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Ashleigh Thornton, Brendan Lay, Michael Rosenberg, Joanna Granich and Rebecca Braham

This study sought to explore the type of fundamental movement skills (FMS) performed during Active Video Game (AVG) play, as well as the frequency with which these FMS are performed. In addition, this study aimed to determine the relationship between FMS performance and energy expenditure during 15 min of AVG play across two Microsoft Xbox Kinect AVGs. Fundamental movement skills were observed via video by two raters and energy expenditure derived using Actiheart monitors in children aged 10–15 years. Six different FMS were observed during AVG play with differences in the number of FMS performed between the two AVGs. The overall energy expended (Joules/kg/minute), however, was similar between the AVGs, suggesting the frequency of FMS did not influence overall energy expended during play. The movements observed during AVG play that possibly accounted for the energy expenditure, were not of a quality that could be classified as FMS. This research demonstrates that children playing these two games have the opportunity to repeatedly perform mostly two FMS, namely jumping and dodging. The goal of the AVGs, however, could be achieved with generalized movements that did not always meet the criteria to be classified as a FMS.

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Joanna Granich, Michael Rosenberg, Matthew W. Knuiman and Anna Timperio

Background:

Individual, home social and physical environment correlates of electronic media (EM) use among children were examined and pattern of differences on school and weekend days.

Methods:

Youth (n = 298) aged 11 to 12 years self-reported time spent using EM (TV, video/DVD, computer use, and electronic games) on a typical school and a weekend day, each dichotomized at the median to indicate heavy and light EM users. Anthropometric measurements were taken. Logistic regression examined correlates of EM use.

Results:

In total, 87% of participants exceeded electronic media use recommendations of ≤ 2 hrs/day. Watching TV during breakfast (OR = 3.17) and after school (OR = 2.07), watching TV with mother (OR = 1.96), no rule(s) limiting time for computer game usage (OR = 2.30), having multiple (OR = 2.99) EM devices in the bedroom and BMI (OR = 1.15) were associated with higher odds of being heavy EM user on a school day. Boys (OR = 2.35) and participants who usually watched TV at midday (OR = 2.91) and late at night (OR = 2.04) had higher odds of being a heavy EM user on the weekend.

Conclusions:

Efforts to modify children’s EM use should focus on a mix of intervention strategies that address patterns and reinforcement of TV viewing, household rules limiting screen time, and the presence of EM devices in the child’s bedroom.

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Karen Martin, Alexandra Bremner, Jo Salmon, Michael Rosenberg and Billie Giles-Corti

Background:

The objective of this study was to develop a multidomain 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 sociocultural, policy and physical environments in supporting class-based PA. Interventions testing optimization of the school physical, sociocultural and policy environments to support physical activity are warranted.

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Areekul Amornsriwatanakul, Kasem Nakornkhet, Piyawat Katewongsa, Chairat Choosakul, Tippawan Kaewmanee, Kurusart Konharn, Atchara Purakom, Anoma Santiworakul, Patraporn Sitilertpisan, Sonthaya Sriramatr, Araya Yankai, Michael Rosenberg and Fiona C. Bull

Background:

Physical activity (PA) is recognized as one of the core modifiable risk factors of noncommunicable diseases. However, little is known about PA in the Thai population, particularly in children. The report card (RC) project provided Thailand with an opportunity to assess PA behaviors in children. This paper summarizes the methodology, grading process, and the final grades of the Thai RC.

Methods:

A school-based survey was conducted to collect data from a nationally representative sample of children aged 6 to 17 years. Survey results provided the primary source for the RC. Nine indicators were graded using the Global Matrix 2.0 framework. Grading was undertaken by a national committee comprising experts from key stakeholders.

Results:

Grades ranged from F to B. Overall PA and Sedentary Behaviors both received the grade D-. Organized Sport Participation scored a C. Active Play scored the grade F. Active Transport and support from Family and Peers were both graded B. School, Community, and Government indicators were scored C.

Conclusions:

In Thai children, participation in PA and active play is very low; conversely, sedentary behaviors are high. These first data on patterns of activity for the Thailand RC will serve to guide national actions and advocacy aimed at increasing PA in children.

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Michael J. LaMonte, I-Min Lee, Eileen Rillamas-Sun, John Bellettiere, Kelly R. Evenson, David M. Buchner, Chongzhi Di, Cora E. Lewis, Dori E. Rosenberg, Marcia L. Stefanick and Andrea Z. LaCroix

Background: Limited data are available regarding the correlation between questionnaire and device-measured physical activity (PA) and sedentary behavior (SB) in older women. Methods: We evaluated these correlations in 5,992 women, aged 63 and older, who completed the Women’s Health Initiative (WHI) and Community Healthy Activities Model Program for Seniors (CHAMPS) PA questionnaires and the CARDIA SB questionnaire prior to wearing a hip-worn accelerometer for 7 consecutive days. Accelerometer-measured total, light, and moderate-to-vigorous PA (MVPA), and total SB time were defined according to cutpoints established in a calibration study. Spearman coefficients were used to evaluate correlations between questionnaire and device measures. Results: Mean time spent in PA and SB was lower for questionnaire than accelerometer measures, with variation in means according to age, race/ethnicity, body mass index, and functional status. Overall, correlations between questionnaires and accelerometer measures were moderate for total PA, MVPA, and SB (r ≈ 0.20–0.40). Light intensity PA correlated weakly for WHI (r ≈ 0.01–0.06) and was variable for CHAMPS (r ≈ 0.07–0.22). Conclusion: Questionnaire and accelerometer estimates of total PA, MVPA, and SB have at best moderate correlations in older women and should not be assumed to be measuring the same behaviors or quantity of behavior. Light intensity PA is poorly measured by questionnaire. Because light intensity activities account for the largest proportion of daily activity time in older adults, and likely contribute to its health benefits, further research should investigate how to improve measurement of light intensity PA by questionnaires.