Threshold of Energy Deficit and Lower-Body Performance Declines in Military Personnel: A Meta-Regression

Abstract

Background

Negative energy balance (EB) is common during military operations, diminishing body mass and physical performance. However, the magnitude of negative EB where performance would still be maintained is not well defined.

Objective

Our objective was to explore relationships between EB and physical performance during military operations and define an acceptable negative EB threshold where performance may be maintained.

Methods

A systematic search was performed for studies that measured EB and physical performance during military training. A total of 632 articles and technical reports were screened. Lower-body power and strength were the most common performance tests across investigations and were used as physical performance outcomes. Data were extracted from nine eligible studies containing 15 independent subgroups. Meta-regression assessed changes in performance in relation to study duration (days), average daily EB, and total EB (daily EB × duration).

Results

Changes in physical performance were not associated with average daily EB or training duration. Total EB was associated with changes in lower-body power (r2 = 0.764, P < 0.001) and strength (r2 = 0.836, P < 0.001) independently and combined (r2 = 0.454, P = 0.002). Predictive equations generated from the meta-regression indicated that, for a zero to small (2%) decline in performance, total EB should be limited to − 5686 to − 19,109 kcal, for an entire operation, whereas total EB of − 39,243 to − 59,377 kcal will result in moderate (7%) to large (10%) declines in performance.

Conclusion

These data demonstrated that greater total negative EB is associated with declines in lower-body performance during military operations.

FormalPara Key Points
Declines in lower-body power and strength following strenuous military training were associated with the combination of daily energy balance (EB) and duration, expressed as total EB (daily EB × duration).
Based on the slope equations generated from the meta-regression analysis, the threshold for a zero to small (2%) decline in lower-body power and strength, total EB should be limited to − 5686 to − 19,109 kcal, respectively, for an entire operation, corresponding to a percent change in body mass of ≤ 3%.
Total EB exceeding − 39,243 to − 59,377 kcal, resulting in a percent change in body mass of ≥ 8%, will lead to moderate (7%) to large (10%) declines in lower-body power and strength.

Introduction

Periods of high energy expenditure coupled with inadequate energy intake are common during military training and combat operations, leading to periods of negative energy balance (EB; energy expenditure > energy intake) and subsequent reductions in body mass [1]. High energy expenditures largely result from sustained low- to moderate-intensity (~ 35–50% maximum oxygen uptake; VO2max) physical activity while carrying heavy loads [2,3,4], and suboptimal energy intakes have been attributed to various logistical and environmental factors that may limit both the ability and the desire to eat [5]. The duration and intensity of different military training courses vary greatly [4, 6,7,8,9,10,11]. For example, US Army soldiers endure EB of − 2700 kcal·day−1 during 10 days of Special Forces Small Unit Tactics training [9] and − 1000 kcal·day−1 during the 8-week Ranger training [10]. Under these strenuous training and field conditions, negative EB has historically been a consequence due to high energy expenditures. The resulting negative EB not only results in undesired reductions in body mass but may also compromise physical performance [11, 12].

Performance decrements sustained during military training have mainly been attributed to body mass loss, potentially driven by associated reductions in fat-free mass. Taylor et al. [13] concluded that negative EB resulting in less than a 10% loss in body mass did not impede muscle strength and VO2max in soldiers, which was corroborated by a qualitative comparison of multiple studies involving body mass loss in soldiers and athletes by Friedl [14]. However, these studies did not include the severity of negative EB or study duration as contributing factors to body mass loss. Our laboratory recently reported that during a short-term (7-day) Arctic military training where participants experienced negative EB of ~ 2900 kcal·day−1, lower-body peak power declined ~ 330 W (~ 7%) compared with baseline despite participants losing only 3% of their initial body mass during the training [8]. These findings suggest that severe negative EB may degrade physical performance before 10% body mass loss is reached, indicating that study duration and magnitude of negative EB likely contribute to declines in physical performance beyond body mass loss alone.

While sustained negative EB is common during military operations and has been shown to impact performance in both laboratory and field study conditions [8, 15,16,17], declines in performance vary by study, potentially due to differences in the severity of negative EB or training duration. With similar exposure to negative EB (~ 2500 kcal·day−1) and duration (~ 7-day) of military training, magnitude of declines in lower-body power (~ 7%) appear to be the same across studies [8, 18]. However, the exact relationship between negative EB and declines in physical performance during military training remains unclear.

The objective of this analysis was to determine the associations between EB and change in lower-body power and strength following military training operations. As it is challenging, if not impractical, for service members to achieve EB during military field operations, this analysis also sought to identify a threshold at which an acceptable level of negative EB does not significantly impact physical performance.

Methods

Literature Search Strategy

Abstracts of publications identified in PubMed (http://www.ncbi.nlm.nih.gov/pubmed) were reviewed for relevance using the Abstrackr citation program (http://abstrackr.cebm.brown.edu [16]) by two researchers (NEM and CTC). Additionally, the Defense Technical Information Center (DTIC, http://www.DTIC.mil, Fort Belvoir, VA, USA) database was searched for Department of Defense technical reports. Searches took place on 22 February 2017 and were not restricted by publication date. Exact search terms are described in Table S1 in the Electronic Supplementary Material (ESM). All terms were included in a single search of each database. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy and subsequent reference narrowing is described in Fig. 1 [19]. Reference lists from these publications were hand-searched for any reports missed by database searches. There were no language restrictions, but English search terms were used. Full-text publications and technical reports were reviewed independently by two researchers (NEM and CTC) for military relevance and the presence of physical performance and EB measurements. Discrepancies between researchers were assessed by a third investigator (LMM). The final nine publications were reviewed by all involved researchers (Table 1 [8, 10, 15, 16, 18, 20,21,22,23,24,25,26]. Further search strategy details can be found at the University of York Centre for Reviews and Dissemination (PROSPERO) website (https://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42017057607).

Fig. 1
figure1

PRISMA meta-regression search strategy diagram

Table 1 Publications included in meta-regression

Inclusion Criteria

Publications were included if military members and/or civilians performed military training or training simulations. Following the literature search, lower-body power and strength were the most common performance tests across investigations and were thus used as main physical performance outcomes. Additionally, lower-body power and strength has been reported in studies [27,28,29,30] to be a strong predictor for success of military-specific tasks (e.g., 30-m rush, 400-m run time, casualty rescue, and obstacle course time completion), indicating these outcome measures are relevant performance metrics for a military population. Included studies contained lower-body power and/or strength measurement before the military training operations (pre) and at conclusion of the training (post). Studies must also have included sufficient data to calculate EB, either by measured energy expenditure (double-labeled water [DLW]) minus measured energy intake (food records, analysis of food consumed in dining facility, and analysis of combat ration items consumed), or change in energy stores as described in Sect. 2.5. Both observational and experimental study designs were included.

Exclusion Criteria

Studies using populations outside the age range 18–62 years and studies that lacked military relevance (e.g., studies not performed in a military population and/or under military training operation conductions) were excluded. Studies were excluded if energy intake or expenditure data lacked specificity or clarity or EB could not be calculated using the available data. While maintenance of self-selected pace during sustained or endurance-related tasks is a critical component of military performance (cardiovascular capacity [e.g. VO2max] or occupational tasks), studies delineating the effects of negative EB on aerobic or endurance performance were limited, thus, studies were restricted to those that measured strength and power. Other studies have attempted to capture performance decrements using handgrip strength because of its convenience in the field, but handgrip strength has not been shown to be sensitive enough to reflect low or moderate nutritional changes and generally only begins to decrease after a severe (≥ 10%) loss in body mass [14, 22]. Since this meta-regression included trainings resulting in zero, low (3%), moderate (7%), and severe (≥ 10%) body mass loss, studies reporting handgrip strength as the sole performance measurement were omitted.

Bias and Limitations

A bias analysis was performed by LMM and JPK according to PRISMA guidelines recommended by Moher et al. [19]. Ratings of low, unclear, or high risk of selection, performance, attrition, and reporting bias were assigned for each study. Differences were resolved by discussion. The resulting risk of bias assessment is reported in Table S2 in the ESM.

Data Extraction

Data were extracted from nine studies; all performance measurements were taken before (pre) and after (post) military training or operations. Intervention and control groups in a single study were treated as independent subgroups. When a single study measured both power and strength, data were grouped separately and analyzed as independent subgroups. Lower-body peak power (W) was determined in all investigations using the vertical or squat jump test, with all data calculated relative to mass. Lower-body strength was determined using multiple maximum lift movements (e.g., power clean, squat, and deadlift) between studies. Strength was standardized as relative strength for all maximum lift movements, to calculate effect size (ES). Relative strength was calculated as mean maximum lift capacity (kg) over mean body mass (kg). Average daily EB was measured as daily energy expenditure, as determined by DLW, minus daily energy intake. When EB could not be directly determined by measured energy expenditure and intake, change in energy stores was used to determine EB from differences in fat mass (FM) and fat-free mass (FFM) at the conclusion of the study compared with baseline values [31]:

$${\text{EB}}\, = \,((\Delta {\text{FM g}}\, \times \, 9. 5 1\, {\text{kcal}}/{\text{g}})\, + \,(\Delta {\text{FFM g}}\, \times \,( 1 { }{-} \, 0. 7 3)\, \times \, 4. 40 \,{\text{kcal}}/{\text{g}}))/{\text{study}}\;{\text{duration}},$$

where 0.73 represents the estimated aqueous fraction of FFM [32]. Total EB was determined as daily EB multiplied by study duration. Performance data by Fortes et al. [26] and energy expenditure and intake data by Jacobs et al. [21] were not reported numerically, so values were generated from provided figures by digitally measuring the height of histogram bars and calculating relative to measured y-axis units [33].

Statistical Analysis

ES for changes in power and strength were determined as standard mean difference of pre- and post-military operations divided by pooled standard deviation. Due to lack of correlation coefficients for included studies, sensitivity analysis testing multiple correlation coefficients was conducted to impute a conservative correlation coefficient (r = 0.97) to use across all studies [34]. Meta-essentials by Van Rhee [35] with Microsoft Excel 2010 (Microsoft Corp, Redmond, WA, USA) was used to conduct a meta-analysis and meta-regression. To account for heterogeneity and small sample bias, random effects was applied, and ES was generated as Hedges’ g, respectively [36]. As defined by Cohen [37], ES values of 0.2, 0.5, and 0.8 were considered small, moderate, and large effects, respectively. To assess heterogeneity, both the Q and I2 statistics were used to assess between-study variations in ES [36]. Statistical significance was set at P < 0.05. Meta-regression analysis was then performed using study duration (days), daily EB, and total EB (daily EB multiplied by duration) as moderators (i.e., independent variables) with the ES of change in physical performance as the outcome. Additionally, as percent change in body mass has historically been used as the primary indicator for the impact of military operations on declines in physical performance [14], a meta-regression analysis was performed using percent change of body mass (delta absolute body mass divided by initial body mass) as a moderator on ES of physical performance to determine whether similar associations were observed between EB and percent change in body mass.

Results

Study Characteristics

Of the 632 studies captured by the initial literature search, nine (two randomized, controlled trials [RCTs] and seven observational studies) met the inclusion criteria of the current investigation (Fig. 1). Within these studies, 15 independent subgroups were identified for analysis (Table 1). Performance was measured using vertical or squat jump in seven groups and maximum lift in eight groups. A total of 324 subjects participated in the 15 groups combined; 60 subjects performed vertical or squat jump only, 146 performed maximum lift only, and 118 subjects performed both tests. Participant demographics are reported in Table S3 in the ESM. Daily and total negative EB and percent change in body mass by subgroup are reported in Table 2. Pre- and post-performance data are reported in Fig. 2.

Table 2 Daily and total energy balance by study and percent change in body mass before and after military training
Fig. 2
figure2

Performance data are power in watts (W) calculated from vertical jump, and strength calculated as maximum lift using kilograms lifted over kilograms body mass (kgLift/kgBody Mass), both ± standard deviation (SD), followed by the difference (post minus pre). Meta-analysis data including standard mean difference or effect size (ES), 95% confidence interval (CI), and standard error (SE) for each subgroup are listed. ES of − 0.2, − 0.5, and − 0.8 denote small, moderate, and large impacts on performance. Individual squares (black [vertical jump; power] and white [maximum lift; strength]) represent the ES of 14 subgroups and overall mean (gray) of each performance test and both tests combined. aVertical jump. bSquat jump. cSimulated power clean. dDeadlift. eStanding squat (knee and hip extension). *Difference in ES, P < 0.001. Difference in ES, P = 0.004

Energy Balance and Physical Performance

The investigations included in this analysis had a moderate (ES − 0.35; 95% confidence interval [CI] − 0.60 to − 0.09; P = 0.004) effect on overall change in physical performance with a high level of heterogeneity (Q = 1278, P < 0.001, I2 = 98.90%; Fig. 2) between these nine studies. When stratified by performance test type, a moderate (ES − 0.59; 95% CI − 0.94 to − 0.24; P < 0.001) effect was observed for power output, and no effect (ES − 0.13; 95% CI − 0.39 to 0.12; P = 0.299) was observed on relative strength.

Individually, neither study duration (days) nor daily EB were associated with lower-body power (duration: r2 = 0.199, P = 0.254; daily EB: r2 = 0.116, P = 0.417) or strength (duration: r2 = 0.186, P = 0.283; daily EB: r2 = 0.027, P = 0.719). However, the combination of the duration and magnitude of EB, expressed as total EB (daily EB × duration), was associated with change in both lower-body power (r2 = 0.764, P < 0.001, Fig. 3a) and strength (r2 = 0.836, P < 0.001; Fig. 3b) independently and collectively (r2 = 0.454, P = 0.002; Fig. 3c). Similarly, percent change in body mass correlated with change in lower-body power (r2 = 0.828, P < 0.001; Fig. 3d), strength (r2 = 0.791, P < 0.001; Fig. 3e), and a combination of the two (r2 = 0.464, P = 0.002; Fig. 3f).

Fig. 3
figure3

Meta-regression showing associations between total energy balance and (a) power, (b) strength, and (c) all groups combined. Correlations are shown between percent change in body mass and (d) power, (e) strength, and (f) all groups combined. Individual circles (black [vertical jump; power] and white [maximum lift; strength]) represent the effect size of 14 subgroups. y-axis for all panels is effect size using the scale shown on panels (c) and (f)

Based on the linear regression analysis using total EB and percent change in body mass as moderators on ES of lower-body power and strength, an equation was generated to determine the threshold of the two moderators to maintain performance during military training.

$${\text{Energy}}\;{\text{balance }}\;({\text{kcal}}) = \frac{{{\text{ES}} - 0.08472}}{{1.49{\text{E}}^{ - 5} }}$$
$${\text{Percent}}\;{\text{change}}\;{\text{body}}\;{\text{mass}}\;(\%) = \frac{{{\text{ES}} - 0.02064}}{0.0678}$$

Calculated levels of total EB and percent change in body mass corresponding to a zero, small, moderate, or large ES for combined lower-body power and strength are presented in Table 3. Within the present analysis, zero, small, moderate, or large ES were equivalent to 0, 2, 7, and 10% reductions in lower-body physical performance.

Table 3 Effects of energy balance and percent change in body mass on change in physical performance

Discussion

The primary outcome of this study was that decrements in lower-body power and strength following strenuous military training were not associated with daily EB or training duration. However, the combination of daily EB and duration, expressed as total EB (daily EB × duration), was associated with decreases in both lower-body power and strength after military training operations. Based on the slope equations generated from the meta-regression analysis, for a zero to small effect on physical performance, total EB should be limited to − 5686 to − 19,109 kcal, respectively, for an entire operation, corresponding to a percent change in body mass of ≤ 3.3% from baseline. Total EB exceeding − 39,243 to − 59,377 kcal, resulting in a percent change in body mass of ≥ 7.7%, will result in moderate to large declines in performance. As lower-body power and strength can predict success of military-specific tasks such as 30-m rush, 400-m run time, casualty rescue, and obstacle course time completion [27,28,29], understanding how negative EB contributes to declines in lower-body physical performance is important to create appropriate feeding programs and doctrine aimed toward maintaining performance during periods of sustained negative EB.

Findings from this analysis suggest that military training results in an overall decline in physical performance, with greater total negative EB resulting in greater declines in lower-body power and strength. Previously, performance decrements sustained during military operations were primarily attributed to severe (≥ 10%) declines in body mass, likely due to concomitant reductions in skeletal muscle mass [13, 14]. In terms of percent changes in body mass, results from the current meta-regression are largely in agreement with past recommendations, which suggest that body mass losses of ≥ 10% result in negative performance outcomes [13, 14]. However, in the present investigation, losses of 7.6% body mass were reported to have a moderate, and likely meaningful, negative impact on physical performance. Greater sensitivity to detect moderate changes in performance can be attributed to the quantitative nature of this meta-regression, compared with the more qualitative interpretations in other works. Furthermore, the current analysis may enhance previous recommendations through assessment of the impact of EB on physical performance, as appropriate energy intake to maintain EB is an important factor supporting physical performance [38]. In the present investigation, it was observed that both total EB and percent change in body mass were similarly associated with declines in lower-body power and strength. It is not surprising that outcomes for both total EB and percent change in body mass were nearly identical, as these two factors are related, with more severe negative EB resulting in greater losses in body mass [39]. Recommendations based on achieving a desired level of EB may be effective for mitigating performance declines, as feeding regimens and nutrition doctrine can be created to minimize negative EB, thus preventing severe reductions in body mass and declines in physical performance.

Predictive equations generated from the present meta-regression identify thresholds at which total negative EB maintains or minimizes performance decrements during military operations. This is an important outcome as, under strenuous military operations resulting in high daily energy expenditures, it is unlikely that service members will achieve EB because of limited food availability, as well as the time and desire to eat [11, 40]. As such, service members must commonly operate under some level of negative EB. Previous work from our laboratory corroborates that some degree of negative EB may be acceptable during military training. In unpublished data from a 7-d Arctic military training [8], an association (r2 = 0.389) was observed between negative EB and decline in lower-body power, determined by vertical jump, indicating more severe negative EB resulted in greater decreases in performance. Regression analysis indicated that the negative EB that subjects could endure without eliciting declines in performance was − 1166 kcal·day−1 or − 8162 total kcal. According to the predictive equation generated by our meta-regression, this degree of total negative EB would have a zero (− 5686 kcal) to small (− 19,109 kcal) effect on performance. Additionally, two past feeding studies conducted during military training reported that minimizing negative EB through increased energy intake mitigates declines in lower-body power [26] as well as exercise time to exhaustion and VO2max [41] compared with participants with lower energy intake. Agreement of these studies [8, 26, 41] with the present meta-regression further strengthens the finding that decreasing negative EB minimizes reductions in lower-body physical performance. Additionally, as negative EB is an issue for physical performance not only in military populations but also in weight-restricted sports [42], results from the current analysis may also be translatable to athletes.

Physical performance in service members can be evaluated using varied test metrics. Selection of the appropriate performance metric is critical to ensure that findings are relevant to the military population. Because many common military tasks primarily utilize the lower body (e.g., marching, lifting, and loading heavy [~ 45 kg] equipment [5]), performance tests most likely to demonstrate changes in lower-body power (e.g., vertical jump, etc.) and strength (e.g., squats, deadlifts, power cleans, etc.) that have largely standardized data-collection methods were utilized in this analysis. Results from this analysis showed that power, but not strength, was negatively impacted by military training. Furthermore, while both lower-body power and strength were associated with total EB and percent change in body mass, power appeared to be more sensitive, with declines experienced more rapidly to greater severity in total negative EB compared with strength. Given that power is the ability to perform work (work = force × distance) relative to time (power = work/time), power may be a more practical performance metric for military applications. The ability of service members to generate significant force while carrying heavy loads over a given distance as quickly as possible is likely much more critical than the amount of weight they can pick up once (i.e., strength) during an operation. In the absence of negative EB, other studies [27,28,29,30] have reported lower-body power is the most useful variable in predicting outcomes of military-specific tasks such as 30-m rush, 400-m run time, casualty rescue, and obstacle course time completion. Together, these results indicate that assessment of lower-body power is the most appropriate metric to determine change in physical performance following military operations.

While the present analysis highlights important relationships between total EB and changes in lower-body power and strength, a relatively high risk of bias across studies and within all domains of risk assessed was observed. High bias was largely attributable to the high number of observational studies and few RCTs identified by our search. Conducting RCTs designed to empirically examine relations between negative EB and physical performance in military operations is generally not feasible, resulting in reliance on observational study designs. Studies in the military population are generally limited compared with those in civilian populations, and detailed studies involving energy intake and expenditure measurements as well as comparable performance testing are rare, thus, the scope of the meta-regression and resulting sample size is small. The observational nature of these studies and the uncontrolled environments in which the studies were conducted introduce additional limitations. Despite limited sample size and lack of controlled trials, the included studies were reflective of different militaries, types of soldiers at different levels of fitness and military experience (i.e., new recruits vs. elite service members), data-collection locations (i.e., in garrison vs. deployment), training courses, training time periods (e.g., 5-day study, 207-day study, etc.), and environmental conditions, broadening the generalizable application of these findings across militaries. Additionally, not all data from the included studies were published in numeric format, which led to data extraction using digital measurements of histograms and error bars, when numerical data would have been ideal. Discrepancies in how EB was determined may also impact on the current study’s outcomes. While most studies relied on measured energy expenditure and intake, we estimated EB from change in body composition in two manuscripts, which relied on an assumption of the aqueous portion of muscle being 73% of its mass [16, 26]. As relative hydration of fat-free mass may vary between individuals and potentially change over time [43], some error may exist in use of this equation compared with measured energy expenditure and intake.

It is important to note that, although this analysis observed meaningful associations between total EB and lower-body performance, this analysis cannot prove the mechanism results in these declines in performance, and other factors may also contribute to performance decrements during military operations. Of note are decline in motivation, injuries that may occur during training operations, and neuromuscular fatigue [44, 45]. Results from the present analysis indicated the EB explains 46% (r2 = 0.464) of the variance within the data set of declines in lower body-power. It is likely that these other factors contributed to the remaining 54% of the variance in declines in lower-body performance that cannot be explained in the present analysis. While these factors have merit for examination, the published information on them is limited and they were outside the scope of the present analysis. It is likely that declines in physical performance during military operations are multi-factorial, with EB being one factor that can be manipulated in attempts to minimize these performance decrements. These limitations have implications for the interpretation of this meta-analysis. While the conclusion that lower-body physical performance declines as a function of both the duration and the magnitude of negative EB is supported by the data, the identified thresholds at which negative EB degrades performance should be tested for empirical confirmation.

Conclusion

This analysis determined that total negative EB was associated with decrements in lower-body power and strength following military training. Predictive equations generated from this analysis suggest that total EB between − 5686 and − 19,109 kcal and percent change in body mass less than − 3.3% are acceptable to maintain or minimize declines in physical performance. These numbers are a function of both the duration and the magnitude of negative EB, with smaller decrements in performance being observed as either metric is decreased, but should be interpreted cautiously given the high risk of bias present in the available evidence base. Future investigations are warranted to determine whether the acceptable threshold of total negative EB to maintain physical performance can be validated under controlled laboratory conditions. Understanding the relationship between negative EB and physical performance during military training, as well as the threshold of acceptable negative EB that may mitigate performance declines and body mass loss, may help generate feeding programs aimed toward maintaining service member performance and readiness during intense or extended bouts of military training.

References

  1. 1.

    Tharion WJ, Lieberman HR, Montain SJ, Young AJ, Baker-Fulco CJ, Delany JP, et al. Energy requirements of military personnel. Appetite. 2005;44(1):47–65.

    Article  PubMed  Google Scholar 

  2. 2.

    Alemany JA, Nindl BC, Kellogg MD, Tharion WJ, Young AJ, Montain SJ. Effects of dietary protein content on IGF-I, testosterone, and body composition during 8 days of severe energy deficit and arduous physical activity. J Appl Physiol (1985). 2008;105(1):58–64.

    Article  CAS  Google Scholar 

  3. 3.

    Opstad PK. The hypothalamo-pituitary regulation of androgen secretion in young men after prolonged physical stress combined with energy and sleep deprivation. Acta Endocrinol (Copenh). 1992;127(3):231–6.

    PubMed  CAS  Article  Google Scholar 

  4. 4.

    Henning PC, Park BS, Kim JS. Physiological decrements during sustained military operational stress. Mil Med. 2011;176(9):991–7.

    Article  PubMed  Google Scholar 

  5. 5.

    Nindl BC, Castellani JW, Warr BJ, Sharp MA, Henning PC, Spiering BA, et al. Physiological Employment Standards III: physiological challenges and consequences encountered during international military deployments. Eur J Appl Physiol. 2013;113(11):2655–72.

    Article  PubMed  Google Scholar 

  6. 6.

    Margolis LM, Crombie AP, McClung HL, McGraw SM, Rood JC, Montain SJ, et al. Energy requirements of US Army Special Operation Forces during military training. Nutrients. 2014;6(5):1945–55.

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Margolis LM, Murphy NE, Martini S, Gundersen Y, Castellani JW, Karl JP, et al. Effects of supplemental energy on protein balance during 4-d Arctic military training. Med Sci Sports Exerc. 2016;48(8):1604–12.

    Article  PubMed  CAS  Google Scholar 

  8. 8.

    Margolis LM, Murphy NE, Martini S, Spitz MG, Thrane I, McGraw SM, et al. Effects of winter military training on energy balance, whole-body protein balance, muscle damage, soreness, and physical performance. Appl Physiol Nutr Metab. 2014;39(12):1395–401.

    Article  PubMed  CAS  Google Scholar 

  9. 9.

    Margolis LM, Rood J, Champagne C, Young AJ, Castellani JW. Energy balance and body composition during US Army special forces training. Appl Physiol Nutr Metab. 2013;38(4):396–400.

    Article  PubMed  CAS  Google Scholar 

  10. 10.

    Nindl BC, Barnes BR, Alemany JA, Frykman PN, Shippee RL, Friedl KE. Physiological consequences of US Army Ranger training. Med Sci Sports Exerc. 2007;39(8):1380.

    Article  PubMed  Google Scholar 

  11. 11.

    Montain SJ, Young AJ. Diet and physical performance. Appetite. 2003;40(3):255–67.

    Article  PubMed  Google Scholar 

  12. 12.

    Friedl KE, Moore RJ, Martinez-Lopez LE, Vogel JA, Askew EW, Marchitelli LJ, et al. Lower limit of body fat in healthy active men. J Appl Physiol (1985). 1994;77(2):933–40.

    Article  CAS  Google Scholar 

  13. 13.

    Taylor HL, Buskirk ER, Brozek J, Anderson JT, Grande F. Performance capacity and effects of caloric restriction with hard physical work on young men. J Appl Physiol. 1957;10(3):421–9.

    Article  PubMed  CAS  Google Scholar 

  14. 14.

    Friedl KE. When does energy deficit affect soldier performance. In: Marriott BM, editor. Not eating enough. Washington, DC: National Academy Press; 1995. p. 253–83.

    Google Scholar 

  15. 15.

    Fairbrother B, Shippee R, Kramer T, Askew W, Mays M, Popp K, et al. Nutritional and immunological assessment of soldiers during the special forces assessment and selection course. USARIEM tech report. 1995. p. 102.

  16. 16.

    Nindl BC, Leone CD, Tharion WJ, Johnson RF, Castellani JW, Patton JF, et al. Physical performance responses during 72 h of military operational stress. Med Sci Sports Exerc. 2002;34(11):1814–22.

    Article  PubMed  Google Scholar 

  17. 17.

    Wallace B, Small K, Brodley CE, Lau J, Trikalinos TA. Deploying an interactive machine learning system in an evidence-based practice center: abstrackr. In: ACM international health informatics symposium; 2012; Miami, FL; 2012. p. 819–24.

  18. 18.

    Welsh TT, Alemany JA, Montain SJ, Frykman PN, Tuckow AP, Young AJ, et al. Effects of intensified military field training on jumping performance. Int J Sports Med. 2008;29(1):45–52.

    Article  PubMed  CAS  Google Scholar 

  19. 19.

    Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Fallowfield JL, Delves SK, Hill NE, Cobley R, Brown P, Lanham-New SA, et al. Energy expenditure, nutritional status, body composition and physical fitness of Royal Marines during a 6-month operational deployment in Afghanistan. Br J Nutr. 2014;112(5):821–9.

    Article  PubMed  CAS  Google Scholar 

  21. 21.

    Jacobs I, van Loon Capt D, Pasut L, Pope J, Bell D, Kavanagh Maj M, et al. Physical performance and carbohydrate consumption in CF commandos during a 5-day field trial. USARIEM tech report. 1989. p. 36.

  22. 22.

    Johnson MJ, Friedl KE, Frykman PN, Moore RJ. Loss of muscle mass is poorly reflected in grip strength performance in healthy young men. Med Sci Sports Exerc. 1994;26(2):235–40.

    Article  PubMed  CAS  Google Scholar 

  23. 23.

    Moore RJ, Friedl KE, Kramer TR, Martinez-Lopez LE, Hoyt RW, Tulley RE, et al. Changes in soldier nutritional status and immune function during the ranger training course. USARIEM Tech Rep. 1992;T13–92:162.

    Google Scholar 

  24. 24.

    Nindl BC, Friedl KE, Frykman PN, Marchitelli LJ, Shippee RL, Patton JF. Physical performance and metabolic recovery among lean, healthy men following a prolonged energy deficit. Int J Sports Med. 1997;18(5):317–24.

    Article  PubMed  CAS  Google Scholar 

  25. 25.

    Shippee R, Friedl KE, Kramer T, Mays M, Popp K, Askew EW, et al. Nutritional and immunological assessment of ranger students with increased caloric intake. USARIEM Tech Rep. 1994;T95–5:212.

    Google Scholar 

  26. 26.

    Fortes MB, Diment BC, Greeves JP, Casey A, Izard R, Walsh NP. Effects of a daily mixed nutritional supplement on physical performance, body composition, and circulating anabolic hormones during 8 weeks of arduous military training. Appl Physiol Nutr Metab. 2011;36(6):967–75.

    Article  PubMed  CAS  Google Scholar 

  27. 27.

    Williams AG, Rayson MP. Can simple anthropometric and physical performance tests track training-induced changes in load-carriage ability? Mil Med. 2006;171(8):742–8.

    Article  PubMed  Google Scholar 

  28. 28.

    Harman EA, Gutekunst DJ, Frykman PN, Sharp MA, Nindl BC, Alemany JA, et al. Prediction of simulated battlefield physical performance from field-expedient tests. Mil Med. 2008;173(1):36–41.

    Article  PubMed  Google Scholar 

  29. 29.

    Bishop PA, Fielitz LR, Crowder TA, Anderson CL, Smith JH, Derrick KR. Physiological determinants of performance on an indoor military obstacle course test. Mil Med. 1999;164(12):891–6.

    Article  PubMed  CAS  Google Scholar 

  30. 30.

    Friedl KE, Knapik JJ, Hakkinen K, Baumgartner N, Groeller H, Taylor NA, et al. Perspectives on aerobic and strength influences on military physical readiness: report of an International Military Physiology Roundtable. J Strength Cond Res. 2015;29(Suppl 11):S10–23.

    Article  PubMed  Google Scholar 

  31. 31.

    Hoyt RW, Opstad PK, Haugen AH, DeLany JP, Cymerman A, Friedl KE. Negative energy balance in male and female rangers: effects of 7 d of sustained exercise and food deprivation. Am J Clin Nutr. 2006;83(5):1068–75.

    Article  PubMed  CAS  Google Scholar 

  32. 32.

    Siri WE. The gross composition of the body. Adv Biol Med Phys. 1956;4:239–80.

    Article  PubMed  CAS  Google Scholar 

  33. 33.

    Tsafnat G, Glasziou P, Choong MK, Dunn A, Galgani F, Coiera E. Systematic review automation technologies. Syst Rev. 2014;3:74.

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Higgins JPT, Green S, editors. Cochrane handbook for systematic reviews of interventions. Chichester: Wiley; 2011. p. 488–524.

  35. 35.

    Van Rhee HJ. User manual for meta-essentials: workbooks for meta-analysis. Rotterdam: Erasmus Research Institute of Management; 2015. p. 1–43.

    Google Scholar 

  36. 36.

    Borenstein M, Hedges LV, Higgins JPT. Introduction to meta-analysis. Chichester: Wiley; 2009. p. 17–49.

    Google Scholar 

  37. 37.

    Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale: Lawrence Erlbaum Associates; 1988. p. 19–66.

    Google Scholar 

  38. 38.

    Thomas DT, Erdman KA, Burke LM. American College of Sports Medicine Joint Position Statement. Nutrition and athletic performance. Med Sci Sports Exerc. 2016;48(3):543–68.

    Article  PubMed  CAS  Google Scholar 

  39. 39.

    Fogelholm M. Effects of bodyweight reduction on sports performance. Sports Med. 1994;18(4):249–67.

    Article  PubMed  CAS  Google Scholar 

  40. 40.

    Pasiakos SM, Margolis LM. Negative energy balance and loss of body mass and fat-free mass in military personnel subsisting on combat rations during training and combat operations: a comment on Tassone and Baker. Br J Nutr. 2017;117(6):894–6.

    Article  PubMed  CAS  Google Scholar 

  41. 41.

    Guezennec CY, Satabin P, Legrand H, Bigard AX. Physical performance and metabolic changes induced by combined prolonged exercise and different energy intakes in humans. Eur J Appl Physiol Occup Physiol. 1994;68(6):525–30.

    Article  PubMed  CAS  Google Scholar 

  42. 42.

    Franchini E, Brito CJ, Artioli GG. Weight loss in combat sports: physiological, psychological and performance effects. J Int Soc Sports Nutr. 2012;9(1):52.

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Friedl KE, Moore RJ, Hoyt RW, Marchitelli LJ, Martinez-Lopez LE, Askew EW. Endocrine markers of semistarvation in healthy lean men in a multistressor environment. J Appl Physiol (1985). 2000;88(5):1820–30.

    Article  CAS  Google Scholar 

  44. 44.

    Johnson HL, Krzywicki HJ, Canham JE, Skala TA, Daws RA, Nelson RA, et al. Evaluation of calorie requirements for ranger training at Fort Benning, Georgia. Letterman Army Institute of Research tech report 1976; 34, AD A070 880.

  45. 45.

    Peltonen H, Walker S, Hakkinen K, Avela J. Neuromuscular fatigue to power loading using a weight-stack device fitted with or without additional rubber band resistance. J Strength Cond Res. 2014;28(7):1802–11.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors wish to acknowledge Dr. Scott Montain for his critical review of this manuscript, as well as the authors of the papers included in this meta-analysis, and the subjects who volunteered their time and effort to further military research.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lee M. Margolis.

Ethics declarations

The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The investigators adhered to the policies for protection of human subjects as prescribed in Army Regulation 70-25, and the research was conducted in adherence with the provisions of 32 CFR part 219. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the Army or the Department of Defense. Any citations of commercial organizations and trade names in this report do not constitute an official Department of the Army endorsement of approval of the products or services of these organizations.

Funding

This work was supported by the U.S. Army Medical Research and Material Command.

Conflicts of interest

Nancy Murphy, Christopher Carrigan, J. Philip Karl, Stefan Pasiakos, and Lee Margolis have no conflicts of interest relevant to the content of this article.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Murphy, N.E., Carrigan, C.T., Philip Karl, J. et al. Threshold of Energy Deficit and Lower-Body Performance Declines in Military Personnel: A Meta-Regression. Sports Med 48, 2169–2178 (2018). https://doi.org/10.1007/s40279-018-0945-x

Download citation