166 posts · 171,464 views
Obesity is a complex and multifactorial chronic disease that remains a military and public health priority in the United States. Recently, we’ve identified a strong association between obesity prevalence and altitude within the US. Our findings were surprising because they indicated the magnitude of this association was large and the pattern of association exhibited a curvilinear dose response in 500 meter categories of altitude. There was a 4-5 fold increase in obesity prevalence at low altitude as compared with the highest altitude category after controlling for diet, activity level, smoking, demographics, temperature, and urbanization. We published our findings in the International Journal of Obesity (advance online publication doi:10.1038/ijo.2013.5) and presented at the 2013 American College of Preventive Medicine conference.... Read more »
Voss, J., Masuoka, P., Webber, B., Scher, A., & Atkinson, R. (2013) Association of elevation, urbanization and ambient temperature with obesity prevalence in the United States. International Journal of Obesity. DOI: 10.1038/ijo.2013.5
Today’s post comes courtesy of my friend and frequent collaborator Dr Jean-Philippe Chaput. It is a Letter to the Editor that was written by Dr Chaput along with Angelo Tremblay and Eric Doucet in response to a recent paper in the Journal of the American Medical Association.... Read more »
One factor that may link sedentary behaviour with increased morbidity and mortality is the accumulation of visceral fat (see figure below), which has been linked with various chronic diseases and even death. For example, see this study by our former labmate Jen Kuk, which found that visceral fat was an independent predictor of premature death in men. While other types of body fat (e.g. butt fat) don’t tend to have a huge health impact, excess visceral fat is definitely a bad thing (more details on how body fat distribution influences health here).... Read more »
Saunders, T., Tremblay, M., Després, J., Bouchard, C., Tremblay, A., & Chaput, J. (2013) Sedentary Behaviour, Visceral Fat Accumulation and Cardiometabolic Risk in Adults: A 6-Year Longitudinal Study from the Quebec Family Study. PLoS ONE, 8(1). DOI: 10.1371/journal.pone.0054225
Mental work stimulates cardiovascular functions in healthy adults and a reduction in cardiac parasympathetic modulation could be one mechanism involved in such a response. The influence of sex on these cardiovascular responses remains ambiguous. The aim of the study was to evaluate cardiovascular impacts of mental work in healthy individuals and whether sex influences cardiovascular responses induced by mental work.... Read more »
Emilie Perusse-Lachance, Angelo Tremblay, Jean-Philippe Chaput, Paul Poirier, Normand Teasdale, Vicky Drapeau, Caroline Senecal, & Patrice Brassard. (2012) Mental Work Stimulates Cardiovascular Responses through a Reduction in Cardiac Parasympathetic Modulation in Men and Women. Bioenergetics: Open Access. info:/http://dx.doi.org/10.4172/2167-7662.S1-001
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As many of our regular readers know (and often remind each other in the comments), body mass index is not a great predictor of an individual’s risk of disease. It predicts health risk at the population level, but even then there is considerable variation within a given BMI range. Peter has talked about this at length in the past, when he summarized 3 reasons why BMI is a poor measure of your health:... Read more »
Krakauer NY, & Krakauer JC. (2012) A new body shape index predicts mortality hazard independently of body mass index. PloS one, 7(7). PMID: 22815707
A very quick post today to point out an interesting new paper in the journal BMJ Open. Written by Peter Katzmarzyk (Peter J and I took his epi course while at Queen’s University) and I-Min Lee, the paper estimates the impact of both sedentary behaviour (e.g. all sitting) and television viewing on the life expectancy of Americans.... Read more »
Katzmarzyk, P, & Lee, I-Min. (2012) Sedentary behaviour and life expectancy in the USA: a cause-deleted life table analysis. BMJ Open. DOI: 10.1136/bmjopen-2012-000828
That’s right – the average Canadian kid spends almost 8 full hours in front of a screen based device everyday. I hadn’t heard the stat before so I went to the reference paper, which can be accessed for free here.
The data comes from the Canadian Youth Smoking Survey (YSS), which is a nationally representative survey of nearly 52 000 Canadians in grades 6-12 (when a survey is nationally representative, it means that the distribution of participants from various regions, ethnic and linguistic backgrounds match pretty closely with that of the nation as a whole).... Read more »
Leatherdale ST, & Ahmed R. (2011) Screen-based sedentary behaviours among a nationally representative sample of youth: are Canadian kids couch potatoes?. Chronic diseases and injuries in Canada, 31(4), 141-6. PMID: 21978636
Exciting news – this week the Sedentary Behaviour Research Network published an updated definition of the terms “sedentary” and “sedentary behaviour” in French and English in the journals Applied Physiology, Nutrition and Metabolism and Movement & Sport Sciences – Science & Motricité.
In brief, the new definition states that to be engaging in “sedentary behaviour”, you must meet three very basic criteria:
You must be expending very little energy (≤1.5 Metabolic equivalents)
You must be sitting or lying down
You must be awake... Read more »
Sedentary Behaviour Research Network. (2012) Letter to the Editor: Standardized use of the terms "sedentary" and "sedentary behaviours". Applied physiology, nutrition, and metabolism . PMID: 22540258
Have you ever wondered how much salt is actually in those French fries from your favourite fast food outlet? New research published this week in the Canadian Medical Association Journal suggests that the answer to this question depends largely on your home address.... Read more »
Dunford, E., Webster, J., Woodward, M., Czernichow, S., Yuan, W., Jenner, K., Mhurchu, C., Jacobson, M., Campbell, N., & Neal, B. (2012) The variability of reported salt levels in fast foods across six countries: opportunities for salt reduction. Canadian Medical Association Journal. DOI: 10.1503/cmaj.111895
Last week a fascinating study was published by SBRN member David Dunstan and colleagues in Australia, which examined the acute (e.g. short-term) impact of uninterrupted sitting on metabolic health. In this new study, individuals with overweight or obesity were asked to perform 3 separate conditions in random order.
Uninterrupted sitting – participants sat for 5 consecutive hours
Sitting plus light intensity breaks – similar to the uninterrupted sitting condition, except that participants had a 2 minute walk break at a light intensity every 20 minutes throughout the day
Sitting plus moderate intensity breaks – similar to the light intensity breaks condition, except that the breaks were at a moderate intensity
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Dunstan, D., Kingwell, B., Larsen, R., Healy, G., Cerin, E., Hamilton, M., Shaw, J., Bertovic, D., Zimmet, P., Salmon, J.... (2012) Breaking Up Prolonged Sitting Reduces Postprandial Glucose and Insulin Responses. Diabetes Care. DOI: 10.2337/dc11-1931
Nygaard, H., Tomten, S., & Høstmark, A. (2009) Slow postmeal walking reduces postprandial glycemia in middle-aged women. Applied Physiology, Nutrition, and Metabolism, 34(6), 1087-1092. DOI: 10.1139/H09-110
Until recently, only one set of physical activity guidelines was available for children under the age of 5 years (National Association for Sport and Physical Education, 2009). These guidelines were useful recommendations for parents and caregivers with advice on healthy living, but were informed largely on expert consensus and not by the rigor of a systematic review. Last year, Australia and the United Kingdom (UK) were the first to release evidence-based guidelines and recommend that preschoolers be physically active for at least 180 minutes per day (Department of Health and Ageing, Australia 2011; Start Active, Stay Active, United Kingdom, 2011). The full guidelines can be found here and here. However, a systematic review was still not publicly available to inform the development Canadian guidelines for this age group.... Read more »
Tremblay MS, LeBlanc AG, Carson V, Choquette L, Connor Gorber S, Dillman C, Duggan M, Gordon MJ, Hicks A, Janssen I.... (2012) Canadian Physical Activity Guidelines for the Early Years (aged 0–4 years). Applied Physiology, Nutrition and Metabolism. info:/
I recently came across a very interesting study published in Circulation in 2001. In it, authors Darren McGuire and colleagues perform the 30-year follow-up on a group of 5 men who had taken part in the Dallas Bed Rest and Training Study (DBRTS). The DBRTS took place in 1966, when all 5 men were healthy 20 year-olds. They were assessed extensively at baseline, following 3 months of bed rest, and following 8 weeks of physical training. In 1996 these same 5 men were re-assessed, allowing the researchers to compare the influence of 3 weeks of bed rest and 30 years of aging on markers of fitness.... Read more »
McGuire DK, Levine BD, Williamson JW, Snell PG, Blomqvist CG, Saltin B, & Mitchell JH. (2001) A 30-year follow-up of the Dallas Bedrest and Training Study: I. Effect of age on the cardiovascular response to exercise. Circulation, 104(12), 1350-7. PMID: 11560849
Today we will look at other potential contributors to the pediatric obesity epidemic which I didn’t include in my paper. There are a few reasons for that – some risk factors are ones that I just felt didn’t have much evidence behind them, others were similar to ones that were included, and some just didn’t fit within the space constraints (since this paper was originally written for my comprehensive exams, it was limited to 15 pages).... Read more »
In Part 1 we examined the impact of changes in physical activity and sedentary behaviour, in Part 2 we looked at changes in food intake, and in Part 3 we looked at sleep, breastfeeding, maternal age and pollution. Today we look at the evidence (or lack thereof) linking adult obesity with the pediatric obesity epidemic, then examine the relative contributions of all of the risk factors we’ve discussed so far.... Read more »
In Part 1 we examined the impact of changes in physical activity and sedentary behaviour, and in Part 2 we looked at changes in food intake. Today we look at the evidence (or lack thereof) linking sleep, pollution, maternal age and breastfeeding with the pediatric obesity epidemic.... Read more »
Just because one study finds a relationship between A and B, does not mean that other studies will be able to replicate that finding, or that it will extend to other situations. On the face of it, this seems like an incredibly obvious statement. And yet it’s something that newspapers often forget, and which I think could have some very negative consequences.... Read more »
Goldfield, G., Kenny, G., Hadjiyannakis, S., Phillips, P., Alberga, A., Saunders, T., Tremblay, M., Malcolm, J., Prud'homme, D., Gougeon, R.... (2011) Video Game Playing Is Independently Associated with Blood Pressure and Lipids in Overweight and Obese Adolescents. PLoS ONE, 6(11). DOI: 10.1371/journal.pone.0026643
Carson, V., & Janssen, I. (2011) Volume, patterns, and types of sedentary behavior and cardio-metabolic health in children and adolescents: a cross-sectional study. BMC Public Health, 11(1), 274. DOI: 10.1186/1471-2458-11-274
Some exciting news this week - the world’s first systematic review on the relationship between sedentary behaviour and health in school-aged children has just been published online in the International Journal of Behavioural Nutrition and Physical Activity. I am one of 8 authors on the review (nestled nicely in the middle), which was created to inform the Canadian Sedentary Behaviour Guidelines, released earlier this year.... Read more »
Tremblay, M., LeBlanc, A., Kho, M., Saunders, T., Larouche, R., Colley, R., Goldfield, G., & Connor Gorber, S. (2011) Systematic review of sedentary behaviour and health indicators in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 98. DOI: 10.1186/1479-5868-8-98
by Travis Saunders, MSc, CEP in Obesity Panacea
Image by mhowry
Travis’ Note: Today’s post comes from PhD Student Ash Routen. You can find out more about Ash and his work at the bottom of this post.
Consistent with the majority of developed countries, a significant proportion of children here in the UK are overweight or obese (around 30% of 10-11 year olds as of 2010). How do we know this? Well, since 2005 the UK Department of Health have been operating the ‘National Child Measurement Programme’ (NCMP) a nationwide public health surveillance initiative, which to the best of my knowledge is the biggest in Europe. Annually over one million children (aged 4-5 and 10-11 years) have their height and weight measured by teams of school nurses, these measures are then used to calculate body mass index (BMI), and then BMI determined weight status. This data is used to inform local planning and delivery of weight intervention/healthy lifestyle services, and to produce national overweight and obesity prevalence figures.
As BMI differs between genders and increases with age during childhood, the NCMP categorise the children’s weight status as underweight, healthyweight, overweight or obese, by comparing their BMI to children the same age and gender using a BMI growth reference chart. So, children above the 91st percentile (91% of all children used in the reference sample) are defined as overweight, and above the 98th as obese. In most regions, the child’s weight status is fed back to the parent’s and children by letter, with information on local weight management initiatives if they are categorised as underweight, overweight, or obese. Of course you can imagine the furore of some parent’s and thus the NCMP has attracted quite a lot of negative media attention (e.g.http://www.telegraph.co.uk/health/children_shealth/7514267/Letter-to-fat-four-year-old-prompts-complaint-from-obesity-group.html) as a result. As such there is great onus on ensuring the quality of data and identifying any sources of potential ‘error’, which include human (i.e. reliability of the nurses measurements), technical (i.e. reliability of the scales/height measuring device) and biological ‘error’(i.e. both daily and monthly variation in BMI).
I was interested in the impact of the time of day when the measurements are conducted. The nurses follow a standardised protocol, but can take the measurements at any time of day (and indeed the month of measurement may also vary). We know that our weight fluctuates throughout the day, and that we shrink a little after rising due to gravity pushing us back down! What we didn’t know was if combined variation in these measures would result in a change in BMI. Who cares right? they would be at no more or less risk of adverse health if their BMI shifts a little…but could it be enough for those whom are on the cusp of a BMI weight category (e.g. 90.5 percentile) to be differently classified due to the time of day they are measured?
What did we do?
To investigate this issue we took a sample of 74 children (aged 10-11 years) and measured their height and weight in the morning (0900-1045 hr) and again in the afternoon (1300-1500). From this we calculated their BMI, BMI percentile and weight status category using two set’s of BMI percentile cut-off’s, namely clinical cut-off’s (overweight: 91st and obese: 98th) as used by the NCMP and clinicians, and population monitoring cut-off’s as used mainly by researchers (85th and 95th centiles).
What did we find?
Not surprisingly in the afternoon all the children were shorter (-0.5 cm), however only girls were heavier (+0.1 kg), and BMI (+0.12 kg.m2), and BMI percentile was greater (+2.5 centiles) in all children. In relation to weight status categories there were no shifts in the number of people in each category from morning to afternoon, but on an individual level there were some interesting findings. When applying the clinical BMI cut-off’s we saw that one girl moved from healthyweight to overweight, and using the population monitoring cut-off’s , two girls moved from the healthyweight to overweight category, and one moved from the overweight to obese category with BMI increases of only 0.30, 0.55 and 0.26 kg/m2, respectively.
What are the implications?
We saw that it only takes a height loss of about 1 cm and an increase in weight of about 150g to shift a girls BMI category if they are near to the cut-off threshold. As only a few individuals changed (and this was a small sample) the results may seem inconsequential. However both on an individual and national level there may be some impact. Nationally, comparison of prevalence data (and thus future direction of resources) between schools and regions (and this is where I speculate) may be may be clouded if they measure a greater proportion of their children either in the morning or the afternoon. Whilst the extrapolation of the present observations using the clinical BMI cut-off’s (which the NCMP use in parental feedback) to the potential impact on the national NCMP data is tenuous, it is worthy of consideration. As the time of day when measurements are taken is not standardised, or recorded by the NCMP it could be supposed that 50% of the measurements taken are performed in the morning and 50% in the afternoon. If one in every 27 (3.7%) healthy weight girls (as we found in our sample) were on the cusp of overweight in samples measured in the morning they could well have been categorised as overweight had they been measured in the afternoon. Out of the 162,640 healthyweight girls measured by the NCMP in 2009/10 this would represent 6017 girls being classified as overweight instead of healthyweight, which hinders a ‘true’ assessment of prevalence data.
Arguably of more importance is that potentially 6017 parents and children would be informed that their child is overweight, due to their misfortune of being measured in the afternoon as opposed to the morning. We know that children labelled as overweight may be at greater risk of stigmatisation, teasing and anxiety; it is not unimaginable therefore that such a letter could trigger unhealthy activity and dietary habits and unnecessary parental intervention. For all the useful information such screening programmes provide us researchers, we must be cognisant of the discourse surrounding the issue of childhood obesityand consider the impact of such surveillance programmes on individual children and families (see Michael Gard’s work for a thought provoking viewpoint: http://bod.sagepub.com/content/13/4/118.extract).
What can we do?
In our paper we conclude that arguably, to increase data reliability the time of day in which the measurements are performed should be standardized (to either morning or afternoon) by the NCMP and indeed any public health surveillance programme that does not standardise measurement – this would at least ensure that all children are treated equitably. We do not have one ‘true BMI’, but fluctuate about a mean value on a daily and weekly basis. Therefore for the purposes of comparison, and analysis of trends year-on year, we should choose either to measure in the morning or afternoon. However, on the individual level it appears wise to ensure that children are measured in the morning to avoid unfavourable shifts in weight category and associated psychosocial implications of labelling. Standardisation of the timing of taking the measurements is one simple revision to the procedures of such surveillance programmes that could help to limit the impact of at least one potential ‘error’ variable.
About the author: Ash Routen is in the final months of his doctoral studies at the University of Worcester, UK examining the impact of pedometer interventions on habitual PA in kids, with an interest in the assessment of body composition and objective physical activity measurement in kids. He can be found on Twitter @AshRouten.
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Routen, A., Edwards, M., Upton, D., & Peters, D. (2011) The impact of school-day variation in weight and height on National Child Measurement Programme body mass index-determined weight category in Year 6 children. Child: Care, Health and Development, 37(3), 360-367. DOI: 10.1111/j.1365-2214.2010.01204.x
by Travis Saunders, MSc, CEP in Obesity Panacea
Earlier this year I posted an infographic on the health impact of sedentary behaviour which has generated plenty of discussion both here and elsewhere. Many people are understandably skeptical about the relationship between sedentary behaviour and mortality, so I was excited about the recent publication of two recent systematic reviews focusing on just this issue.
The first, published in the American Journal of Preventive Medicine by Karin Proper and colleagues, focused on the prospective association between sedentary behaviour and obesity, CVD and diabetes risk, as well as mortality. Somewhat surprisingly, they found little evidence that sedentary behaviour was associated with increased body weight or other health risk factors, despite consistent associations between sedentary behaviour and risk of death. From the paper:
Based on the inconsistent findings among the  prospective studies identified, there is insufficient evidence for a longitudinal relationship between sedentary behavior and body weight/BMI gain.
Risk of Being Overweight or Obese:
Based on the inconsistent findings among the  studies, there is insufficient evidence for the relationship between sedentary behavior and the risk for overweight or obesity.
Increased Waist Girth:
Based on this single study, there is insufficient evidence for the relationship between sedentary behavior and waist gain.
Risk of Developing Diabetes:
Based on the consistent findings of… two low-quality studies, there is moderate evidence for a significant positive relationship between the time spent sitting and the risk for type 2 diabetes.
Risk of Cardiovascular Disease:
Based on the findings of the 4 studies identified, there is insufficient evidence for a significant relationship between sedentary behavior and various CVD risk factors.
Based on the inconsistencies found between and within the two studies identified, there is insufficient evidence for the relationship between sedentary behavior andendometrial cancer.
As I said, there’s really not much evidence so far that sedentary behaviour is prospectively linked with these various markers of health. However, it does seem to be associated with increased risk of death from various causes:
Based on the findings of the two high-quality studies, there is strong evidence for a relationship between sedentary behavior and mortality from all causes and from CVD, but no evidence for the relationship between sedentary behavior and mortality from cancer.
Another review published late last year focused specifically on sedentary behaviour and cancer outcomes. It’s conclusions were a bit stronger than the review above, but the evidence for a relationship between sedentary behaviour and cancer still seems pretty weak. From the review:
The literature review identified 18 articles pertaining to sedentary behavior and cancer risk, or to sedentary behavior and health outcomes in cancer survivors. Ten of these studies found statistically significant, positive associations between sedentary behavior and cancer outcomes. Sedentary behavior was associated with increased colorectal, endometrial, ovarian, and prostate cancer risk; cancer mortality in women; and weight gain in colorectal cancer survivors. The review of the literature on sedentary behavior and biological pathways supported the hypothesized role of adiposity and metabolic dysfunction as mechanisms operant in the association between sedentary behavior and cancer.
What’s the take-home message?
Sedentary behaviour seems very likely to be associated with increased risk of all-cause and cardiovascular disease mortality, while the relationship between sedentary behaviour and cancer mortality remain quite speculative. Interestingly, prospective studies have yet to find much strong evidence linking sedentary behaviour with prospective risk of cardiovascular disease, despite being associated with increased risk of cardiovascular disease mortality. Clearly there’s still a lot to be worked out here, especially given that so few prospective studies have been performed to date. Further, 17 of 19 studies in the Proper review used self-report measures of sedentary behaviour, which can be dramatically different from directly measured sedentary behaviour.
The prospective relationships between sedentary behaviour and risk markers is likely to become more clear with time. In the meantime, it seems reasonably clear that the more you sit, the greater your risk of mortality.
Standing workstation, anyone?
Proper, K., Singh, A., van Mechelen, W., & Chinapaw, M. (2011). Sedentary Behaviors and Health Outcomes Among Adults American Journal of Preventive Medicine, 40 (2), 174-182 DOI: 10.1016/j.amepre.2010.10.015
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Proper, K., Singh, A., van Mechelen, W., & Chinapaw, M. (2011) Sedentary Behaviors and Health Outcomes Among Adults. American Journal of Preventive Medicine, 40(2), 174-182. DOI: 10.1016/j.amepre.2010.10.015
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