strategies to reduce rates of sedentariness and health inequalities. (C) 2000 American Health Foundation and Academic Press.

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Purpose: To examine age-related differences in the physical activity behaviors of young adults. Methods: We examined rates of participation in vigorous- and moderate-intensity leisure-time activity and walking, as well as an index of physical activity sufficient for health benefits in three Australian cross-sectional samples, for the age ranges of 18-19, 20-24, and 25-29 yr. Data were collected in 1991, 1996, and 1997/8. Results: There was at least a 15% difference in vigorous-intensity leisure-time physical activity from the 18-19 yr to the 25-29 yr age groups, and at least a 10% difference in moderate-intensity leisure-time physical activity. For the index of sufficient activity there was a difference between 9 and 21% across age groups. Differences in rates of walking were less than 8%. For all age groups, males had higher rates of participation for vigorous and moderate-intensity activity than did females, bur females had much higher rates of participation in walking than males. Age-associated differences in activity levels were more apparent for males. Conclusions: Promoting walking and various forms of moderate-intensity physical activities to young adult males, and encouraging young adult females to adopt other forms of moderate-intensity activity to complement walking may help to ameliorate decreases in physical activity over the adult lifespan.

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Physical inactivity continues to be a significant public health issue for middle-aged and older adults. This review focuses on physical activity interventions targeting older adults in health care settings. The literature in this area is limited and the results to date disappointing. Much remains to be done to develop effective interventions targeting older adults, especially those from underserved groups. Attention also needs to be paid to maintenance of initial treatment gains and to linking primary-care-based physical activity interventions to community-based resources. Recognition in the social and behavioral sciences of the importance of social-environmental influences on health and health behaviors mandates both a multidisciplinary and a multilevel intervention approach to the problem of physical inactivity.

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Background: The physical environment plays an important role in influencing participation in physical activity, although which factors of the physical environment have the greatest effect on patterns of activity remain to be determined. We describe the development of a comprehensive instrument to measure the physical environmental factors that may influence walking and cycling in local neighborhoods and report on its reliability. Methods: Following consultation with experts from a variety of fields and a literature search, we developed a Systematic Pedestrian and Cycling Environmental Scan (SPACES) instrument and used it to collect data over a total of 1987 kilometers of roads in metropolitan Perth, Western Australia. The audit instrument is available from the first author on request. Additional environmental information was collected using desktop methods and geographic information systems (GIS) technology. We assessed inter- and intra-rater reliability of the instrument among the 16 observers who collected the data. Results: The observers reported that the audit instrument was easy to use. Both inter- and intra-rater reliability of the environmental scan instrument were generally high. Conclusions: Our instrument provides a reliable, practical, and easy to-use method for collecting detailed street-level data on physical environmental factors that are potential influences on walking in local neighborhoods.