Effect of Australia's walk to work day campaign on adults' active commuting and physical activity behavior

Purpose. To determine whether Australia's Walk to Work Day media campaign resulted in behavioural change among targeted groups. Methods. Pre- and postcampaign telephone surveys of a cohort of adults aged 18 to 65 years (n = 1100, 55% response rate) were randomly sampled from Australian major melropolitan areas. Tests for dependent samples were applied (McNemax chi(2) or paired t-test). Results. Among participants who did not usually actively commute to work was a significant decrease in car only use an increase in walking combined with public transport. Among those who were employed was a significant increase in total time walking (+16 min/wk; t [780] = 2.04, p < .05) and in other moderate physical activity (+120 min/wk; t [1087] = 4.76, p < .005), resulting in a significant decrease in the proportion who were inactive (chi(2) (1) = 6.1, p < .05). Conclusion. Although nonexperimental, the Walk to Work Day initiative elicited short-term changes in targeted behaviors among target groups. Reinforcement by integrating worksite health promotion strategies may be required for sustained effects.

Can physical activity prevent diabetes?

Most physical activity researchers are familiar with the epidemiological evidence that suggests that physical activity has an important role in the primary prevention of Type 2 diabetes. There is compelling evidence from large well-conducted prospective cohort trials to show that the risk of diabetes is reduced by up to 50% in people who are habitually active. There is also evidence from large randomised controlled trails to support the view that physical activity, as part of a lifestyle change program, can prevent Type 2 diabetes and the onset and progression of metabolic syndrome. This is the strongest evidence in support of a beneficial role for physical activity in the primary prevention of any health problem; much stronger than that for the links between smoking and lung cancer. This presentation will critically evaluate this evidence, and explore the notion that, while physical activity may postpone the development of Type 2 diabetes, it may not actually prevent the onset of problem at the population level. As the (self-reported) prevalence of Type 2 diabetes has more than doubled in the last 20 years, it is critical that we explore effective strategies for ensuring that we can ‘activate’ Australians sufficiently to prevent, rather than simply postpone, the development of this significant health problem.

Correcting physical activity energy expenditure for body size in young children

Being able to compare the energy cost of physical activity across and between populations is important. However, energy expenditure is related to body size, so it is necessary to appropriately adjust for differences in body size when comparisons are made. This study examined the relationship between the daily energy cost of activity and body weight in 47 children aged 6-10 years. Log-log regression showed weight(1.0) to be an inappropriate adjustment for activity energy expenditure in children, with a more valid adjustment being weight(0.3). Clearly, both weight dependent and non-weight dependent activities are part of everyday living in children. This balance influences how energy expenditure is correctly adjusted for body size. Investigators interpreting data of energy expenditure in children from children of different body sizes need to take this into consideration.

Comparison of surveys used to measure physical activity

Objective: To compare the level of agreement in results obtained from four physical activity (PA) measurement instruments that are in use in Australia and around the world. Methods: 1,280 randomly selected participants answered two sets of PA questions by telephone. 428 answered the Active Australia (AA) and National Health Surveys, 427 answered the AA and CDC Behavioural Risk Factor Surveillance System surveys (BRFSS), and 425 answered the AA survey and the short International Physical Activity Questionnaire (IPAQ). Results: Among the three pairs of survey items, the difference in mean total PA time was lowest when the AA and NHS items were asked (difference=24) (SE:17) minutes, compared with 144 (SE:21) mins for AA/BRFSS and 406 (SE:27) mins for AA/IPAQ). Correspondingly, prevalence estimates for 'sufficiently active' were similar for AA and NHS (56% and 55% respectively), but about 10% higher when BRFSS data were used, and about 26% higher when the IPAQ items were used, compared with estimates from the AA survey. Conclusions: The findings clearly demonstrate that there are large differences in reported PA times and hence in prevalence estimates of 'sufficient activity' from these four measures. Implications: It is important to consistently use the same survey for population monitoring purposes. As the AA survey has now been used three times in national surveys, its continued use for population surveys is recommended so that trend data ever a longer period of time can be established.

Assessing flow in physical activity: The Flow State Scale-2 and Dispositional Flow Scale-2

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).

A six-year longitudinal study of the relationship of physical activity to bone mineral accrual in growing children: The University of Saskatchewan bone mineral accrual study

To investigate the influence of physical activity on bone mineral accrual during the adolescent years, we analyzed 6 years of data from 53 girls and 60 boys. Physical activity, dietary intakes, and anthropometry were measured every 6 months and dual-energy X-ray absorptiometry scans of the total body (TB), lumbar spine (LS), and proximal femur (Hologic 2000, array mode) were collected annually. Distance and velocity curves for height and bone mineral content (BMC) were fitted for each child at several skeletal sites using a cubic spline procedure, from which ages at peak height velocity (PHV) and peak BMC velocity (PBMCV) were identified. A mean age- and gender-specific standardized activity (Z) score was calculated for each subject based on multiple yearly activity assessments collected up until age of PHV. This score was used to identify active (top quartile), average (middle 2 quartiles), or inactive (bottom quartile) groups. Two-way analysis of covariance, with height and weight at PHV controlled for, demonstrated significant physical activity and gender main effects (but no interaction) for PBMCV, for BMC accrued for 2 years around peak velocity, and for BMC at 1 year post-PBMCV for the TB and femoral neck and for physical activity but not gender at the LS (all p < 0.05). Controlling for maturational and size differences between groups, we noted a 9% and 17% greater TB BMC for active boys and girls, respectively, over their inactive peers 1 year after the age of PBMCV. We also estimated that, on average, 26% of adult TB bone mineral was accrued during the 2 years around PBMCV.

Developing a reliable audit instrument to measure the physical environment for physical activity

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.

Participation in recreational physical activity: Why do socioeconomic groups differ?

This qualitative study explored how influences on recreational physical activity (RPA) were patterned by socioeconomic position. Face-to-face interviews were conducted with 10 males and 10 females in three socioeconomic groups (N = 60). Influences salient across all groups included previous opportunities, physical health, social assistance, safety, environmental aesthetics and urban design, physical and health benefits, and barriers of self-consciousness, low skill, and weather/time of year. Influences more salient to the high socioeconomic group included social benefits, achieving a balanced lifestyle, and the barrier of an unpredictable lifestyle. Influences more salient to the high and mid socioeconomic groups included efficacy, perceived need, activity demands, affiliation, emotional benefits, and the barrier of competing demands. Influences more salient to the low socioeconomic group included poor health and barriers of inconvenient access and low personal functioning. Data suggest that efforts to increase RPA in the population should include both general and socioeconomically targeted strategies.