Adrian Bauman and Josephine Chau
This paper reviewed a) mass media campaigns and b) ‘new media’ interventions to promote physical activity. They are different kinds of interventions, with campaigns being mass-reach communications efforts to increase population awareness of physical activity. ‘New media’ interventions assess the impact of web-based, internet, other ’new media’ and e-mail-delivered interventions to increase physical activity.
Previous reviews of mass media campaigns and ‘new media’ interventions were assessed, and more recent peer-reviewed publications identified using routine electronic databases. For each area, a framework for interventions was described, and evidence for the effectiveness of these interventions, the main outcomes of interest, and methodological strengths and weaknesses were identified.
For mass media campaigns, key recommendations were to use consistent and well-branded messages, and for campaigns to be integrated across local, State and national levels, with sufficient resources to purchase sufficient media. Mass media campaigns should be subject to rigorous formative, process and impact evaluation. For ‘new media’ interventions, there is clear evidence of effectiveness, but efforts should be made to increase the reach and generalizability of these interventions. They should be provided as a low cost component of integrated communitywide physical activity programs.
Niamh M. Murphy and Adrian Bauman
Large-scale, one-off sporting or physical activity (PA) events are often thought to impact population PA levels. This article reviews the evidence and explores the nature of the effect.
A search of the published and grey literature was conducted to July 2005 using relevant databases, web sources, and personal contacts. Impacts are described at the individual, societal and community, and environmental levels.
Few quality evaluations have been conducted. While mass sporting events appear to influence PA-related infrastructure, there is scant evidence of impact on individual participation at the population level. There is some evidence that events promoting active transport can positively affect PA.
The public health potential of major sporting and PA events is often cited, but evidence for public health benefit is lacking. An evaluation framework is proposed.
Michael L. Booth, Adrian Bauman, and Neville Owen
In a cross-sectional survey, older Australians (N = 402) were asked to report their physical activity habits and the 3 main barriers to more physical activity. Active and inactive men and women differed only in how many reported being sufficiently active or that their health was too poor to be more active. Six barriers were reported by more than 10% of inactive men and women: “already active enough,” “have an injury or disability,” “poor health,” “too old,” “don't have enough time,” and “I'm not the sporty type.” Insufficient time was identified by significantly fewer respondents as age increased. More respondents 65–70+ years old identified poor health as a barrier than did those 60–64. The proportion who had an injury or disability decreased from 60–64 to 65–69 and increased markedly among those 70+. Programs for older adults should take into account the age of the target group and the limitations imposed by poor health or disability.
Barry Lambe, Niamh Murphy, and Adrian Bauman
There is a paucity of intervention studies assessing active travel to school as a mechanism to increase physical activity. This paper describes the impact of a community-wide intervention on active travel to primary schools in 2 Irish towns.
This was a repeat cross-sectional study of a natural experiment. Self-report questionnaires were completed by 5th and 6th grade students in 3 towns (n = 1038 students in 2 intervention towns; n = 419 students in 1 control town) at baseline and by a new group of students 2 years later at follow-up. The absolute change in the proportion of children walking and cycling to school (difference in differences) was calculated.
There was no overall intervention effect detected for active travel to or from school. This is despite an absolute increase of 14.7% (1.6, 27.9) in the proportion of children that indicated a preference for active travel to school in the town with the most intensive intervention (town 2).
Interventions designed to increase active travel to school hold some promise but should have a high-intensity mix of infrastructural and behavioral measures, be gender-specific, address car dependency and focus on travel home from school initially.
Adrian E. Bauman and Harold W. Kohl III
Binh Nguyen, Adrian Bauman, and Ding Ding
To examine the combined effects of body mass index (BMI), physical activity (PA) and sitting on incident type 2 diabetes mellitus (T2DM) among Australian adults.
A sample of 29,572 adults aged ≥45 years from New South Wales, Australia, completed baseline (2006–2008) and follow-up (2010) questionnaires. Incident T2DM was defined as self-reported, physician-diagnosed diabetes at follow-up. BMI was categorized as normal/overweight/obese. PA was tertiled into low/medium/ high. Sitting was dichotomized as higher/lower sitting (≥ 8 hours/day or < 8 hours/day). Odds ratios (OR) were estimated for developing T2DM using logistics regression for individual and combined risk factors, and data stratified by BMI categories.
During a mean 2.7 (SD: 0.9) years of follow-up, 611 (2.1%) participants developed T2DM. In fully adjusted models, BMI was the only independent risk factor for incident T2DM. In stratified analyses, the association between BMI and T2DM did not differ significantly across sitting or PA categories. Overweight/obese individuals with high PA and lower sitting had higher odds of incident T2DM than normal counterparts with low PA and higher sitting.
High PA/low sitting did not attenuate the risk of T2DM associated with overweight/obesity. Maintaining a healthy weight, by adopting healthy lifestyle behaviors, is critical for T2DM prevention.
Katie M. Heinrich, Jay Maddock, and Adrian Bauman
Despite clear health benefits of physical activity, previous research has been limited in linking knowledge of physical activity recommendations to actual behavior.
Using Expectancy Theory, we examined whether an individual’s health outcome expectancies from physical activity might provide the missing link between knowledge and behavior. With data from a cross-sectional survey, we assessed differences between how much moderate physical activity people thought they needed for health benefits compared with what they thought experts recommended and the relationship of these differences to physical activity behaviors.
Our hypothesis that people with positive health outcome expectancies would report more minutes of physical activity than those with neutral or negative health outcome expectancies was supported for all self-reported physical activity behaviors (P < .001).
It appears that the health outcome expectancy of needing more physical activity than recommended by experts is correlated with achieving more physical activity, regardless of type. Future research should address health outcome expectancies as a way to impact physical activity.
Adrian E. Bauman, Niamh Murphy, and Victor Matsudo
Adrian E. Bauman and Justin A. Richards
Background: Sport New Zealand conducts continuous representative “Active NZ” surveys. Between 2019 and 2020 (n = 13,887), these surveys asked International Physical Activity Questionnaire (IPAQ)—long-form questions, the single-item days (SI-days) per week question, and 1 question on hours per week (single-item hours [SI-hours] per week). This study examines relationships between the established SI-days question and meeting physical activity (PA) guidelines (150 min moderate-to-vigorous physical activity per week from SI-hours question and IPAQ). Methods: Analyses were descriptive, and the best fit between SI-days and the PA thresholds was estimated using area under the receiver operator characteristic curves and Youden index. Results: Using SI-hours, 60.6% achieved 150+ minutes; 85.2% reported the IPAQ-total minimum threshold, and 40.8% met the IPAQ-leisure time PA-only threshold. Receiver operator characteristic analyses showed area under the curve values with IPAQ between 0.63 and 0.76, but the SI-days showed a very good area under the curve of 0.82 (0.81–0.83) with the SI-hours 150-minute threshold. Youden index suggested the best fit was at 3+ days per week for maximizing Sensitivity and Specificity to meet IPAQ or SI-hours-defined PA guidelines. Discussion: The SI-days per week question reflects achieving PA guidelines, and the best fit was with the SI-hours per week question. This provides surveillance-relevant concurrent validity for the SI-days measure, but the cut point for broadly meeting guidelines appears to be at least 3 days per week, not 5 days per week as previously thought.