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Angelika Wientzek, Anna Floegel, Sven Knüppel, Matthaeus Vigl, Dagmar Drogan, Jerzy Adamski, Tobias Pischon and Heiner Boeing

The aim of our study was to investigate the relationship between objectively measured physical activity (PA) and cardiorespiratory fitness (CRF) and serum metabolites measured by targeted metabolomics in a population- based study. A total of 100 subjects provided 2 fasting blood samples and engaged in a CRF and PA measurement at 2 visits 4 months apart. CRF was estimated from a step test, whereas physical activity energy expenditure (PAEE), time spent sedentary and time spend in vigorous activity were measured by a combined heart rate and movement sensor for a total of 8 days. Serum metabolite concentrations were determined by flow injection analysis tandem mass spectrometry (FIA-MS/MS). Linear mixed models were applied with multivariable adjustment and p-values were corrected for multiple testing. Furthermore, we explored the associations between CRF, PA and two metabolite factors that have previously been linked to risk of Type 2 diabetes. CRF was associated with two phosphatidylcholine clusters independently of all other exposures. Lysophosphatidylcholine C14:0 and methionine were significantly negatively associated with PAEE and sedentary time. CRF was positively associated with the Type 2 diabetes protective factor. Vigorous activity was positively associated with the Type 2 diabetes risk factor in the mutually adjusted model. Our results suggest that CRF and PA are associated with serum metabolites, especially CRF with phosphatidylcholines and with the Type 2 diabetes protective factor. PAEE and sedentary time were associated with methionine. The identified metabolites could be potential mediators of the protective effects of CRF and PA on chronic disease risk.

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Herculina S. Kruger, Lize Havemann-Nel, Chrisna Ravyse, Sarah J. Moss and Michael Tieland

Background:

Black women are believed to be genetically less predisposed to age-related sarcopenia. The objective of this study was to investigate lifestyle factors associated with sarcopenia in black South African (SA) urban women.

Methods:

In a cross-sectional study, 247 women (mean age 57 y) were randomly selected. Anthropometric and sociodemographic variables, dietary intakes, and physical activity were measured. Activity was also measured by combined accelerometery/heart rate monitoring (ActiHeart), and HIV status was tested. Dual energy x-ray absorptiometry was used to measure appendicular skeletal mass (ASM). Sarcopenia was defined according to a recently derived SA cutpoint of ASM index (ASM/height squared) < 4.94 kg/m2.

Results:

In total, 8.9% of the women were sarcopenic, decreasing to 8.1% after exclusion of participants who were HIV positive. In multiple regressions with ASM index, grip strength, and gait speed, respectively, as dependent variables, only activity energy expenditure (β = .27) was significantly associated with ASM index. Age (β = –.50) and activity energy expenditure (β = .17) were significantly associated with gait speed. Age (β = –.11) and lean mass (β = .21) were significantly associated with handgrip strength.

Conclusions:

Sarcopenia was prevalent among these SA women and was associated with low physical activity energy expenditure.

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Nerissa Campbell, Anca Gaston, Casey Gray, Elaine Rush, Ralph Maddison and Harry Prapavessis

Background:

Accurate assessment of physical activity energy expenditure (PAEE) among adolescents is important for surveillance, evaluating interventions, and understanding the relation between energy balance and normal physiological and behavioral growth and development. The purpose of this study was to examine the validity of the Short Questionnaire to Assess Health-Enhancing Physical Activity (SQUASH)13 for measuring PAEE among adolescents.

Methods:

The participants were seventeen adolescents (9 females; Mean age = 17.53; SD = 0.62). Energy expenditure was measured during a 9-day period with doubly labeled water (DLW). The SQUASH was self-administered on the morning of the 10th day and assessed commuting activities, leisure time activities, household activities, and activities at work and school over the previous 9 days.

Results:

A Bland-Altman plot indicated that the SQUASH underestimated PAEE compared with DLW by a mean difference of 126 kcal·d−1 (95% limits of agreement: –1,207 to 1,459 kcal·d−1), representative of a 10% underestimation. The Spearman rank order correlation coefficient showed there was a significant association between the SQUASH and DLW (r = .50, P = .04), for estimating PAEE.

Conclusion:

When using a sample of highly active adolescents, the SQUASH is a valid self-report tool for measuring PAEE at the group and individual rank order level.

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Molly P. O’Sullivan, Matthew R. Nagy, Shannon S. Block, Trevor R. Tooley, Leah E. Robinson, Natalie Colabianchi and Rebecca E. Hasson

completed the moderate-intensity condition first, 10 completed the high-intensity condition first, and 4 completed the sitting condition first. Outcome Measures Physical activity energy expenditure (PAEE) was assessed via accelerometry. The devices were initialized to collect raw data at a frequency of 30

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Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham and Søren Brage

physical activity energy expenditure (PAEE) expressed in kJ·day −1 ·kg −1 as measured using the gold standard criterion. Although the gold standard assessment method for PAEE has been used in the present study, the following methods are applicable to other target variables (e

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Ignacio Perez-Pozuelo, Thomas White, Kate Westgate, Katrien Wijndaele, Nicholas J. Wareham and Soren Brage

(VM HPF ≤ 47.61 m g ). This is based on principles from previously developed methodology which derives sedentary time estimates from wrist accelerometry data (i.e., sedentary sphere methodology [ Rowlands et al., 2016 ]), as well as estimations of physical activity energy expenditure in free

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Karin A. Pfeiffer, Kathleen B. Watson, Robert G. McMurray, David R. Bassett, Nancy F. Butte, Scott E. Crouter, Stephen D. Herrmann, Stewart G. Trost, Barbara E. Ainsworth, Janet E. Fulton, David Berrigan and For the CDC/NCI/NCCOR Research Group

The Compendium of Physical Activities standardized the coding of physical activity energy expenditure (PAEE) by activity type and intensity [metabolic equivalents (METs)] for adults ( 2 ). According to the Adult Compendium, an MET is the activity metabolic rate divided by the resting metabolic rate

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Lisa H. Colbert, Charles E. Matthews, Dale A. Schoeller, Thomas C. Havighurst and KyungMann Kim

This study examined the intensity of activity contributing to physical activity energy expenditure in older adults. In 57 men and women aged ≥ 65, total energy expenditure (TEE) was measured using doubly labeled water and resting metabolic rate was measured using indirect calorimetry to calculate a physical activity index (PAI). Sedentary time and physical activity of light and moderate to vigorous (mod/vig) intensity was measured using an accelerometer. The subjects were 75 ± 7 yrs (mean ± SD) of age and 79% female. Subjects spent 66 ± 8, 25 ± 5, and 9 ± 4% of monitor wear time in sedentary, light, and mod/vig activity per day, respectively. In a mixture regression model, both light (β = 29.6 [15.6–43.6, 95% CI]), p < .001) and mod/vig intensity activity (β = 28.7 [7.4−50.0, 95% CI]), p = .01) were strongly associated with PAI, suggesting that both light and mod/vig intensity activities are major determinants of their physical activity energy expenditure.

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Karry Dolash, Meizi He, Zenong Yin and Erica T. Sosa

Background:

Park features’ association with physical activity among predominantly Hispanic communities is not extensively researched. The purpose of this study was to assess factors associated with park use and physical activity among park users in predominantly Hispanic neighborhoods.

Methods:

Data were collected across 6 parks and included park environmental assessments to evaluate park features, physical activity observations to estimate physical activity energy expenditure as kcal/kg/minute per person, and park user interviews to assess motivators for park use. Quantitative data analysis included independent t tests and ANOVA. Thematic analysis of park user interviews was conducted collectively and by parks.

Results:

Parks that were renovated had higher physical activity energy expenditure scores (mean = .086 ± .027) than nonrenovated parks (mean = .077 ± .028; t = −3.804; P < .01). Basketball courts had a significantly higher number of vigorously active park users (mean = 1.84 ± .08) than tennis courts (mean = .15 ± .01; F = 21.9, η2 = 6.1%, P < .01). Thematic analysis of qualitative data revealed 4 emerging themes—motivation to be physically active, using the play spaces in the park, parks as the main place for physical activity, and social support for using parks.

Conclusion:

Renovations to park amenities, such as increasing basketball courts and trail availability, could potentially increase physical activity among low-socioeconomic-status populations.

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Jeffery J. Honas, Richard A. Washburn, Bryan K. Smith, Jerry L. Greene, Galen Cook-Wiens and Joseph E. Donnelly

The aim of this investigation was to develop an equation to estimate physical activity energy expenditure (PAEE) during a 10-min physically active academic lesson using The System for Observing Fitness Instruction Time (SOFIT) and demographic information. PAEE (portable indirect calorimeter) and physical activity (SOFIT) were simultaneously assessed in 38, 2nd through 5th grade children. PAEE and SOFIT were 3.04 ± 1.1 (kcal/min) and 3.8 ± 0.4 (score), respectively. PAEE was predicted from SOFIT score and body weight [PAEE (kcal/min) = (1.384*SOFIT + 0.084*weight (kg)—5.126), R = .81, SEE = 1.23 kcal/min]. PAEE measured by indirect calorimeter and predicted from SOFIT and body weight were 3.04 ± 1.1 (kcal/min) and 3.04 ± 0.9 kcal/min) respectively. SOFIT and body weight may provide a useful measure of PAEE associated with classroom based physical activity.