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Prediction equation for estimating total daily energy requirements of special operations personnel

Overview of attention for article published in Journal of the International Society of Sports Nutrition, April 2022
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  • Above-average Attention Score compared to outputs of the same age (57th percentile)

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Title
Prediction equation for estimating total daily energy requirements of special operations personnel
Published in
Journal of the International Society of Sports Nutrition, April 2022
DOI 10.1186/s12970-018-0219-x
Pubmed ID
Authors

N. D. Barringer, S. M. Pasiakos, H. L. McClung, A. P. Crombie, L. M. Margolis

Abstract

Special Operations Forces (SOF) engage in a variety of military tasks with many producing high energy expenditures, leading to undesired energy deficits and loss of body mass. Therefore, the ability to accurately estimate daily energy requirements would be useful for accurate logistical planning. Generate a predictive equation estimating energy requirements of SOF. Retrospective analysis of data collected from SOF personnel engaged in 12 different SOF training scenarios. Energy expenditure and total body water were determined using the doubly-labeled water technique. Physical activity level was determined as daily energy expenditure divided by resting metabolic rate. Physical activity level was broken into quartiles (0 = mission prep, 1 = common warrior tasks, 2 = battle drills, 3 = specialized intense activity) to generate a physical activity factor (PAF). Regression analysis was used to construct two predictive equations (Model A; body mass and PAF, Model B; fat-free mass and PAF) estimating daily energy expenditures. Average measured energy expenditure during SOF training was 4468 (range: 3700 to 6300) Kcal·d-1. Regression analysis revealed that physical activity level (r = 0.91; P < 0.05) and body mass (r = 0.28; P < 0.05; Model A), or fat-free mass (FFM; r = 0.32; P < 0.05; Model B) were the factors that most highly predicted energy expenditures. Predictive equations coupling PAF with body mass (Model A) and FFM (Model B), were correlated (r = 0.74 and r = 0.76, respectively) and did not differ [mean ± SEM: Model A; 4463 ± 65 Kcal·d- 1, Model B; 4462 ± 61 Kcal·d- 1] from DLW measured energy expenditures. By quantifying and grouping SOF training exercises into activity factors, SOF energy requirements can be predicted with reasonable accuracy and these equations used by dietetic/logistical personnel to plan appropriate feeding regimens to meet SOF nutritional requirements across their mission profile.

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 72 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 19%
Student > Bachelor 9 13%
Student > Ph. D. Student 9 13%
Student > Doctoral Student 8 11%
Professor 2 3%
Other 6 8%
Unknown 24 33%
Readers by discipline Count As %
Sports and Recreations 12 17%
Medicine and Dentistry 7 10%
Nursing and Health Professions 6 8%
Social Sciences 4 6%
Psychology 3 4%
Other 10 14%
Unknown 30 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 April 2018.
All research outputs
#13,354,637
of 23,041,514 outputs
Outputs from Journal of the International Society of Sports Nutrition
#719
of 887 outputs
Outputs of similar age
#182,121
of 440,467 outputs
Outputs of similar age from Journal of the International Society of Sports Nutrition
#686
of 851 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 887 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 58.5. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 440,467 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 851 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.