Rethinking our approach to physical activity

Das, Pam; Horton, Richard

doi:10.1016/s0140-6736(12)61024-1

Cited by 209 publications

(135 citation statements)

References 1 publication

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“…Using Mplus version 6.1, 21 decisions about the optimal number of latent classes were based on model fit statistics including Akaike Information Criterion (AIC); Bayesian Information Criterion (BIC); sample-size adjusted Bayesian Information Criterion (ABIC); entropy; and the Lo-Mendell-Rubin's likelihood ratio (LMR) and bootstrapped Lo-Mendell-Rubin's likelihood ratio tests (BLMR). 22 To select the appropriate number of latent classes, we analyzed a two-class model and compared this analysis with successive models, which specified an increasing number of latent classes (up to four). We examined specific model estimates for both three-and four-class models to select a final specification based on the interpretability of the results.…”

Section: Discussionmentioning

confidence: 99%

Patterns of multiple health risk–behaviours in university students and their association with mental health: application of latent class analysis

Kwan

Arbour-Nicitopoulos

Duku

et al. 2016

Health Promot Chronic Dis Prev Can

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Introduction: University and college campuses may be the last setting where it is possible to comprehensively address the health of a large proportion of the young adult population. It is important that health promoters understand the collective challenges students are facing, and to better understand the broader lifestyle behavioural patterning evident during this life stage. The purpose of this study was to examine the clustering of modifiable health-risk behaviours and to explore the relationship between these identified clusters and mental health outcomes among a large Canadian university sample.Methods: Undergraduate students (n = 837; mean age = 21 years) from the University of Toronto completed the National College Health Assessment survey. The survey consists of approximately 300 items, including assessments of student health status, mental health and health-risk behaviours. Latent class analysis was used to identify patterning based on eight salient health-risk behaviours (marijuana use, other illicit drug use, risky sex, smoking, binge drinking, poor diet, physical inactivity, and insufficient sleep). Results:A three-class model based on student behavioural patterns emerged: "typical," "high-risk" and "moderately healthy." Results also found high-risk students reporting significantly higher levels of stress than typical students (χ 2 (1671) = 7.26, p < .01). Conclusion:Students with the highest likelihood of engaging in multiple health-risk behaviours reported poorer mental health, particularly as it relates to stress. Although these findings should be interpreted with caution due to the 28% response rate, they do suggest that interventions targeting specific student groups with similar patterning of multiple health-risk behaviours may be needed.

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Section: Discussionmentioning

confidence: 99%

Patterns of multiple health risk–behaviours in university students and their association with mental health: application of latent class analysis

Kwan

Arbour-Nicitopoulos

Duku

et al. 2016

Health Promot Chronic Dis Prev Can

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“…Morris and coworkers showed in 1953 that bus drivers were at a substantially increased risk of cardiac events than were their physically active conductor peers (91). Physical inactivity is indeed a risk factor of the same magnitude as tobacco for poor health (92). Physical inactivity may account for 6-10% of all deaths from the major chronic diseases (coronary heart disease, type 2 diabetes, breast and colon cancer), which means 5.3 million of the 57 million premature deaths worldwide (93).…”

Section: Sedentary Lifestylementioning

confidence: 99%

Helsinki alert of biodiversity and health

et al. 2015

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Urban living in built environments, combined with the use of processed water and food, may not provide the microbial stimulation necessary for a balanced development of immune function. Many chronic inflammatory disorders, including allergic, autoimmune, metabolic, and even some behavioural disorders, are linked to alteration in the human commensal microbiota. Sedentary lifestyle is associated with reduced exposure to a broad spectrum of environmental micro-organisms and surplus energy balance, both risk factors of chronic inflammatory disorders. According to the Biodiversity Hypothesis, an environment with diverse macrobiota and microbiota modifies and enriches the human microbiota, which in turn is crucial in the development and maintenance of appropriate immune function. These issues were discussed in the symposium Chronic Inflammation, Lifestyle and Environment , held in Helsinki, 20 - 22 August 2014, under the sponsorship of the Yrjo Jahnsson Foundation. This paper briefly outlines the recent findings in the context of the environment, lifestyle, and health; discusses the forces that undermine immune tolerance in urban environments; and highlights the possibilities to restore broken immune tolerance among urban dwellers, summarizing the main messages in four statements and calling for actions to combat major public health threats

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“…[1][2][3] Yet, research evidence on PA behavior among children and youth remains relatively scarce in the developing world. Although Ghana is experiencing a rapid increase in obesity, [4][5][6] there is limited evidence on PA levels of children and youth and less information about community-based practices or governmental programs for the promotion of PA.…”

mentioning

confidence: 99%

Results from Ghana’s 2014 Report Card on Physical Activity for Children and Youth

Ocansey¹,

Aryeetey²,

Sofo³

et al. 2014

Journal of Physical Activity and Health

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Background: Limited evidence exists on indicators of physical activity (PA) and guidelines for children and youth in Ghana, despite the growing burden of physical inactivity, obesity, and related morbidity. A baseline description of PA indicators of Ghanaian children and youth is hereby presented in the 2014 Ghanaian Physical Activity Report Card. Methods: Data for the report card were obtained from a very limited available literature on PA among children and youth in Ghana. PA experts independently assigned grades to indicators based on available evidence, which were then harmonized and agreed to by group consensus. Results: The report card is based on limited evidence. Thus, 2 indicators were not graded (Active Play, and Family and Peer Support). For sedentary behavior, a B grade was assigned based on evidence from the 2012 Ghana School Health Survey which indicated that 21% of children and youth were sedentary. Organized Sports was graded a C, while the remainder of indicators (Overall PA levels, Active Transportation, School, Community, and Government) were graded a D. Conclusions: About one-third of Ghanaian children and youth engage in inadequate PA. More research on PA behavior and enabling environments is needed to better grade the indicators of PA in the future and to inform policy and interventions in Ghana. Appropriate school physical education and after-school sports policies and programs are warranted.

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Rethinking our approach to physical activity

Cited by 209 publications

References 1 publication

Patterns of multiple health risk–behaviours in university students and their association with mental health: application of latent class analysis

Patterns of multiple health risk–behaviours in university students and their association with mental health: application of latent class analysis

Helsinki alert of biodiversity and health

Results from Ghana’s 2014 Report Card on Physical Activity for Children and Youth

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