Katia Ferrar

Trends in the prevalence of childhood overweight and obesity in Australia between 1985 and 2008

Background:Popular media, health experts and researchers talk about a paediatric ‘obesity epidemic’ with exponentially increasing rates of obesity and overweight. However, some recent reports suggest that prevalence may have plateaued. This study examined trends in the prevalence of Australian childhood overweight and obesity since 1985. Specifically, it aimed to determine whether there have been (a) overall increases in average body mass index (BMI), (b) differential patterns of change within age groups and (c) increases in BMI within each weight-status category.Method:Forty-one Australian studies of childhood weight status conducted between 1985 and 2008 were reviewed. The studies included data on 264 905 Australians aged 2–18 years, with raw data being available on 70 758 children (27%). Children were classified as overweight or obese based on BMI using the criteria of Cole et al. (BMJ, 2000). The prevalence estimates were adjusted for age and sex, and plotted against measurement year using Lowess plots and two-linear-segment models. Where raw data were available, BMI z-scores (UK 1990 standard) were plotted against measurement year for all children and children in various age groups. Lowess plots and two-linear-segment models were used to assess secular trends in BMI z-scores pre- and post-1996 within age, gender and weight-status categories.Results:There has been a plateau, or only slight increase, in the percentage of boys and girls classified as overweight or obese, with almost no change over the last 10 years. In boys and girls, prevalence rates have settled around 21–25% for overweight and obesity together, and 5–6% for obesity alone. Similar trends were found for BMI z-scores. These patterns were fairly consistent across the age span. Within each weight-status category, average BMI has not increased.Conclusions:Although levels of Australian paediatric overweight remain high, the prevalence of overweight and obesity seems to have flattened and has not followed the anticipated exponential trajectory.

Are health behavior change interventions that use online social networks effective? A systematic review /

Background The dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change. Objective The intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions. Methods Eight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where “population” included child or adult populations, including healthy and disease populations; “intervention” involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; “comparator” was either a control group or within subject in the case of pre-post study designs; “outcomes” included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg, self-efficacy); and “study design” included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen’s d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized. Results A total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online health social network websites (n=2), research health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine of the 10 included studies reported significant improvements in some aspect of health behavior change or outcomes related to behavior change. Effect sizes for behavior change ranged widely from −0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement and fidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity). Conclusions To date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether behavior change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided.

Adolescent Time Use Clusters: A Systematic Review

PURPOSE Recent research suggests that patterns or clusters of time use may affect health in ways that cannot be explained by the effect of individual behaviors alone. The aim of this research was to systematically review the literature examining adolescent time use clusters and associated correlates. METHODS Systematic searches of six online databases for relevant observational studies were conducted. At least two authors reviewed abstract and full text selection meeting eligibility criteria. Included studies were quality scored, had data extracted, and cluster types and cluster associations interpreted. RESULTS Nineteen studies were identified for inclusion, and 18 of them investigated cluster-correlate associations. Twenty-nine cluster types were identified, characterized by both individual (e.g., church) and co-occurring behaviors (e.g., physical activity and screen [technoactive]). Nineteen correlate categories were identified (e.g., socioeconomic and weight status). Consistent patterns of cluster-correlate association were found. For example, the technoactive cluster type is more likely to be male and to have low school orientation. CONCLUSIONS Despite the between-study differences, consistent cluster and cluster-correlate patterns were still evident. Cluster analysis of adolescent time use behaviors appears to be an emerging and useful classification technique, one which may have implications for targeted health-related interventions.

Obese adolescents are less active than their normal-weight peers, but wherein lies the difference?

BACKGROUND Obese adolescents achieve less moderate to vigorous physical activity (MVPA) than normal-weight adolescents, but the nature and extent of the deficit is unclear. This study aimed to describe differences in MVPA across weight status categories by domain of activity (sport, play, and active transport) and specific activity-sets (e.g. team sports, playground games, and chores) using minutes of activity, estimated intensity, and estimated energy expenditure as metrics of MVPA. METHOD Anthropometric, use-of-time, and pedometer data were collected on a random sample of 2,200 Australian children aged 9-16 years. Minutes of activity, intensity metabolic equivalents of tasks (METs), and energy expenditure (MET.min) within each domain were estimated using an energy cost compendium. RESULTS Obese adolescents reported 174 MET.min/d lower MVPA energy expenditure than normal-weight peers (p < .0001), more than accounting for the entire difference in total daily energy expenditure (154 MET.min/d). Of this difference, 121 MET.min/d was associated with lower sports participation (p < .0001) and 45 MET.min/d with less free play (p = .03). There were no differences in minutes of active transport or in reported activity intensity across weight status categories in sport, free play, or active transport. The differences in MVPA participation between obese and normal-weight adolescents were largely because of different levels of participation in team sports, cycling, partner sports (boys), and dancing (girls). CONCLUSION More than two-thirds of the difference in energy expenditure between obese and normal-weight Australian adolescents was because of lower participation in sport. Strategies for engaging obese adolescents in sport may help to redress deficits in energy expenditure.

Does Physical Activity Increase After Total Hip or Knee Arthroplasty for Osteoarthritis? A Systematic Review

Study Design Systematic review. Background Despite improvements in self-reported symptoms and perceived functional ability after total hip arthroplasty (THA) and total knee arthroplasty (TKA), it is unclear whether changes in objectively measured physical activity (PA) occur after surgery. Objective To determine if objectively measured PA increases after THA and TKA in adults with osteoarthritis. Methods Five electronic databases were searched from inception to March 3, 2015. All study designs objectively measuring PA before and after THA or TKA were eligible, including randomized controlled trials, cohort studies, and case-control studies. Two reviewers independently screened abstracts and full texts and extracted study demographic, PA, and clinical outcome data. Standardized mean differences (SMDs) and 95% confidence intervals were calculated for accelerometer- and pedometer-derived estimates of PA. Risk of methodological bias was assessed with Critical Appraisal Skills Programme checklists. Results Eight studies with a total of 373 participants (238 TKA, 135 THA) were included. Findings were mixed regarding improvement in objectively measured PA at 6 months after THA (SMDs, -0.20 to 1.80) and TKA (SMDs, -0.36 to 0.63). Larger improvements from 2 studies at 1 year postsurgery were generally observed after THA (SMDs, 0.39 to 0.79) and TKA (SMDs, 0.10 to 0.85). However, at 1 year, PA levels were still considerably lower than those of healthy controls (THA SMDs, -0.25 to -0.77; TKA SMDs, -1.46 to -1.80). Risk-of-bias scores ranged from 3 to 9 out of 11 (27%-82%) for cohort studies, and from 3 to 8 out of 10 (30%-80%) for case-control studies. Conclusion The best available evidence indicates negligible changes in PA at 6 months after THA or TKA, with limited evidence for larger changes at 1 year after surgery. In the 4 studies that reported control-group data, postoperative PA levels were still considerably less than those of healthy controls. Improved perioperative strategies to instill behavioral change are required to narrow the gap between patient-perceived functional improvement and the actual amount of PA undertaken after THA and TKA. Registered with PROSPERO (registration number CRD42014010155). Level of Evidence Therapy, level 2a. J Orthop Sports Phys Ther 2016;46(6):431-442. Epub 26 Apr 2016. doi:10.2519/jospt.2016.6449.

All the Stereotypes Confirmed Differences in How Australian Boys and Girls Use Their Time

Background. To influence adolescent health, a greater understanding of time use and covariates such as gender is required. Purpose. To explore gender-specific time use patterns in Australian adolescents using high-resolution time use data. Method. This study analyzed 24-hour recall time use data collected as part of the 2007 Australian National Children’s Nutrition and Physical Activity Survey (n = 2,200). Univariate analyses to determine gender differences in time use were conducted. Results. Boys spent more (p < .0001) time participating in screen-based (17.7 % vs. 14.2% daily time) and physical activities (10.7% vs. 9.2%). Girls spent more (p < .0001) time being social (4.7% vs. 3.4% daily time), studying (2.0% vs. 1.7%), and doing household chores (4.7% vs. 3.4%). Conclusions. There are gender-specific differences in time use behavior among Australian adolescents. The results reinforce existing time use gender-based stereotypes. Implications. The gender-specific time use behaviors offer intervention design possibilities.

Thin adolescents: Who are they? What do they do? Socio-demographic and use-of-time characteristics.

OBJECTIVES Examine: (1) the anthropometric, socio-demographic and use-of-time characteristics of thin adolescents, and (2) compare these characteristics to other weight status categories. METHODS Data were from the 2007 National Childrens Nutrition and Physical Activity Survey which collected data on a random sample of 2200 9 to 16 year old Australians from February to August 2007. Seven socio-demographic variables, anthropometric data (height and weight were measured) and nine use-of-time variables were used, and compared across the weight status categories. Physical activity was measured using pedometers and the Multimedia Activity Recall for Children and Adults. RESULTS 5.3% of adolescents were classified as thin, a percentage which did not significantly vary by age, sex, indigenous status, household income, education level or family structure. Relative to other adolescents, thin adolescents were shorter and lighter. Thin adolescents were less active than their normal weight peers, but walked further and accumulated significantly less screen and TV time than obese adolescents. CONCLUSION Thin adolescents were found in similar proportions across all socio-demographic bands. Thin adolescents recorded similar physical activity levels to their normal weight peers, but were more active than obese adolescents. The findings from the study support in part the theory of thinness related developmental delay.

The Elasticity of Time: Associations Between Physical Activity and Use of Time in Adolescents

The way an individual uses one’s time can greatly affect his or her health. The purpose of this article was to examine the cross-sectional cross-elasticity relationships for use of time domains in a sample of Australian adolescents. This study analyzed 24-hour recall time use data collected using the Multimedia Activity Recall for Children and Adults (N = 2,200). Using simple linear regression, the authors calculated the difference in time devoted to a reference activity (i.e., screen time, sleep, or social) given 1 hour extra in the time devoted to a criterion activity (i.e., physical activity). The most elastic activities were screen time and school-related time. Every additional hour committed to physical activity was associated with 32 minutes less screen time. This relationship was more pronounced in obese adolescents (−56 minutes screen time) compared with normal (−31 minutes) and overweight (−27 minutes) adolescents. Promising behavior patterns exist, with screen time appearing as a highly elastic behavior.

Changes in sedentary behaviours across the retirement transition: a systematic review

BACKGROUND Prolonged sedentary behaviour has been associated with a number of chronic health conditions. This issue is compounded by inactivity increasing with age. OBJECTIVE This systematic review aimed to identify evidence regarding changes in sedentary behaviours as people move into retirement. SEARCH STRATEGY AND SELECTION CRITERIA Nine databases (Ageline, CINAHL, Cochrane, Embase, MEDLINE, ProQuest, PubMed, SportDiscus and Web of Science) were searched in May 2014. Search terms included retirement, time use and a range of sedentary behaviours, with no date limit. Articles were selected and appraised for risk of bias by two independent reviewers. Due to the variations in measures used for reporting, data synthesis of results was qualitative. RESULTS Two studies measured total sitting time and reported declines across retirement. Several studies examined self-reported time spent in specific sedentary leisure activities and generally reported increases in duration, prevalence or frequency (television: 7/9 studies; reading: 4/6 studies). Few other sedentary behaviours were considered. CONCLUSIONS Changes in sedentary time across retirement are currently poorly understood with varying patterns of change identified by different study methodologies (total sitting time versus specific leisure sedentary activities). Future research that simultaneously investigates changes in a comprehensive range of sedentary behaviours across retirement is required. To date, findings suggest that interventions aimed at improving the health of this population need to be targeted at specific sedentary behaviours to provide maximum benefit.

Time use clusters of New Zealand adolescents are associated with weight status, diet and ethnicity.

Objective : To describe New Zealand adolescent time use clusters and correlate cluster profiles.