Deakin University

About: Deakin University is a education organization based out in Burwood, Victoria, Australia. It is known for research contribution in the topics: Population & Context (language use). The organization has 12118 authors who have published 46470 publications receiving 1188841 citations. The organization is also known as: Deakin.

Perceived neighborhood environmental attributes associated with walking and cycling for transport among adult residents of 17 cities in 12 countries: the IPEN study

Abstract: IntroductionPrevalence of walking and cycling for transport is low and varies greatly across countries. Few studies have examined neighborhood perceptions related to walking and cycling for transpo...

The Sources of Stress Experienced by Professional Australian Footballers

Abstract: Previous studies looking at the sources of stress in sport have generally overlooked the unique experiences of professional athletes participating in team sports. This paper describes the results of a qualitative study aimed at identifying the sources of stress experienced by a cross-section of professional Australian footballers. Players from two Australian Football League clubs took part in the study involving in-depth, one-to-one interviews and focus group discussions. The results revealed that players identified sources of stress that went beyond those associated with the competitive event (such as poor performances) and included a lack of feedback, difficulty balancing football and study commitments, and job insecurity. The influence of both competition and non-competition sources of stress parallels previous research involving non-professional athletes and indicates that the entire sporting experience needs to be taken into account when developing stress management strategies.

Smart-Phone Obesity Prevention Trial for Adolescent Boys in Low-Income Communities: The ATLAS RCT

Abstract: OBJECTIVE: The goal of this study was to evaluate the impact of the Active Teen Leaders Avoiding Screen-time (ATLAS) intervention for adolescent boys, an obesity prevention intervention using smartphone technology. METHODS: ATLAS was a cluster randomized controlled trial conducted in 14 secondary schools in low-income communities in New South Wales, Australia. Participants were 361 adolescent boys (aged 12–14 years) considered at risk of obesity. The 20-week intervention was guided by self-determination theory and social cognitive theory and involved: teacher professional development, provision of fitness equipment to schools, face-to-face physical activity sessions, lunchtime student mentoring sessions, researcher-led seminars, a smartphone application and Web site, and parental strategies for reducing screen-time. Outcome measures included BMI and waist circumference, percent body fat, physical activity (accelerometers), screen-time, sugar-sweetened beverage intake, muscular fitness, and resistance training skill competency. RESULTS: Overall, there were no significant intervention effects for BMI, waist circumference, percent body fat, or physical activity. Significant intervention effects were found for screen-time (mean ± SE: –30 ± 10.08 min/d; P = .03), sugar-sweetened beverage consumption (mean: –0.6 ± 0.26 glass/d; P = .01), muscular fitness (mean: 0.9 ± 0.49 repetition; P = .04), and resistance training skills (mean: 5.7 ± 0.67 units; P CONCLUSIONS: This school-based intervention targeting low-income adolescent boys did not result in significant effects on body composition, perhaps due to an insufficient activity dose. However, the intervention was successful in improving muscular fitness, movement skills, and key weight-related behaviors.

A New Fuzzy-Based Combined Prediction Interval for Wind Power Forecasting

Abstract: This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.