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Abstract Background Increased understanding of why and how physical activity impacts on health outcomes is needed to increase the effectiveness of physical activity interventions. A recent randomized controlled trial of an active video game (PlayStation EyeToy™) intervention showed a statistically significant treatment effect on the primary outcome, change from baseline in body mass index (BMI), which favored the intervention group at 24 weeks. In this short paper we evaluate the mediating effects of the secondary outcomes. Objective To identify mediators of the effect of an active video games intervention on body composition. Methods Data from a two-arm parallel randomized controlled trial of an active video game intervention (n = 322) were analyzed. The primary outcome was change from baseline in BMI. A priori secondary outcomes were considered as potential mediators of the intervention on BMI, including aerobic fitness (VO2Max), time spent in moderate-to-vigorous physical activity (MVPA), and food snacking at 24 weeks. Results Only aerobic fitness at 24 weeks met the conditions for mediation, and was a significant mediator of BMI. Conclusion Playing active video games can have a positive effect on body composition in overweight or obese children and this effect is most likely mediated through improved aerobic fitness. Future trials should examine other potential mediators related to this type of intervention. Trial registration Australian New Zealand Clinical Trials Registry Website: http://www.anzctr.org.au Study ID number: ACTRN12607000632493

Abstract Background Sedentary behaviour has been linked with adverse health outcomes in young people; however, the nature and context of being sedentary is poorly understood. Accurate quantification and description of sedentary behaviour using population-level data is required. The aim of this research was to describe sedentary behaviour among New Zealand (NZ) youth and examine whether sedentary behaviour differs by Body Mass Index (BMI) status in this population. Methods A national representative cross-sectional survey of young people aged 5-24 years (n = 2,503) was conducted in 2008-2009. Data from this survey, which included subjectively (recall diary; n = 1,309) and objectively (accelerometry; n = 960) measured sedentary behaviour for participants aged 10-18 years were analysed using survey weighted methods. Results Participants self-reported spending on average 521 minutes per day (standard error [SE] 5.29) in total sedentary behaviour, 181 minutes per day (SE 3.91) in screen-based sedentary activities (e.g., television and video games), and 340 minutes per day (SE 5.22) in other non-screen sedentary behaviours (e.g., school, passive transport and self-care). Accelerometer-measured total sedentary behaviour was on average 420 minutes per day (SE 4.26), or 53% (SE 0.42%) of monitored time. There were no statistically significant differences in time spent in sedentary behaviour among overweight, obese and healthy/underweight young people. Conclusions Both subjective and objective methods indicate that NZ youth spend much of their waking time being sedentary. No relationships were found between sedentary behaviour and BMI status. These findings extend previous research by describing engagement in specific sedentary activities, as well as quantifying the behaviour using an objective method. Differences in what aspects of sedentary behaviour the two methods are capturing are discussed. This research highlights the potential for future interventions to target specific sedentary behaviours or demographic groups.

The aim of the study was to validate the self-report Multimedia Activity Recall for Children and Adolescents (MARCA) against accelerometry for the assessment of physical activity in New Zealand children. Participants (n = 716, 10–18 years) recalled 3–4 days of activity using the MARCA and underwent a partially overlapping 7-day accelerometry protocol during a national survey. Spearman correlation coefficients (ρ) assessed the association between accelerometer-derived counts per minute and MARCA-derived physical activity level and time in locomotion. Both data sources estimated time spent in light and moderate–vigorous physical activity. Association and agreement between methods for light physical activity and moderate–vigorous physical activity was assessed using correlations and Bland–Altman plots respectively, and pairedt-tests conducted. Accelerometer-derived activity counts were moderately correlated with both MARCA-derived physical activity level and locomotion (ρ = 0.38,P < 0.0001). The correlation between methods was –0.14 for light physical activity and 0.28 for moderate–vigorous physical activity (P < 0.0001). The MARCA overestimated moderate–vigorous physical activity compared with accelerometry (120 min,P < 0.0001), which increased as moderate–vigorous physical activity time increased. Some sex and ethnicity (Māori [indigenous] versus non-Māori) differences were observed. Overall, the MARCA indicated moderate validity for assessment of physical activity level, locomotion and moderate–vigorous physical activity and poor validity for assessment of light physical activity. This was comparable to other self-report tools. The MARCA has utility for future large-scale research. [ABSTRACT FROM PUBLISHER]

The purpose of the present study was to integrate and test the utility of two models for explaining and predicting physical activity intentions and behavior--the theory of planned behavior (TPB) and self-efficacy theory. A total of 645 (male = 347) New Zealand children between the ages of 11 and 13 completed demographic information and measures corresponding to the integrated model. One week later, children completed a self-report questionnaire in which they were asked about their physical activity behaviors in the previous week. Two hierarchical regression analyses were conducted to predict goal intention and physical activity behavior, respectively. Results showed that the constructs of the TPB (attitude, subjective norm, perceived behavioral control [PBC]) significantly predicted goal intention (p < .001) in Step 1, explaining 57.3% of the variance. Task and barrier efficacy also contributed significantly to the prediction (R² = .01, p < .001) when entered in Step 2. When all variables were considered together--attitude, β = .15, p < .001; subjective norm, β = .29, p < .001; PBC, β = .37, p < .001; task efficacy, β = .12, p < .01; and barrier efficacy, β = .02, p > .05--only barrier efficacy did not predict goal intention. With regard to physical activity behavior, goal intention was entered in Step 1 and was a significant predictor (p < .001), accounting for 13.5% of the variance. Step 2 revealed that PBC contributed significantly to the prediction of behavior (R² = .01, p < .01), and the inclusion of task and barrier efficacy in Step 3 also resulted in a significant increase in the amount of explained variance (R² = .16, p < .001). When all variables were considered together--goal intention, β = .12, p < .05; PBC, β = .03, p > .05; task efficacy, β = .16, p < .01; and barrier efficacy, β = .35, p < .001--task and barrier efficacy emerged as the two strongest predictors of physical activity behavior. Future research should examine the utility of task and barrier efficacy in predicting objectively measured physical activity behaviors in children. [ABSTRACT FROM AUTHOR]