Schoeppe S, MJ Duncan, H Badland, M Oliver, C Curtis. (In Press) Associations of children’s independent mobility and active travel with physical activity, sedentary behaviour and weight status: A systematic review. Journal of Science and Medicine in Sport.

Health benefits from children’s independent mobility and active travel beyond school travel are largely unexplored. Objectives: This review synthesised the evidence for associations of independent mobility and active travel to various destinations with physical activity, sedentary behaviour and weight status. Design: Systematic review. Methods: A systematic search in six databases (PubMed, Scopus, CINAHL, SportDiscus, PsychInfo, TRIS) for papers published between January 1990 and March 2012 was undertaken, focussing on children aged 3-18 years. Study inclusion and methodological quality were independently assessed by two reviewers. Results: 52 studies were included. Most studies focussed solely on active travel to and/or from school, and showed significant positive associations with physical activity. The same relationship was detected for active travel to leisure-related places and independent mobility with physical activity. An inverse relationship between active travel to school and weight status was evident but findings were inconsistent. Few studies examined correlations between active travel to school and self-reported screen-time or objectively measured sedentary behaviour, and findings were unclear. Conclusions: Studies on independent mobility suggested that children who have the freedom to play outdoors and travel actively without adult supervision accumulate more physical activity than those who do not. Further investigation of children’s active travel to leisure-related destinations, measurement of diverse sedentary behaviour beyond simply screen-based activities, and consistent thresholds for objectively measured sedentary behaviour in children will clarify the inconsistent evidence base on associations of active travel with sedentary behaviour and weight status.

C Davies, C Vandelanotte, M Duncan, J van Uffelen. (2012) Associations of Physical Activity, Screen time on Health related quality of life in adults. Preventive Medicine, 55, 46-49.

BACKGROUND: Associations between the combined effect of physical activity and screen based activities on health related quality of life remain largely undetermined. METHODS: During 2008-2010, cross-sectional data for self-reported health related quality of life, physical activity, and screen-time were collected for 3796 Australian adults. Logistic regression was conducted to examine associations for six combinations of physical activity (none, insufficient, and sufficient), and screen-time (low and high) on health related quality of life.RESULTS: In comparison to the reference category (sufficient physical activity and low screen-time) men and women who reported no physical activity and either high (OR=4.52, 95% CI 2.82-7.25) or low (OR=2.29, 95% CI 1.37-3.80) screen-time, were significantly more likely to report over 14 unhealthy days. Men reporting either; no physical activity and high (OR=3.15, 95% CI 1.92-5.15), or low (OR=2.17, 95% CI 1.30-3.63) screen-time; insufficient physical activity and high (OR=1.68, 95% CI 1.08-2.60), or low (OR=1.79, 95% CI 1.14-2.82) screen-time were more likely to rate their health as poor or fair. In women this was significant for those who reported no physical activity and high screen-time (OR=1.98, 95% CI, 1.19-3.31).CONCLUSIONS: Results suggest that the combination of no physical activity and high screen-time demonstrated the greatest negative impact on health related quality of life.

Parekh P, C Vandelanotte, D King, F Boyle (2012). Improving diet, physical activity and other lifestyle behaviours using computer-tailored advice in general practice: a randomised controlled trial. International Journal of Behavioral Nutrition and Physical Activity, 9:108.

BACKGROUND: The adoption and maintenance of healthy behaviours is essential in the primary prevention of chronic non-communicable diseases. This study evaluated the effectiveness of a minimal intervention on multiple lifestyle factors such as diet, physical activity, smoking and alcohol, delivered through general practice, using computer-tailored feedback.METHODS: Adult patients visiting 21 general practitioners in Brisbane, Australia, were surveyed about ten health behaviours that are risk factors for chronic, non-communicable diseases. Those who completed the self-administered baseline questionnaire entered a randomised controlled trial, with the intervention group receiving computer-tailored printed advice, targeting those health behaviours for which respondents were not meeting current recommendations. The primary outcome was change in summary lifestyle score (Prudence Score) and individual health behaviours at three months. A repeated measures analysis compared change in these outcomes in intervention and control groups after adjusting for age and education.RESULTS: 2306 patients were randomised into the trial. 1711 (76%) returned the follow-up questionnaire at 3 months. The Prudence Score (10 items) in the intervention group at baseline was 5.88, improving to 6.25 at 3 months (improvement = 0.37), compared with 5.84 to 5.96 (improvement = 0.12) in the control group (F = 13.3, p = 0.01). The intervention group showed improvement in meeting recommendations for all individual health behaviours compared with the control group. However, these differences were significant only for fish intake (OR 1.37, 95% CI 1.11-1.68), salt intake (OR 1.19, 95% CI 1.05-1.38), and type of spread used (OR 1.28, 95% CI 1.06-1.51). CONCLUSION: A minimal intervention using computer-tailored feedback to address multiple lifestyle behaviours can facilitate change and improve unhealthy behaviours. Although individual behaviour changes were modest, when implemented on a large scale through general practice, this intervention appears to be an effective and practical tool for population-wide primary prevention.

Caperchione C, Vandelanotte C, Kolt G, Duncan, Ellison M, George E, Mummery K (2012). What a man wants: Understanding the challenges and motivations to physical activity participation and healthy eating in middle-aged Australian Men. American Journal of Men’s Health, 6(6), 453-461.

Little attention has been paid to the physical activity (PA) and nutrition behaviors of middle-aged men; thus, the aim of this study was to gather information and gain insight into the PA and nutrition behaviors of these men. Six focus group sessions were undertaken with middle-aged men (N = 30) from regional Australia to explore the challenges and motivations to PA participation and healthy eating. Men had a good understanding of PA and nutrition; however, this was sometimes confounded by inconsistent media messages. Work commitments and family responsibilities were barriers to PA, while poor cooking skills and abilities were barriers to healthy eating. Disease prevention, weight management, and being a good role model were motivators for PA and healthy eating. By understanding what a man wants, PA and nutrition interventions can be designed and delivered to meet the needs of this hard-to-reach population.

De Cocker K, Spittaels H, Cardon G, De Bourdeaudhuij I and Vandelanotte C (2012). Online pedometer-based and computer-tailored physical activity advice: development, dissemination through general practice, acceptability and preliminary efficacy. Journal of Medical Internet Research, 14(2):e53.

BACKGROUND: Computer tailoring is a relatively innovative and promising physical activity intervention approach. However, few computer-tailored physical activity interventions in adults have provided feedback based on pedometer use. OBJECTIVES: To (1) describe the development of a Web-based, pedometer-based, computer-tailored step advice intervention, (2) report on the dissemination of this tool through general practice, (3) report on its perceived acceptability, and (4) evaluate the preliminary efficacy of this tool in comparison with a standard intervention. METHODS: We recruited 92 participants through general practitioners and randomly assigned them to a standard condition (receiving a pedometer-only intervention, n = 47) and a tailored condition (receiving a pedometer plus newly developed, automated, computer-tailored step advice intervention, n = 45). Step counts, self-reported data obtained via telephone interview on physical activity, time spent sitting, and body mass index were assessed at baseline and postintervention. The present sample was mostly female (54/92, 59%), highly educated (59/92, 64%), employed (65/92, 71%), and in good health (62/92, 67%). RESULTS: Recruitment through general practitioners was poor (n = 107, initial response rate 107/1737, 6.2%); however, the majority of participants (50/69, 73%) believed it is useful that general practitioners help patients find ways to increase physical activity. In the tailored condition, 30/43 (70%) participants requested the computer-tailored step advice and the majority found it understandable (21/21, 100%), credible (17/18, 94%), relevant (15/18, 83%), not too long (13/18, 72%), instructive (13/18, 72%), and encouraging to increase steps (16/24, 67%). Daily step counts increased from baseline (mean 9237, SD 3749 steps/day) to postintervention (mean 11,876, SD 4574 steps/day) in the total sample (change of 2639, 95% confidence interval 105-5172; F(1 )= 5.0, P = .04). No interaction or other time effects were found. CONCLUSIONS: The majority of participants in the tailored condition accepted the step advice and indicated it was useful. However, in this selected sample of adults, the tailored condition did not show superior effects compared with the standard condition.