The Evolving Definition of "Sedentary"

Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA.
Exercise and sport sciences reviews (Impact Factor: 4.26). 11/2008; 36(4):173-8. DOI: 10.1097/JES.0b013e3181877d1a
Source: PubMed


Studies that did not directly measure sedentary behavior often have been used to draw conclusions about the health effects of sedentariness. Future claims about the effects of sedentary, light, and moderate-to-vigorous activities on health outcomes should be supported by data from studies in which all levels of physical activity are differentiated clearly and measured independently.

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Available from: Felipe Lobelo, Mar 13, 2014
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    • "Sedentary behaviours, defined as activities with low levels of energy expenditure (Pate et al., 2008), are highly prevalent in children and adolescents (Foley et al., 2011; Pate et al., 2011; Basterfield et al., 2011). A common form of sedentary behaviour among children is screen viewing (SV), (e.g. "
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    ABSTRACT: Background: Child screen viewing (SV) is positively associated with poor health indicators. Interventions addressing rule-based parenting practices may offer an effective means of limiting SV. This study examined associations between rule-based parenting practices (limit and collaborative rule setting) and SV in 6-8-year old children. Methods: An online survey of 735 mothers in 2011 assessed: time that children spent engaged in SV activities; and the use of limit and collaborative rule setting. Logistic regression was used to examine the extent to which limit and collaborative rule setting were associated with SV behaviours. Results: 'Always' setting limits was associated with more TV viewing, computer, smartphone and game-console use and a positive association was found between 'always' setting limits for game-console use and multi-SV (in girls). Associations were stronger in mothers of girls compared to mothers of boys. 'Sometimes' setting limits was associated with more TV viewing. There was no association between 'sometimes' setting limits and computer, game-console or smartphone use. There was a negative association between collaborative rule setting and game-console use in boys. Conclusions: Limit setting is associated with greater SV. Collaborative rule setting may be effective for managing boys' game-console use. More research is needed to understand rule-based parenting practices.
    12/2015; 2:84-89. DOI:10.1016/j.pmedr.2015.01.005
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    • "<1 . 5 METs ) , light activity ( 1 . 5 – 3 . 0 METs ) ; MVPA ( >3 . 0 METs ; NIH , 2008 ; Pate et al . , 2008"
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    ABSTRACT: Given the world’s aging population, the staggering economic impact of dementia, the lack of effective treatments, and the fact a cure for dementia is likely many years away – there is an urgent need to develop interventions to prevent or at least delay dementia’s progression. Thus, lifestyle approaches to promote healthy aging are an important line of scientific inquiry. Good sleep quality and physical activity (PA) are pillars of healthy aging, and as such, are an increasing focus for intervention studies aimed at promoting health and cognitive function in older adults. However, PA and sleep quality are difficult constructs to evaluate empirically. Wrist-worn actigraphy (WWA) is currently accepted as a valid objective measure of sleep quality. The MotionWatch 8© (MW8) is the latest WWA, replacing the discontinued Actiwatch 4 and Actiwatch 7. In the current study, concurrent measurement of WWA and indirect calorimetry was performed during 10 different activities of daily living for 23 healthy older adults (aged 57–80 years) to determine cut-points for sedentary and moderate-vigorous PA – using receiver operating characteristic curves – with the cut-point for light activity being the boundaries between sedentary and moderate to vigorous PA. In addition, simultaneous multi-unit reliability was determined for the MW8 using inter-class correlations. The current study is the first to validate MW8 activity count cut-points – for sedentary, light, and moderate to vigorous PA – specifically for use with healthy older adults. These cut-points provide important context for better interpretation of MW8 activity counts, and a greater understanding of what these counts mean in terms of PA. Hence, our results validate another level of analysis for researchers using the MW8 in studies aiming to examine PA and sleep quality concurrently in older adults.
    Frontiers in Aging Neuroscience 08/2015; 7. DOI:10.3389/fnagi.2015.00165 · 4.00 Impact Factor
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    • "While this 4MET value is now commonly used for defining the minimum threshold for moderate-to-vigorousintensity physical activity (MVPA) in youth, the threshold for sedentary/light-intensity activity has not been similarly addressed. Sedentary behavior is currently defined as any activity that yields EE values not substantively greater than REE, and this has been operationally defined as activities producing METs between 1 and 1.5 (Owen et al. 2010; Pate et al. 2008; Sedentary Behaviour Research Network 2012). "
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    ABSTRACT: The study compares MET-defined cutpoints used to classify sedentary behaviors in children using a simulated free-living design. A sample of 102 children (54 boys and 48 girls; 7-13 years) completed a set of 12 activities (randomly selected from a pool of 24 activities) in a random order. Activities were predetermined and ranged from sedentary to vigorous intensities. Participant's energy expenditure was measured using a portable indirect calorimetry system, Oxycon mobile. Measured minute-by-minute VO2 values (i.e., ml/kg/min) were converted to an adult- or child-MET value using the standard 3.5 ml/kg/min or the estimated child resting metabolic rate, respectively. Classification agreement was examined for both the "standard" (1.5 adult-METs) and an "adjusted" (2.0 adult-METs) MET-derived threshold for classifying sedentary behavior. Alternatively, we also tested the classification accuracy of a 1.5 child-MET threshold. Classification accuracy of sedentary activities was evaluated relative to the predetermined intensity categorization using receiver operator characteristic curves. There were clear improvements in the classification accuracy for sedentary activities when a threshold of 2.0 adult-METs was used instead of 1.5 METs (Se1.5 METs = 4.7 %, Sp1.5 METs = 100.0 %; Se2.0 METs = 36.9 %, Sp2.0 METs = 100.0 %). The use of child-METs while maintaining the 1.5 threshold also resulted in improvements in classification (Se = 45.1 %, Sp = 100.0 %). Adult-MET thresholds are not appropriate for children when classifying sedentary activities. Classification accuracy for identifying sedentary activities was improved when either an adult-MET of 2.0 or a child-MET of 1.5 was used.
    Arbeitsphysiologie 08/2015; DOI:10.1007/s00421-015-3238-1 · 2.19 Impact Factor
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