Army Body Composition Program Study Results Concerning: Enrollees Are More Over Fat Than Expected

Abstract

Introduction

Whether implementation of the Army Body Composition Program (ABCP) is meeting readiness objectives is unknown.

Objective

This study sought to primarily describe the extent of Active Duty Soldiers’ over-fatness when attending the initial ABCP nutrition class at an Army Nutrition clinic in Washington State; and secondarily to describe the proportion of these Soldiers meeting metabolic syndrome (MS) criteria.

Methods

Soldiers (189) in this cross-sectional study completed the following: a questionnaire developed for this study, anthropometric measurements, body fat assessment via AR 600–9 standards, and a laboratory blood draw for fasting glucose and lipid panel.

Results

Soldiers were predominantly male (76%), obese (BMI 32 kg/m² for males and 30 kg/m² for females), exceeded body fat standards by 3.8% for males and 7.3% for females, and 16% had three or more risk factors meeting MS diagnostic criteria. Waist circumference was the predominant MS risk factor for males and females.

Conclusion

Soldiers in this study had higher body fat percentages than expected with a majority of Soldiers classified as obese. Achieving and maintaining ABCP standards may be more challenging for obese Soldiers. To maintain Soldier readiness, commanders should consider intervening earlier when signs of weight gain are observed.

Overweight, Obesity, Military, Metabolic Syndrome, Body Composition

INTRODUCTION

Overweight and obesity prevalence in the military reflects an increasing trend of Service Members (SMs) who meet the criteria.^1–3 Of particular concern is the proportion of SMs classified as obese, defined as body mass index (BMI) ≥30 kg/m², which rose by over five percentage points between 1995 and 2008.¹ Obesity proportions rose the highest among males, African Americans and Hispanics, and married SMs.^1,4 Soldiers (15.8%) are more obese than other SMs, and males (17.2%) at a higher proportion than females (7.1%).⁵ While BMI does not differentiate weight from lean mass and fat mass, a higher BMI is associated with higher body fat mass.^6–9 Of relevance to Soldiers is the association of higher body fat with reduced cardiorespiratory fitness compared to leaner individuals, which is in opposition of Soldier fitness and readiness requirements.^6,10–12

The costs of overweight and obesity to individual and unit readiness, and the health care system are also a concern. In addition to the impact on physical performance, overweight and obesity are associated with musculoskeletal injury,^13–16 military discharge,^17,18 and chronic health conditions such as hypertension and impaired glucose metabolism.^4,19–21 Soldiers experiencing the health-related repercussions of excess body weight may use the health care system more often. Beyond the estimated cost of $167 million in time away from mission requirements, injury and chronic health conditions contribute to the $1.1B in Military Healthcare System (MHS) health care costs for treatment of co-morbidities associated with overweight and obesity.²² Peake et al. similarly found that health care costs, absenteeism, and productivity losses were higher for obese Australian defense force personnel compared to their normal weight peers.²³

Overweight and obesity are associated with an increased risk of metabolic syndrome (MS), which increases cardiovascular disease and diabetes risk.^24,25 An assessment of Active Duty SMs’ medical records found that high blood pressure, low high-density lipoprotein cholesterol (HDL), and high triglycerides were most prevalent of the MS risk factors.²⁶ MS is diagnosed when an individual presents with three or more of the following risk factors: (1) a waistline ≥88 centimeters (35 inches) for women and ≥102 centimeters (40 inches) for men; (2) HDL < 50 mg/dL for women and <40 mg/dL for men, or taking medication for low HDL-cholesterol; (3) blood pressure ≥130/85 mmHg or taking medication for hypertension; (4) fasting blood sugar level >100 mg/dL; and (5) triglycerides ≥150 mg/dL or higher, or taking medication to treat high triglycerides.²⁵ Physical activity may not reduce the cardiovascular disease risk factors associated with excess body fat as studies have previously stated, particularly if obese.^24,27 Excess body fat carried in the abdomen, resulting in a higher waist circumference (WC) measurement, has an especially deleterious impact on cardiovascular disease risk.^10,28–30

Soldiers’ weight-for-height is measured at least every 6 months, and Soldiers are further assessed for body composition via the “tape test” when weight exceeds the Army’s weight allowance for their height, age and gender per the Army Body Composition Program (ABCP) Regulation, AR 600–9.³¹ If a Soldier exceeds body fat standards for their age and gender per the “tape test,” the Soldier is enrolled in the ABCP. A Soldier is enrolled in the ABCP once the commander or designee counsels the Soldier verbally and in writing. A Soldier may self-enroll as a proactive weight management effort. The ABCP aims to “ensure all Soldiers achieve and maintain optimal well-being and performance under all conditions” to “…establish and maintain operational readiness, physical fitness, health, and a professional military appearance…”.³¹

Whether implementation of the ABCP is meeting the objectives of maintaining operational readiness, health and professional appearance has not been explored. This study’s primary purpose was to describe the extent of Active Duty Soldiers’ over-fatness when attending the initial ABCP nutrition class at an Army Nutrition Clinic in Washington State. A secondary objective was to describe the proportion of the Soldiers meeting diagnostic criteria for MS.

METHODS

Subjects and Recruitment

This descriptive study included a convenience sample of Active Duty Army Soldiers aged ≥18 years attending the initial ABCP nutrition class, command or self-referred, at the Nutrition Clinic on Joint Base Lewis-McChord in Washington from January 2014 to January 2016. Soldiers were excluded from the study if they were pregnant or postpartum <180 days, or had a diagnosed condition that could impact weight management efforts. The study was approved by the Madigan Army Medical Center Institutional Review Board. Soldiers attending the initial ABCP nutrition class were provided a short information briefing about the study immediately after the class by a study team member wearing civilian clothes. Interested Soldiers remained after the briefing and completed a consent and screening form to verify eligibility before proceeding. Soldiers were not compensated for their participation.

Procedures

Soldiers were weighed and measured in duplicate on a calibrated scale and stadiometer to the nearest 0.1 pound and 0.5 inch, respectively. Soldiers typically reported to the clinic in the Army Combat Uniform and were measured without boots, blouse, or belt, and were asked to empty all pockets; weight was adjusted by 1.4 pounds after identifying the average weight of several pairs of uniform pants of differing sizes. Body composition measurements were taken using a measuring tape made of non-stretchable material. Results were calculated for males and females per AR 600-9 standards and documented on the Department of the Army Forms 5,500 and 5,501.³¹ WC was measured in duplicate to the nearest 0.1 cm per the 2007 National Health and Nutrition Examination Survey guidelines using the same measuring tape.³² Soldiers completed a questionnaire developed for this study that included questions about their weight over the past 5 years, physical activity habits, diet quality using the Healthy Eating Score-5 (HES-5),³³ and a medical profile history over the previous 12 months (Fig. 1). A medical profile may require Soldiers to alter the type(s) and amount of physical activity for a period of time, thus impacting the physical activity habit data. The HES-5 correlates highly with the Healthy Eating Index 2005 (Cronbach α = 0.81).³³ Blood pressure was measured in duplicate using a calibrated digital sphygmomanometer with a 30 second break between measurements. Soldiers were asked to present to the laboratory on the installation most convenient to them after a 12-hour fast to provide a 15-mL blood sample to assess fasting blood glucose and a lipid panel that included HDL-cholesterol, low-density lipoprotein (LDL)-cholesterol and triglyceride levels.

FIGURE 1.

Select questions from the study questionnaire. *Questions taken from the 2011 Behavioral Risk Factor Surveillance System Questionnaire, https://www.cdc.gov/brfss/questionnaires/pdfques/2011brfss.pdf.

Data Analysis

The primary dependent variable was excess fat (overfat, reported in percentage and estimated pounds). Data were also categorized as: within the ABCP standard (were not over the fat limit), at the fat limit, 1–9 lbs over the fat limit, 10–19 lbs over the fat limit, 20–29 lbs over the fat limit, and 30 lbs or more over the fat limit. A secondary dependent variable was meeting the WC MS risk criteria (yes/no).

Physical activity was recorded as a dichotomous variable based on Soldiers reported minutes of activity meeting ≥150 minutes per week (yes) or <150 minutes per week (no).³⁴ Diet quality scores using the HES-5 were calculated per previously reported methods,³³ with potential scores ranging from 0 to 25 points, and scores ≤12 defined as low diet quality.

Data were analyzed for descriptive statistics using IBM Statistical Package for Social Sciences v24 with statistical significance set at p < 0.05. Descriptive data were reported as mean ± standard deviation or frequency. Data were stratified by body fat status category, sex, and medical profile within the past 12 months. Inferential statistics between groups included independent t-test, Mann–Whitney U, Chi-square, or 1-ANOVA tests based upon parametric and non-parametric requirements to compare stratified data groups noted above. Binary logistic regression was performed to identify if significant independent variables contributed to predicting overfat and meeting the WC MS criteria.

RESULTS

Subjects

Study participants (n = 189) were predominantly male (76%), white (52%), junior enlisted (68%; Private to Specialist), married (61%) with some college education (48%). The mean age was 28.5 ± 7.2 years (range 18–53 years), the mean BMI at 31.8 ± 3.2 kg/m² (range 25.6–45.3 kg/m²) with 30% classified as overweight and 70% as obese. Sixty-four percent of volunteers were on a medical profile within the past 12 mo and 60% met physical activity recommendations (>150 minutes of physical activity per week). One female participant was excluded from analysis because her BMI (20.8 kg/m²) is classified as normal and does not represent the overweight population examined. Table I depicts the demographic data with several significant differences identified: more males were married and had a higher BMI, whereas proportionally more females were college educated, on a medical profile in the past 12 months, and had a higher percentage of excess body fat.

TABLE I.

Participant Descriptive Demographic Data by Sex

Participant Demographics (n = 189)	Males	Females
	n = 144	n = 45
	76%	24%
Age [mean years ± SD]a	28.3 ± 7.0	28.7 ± 8.0
BMI [mean kg/m2 ± SD]**,a	32.4 ± 3.1	30.2 ± 2.7
Body fat [mean % ± SD]**,a	26.7 ± 3.8	40.3 ± 3.7
Body fat over maximum limit [% ± SD]**,a	3.8 ± 3.6	7.3 ± 3.7
Meets MS WC criterion [n(%)]**,c	81 (56%)	37 (82%)
Race [n(%)]*,b
White	76 (53%)	21 (47%)
Black	23 (16%)	14 (31%)
Other (includes Asian, Native Hawaiian or Pacific Islander,  American Indian or Alaskan Native)	44 (31%)	10 (22%)
Ethnicity [n(%)]c
Hispanic/Latino	46 (32%)	14 (31%)
Rank [n(%)]b
E1–E4 (Private to specialist)	98 (68%)	27 (61%)
E5–E7 (Sergeant to sergeant 1st class)	40 (28%)	12 (27%)
Master sergeant (E8) and higher	5 (4%)	5 (9%)
Education [n (%)]**,b
High school, GED or Trade School Grad	65 (45%)	8 (18%)
Some college/associates degree	63 (44%)	27 (60%)
Bachelor’s degree or equivalent	11 (8%)	4 (9%)
Graduate classes or degree	5 (4%)	6 (13%)
Marital status [n(%)]*,c
Married	97 (67%)	19 (42%)
Not married	47 (33%)	26 (58%)
Met physical activity recommendations [Yes; n(%)]c	90 (63%)	24 (52%)
Medical Profile in the past 12 months [Yes; n(%)]*,c	63 (59%)	26 (81%)
Health Eating Scale Score [HES-5; mean points ± SD]a	12.5 ± 4.7	11.5 ± 5.2
Following a Diet Plan in the past 30 days [Yes; n(%)]c	34 (24%)	12 (27%)

*Significant difference between sex p < 0.05.

**Significant difference by sex p ≤ 0.001.

Note: E = enlisted; GED = general education degree; Grad, graduate; SD, standard deviation.

^aIndependent t-test by sex was conducted on continuous variables.

^bWilcoxon-ranked sum test by sex was conducted on categorical variables (three or more response options).

^cChi-square analysis by sex was conducted on dichotomous variables.

Body Fat

The overall mean body fat percentage was 29.8 ± 7.0% with the mean body fat exceeding the maximum ABCP percentage limit at 9.8 ± 8.9%. A significant increase in male excess percent body fat was identified between the lowest age group (17–20 years) and the remaining three age categories; however, no differences were noted between any of the female age categories (Fig. 2). Significantly more males (48%) were 1–9 pounds over the maximum allowable fat limit compared to females (42%) who were predominantly 10–19 pounds over (all p < 0.03) (Fig. 3). When data were analyzed by body fat status, within or at body fat limit versus exceeding body fat limit, significant differences were identified as depicted in Table II. Participants who exceeded body fat gained more weight in the 12 months prior to the study, had a higher BMI, had a lower HES-5 diet quality score, were predominantly male, met MS WC criteria in a higher proportion, and had at least two of the five MS diagnostic criteria.

FIGURE 2.

Participant body fat percentages by sex and age category. *Significant difference (p < 0.05) in male body fat percent for 17-20 years age group compared to the other age categories. Body fat changes at 21-27 years, 28-39 years, and 40+ years were not significantly different.

FIGURE 3.

Percent of participants in each body fat status category by sex. *Significant difference between sex (all p < 0.03). Note: Pounds of body fat were estimated by taking the Soldiers' weight and multiplying it by the actual body fat and AR 600-9 standard for percent body to calculate the pounds of body fat in excess of the standard. Within Standard = 4.9 lbs under the allowable fat limit. At Limit = Measured at the maximum allowable fat limit (0 lbs over).

TABLE II.

Significant Descriptive Differences in Participants Grouped by Meeting or Exceeding Body Fat Limit

Variables	Within or At Body Fat Limit (n = 22)	Exceed Body Fat Limit (n = 166)	p-Value
Weight gained in the last 12 months [mean pounds ± SD; n = 152]a	16.8 ± 10.4	22 ± 12.9	0.042
BMI [mean kg/m2 ± SD; n = 188]a	30.1 ± 2.2	32.1 ± 3.2	0.005
Healthy Eating Scale-5 [HES-5; Mean Points ± SD; n = 186]a	14.8 ± 4.3	11.9 ± 4.8	0.009
Sex [Males; n (%); n = 188]c	21 (96%)	123 (74%)	0.030
Ethnicity [Hispanic; n(%); n = 186]c	2 (9%)	57 (35%)	0.014
Meets MS WC criterion [Yes; n(%); n = 183]c	2 (10%)	116 (71%)	<0.001
Meets 2 MS diagnosis criteria [Yes; n(%); n = 186]c	0 (0%)	33 (20%)	0.016

Note: ^aIndependent t-test by Body Fat group was conducted on continuous variables.

^bWilcoxon-ranked sum test by Body Fat group was conducted on categorical variables (three or more response options).

^cChi-square analysis by Body Fat group was conducted on dichotomous variables.

Sample size for each variable differed because some participants had missing entries on the survey or did not complete the biochemical measures.

Biochemical Indices

Biochemical indices were completed on 106 participants (77 males and 29 females) due to lack of participant compliance despite reminders to complete the blood draw. Soldiers did not provide a reason for not completing the blood draw. There were no significant differences between the sexes with mean values of 181 ± 38 mg/dL for total cholesterol, 114 ± 32 mg/dL for LDL-cholesterol, 51 ± 13 mg/dL for HDL-cholesterol, 112 ± 65 mg/dL for triglycerides, and 92 ± 11 mg/dL for fasting blood glucose.

MS Risk Factors

MS syndrome risk factors were assessed in the overall group and stratified by sex. Overall, 26% met two of the five risk factors while 16% met three of the five risk factors required for diagnosis. Table III depicts the proportion of participants who met each of the MS risk factor criteria. Elevated WC was the only risk factor significantly different between sexes with more females exceeding WC standards, which was consistent when assessed overall (n = 184; 57% males vs. 84% females; p = 0.002). Data were analyzed after stratifying by WC MS criteria met or not met (Table IV), revealing significant differences between the two groups. Soldiers who met the MS criteria reported the highest weight in the past 5 years, gained more weight in the 12 months preceding the study, had a higher BMI, higher systolic and diastolic blood pressure values, and were predominantly male ≥28 years old and female ≤28 years old. When age category was assessed by 1-ANOVA, only the 17–20 year age category had a significantly lower proportion who met the MS WC risk criteria: 2% in 17–20 years compared to other three age categories (47% for 21–27 years, 42% for 28–39 years, and 11% for 40+ years) (p < 0.01; results not shown). When data were analyzed by medical profile status, significant differences were identified as depicted in Table V. Compared to Soldiers without a medical profile, male and female Soldiers with a medical profile had higher average BMI, WC, percent body fat, and excess pounds of body fat.

TABLE III.

Percent from a Subset of Participants Meeting Each MS Risk Factor by Sex

Met MS Risk Factor Criteria for:	Overall	Male	Female
	n (%)	n (%)	n (%)
Blood glucose [Yes; n(%); n = 105]	24 (22)	21 (28)	3 (11)
HDL-cholesterol [Yes; n(%); n = 106]	22 (21)	14 (18)	8 (28)
Triglycerides [Yes; n(%); n = 106]	25 (23)	20 (26)	5 (17)
Blood pressure [Yes; n(%); n = 106]	11 (10)	9 (12)	2 (7)
WC [Yes; n(%); n = 106]*	69 (65)	46 (60)	23 (79)
2 MS risk factors [Yes; n(%); n = 106]	28 (26)	22 (29)	6 (21)
3 MS risk factors [Yes; n(%); n = 106]	16 (15)	11 (14)	5 (17)

*Significant difference by sex (p < 0.05). Note that these data represent a subset of the sample who had a complete data set for all five risk factors. Chi-square analysis by sex was conducted for all dichotomous (Yes/No) variables (met vs. did not meet each risk factor category).

TABLE IV.

Significant Descriptive Differences in Participants Grouped by MS WC Risk Criterion

Variables	Meet MS	Did Not Meet MS	p-Value
	WC Criterion	WC Criterion
Lowest weight in past 5 years [mean pounds ± SD; n = 183]a	182.3 ± 30.5	171.7 ± 25.1	0.005
Highest weight in past 5 years [mean pounds ± SD; n = 180]a	229.9 ± 39.1	214.6 ± 35.7	0.003
Weight gained in the last 12 months [mean pounds ± SD; n = 148]a	23.9 ± 13.6	17.6 ± 9.7	0.002
BMI [mean kg/m2 ± SD; n = 184]a	32.8 ± 3.2	30.3 ± 2.4	<0.001
Systolic blood pressure [Mean mmHg ± SD; n = 181]a	120 ± 12	116 ± 11	0.030
Diastolic blood pressure [Mean mmHg ± SD; n = 181]a	77 ± 9	72 ± 9	0.001
Sex [Males; n (%); n = 184]c	81 (69%)	59 (89%)	0.001
Age category [n(%)]b
17–20 years [n = 12]	2 (17%)	10 (83%)	<0.001
21–27 years [n = 93]	55 (59%)	38 (41%)
28–39 years [n = 64]	49 (77%)	15 (23%)
40 or more years [n = 16]	12 (75%)	3 (25%)
Race [n(%)]b
White [n = 96]	66 (69%)	30 (31%)	0.016
Black [n = 35]	23 (66%)	12 (34%)
Asian [n = 6]	0 (0%)	6 (100%)
Other (includes Native Hawaiian or Pacific Islander,  American Indian or Alaskan Native) [n = 12]	29 (63%)	17 (37%)

^aIndependent t-test by MS WC group was conducted on continuous variables.

^bWilcoxon-ranked sum test by MS WC group was conducted on categorical variables (three or more response options) with p-values based upon the overall proportion distribution between responses.

^cChi-square analysis by MS WC group was conducted on dichotomous variables.

Table V.

Significant Descriptive Differences in Participants Grouped by Profile Status.

Variable	Malea			Femalea
	Profile (n = 63)	No Profile (n = 43)	p-Value	Profile (n = 26)	No Profile (n = 6)	p-Value
Race [n(%); n = 138]c
White	40 (70%)	17 (30%)	ns	13 (100%)	0 (0%)	0.005
Black/African American	6 (38%)	10 (63%)		9 (82%)	2 (18%)
Asian	2 (50%)	2 (50%)		2 (100%)	0 (0%)
Other (includes Native Hawaiian or Pacific Islander, American Indian or Alaskan Native)	14 (50%)	14 (50%)		3 (43%)	4 (57%)
Marital status [n(%); n = 138]d
Married	46 (67%)	23 (33%)	0.038	10 (83%)	2 (17%)	ns
Not married	17 (46%)	20 (54%)		17 (81%)	4 (19%)
BMI [mean kg/m2 ± SD; n = 138]b	32.8 ± 3.5	31.2 ± 2.7	0.01	30.8 ± 3.0	27.1 ± 1.2	0.007
WC [mean cm ± SD; n = 138]b	104.9 ± 8.4	101.4 ± 7.2	0.032	95.5 ± 7.6	89.2 ± 5.8	ns
% Body fat [mean % ± SD; n = 138]b	27.3 ± 4.1	25.7 ± 3.6	0.049	41.0 ± 43.7	37.2 ± 1.9	0.021
Pounds of body fat over limit* [mean lbs ± SD; n = 138]b	10.2 ± 10.2	6.9 ± 7.4	ns	15.0 ± 7.2	5.5 ± 3.5	0.004
LDL-cholesterol [mean mg/dL ± SD; n = 78]b	120 ± 36	110 ± 34	ns	115 ± 22	89 ± 17	0.038
Systolic blood pressure [mean mmHg ± SD; n = 137]b	120 ± 9	122 ± 12	ns	112 ± 12	101 ± 10	0.026
Diastolic blood pressure [mean mmHg ± SD; n = 137]b	75 ± 8	76 ± 10	ns	74 ± 11	63 ± 9	0.025

*Significant difference between sex by profile (p = 0.034).

^a38 males and 13 females did not answer question regarding medical profile status.

^bIndependent t-test by Profile status group split by sex was conducted on continuous variables.

^cWilcoxon-ranked sum test by Profile status group split by sex was conducted on categorical variables (three or more response options) with p-values based upon the overall proportion distribution between responses.

^dChi-square analysis by Profile status group split by sex was conducted on dichotomous variables.

Binary logistic regression was performed to ascertain the effects age, sex, rank, education, marital status, race, ethnicity, and BMI on the likelihood of meeting the WC MS risk criteria. The regression model was statistically significant, Χ² (3) = 81.369, p < 0.001 for age, BMI and sex. The model explained 50% (Naglekerke R²) of the variance in meeting MS WC risk criteria and correctly classified 77.8% of cases. The odds of meeting the MS WC criteria is 9.4 times greater with each 10-year increase in age (p < 0.039), 0.63 times greater with each increased 1 unit kg/m² increase in BMI (p < 0.001), and females have a 0.07 increased odds (probability of 2.6 higher) relative to males (p < 0.001).

DISCUSSION

Soldiers attending the initial ABCP class were predominantly obese with body fat measurements higher than expected. Based on this study’s findings, implementation of AR 600-9 does not appear to be meeting the objective of “…establish and maintain operational readiness, physical fitness, health, and a professional military appearance…”³¹ Similar to BMI findings in the Smith et al.'s study.³ This study found that percent excess body fat was associated with age and sex, though participants under 28 years, regardless of sex, had higher proportions of excess body fat. The majority of individuals exceeding body fat standards had WC measurements meeting MS criteria, a modifiable cardiovascular disease risk factor.

The prevalence of MS in the sample is lower for males than found in the MHS study.²⁶ However, not all study participants completed the biochemical measures to assess MS diagnostic criteria. While 16% of Soldiers in this sample met diagnostic criteria for MS, 26% met two criteria, and over half exceeded the WC cut-off, indicating central adiposity. Central adiposity particularly increases the risk for cardiovascular disease risk factors, including hypertension, impaired glucose metabolism, and elevated triglycerides.^10,28,30,35 Future studies of this population should assess for MS risk factors.

Soldiers in this study had low diet quality scores as measured by the HES-5, while over half met national physical activity recommendations, both of which are consistent with findings from the 2011 DoD Health Related Behaviors Survey.⁵ A low diet quality score, regardless of the measurement tool, is associated with higher body mass index.^36–39 The risk for developing diabetes is also associated with low diet quality when controlling for age, physical activity, and BMI.⁴⁰ After adjusting for BMI, age, education, and health behaviors, men with higher diet quality had a lower odds of having abdominal obesity, hypertension, and type 2 diabetes.⁴¹ For women, after adjusting for the same variables, higher diet quality resulted in a lower odds of developing pre-diabetes.⁴¹ Pate and colleagues found age and sex-specific differences regarding diet quality and moderate to vigorous physical activity’s (MVPA) impact on BMI and WC.⁴² For men over 30 years old, diet quality had an inverse relationship to BMI and WC, and that controlling for MVPA only affected WC for men aged 40–49 years.⁴² For women, Pate and colleagues found that only women aged 50–59 years showed an inverse relationship between BMI and WC; otherwise MVPA was more associated with BMI and WC for women.⁴² The “FAT associated CariOvasculaR dysfunction” (FATCOR) study found that in a sample of overweight and obese subjects, fit individuals did not have a lower prevalence of biochemical MS risk factors, but they did have a lower WC, BMI, and higher muscle mass.²⁴ Drenowatz et al. found that men and women’s MVPA were more related to BMI and body fat percentage than dietary intake, but that the relationship was dependent on overweight/obesity status, with normal weight individuals engaging in more MVPA.⁴³ Perhaps this study sample’s low diet quality impacted body composition more than physical activity habits given over half of the sample met national physical activity guidelines.

The majority of Soldiers in this study had a medical profile at some point over the twelve months. While this study does not allow for determining directionality, there appears to be a relationship between having a medical profile and body composition. It is logical to presume that Soldiers with a medical profile are less physically active than their peers, which could mean a decrease in amount of physical activity, a change in the type of physical activity (e.g., resistance or aerobic training, or a combination of both), or little to no activity, depending on the limitations imposed by the medical profile. Weight gain is likely when physical activity is limited but energy consumption is not modified. Desirable body composition changes, meaning loss of body fat and maintenance of lean body mass, for individuals engaged in weight loss are more favorable with a combination of reduced energy intake and physical activity, while energy restriction alone often leads to a reduction in lean body mass.⁴⁴ Physical activity helps maintain lean body mass and limits body fat mass accretion in overweight and obese individuals engaged in moderate and moderate to vigorous physical activity.⁴⁵ A study of overweight and obese individuals found that body fat percentage decreased with aerobic exercise only as well as aerobic exercise plus resistance training, but only participants who had resistance training added lean body mass.⁴⁶ These findings may have important implications for Soldiers on a medical profile that also exceed body composition standards.

Strengths of this study are that Soldiers were weighed and measured for height, body fat, and WC. Blood samples were also collected for this study, providing the most recent biomarker data. There are several study limitations. The study was descriptive using a convenience sample that may not represent the entire population who exceeded weight-for-height standards on Joint Base Lewis-McChord, thus contributing to a potential selection bias given the sampling method. The questionnaire used was a compilation of validated questionnaires developed for this study because no validated questionnaire exists to answer the study question. The AR 600-9 regulation encourages 7-days separation between the mandatory weigh-in and physical fitness test, which may result in Soldiers implementing strategies to temporarily meet the standard when they would otherwise exceed the standard and be flagged for ABCP enrollment. We did not assess the time between Soldiers being flagged and attendance of the initial nutrition class, and Soldiers may have taken measures to reduce body fat prior to attending the first class.

This is the first study to identify the extent of over-fatness in Soldiers attending the initial ABCP nutrition class and the results are concerning. The ABCP regulation allows Commanders to assess Soldiers’ weight-for-height status whenever they are not viewed as maintaining a Soldierly appearance. Twice a year weight-for-height assessments also provide an opportunity to re-evaluate weight and body composition status. The majority of Soldiers in this sample had between one and nineteen estimated pounds to lose. Assuming a one pound per week weight loss, with no weight loss plateau, Soldiers may be expected to lose up to 24 pounds in 6 months.⁴⁷ However, weight loss does not typically occur so predictably due to physiological adaptations of energy expenditure, hormonal changes impacting hunger signals, and other factors.⁴⁸ Additionally, once obese, the probability of weight loss, even a 5% weight loss, was estimated to be 1:12 for men and 1:10 for women, making it important to prevent obesity.⁴⁹ Soldiers in this study may be unable to meet and maintain ABCP standards. Consideration should be given to enforcing the Soldierly appearance standard and to evaluate weight changes between biannual mandatory weight-for-height assessments to support the goal of maintaining Soldier health and readiness. Further research should assess a larger cohort of Soldiers initially enrolled in the ABCP across multiple sites to better define whether Soldiers are enrolled when weight management challenges first arise. This information can potentially inform leaders about when to offer Soldiers assistance with meeting body composition standards.

CONCLUSION

Soldier readiness remains a top priority. To increase success of maintaining Soldier readiness, implementation of AR 600-9 to its fullest extent should include preventive referrals for weight management guidance for Soldiers when: (1) a Soldier’s weight trends up between physical fitness assessments; (2) a Soldier’s appearance is not in keeping with expectations; and perhaps (3) a Soldier is placed on a medical profile that limits physical activity to educate on how to adjust energy consumption to avoid weight gain.

Presentations

MHSRS 27-30 August 2017, Poster Presentation, Abstract # MHSRS-17-0380.

Funding

The Retired Army Medical Specialist Corps Association (RAMSCA) awarded a $725.90 endowment for the purchase of a scale and measuring tapes for the study April 2013. This supplement was sponsored by the Office of the Secretary of Defense for Health Affairs.

Acknowledgments

A special thanks to CPT Nicolette Cherney, CPT Kira Brown, CPT Cara Beavert, CPT Taylor Newman, CPT Nicolle Curtis, CPT Simon, CPT Asia Nakakura, CPT Lilly Vanek, 1LT Carl Barnes, 1LT Stephanie Carroll and 1LT Jourdin Stewart for assistance with data collection. I have obtained written permission from all persons named in the Acknowledgment.

Author notes

The views expressed are those of the authors and do not reflect the official policy or position of the U.S. Army Medical Department, Department of the Army, Department of Defense or the U.S. Government. The investigators have adhered to the policies for the protection of human subjects as prescribed in 45 CFR 46.