Jo Salmon

University of Tasmania, Hobart Town, Tasmania, Australia

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Publications (165)477.44 Total impact

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    ABSTRACT: The time that children and adults spend sedentary-put simply, doing too much sitting as distinct from doing too little physical activity-has recently been proposed as a population-wide, ubiquitous influence on health outcomes. It has been argued that sedentary time is likely to be additional to the risks associated with insufficient moderate-to-vigorous physical activity. New evidence identifies relationships of too much sitting with overweight and obesity, type 2 diabetes, cardiovascular disease, some cancers and other adverse health outcomes. There is a need for a broader base of evidence on the likely health benefits of changing the relevant sedentary behaviours, particularly gathering evidence on underlying mechanisms and dose-response relationships. However, as remains the case for physical activity, there is a research agenda to be pursued in order to identify the potentially modifiable environmental and social determinants of sedentary behaviour. Such evidence is required so as to understand what might need to be changed in order to influence sedentary behaviours and to work towards population-wide impacts on prolonged sitting time. In this context, the research agenda needs to focus particularly on what can inform broad, evidence-based environmental and policy initiatives. We consider what has been learned from research on relationships of environmental and social attributes and physical activity; provide an overview of recent-emerging evidence on relationships of environmental attributes with sedentary behaviour; argue for the importance of conducting international comparative studies and addressing life-stage issues and socioeconomic inequalities and we propose a conceptual model within which this research agenda may be addressed.
    British journal of sports medicine 02/2014; 48(3):174-7. · 3.67 Impact Factor
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    ABSTRACT: AIMS/HYPOTHESIS While detrimental associations of sitting time and health indicators have been observed in young adults, evidence on pathophysiological mechanisms is lacking. Therefore, this study tested the hypothesis that the acute cardiometabolic effects of prolonged sitting can be compensated by hourly interruptions to sitting in healthy young adults. Additionally, leg muscle activation during sitting and moderate-intensity physical activity interruptions was assessed. METHODS 11 apparently healthy adults (18-24years; 5 male/6 female) participated in this randomized, crossover study, involving 2 experimental conditions: 1) 8-hours prolonged sitting; and 2) 8-hours of sitting interrupted with hourly 8-min moderate-intensity cycling exercise bouts. In both conditions, participants consumed two standardized high-fat mixed meals, after 1 and 5hrs. Capillary blood samples were collected hourly during each 8-hour experimental condition. Muscle activity was measured using electromyography. RESULTS Muscle activity during cycling was 7-8 times higher compared to rest. Postprandial levels of C-peptide were significantly lower (B=-0.19; CI=[-0.55; 0.09]; p=0.017) during interrupted sitting compared with prolonged sitting. Postprandial levels of other cardiometabolic biomarkers (e.g. glucose, triglycerides, cholesterol) were not significantly different between conditions. CONCLUSIONS/INTERPRETATION Hourly physical activity interruptions in sitting time, requiring a muscle activity of 7-8 times the resting value, led to an attenuation of postprandial C-peptide levels, but not for other cardiometabolic biomarkers, compared with prolonged sitting in healthy young adults. Whether this acute effect transfers to chronic effects over time is unknown.
    Journal of Applied Physiology 10/2013; · 3.48 Impact Factor
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    ABSTRACT: This study aimed to investigate whether parents' perceptions of the neighborhood environment moderate associations between the family environment and children's moderate- to vigorous-intensity physical activity (MVPA) outside of school hours. In total, 929 parents of 10-12 year-old children completed a questionnaire concerning the family environment, MVPA levels, and the neighborhood environment. Children wore an Actigraph (AM7164-2.2C) accelerometer. Compared with neighborhood environment factors, the family environment was more frequently associated with children's MVPA. Parental MVPA was positively associated with children's MVPA, but only among children whose parents reported a high presence of sporting venues. Having more restrictive physical activity rules was negatively associated with children's weekday MVPA in neighborhoods with high perceived stranger danger.
    Health & Place 10/2013; 24C:203-209. · 2.42 Impact Factor
  • Health promotion journal of Australia: official journal of Australian Association of Health Promotion Professionals 10/2013; 24(2):155. · 0.59 Impact Factor
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    ABSTRACT: The aim of this study was to identify subgroups of retirement age older adults with respect to their lifestyle patterns of eating, drinking, smoking, physical activity and TV viewing behaviors, and to examine the association between these patterns and socio-demographic covariates. The sample consisted of 3133 older adults aged 55-65 years from the Wellbeing, Eating and Exercise for a Long Life (WELL) study, 2010. This study used latent class analysis (stratified by sex), with a set of lifestyle indicators and including socio-demographic covariates. Statistical analyses were performed by generalized linear latent and mixed models in Stata. Two classes of lifestyle patterns were identified: Healthy (53% men and 72% women) and less healthy lifestyles. Physical activity, TV-viewing time, and fruit intake were good indicators distinguishing the "Healthier" class, whereas consumption of vegetables, alcohol (men) and fast food (women) could not clearly discriminate older adults in the two classes. Class membership was associated with education, body mass index, and self-rated health. This study contributes to the literature on lifestyle behaviors among older adults, and provides evidence that there are meaningful sex differences in lifestyle behaviors between subgroups of older adults. From a policy perspective, understanding indicators or "markers" of healthy and less healthy lifestyle patterns is important for identifying target groups for interventions.
    Maturitas 09/2013; · 2.84 Impact Factor
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    ABSTRACT: This aim of this study was to examine whether frequency of park visitation was associated with time spent in various domains of physical activity among adults living in a disadvantaged neighbourhood of Victoria, Australia. In 2009, participants (n=319) self-reported park visitation and physical activity including; walking and cycling for transport, leisure-time walking, leisure-time moderate- to vigorous-intensity physical activity, and total physical activity. The mean number of park visits per week was 3.3 (SD=3.8). Park visitation was associated with greater odds of engaging in high (as compared to low) amounts of transportation physical activity, leisure-time walking, leisure-time moderate- to vigorous-intensity physical activity (MVPA) and total physical activity. Each additional park visit per week was associated with 23% greater odds of being in the high category for transportation physical activity, 26% greater odds of engaging in high amounts of leisure-time walking, 11% greater odds of engaging in MVPA, and 40% greater odds of high total physical activity. Acknowledging the cross-sectional study design, the findings suggest that park visitation may be an important predictor and/or destination for transportation and leisure-time walking and physical activity. Findings highlight the potentially important role of parks for physical activity.
    Preventive Medicine 08/2013; · 3.50 Impact Factor
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    ABSTRACT: CONTEXT: To date, no reviews have investigated the evidence of tracking of physical activity and sedentary behavior specifically during early childhood (aged 0-5.9 years) or from early childhood to middle childhood (aged 6-12 years). It is important to review the evidence of tracking of these behaviors to determine their stability during the foundational early years of life. EVIDENCE ACQUISITION: A literature search of studies was conducted in seven electronic databases (January 1980 to April 2012). Studies were compared on methodologic quality and evidence of tracking of physical activity or sedentary behavior. Tracking was defined as the stability (or relative ranking within a cohort) of behaviors, such as physical activity and sedentary behavior, over time. EVIDENCE SYNTHESIS: Eleven studies met the inclusion criteria. All studies reporting physical activity outcomes had high methodologic quality; 71% of studies reporting sedentary behavior outcomes had high methodologic quality. Of the tracking coefficients for physical activity, 4% were large, 60% were moderate, and 36% were small. Of the tracking coefficients for sedentary behavior, 33% were large, 50% were moderate, and 17% were small. Overall, there was evidence of moderate tracking of physical activity during early childhood, and from early childhood to middle childhood, and of moderate-to-large tracking of sedentary behavior during early childhood and from early childhood to middle childhood. CONCLUSIONS: This review highlights the importance of establishing recommended levels of physical activity and sedentary behavior during the early years of life. Based on this review, the following recommendations are made: (1) early childhood should be targeted as a critical time to promote healthy lifestyle behaviors through methodologically sound prevention studies; and (2) future tracking studies should assess a broad range of sedentary behaviors using objective measures.
    American journal of preventive medicine 06/2013; 44(6):651-658. · 4.24 Impact Factor
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    ABSTRACT: BACKGROUND: The afterschool period holds promise for the promotion of physical activity, yet little is known about the importance of this period as children age. PURPOSE: To examine changes in physical activity of children aged 5-6 years and 10-12 years and their sedentary time in the afterschool period over 3 and 5 years, and to determine the contribution of this period to daily physical activity and sedentary behavior over time. METHODS: Data from two longitudinal studies conducted in Melbourne, Australia, were used. Accelerometer data were provided for 2053 children at baseline (Children Living in Active Neighbourhoods Study [CLAN]: 2001; Health, Eating and Play Study [HEAPS]: 2002/2003); 756 at 3-year follow-up (time point 2 [T2]); and 622 at 5-year follow-up (T3). Light (LPA), moderate (MPA) and vigorous (VPA) physical activity were determined using age-adjusted cut-points. Sedentary time was defined as≤100counts/minute. Multilevel analyses, conducted in April 2012, assessed change in physical activity and sedentary time and the contributions of the afterschool period to overall levels. RESULTS: Afterschool MPA and VPA decreased among both cohorts, particularly in the younger cohort, who performed less than half of their baseline levels at T3 (MPA: T1=24minutes; T3=11minutes; VPA: T1=12minutes; T3=4minutes). LPA also declined in the older cohort. Afterschool sedentary time increased among the younger (T1=42minutes; T3=64minutes) and older cohorts (T1=57minutes; T3=84minutes). The contribution of the afterschool period to overall MPA and VPA increased in the older cohort from 23% to 33% over 5 years. In the younger cohort, the contribution of the afterschool period to daily MPA and VPA decreased by 3% over 5 years. CONCLUSIONS: The importance of the afterschool period for children's physical activity increases with age, particularly as children enter adolescence.
    American journal of preventive medicine 06/2013; 44(6):605-611. · 4.24 Impact Factor
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    ABSTRACT: OBJECTIVE: To investigate the individual, social and physical environment correlates of preschool children's compliance with Australian/Canadian and American Academy of Pediatrics (AAP) screen recommendations. METHOD: An Ecological Model (EM) was used to identify constructs potentially associated with children's screen time. In 2008-2009, parents in Melbourne, Australia, reported their child's screen time and on a range of potential correlates. Children (n=935; 54% boys, mean age 4.54±0.70years) were assessed as meeting or not meeting each of screen recommendations. Logistic regression assessed bivariable and multivariable associations. RESULTS: In total, 15 explanatory variables, across the three domains of the EM were associated with boys' and/or girls' compliance with either Australian/Canadian or AAP recommendations. Correlates varied by sex and recommendation. Maternal television viewing time was the only consistent correlate for both boys' and girls' compliance with both recommendations. No demographic groups were identified as being less likely to comply with screen recommendations. CONCLUSION: Public health programs should take account of the sex-specific nature of correlates of preschool children's screen time. Preschool children across all demographic groups need support to engage in less screen use. Parents may benefit from education and parenting skills to minimize potentially harmful effects of excessive screen time for their child.
    Preventive Medicine 05/2013; · 3.50 Impact Factor
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    ABSTRACT: BACKGROUND: The optimal targets and strategies for effectively reducing sedentary behavior among young people are unknown. Intervention research that explores changes in mediated effects as well as in outcome behaviors is needed to help inform more effective interventions. Therefore, the purpose of this study was to examine the mid-intervention mediating effects on children's objectively assessed classroom and total weekday sedentary time in the Transform-Us! intervention. METHODS: The results are based on 293 children, aged 7- to 9-years-old at baseline, from 20 schools in Melbourne, Australia. Each school was randomly allocated to one of four groups, which targeted reducing sedentary time in the school and family settings (SB; n = 74), increasing or maintaining moderate- to vigorous-intensity physical activity in the school and family settings (PA; n = 75), combined SB and PA (SB + PA; n = 80), or the current practice control (C; n = 64). Baseline and mid-intervention data (5--9 months) were collected in 2010 and analyzed in 2012. Classroom and total weekday sedentary time was objectively assessed using ActiGraph accelerometers. The hypothesized mediators including, child enjoyment, parent and teacher outcome expectancies, and child perceived access to standing opportunities in the classroom environment, were assessed by questionnaire. RESULTS: The SB + PA group spent 13.3 min/day less in weekday sedentary time at mid-intervention compared to the control group. At mid-intervention, children in the SB group had higher enjoyment of standing in class (0.9 units; 5-unit scale) and all intervention groups had more positive perceptions of access to standing opportunities in the classroom environment (0.3-0.4 units; 3-unit scale), compared to the control group. However, none of the hypothesized mediator variables had an effect on sedentary time; thus, no mediating effects were observed. CONCLUSIONS: While beneficial intervention effects were observed on some hypothesized mediating variables and total weekday sedentary time at mid-intervention, no significant mediating effects were found. Given the dearth of existing information, future intervention research is needed that explores mediated effects. More work is also needed on the development of reliable mediator measures that are sensitive to change overtime.Trial registrationACTRN12609000715279ISRCTN83725066.
    International Journal of Behavioral Nutrition and Physical Activity 05/2013; 10(1):62. · 3.58 Impact Factor
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    ABSTRACT: Issue addressed Studies examining children's after-school physical activity (PA) and sedentary behaviours (SB) often use arbitrary times to signify the period start and end. A standardised time is required for future research examining this period. The aim of the present study was to compare children's after-school behaviour using three definitions of the after-school period, namely (1) end of school to 6 pm; (2) end of school to dinner time; and (3) end of school to sunset, to determine the extent of variability in PA and SB during the after-school period depending on the definition used. Methods Children (n=308; aged 8 years) from the Melbourne Transform-Us! intervention wore an accelerometer and a subsample (n=112) wore an activPAL inclinometer in 2010. The end of school bell time was obtained from the child's school, parents completed a 2-day log reporting their child's dinner time and sunset times were obtained from Geoscience Australia. ActiGraph accelerometers assessed the proportion of time spent sedentary (SED) and that spent in light (LPA), moderate (MPA) and moderate-to-vigorous (MVPA) PA during the three time periods; activPAL inclinometers assessed the proportion of time spent sitting (SIT). Results Apart from the end of school time (3:30 pm), dinner (range 3:30 pm-8:40 pm) and sunset (range 5:07 pm-7:34 pm) times varied. Despite this, there were no significant differences in estimates of the proportions of time children spent in SED, LPA, MPA, MVPA or SIT between the three after-school periods examined. Conclusion Given the small differences in SED, PA and SIT during the after-school period regardless of the definition (6 pm, sunset or dinner time), it appears that applying a standardised definition of end of school to 6 pm is acceptable for defining children's PA and SB during the after-school period. So what? The use of a standardised after-school definition (end of school to 6 pm), will enable future studies exploring children's after-school PA and SB to be more comparable.
    Health promotion journal of Australia: official journal of Australian Association of Health Promotion Professionals 04/2013; 24(1):65-7. · 0.59 Impact Factor
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    ABSTRACT: OBJECTIVE:To assess the effectiveness of a parent-focused intervention on infants' obesity-risk behaviors and BMI.METHODS:This cluster randomized controlled trial recruited 542 parents and their infants (mean age 3.8 months at baseline) from 62 first-time parent groups. Parents were offered six 2-hour dietitian-delivered sessions over 15 months focusing on parental knowledge, skills, and social support around infant feeding, diet, physical activity, and television viewing. Control group parents received 6 newsletters on nonobesity-focused themes; all parents received usual care from child health nurses. The primary outcomes of interest were child diet (3 × 24-hour diet recalls), child physical activity (accelerometry), and child TV viewing (parent report). Secondary outcomes included BMI z-scores (measured). Data were collected when children were 4, 9, and 20 months of age.RESULTS:Unadjusted analyses showed that, compared with controls, intervention group children consumed fewer grams of noncore drinks (mean difference = -4.45; 95% confidence interval [CI]: -7.92 to -0.99; P = .01) and were less likely to consume any noncore drinks (odds ratio = 0.48; 95% CI: 0.24 to 0.95; P = .034) midintervention (mean age 9 months). At intervention conclusion (mean age 19.8 months), intervention group children consumed fewer grams of sweet snacks (mean difference = -3.69; 95% CI: -6.41 to -0.96; P = .008) and viewed fewer daily minutes of television (mean difference = -15.97: 95% CI: -25.97 to -5.96; P = .002). There was little statistical evidence of differences in fruit, vegetable, savory snack, or water consumption or in BMI z-scores or physical activity.CONCLUSIONS:This intervention resulted in reductions in sweet snack consumption and television viewing in 20-month-old children.
    PEDIATRICS 03/2013; · 4.47 Impact Factor
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    ABSTRACT: BACKGROUND: The objective of this study was to develop a multi-domain model to identify key characteristics of the primary school environment associated with children's physical activity (PA) during class-time. METHODS: Accelerometers were used to calculate time spent in moderate to vigorous physical activity during class-time (CMVPA) of 408 sixth-grade children (mean±SD age 11.1±0.43 years) attending 27 metropolitan primary schools in Perth Western Australia. Child and staff self-report instruments and a school physical environment scan administered by the research team were used to collect data about children and the class and school environments. Hierarchical modeling identified key variables associated with CMVPA. RESULTS: The final multilevel model explained 49% of CMVPA. A physically active physical education (PE) coordinator, fitness sessions incorporated into PE sessions and either a trained PE specialist, classroom teacher or nobody coordinating PE in the school, rather than the deputy principal, were associated with higher CMVPA. The amount of grassed area per student and sporting apparatus on grass were also associated with higher CMVPA. CONCLUSION: These results highlight the relevance of the school's socio-cultural, policy and physical environments in supporting class-based PA. Interventions testing optimization of the school physical, socio-cultural and policy environments to support physical activity are warranted.
    Journal of Physical Activity and Health 02/2013; · 1.95 Impact Factor
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    ABSTRACT: BACKGROUND: Sport participation makes an important contribution to children's overall physical activity. Understanding influences on sports participation is important and the family environment is considered key, however few studies have explored the mechanisms by which the family environment influences children's sport participation. The purpose of this study was to examine whether attitude, perceive behavioural control, health belief and enjoyment mediate associations between the family environment and 10--12 year-old children's sports participation. METHODS: Children aged 10--12 years (n=7234) and one of their parents (n=6002) were recruited from 175 schools in seven European countries in 2010. Children self-reported their weekly duration of sports participation, physical activity equipment items at home and the four potential mediator variables. Parents responded to items on financial, logistic and emotional support, reinforcement, modelling and co-participation in physical activity. Cross-sectional single and multiple mediation analyses were performed for 4952 children with complete data using multi-level regression analyses. RESULTS: Availability of equipment (OR=1.16), financial (OR=1.53), logistic (OR=1.47) and emotional (OR=1.51) support, and parental modelling (OR=1.07) were positively associated with participation in >=30mins/wk of sport. Attitude, beliefs, perceived behavioural control and enjoyment mediated and explained between 21-34% of these associations. Perceived behavioural control contributed the most to the mediated effect for each aspect of the family environment. CONCLUSIONS: Both direct (unmediated) and indirect (mediated) associations were found between most family environment variables and children's sports participation. Thus, family-based physical activity interventions that focus on enhancing the family environment to support children's sport participation are warranted.
    International Journal of Behavioral Nutrition and Physical Activity 02/2013; 10(1):15. · 3.58 Impact Factor
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    ABSTRACT: OBJECTIVES: To investigate individual, behavioral, social and environmental correlates of physical activity (PA) and screen-based behaviors ([SBBs] TV viewing, electronic games playing and Internet use) in Chinese boys and girls in Hong Kong. DESIGN: Cross-sectional study. METHODS: PA and SBBs were self-reported among 303 children. Individual, behavioral, social and environmental correlates were reported by children and parents. Children's height and weight were measured and population density of residence districts was divided into tertiles. Hierarchical multivariable regression analyses were conducted to determine the contributions of correlates in explaining PA and SBBs. RESULTS: Participation in school sport teams and self-efficacy was positively associated with PA in boys. Girls who reported participation in school sport teams, who perceived more peer support, had a more supportive home PA environment, and spent more time doing homework were more physically active. Family support for PA was negatively and homework was positively, associated with boys' SBBs. Body mass index and parent role modeling of TV was positively associated with TV viewing, whilst more sedentary opportunities in the home were associated with higher Internet use/e-games playing among girls. CONCLUSIONS: Sex differences existed among the individual, behavioral, social and home environmental factors related to PA and SBBs. Interventions should consider multiple and sex-specific factors for promoting an active lifestyle and reducing sedentary time among Chinese children.
    Journal of science and medicine in sport / Sports Medicine Australia. 01/2013;
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    ABSTRACT: BACKGROUND: We investigated associations of total sedentary behavior (SB) and objectively-measured and self-reported physical activity (PA) with obesity. METHODS: Data from 1662 adults (26-36 years) included daily steps, self-reported PA, sitting, and waist circumference. SB and PA were dichotomised at the median, then two variables created (SB/self-reported PA; SB/objectively-measured PA) each with four categories: low SB/high PA (reference group), high SB/high PA, low SB/low PA, high SB/low PA. RESULTS: Overall, high SB/low PA was associated with 95-168% increased obesity odds. Associations were stronger and more consistent for steps than self-reported PA for men (OR 2. 68, 95% CI 1.36-5.32 and OR 1.95, 95% CI 1.01-3.79, respectively) and women (OR 2.66, 95% CI 1.58-4.49 and OR 2.00, 95% CI 1.21-3.31, respectively). Among men, obesity was higher when daily steps were low, irrespective of sitting (low SB/low steps OR 2.07, 95% CI 1.03-4.17; high SB/low steps OR 2.68, 95% CI 1.36-5.32). CONCLUSIONS: High sitting and low activity increased obesity odds among adults. Irrespective of sitting, men with low step counts had increased odds of obesity. The findings highlight the importance of engaging in physical activity and limiting sitting.
    Journal of Physical Activity and Health 01/2013; · 1.95 Impact Factor
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    ABSTRACT: Adolescents' physical activity levels during school break time are low and understanding correlates of physical activity and sedentary time in this context is important. This study investigated cross-sectional and longitudinal associations between a range of individual, behavioural, social and policy/organisational correlates and objectively measured school break time physical activity and sedentary time. In 2006, 146 adolescents (50% males; mean age = 14.1±0.6 years) completed a questionnaire and wore an accelerometer for ≥3 school days. Time spent engaged in sedentary, light (LPA) and moderate-to-vigorous physical activity (MVPA) during school break times (recess and lunchtime) were calculated using existing cut-points. Measures were repeated in 2008 among 111 adolescents. Multilevel models examined cross-sectional and longitudinal associations. Bringing in equipment was cross-sectionally associated with 3.2% more MVPA during break times. Females engaged in 5.1% more sedentary time than males, whilst older adolescents engaged in less MVPA than younger adolescents. Few longitudinal associations were observed. Adolescents who brought sports equipment to school engaged in 7.2% less LPA during break times two years later compared to those who did not bring equipment to school. These data suggest that providing equipment and reducing restrictions on bringing in sports equipment to school may promote physical activity during school recess. Strategies targeting females' and older adolescents', in particular, are warranted.
    PLoS ONE 01/2013; 8(2):e56838. · 3.53 Impact Factor
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    ABSTRACT: Many interventions aiming to increase children's physical activity have been developed and implemented in a variety of settings, and these interventions have previously been reviewed; however the focus of these reviews tends to be on the intervention effects on physical activity outcomes without consideration of the reasons and pathways leading to intervention success or otherwise.To systematically review the efficacy of physical activity interventions targeting 5-12 year old children on potential mediators and, where possible, to calculate the size of the intervention effect on the potential mediator. A systematic search identified intervention studies that reported outcomes on potential mediators of physical activity among 5-12 year old children. Original research articles published between 1985 and April 2012 were reviewed. Eighteen potential mediators were identified from 31 studies. Positive effects on cognitive/psychological potential mediators were reported in 15 out of 31 studies. Positive effects on social environmental potential mediators were reported in three out of seven studies, and no effects on the physical environment were reported. Although no studies were identified that performed a mediating analysis, 33 positive intervention effects were found on targeted potential mediators (with effect sizes ranging from small to large) and 73% of the time a positive effect on the physical activity outcome was reported. Many studies have reported null intervention effects on potential mediators of children's physical activity; however, it is important that intervention studies statistically examine the mediating effects of interventions so the most effective strategies can be implemented in future programs.
    BMC Public Health 01/2013; 13:165. · 2.08 Impact Factor
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    ABSTRACT: The Resilience for Eating and Activity Despite Inequality (READI) cohort was established to address the following two key aims: to investigate the pathways (personal, social and structural) by which socio-economic disadvantage influences lifestyle choices associated with obesity risk (physical inactivity, poor dietary choices) and to explore mechanisms underlying 'resilience' to obesity risk in socio-economically disadvantaged women and children. A total of 4349 women aged 18-46 years and 685 children aged 5-12 years were recruited from 80 socio-economically disadvantaged urban and rural neighbourhoods of Victoria, Australia, and provided baseline (T1: 2007-08) measures of adiposity, physical activity, sedentary and dietary behaviours; socio-economic and demographic factors; and psychological, social and perceived environmental factors that might impact on obesity risk. Audits of the 80 neighbourhoods were undertaken at baseline to provide objective neighbourhood environmental data. Three-year follow-up data (2010-11) have recently been collected from 1912 women and 382 children. Investigators welcome enquiries regarding data access and collaboration.
    International Journal of Epidemiology 12/2012; · 6.98 Impact Factor

Publication Stats

5k Citations
477.44 Total Impact Points


  • 2013
    • University of Tasmania
      • Menzies Research Institute
      Hobart Town, Tasmania, Australia
    • Ghent University
      • Department of Movement and Sports Sciences
      Gand, Flanders, Belgium
    • University of Wollongong
      • Faculty of Education
      City of Greater Wollongong, New South Wales, Australia
  • 2009–2013
    • The Chinese University of Hong Kong
      • Department of Sports Science and Physical Education
      Hong Kong, Hong Kong
  • 2003–2013
    • Deakin University
      • • Centre for Physical Activity and Nutrition Research
      • • School of Exercise and Nutrition Sciences
      Geelong, Victoria, Australia
  • 2012
    • RMIT University
      • School of Medical Sciences
      Melbourne, Victoria, Australia
    • University of Victoria
      Victoria, British Columbia, Canada
  • 2010–2012
    • Baker IDI Heart and Diabetes Institute
      • Clinical Diabetes and Epidemiology Research Group
      Melbourne, Victoria, Australia
  • 2011
    • Loughborough University
      • School of Sport, Exercise and Health Sciences
      Loughborough, ENG, United Kingdom
    • Erasmus Universiteit Rotterdam
      • Department of Public Health (MGZ)
      Rotterdam, South Holland, Netherlands
    • VU University Medical Center
      Amsterdamo, North Holland, Netherlands
  • 2007–2010
    • University of Queensland 
      • Cancer Prevention Research Centre
      Brisbane, Queensland, Australia
  • 2006
    • University of Sydney
      Sydney, New South Wales, Australia