Towards Integrated Physical Activity Profiling

Recently, there has been some discussion of whether it is possible to score highly in one dimension of physical activity behaviour (e.g., moderate intensity exercise) whilst also scoring poorly in another (e.g., sedentary time). Interestingly, direct empirical observations to support these proposals are lacking. New technologies now enable the capture of physical activity thermogenesis on a minute-by-minute basis and over a sustained period. We used one of the best available technologies to explore whether individuals can score differently in various physiologically-important physical activity dimensions. We determined minute-by-minute physical activity energy expenditure over 7 days in 100 men aged 28 ± 9 years. We used combined accelerometry and heart rate with branched equation modelling to estimate energy expenditure and extracted data for key physical activity outcomes and descriptors. Although some physical activity outcomes were tightly correlated, the attainment of one threshold for a given physical activity dimension did not automatically predict how well an individual scored in another dimension (with bivariate correlations ranging from 0.05 to 0.96). In one illustrative example of this heterogeneity, although 41 men showed a relatively low Physical Activity Level (total energy expenditure/resting energy expenditure ≤ 1.75), only 17% (n=7) of these men showed consistently low physical activity across other dimensions (moderate intensity activity, vigorous intensity activity, and sedentary time). Thus, physical activity is highly heterogeneous and there is no single outcome measure that captures all the relevant information about a given individual. We propose that future studies need to capture (rather than ignore) the different physiologically-important dimensions of physical activity via generation of integrated, multidimensional physical activity 'profiles'

High-intensity interval exercise training for public health: a big HIT or shall we HIT it on the head?

Background: The efficacy of high-intensity interval training for a broad spectrum of cardio-metabolic health outcomes is not in question. Rather, the effectiveness of this form of exercise is at stake. In this paper we debate the issues concerning the likely success or failure of high-intensity interval training interventions for population-level health promotion. Discussion: Biddle maintains that high-intensity interval training cannot be a viable public health strategy as it will not be adopted or maintained by many people. This conclusion is based on an analysis of perceptions of competence, the psychologically aversive nature of high-intensity exercise, the affective component of attitudes, the less conscious elements of motivated behaviour that reflect our likes and dislikes, and analysis using the RE-AIM framework. Batterham argues that this appraisal is based on a constrained and outmoded definition of high-intensity interval training and that truly practical and scalable protocols have been - and continue to be - developed. He contends that the purported displeasure associated with this type of exercise has been overstated. Biddle suggests that the way forward is to help the least active become more active rather than the already active to do more. Batterham claims that traditional physical activity promotion has been a spectacular failure. He proposes that, within an evolutionary health promotion framework, high-intensity interval training could be a successful population strategy for producing rapid physiological adaptations benefiting public health, independent of changes in total physical activity energy expenditure. Summary: Biddle recommends that we focus our attention elsewhere if we want population-level gains in physical activity impacting public health. His conclusion is based on his belief that high-intensity interval training interventions will have limited reach, effectiveness, and adoption, and poor implementation and maintenance. In contrast, Batterham maintains that there is genuine potential for scalable, enjoyable high-intensity interval exercise interventions to contribute substantially to addressing areas of public health priority, including prevention and treatment of Type 2 diabetes and cardiovascular disease

Gait retraining and incidence of medial tibial stress syndrome in army recruits

Purpose: Gait retraining, comprising bio-feedback and/or an exercise intervention, might reduce the risk of musculoskeletal conditions. The purpose was to examine the effect of a gait retraining program on medial tibial stress syndrome incidence during a 26 week basic military training regimen.Methods: A total of 450 British Army recruits volunteered. On the basis of a baseline plantar pressure variable (mean foot balance during the first 10% of stance), participants classified as at-risk of developing medial tibial stress syndrome (n = 166) were randomly allocated to an intervention (n = 83) or control (n = 83) group. The intervention involved supervised gait retraining, including exercises to increase neuromuscular control and flexibility (3 sessions per week) and bio-feedback enabling internalization of the foot balance variable (1 session per week). Both groups continued with the usual military training regimen. Diagnoses of medial tibial stress syndrome over the 26 week regimen were made by physicians blinded to group assignment. Data were modelled in a survival analysis using Cox regression, adjusting for baseline foot balance and time to peak heel rotation.Results: The intervention was associated with a substantially reduced instantaneous relative risk of medial tibial stress syndrome versus control, with an adjusted hazard ratio of 0.25 (95% confidence interval, 0.05 to 0.53). The number needed to treat to observe one additional injury-free recruit in intervention versus control at 20 weeks was 14 (11 to 23) participants. Baseline foot balance was a nonspecific predictor of injury, with a hazard ratio per 2-SD increment of 5.2 (1.6 to 53.6). Conclusions: The intervention was effective in reducing incidence of medial tibial stress syndrome in an at-risk military sampl

Predicting Future Weight Status From Measurements Made In Early Childhood: A Novel Longitudinal Approach Applied To Millennium Cohort Study Data

Background/objective: There are reports that childhood obesity tracks into later life. Nevertheless, some tracking statistics, e.g. correlations, do not quantify individual agreement, while others, e.g. diagnostic test statistics, can be difficult to translate into practice. We aimed to employ a novel analytic approach, based on ordinal logistic regression, to predict weight status of 11-year-old children from measurements at age 5.Subjects/methods: UK 1990 growth references were used to generate clinical weight status categories of 12 076 children enrolled in the Millennium Cohort Study. Using ordinal regression, we derived the predicted probability (percent chances) of an 11-year-old child becoming underweight, normal weight, overweight, obese and severely obese from their weight status category at age 5.Results: The chances of becoming obese (including severely obese) at age 11 were 5.7% (95% CI: 5.2% to 6.2%) for a normal weight 5-year-old and 32.3% (29.8% to 34.8%) for an overweight 5-year-old. An obese 5-year-old child had a 68.1% (63.8% to 72.5%) chance of remaining obese at 11 years. Severely obese 5-year-old children had a 50.3% (43.1% to 57.4%) c 50 hance of remaining severely obese. There were no substantial differences between sexes. Non-deprived obese 5- year-old boys had a lower probability of remaining obese than deprived obese boys: -21.8% (-40.4% to -3.2%). This association was not observed in obese 5-year-old girls, in whom the non-deprived group had a probability of remaining obese 7% higher (-15.2% to 29.2%). The sex difference in this interaction of deprivation and baseline weight status was therefore -28.8% (-59.3% to 1.6%).Conclusions: We have demonstrated that ordinal logistic regression can be an informative approach to predict the chances of a child changing to, or from, an unhealthy weight status. This approach is easy to interpret and could be applied to any longitudinal dataset with an ordinal outcome