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

• Main Authors: N. D. Barringer (Author), S. M. Pasiakos (Author), H. L. McClung (Author), A. P. Crombie (Author), L. M. Margolis (Author)
• Format: Book
• Published: Taylor & Francis Group, 2018-04-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1550-2783; 10.1186/s12970-018-0219-x
• Keywords: military; energy expenditure; energy balance; energy deficit; Nutrition. Foods and food supply; TX341-641; Sports medicine; RC1200-1245
• Source: Journal of the International Society of Sports Nutrition, Vol 15, Iss 1 (2018)

Background 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. Purpose Generate a predictive equation estimating energy requirements of SOF. Methods 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. Results 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. Conclusion 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.