Multidimensional Physical Activity

Thompson, Dylan; Peacock, Oliver; Western, Max; Batterham, Alan M.

doi:10.1249/jes.0000000000000039

Cited by 83 publications

(68 citation statements)

References 30 publications

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“…Movement in the vertical plane typically occurs more during lower body than upper body physical activities. Thus, our results support using multiple methods to assess the multidimensional aspects of physical activity (D. Thompson et al 2015 ).…”

Section: Discussionsupporting

confidence: 84%

“…A unique aspect of our study is that we used both self-report and objective measures of physical activity to profile the multidimensional nature of physical activity (D. Thompson, Peacock, Western, and Batterham 2015 ). We found similar weak associations using both a self-report questionnaire and an accelerometer to measure physical activity and muscle strength (r = 0.139–0.186); findings which contrast the small body of literature utilizing an accelerometer to examine these relationships (Gerdhem et al 2008 ; Morie et al 2010 ).…”

Section: Discussionmentioning

confidence: 99%

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Relationships between physical activity and muscular strength among healthy adults across the lifespan

et al. 2015

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The purpose of this study was to examine relationships between objective and self-report measures of physical activity and muscle strength among healthy adults ranging in age from 20 to 91 years. Participants (n = 412) were mostly Caucasian men (48 %) and women (52 %) 43.9 ± 16.1 year of age with a body mass index (BMI) of 26.4 ± 4.8 kg/m2. Physical activity was measured objectively with an accelerometer and by self-report with the Paffenbarger Physical Activity Questionnaire. Upper and lower body muscle strength were measured with an isokinetic dynamometer and handgrip strength with a static dynamometer. Multivariate regression assessed relationships between physical activity and muscle strength. The strongest correlates of upper body strength including handgrip strength were gender (r = −0.861 to −0.716), age (r = −0.445 to −0.241), BMI (r = 0.134–0.397), and physical activity (r = 0.093–0.186). The strongest correlates of lower body strength were gender (r = −0.772 to −0.634), age (r = −0.663 to −0.445), BMI (r = 0.160–0.266), and physical activity (r = −0.139 to 0.151). The strongest correlates of muscle strength were gender (explaining 40–74 % of the variance), age (6–44 %), and BMI (2–16 %), while physical activity correlations were weaker (1–3 %). Conflict surrounding the influence of a physically active lifestyle on muscle strength with age may be due to the stronger influences of other factors that supersede those of physical activity whether measured objectively or by self-report methods.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Relationships between physical activity and muscular strength among healthy adults across the lifespan

et al. 2015

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“…Our calibration method focused on total time in MVPA and SB so the estimates may not directly equate with values needed to directly inform public health guidelines for MVPA or quantification of SB patterns. Additional work with related models could make it possible to capture additional metrics from the same data to provide a more complete profile as advocated by some researchers (29, 30). …”

Section: Discussionmentioning

confidence: 99%

Calibration of Self-Report Measures of Physical Activity and Sedentary Behavior

Welk

Beyler

Kim

et al. 2017

Medicine &Amp; Science in Sports &Amp; Exercise

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Introduction Calibration equations offer potential to improve the accuracy and utility of self-report measures of physical activity (PA) and sedentary behavior (SB) by re-scaling potentially biased estimates. The present study evaluates calibration models designed to estimate PA and SB in a representative sample of adults from the Physical Activity Measurement Survey (PAMS). Methods Participants in the PAMS project completed replicate single day trials that involved wearing a Sensewear armband (SWA) monitor for 24 hours followed by a telephone administered 24-hour physical activity recall (PAR). Comprehensive statistical model selection and validation procedures were used to develop and test separate calibration models designed to predict objectively-measured SB and moderate to vigorous PA (MVPA from self-reported PAR data. Equivalence testing was used to evaluate the equivalence of the model-predicted values with the objective measures in a separate holdout sample. Results The final prediction model for both SB and MVPA included reported time spent in SB and MVPA, as well as terms capturing sex, age, education, and BMI. Cross-validation analyses on an independent sample exhibited high correlations with observed SB (r = 0.72) and MVPA (r = 0.75). Equivalence testing demonstrated that the model-predicted values were statistically equivalent to the corresponding objective values for both SB and MVPA. Conclusion The results demonstrate that simple regression models can be used to statistically adjust for over or underestimation in self-report measures among different segments of the population. The models produced group estimates from the PAR that were statistically equivalent to the observed time spent in SB and MVPA obtained from the objective SWA monitor; however additional work is needed to correct for estimates of individual behavior.

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“…I argue that HIT deserves a prominent place among a smörgåsbord of physical activity and exercise options [ 17 ]. A common misunderstanding among HIT detractors is the belief that the purpose of HIT interventions is to increase population physical activity levels.…”

Section: High-intensity Exercise Training Could Be a Big Hit: Time Tomentioning

confidence: 99%

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

Biddle

Batterham

2015

Int J Behav Nutr Phys Act

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273

235

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BackgroundThe 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.DiscussionBiddle 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.SummaryBiddle 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.

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Multidimensional Physical Activity

Cited by 83 publications

References 30 publications

Relationships between physical activity and muscular strength among healthy adults across the lifespan

Relationships between physical activity and muscular strength among healthy adults across the lifespan

Calibration of Self-Report Measures of Physical Activity and Sedentary Behavior

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

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