Abstract
Objectives:
US Army reserve soldiers and active-duty soldiers differ in their daily work demands and supporting resources, yet research on reservists’ health and fitness is lacking. The objectives of this study were to (1) determine whether physical test failure rates and health behaviors differed between active-duty soldiers and reserve soldiers and (2) establish which demographic and health behavioral factors were associated with failing physical tests.
Methods:
We analyzed a sample of 239 329 US Army active-duty and reserve soldiers surveyed from September 2013 through March 2015 using the Global Assessment Tool. We extracted data on soldier demographic characteristics and health behaviors, as well as Body Composition Test (BCT) and Army Physical Fitness Test (APFT) results. We compared the 2 groups using the active-to-reserve adjusted odds ratio (aOR) for each variable. We used logistic regression models to determine which variables were associated with failing these tests.
Results:
The odds of failing the BCT (aOR = 0.76; 95% confidence interval [CI], 0.73-0.78) or the APFT (aOR = 0.31; 95% CI, 0.30-0.32) were lower among active-duty soldiers than among reservists, and the odds of doing high levels of high-intensity interval training (aOR = 1.47; 95% CI, 1.42-1.51), resistance training (aOR = 1.45; 95% CI, 1.42-1.48), and vigorous physical activity (aOR = 2.92; 95% CI, 2.86-2.98) were higher among active-duty soldiers than among reservists. The odds of using tobacco (aOR = 1.37; 95% CI, 1.35-1.40), binge drinking alcohol (aOR = 1.11; 95% CI, 1.09-1.13), having insomnia (aOR = 1.46; 95% CI, 1.43-1.48) or mild depression (aOR = 1.50; 95% CI, 1.48-1.53), and sustaining a physical activity–related injury (aOR = 2.52; 95% CI, 2.47-2.57) were higher among active-duty soldiers than among reservists.
Conclusions:
Policy makers and military leaders could use this information to implement health screenings and tailor health-promotion, intervention, and treatment programs.
Increasing numbers of US soldiers are overweight and considered physically unfit because they do not meet strength and endurance standards. 1,2 The prevalence of active-duty soldiers who are overweight or obese increased from 50.6% in 1995 to 60.8% in 2008. 3 For the US military, these trends have not only major implications for operational readiness but also substantial fiscal consequences related to health care expenditures (eg, more than $1 billion annually) and other associated costs (eg, lost productivity). 4
Understanding the factors that affect the health and physical performance of soldiers requires accounting for multiple stressors associated with life in the military. 5 -7 More information is needed about how these stressors and other health-related behaviors may differ among military subpopulations. 7,8 Although military health–related research has increased during the past 2 decades, most of this research has focused on active-duty soldiers; reserve soldiers (ie, reservists) have been understudied. 9,10 Yet these populations differ in their health, fitness, and daily work demands and in the supporting resources available to them. 11,12
Reservists compose 53% of US Army soldiers and have been mobilized to support military operations at an unprecedented level since 2002. 13 Yet reservists are often prevented from deploying because of medical issues and/or poor physical fitness, and they frequently lack access to the resources available to their active-duty peers to address postdeployment health issues. 14 Additional research to identify differences in the protective and risk factors for the health and fitness of active-duty and reserve soldiers is needed to tailor programs and policies to these 2 populations and, ultimately, to enhance the overall health and well-being of all US Army soldiers. 15
The primary objective of this study was to determine whether body composition and fitness test failure rates, as well as demographic and health behavioral factors, differed between US Army active-duty soldiers and reserve soldiers. A secondary objective was to identify demographic and health behavioral factors that were associated with failing body composition and fitness tests.
Methods
We accessed data from the US Army’s Person-Event Data Environment (PDE), a computer system that collects data from multiple US Department of Defense databases. 16 The PDE assigns each person in the database a unique alphanumeric key that can be used to merge data about that person from multiple sources while protecting privacy. To ensure that no personally identifiable or protected health information is released, PDE staff members review all data files before releasing them to researchers. The Uniformed Services University and the US Army Armament Research Development and Engineering Center Institutional Review Boards approved this study.
Study Sample
We used data from the US Army’s Global Assessment Tool (GAT), a confidential 105-item survey that soldiers are required to complete annually. The survey takes approximately 12 minutes to complete. The GAT allows information to be collected about demographic characteristics, physical and psychological health, nutritional habits, and daily physical activities. 17,18
For this study, we analyzed data from soldiers who had completed the GAT from September 2013 through March 2015, which represented the most recent data available at the time of the study in 2017. We selected the 1.5-year range to capture the largest number of unique respondents, with the assumption that some soldiers may not have completed the GAT at the same time each year.
For this study, we included data from both US Army active-duty soldiers and reserve soldiers. Reservists included those in the US Army Reserve and the Army National Guard. Many soldiers take the GAT but do not consent for their data to be used. Although those data are strictly off limits, about 73% of soldiers consent to have their GAT data used for research. 17,18 Based on our initial data request asking for records of soldiers who (1) completed the GAT between September 2013 and March 2015 and (2) consented for their GAT records to be used, we received 591 010 GAT records. We then removed 127 068 (21.5%) duplicate GAT records. From the remaining 463 942 records, we removed 224 613 (48.4%) records of soldiers who did not have data on the Body Composition Test (BCT), Army Physical Fitness Test (APFT), or demographic characteristics, leaving a total sample size of 239 329 soldiers.
Demographic Measures
We obtained data on age (17-24, 25-34, 35-44, and ≥45), ethnicity (non-Hispanic white or other [American Indian/Alaska Native, Asian, black or African American, Native Hawaiian/other Pacific Islander, and multiracial/multiethnic]), sex (female, male), marital status (married, unmarried), and military status (officer, enlisted) from the GAT.
Physical Test Measures
We used the results of the most recent official US Army BCT and APFT of each soldier to assess overall physical health. Soldiers are required to take and pass these tests every 6 months. The BCT is used as a pass/fail indicator of physical readiness and general health. 19,20 The APFT measures muscular strength, local muscular endurance, and cardiorespiratory fitness based on 3 activities: push-ups, sit-ups, and a 3.2-km run. Performance in each activity is scored from 0 to 100, with scoring adjusted by sex and age, and a minimum score of 60 for each activity is required to pass the test. 21 For our study, we coded the APFT score for each soldier as a dichotomous pass/fail.
Health Behavior Measures
Nutritional habits
For each respondent, we collected data on consumption of breakfast (≥6 vs ≤5 days per week), consumption of recovery snacks within 60 minutes after exercise (often or most of the time vs sometimes/rarely/never), and intake of selected food types. To determine healthy eating vs unhealthy eating, we calculated a composite score (from 0-5) of healthy food intake, which was based on the frequency of consumption of fruit, vegetables, whole grains, dairy products, and fish. We then added the scores for these 5 food types and labeled respondents with total scores in the lowest quartile (ie, a score of <2.2) as unhealthy eaters. 22
In the GAT, respondents were asked if they took any of the following dietary supplements and at what frequency: health-promoting supplements, vitamin D, fish oil, sport protein powders or supplements, weight-loss products, and performance-enhancing or body-building products. We coded these responses dichotomously, based on whether or not respondents had taken a supplement at least once during the previous year.
Substance use
We coded tobacco use dichotomously, based on whether or not respondents reported use of tobacco products (cigarettes, cigars, smokeless tobacco, chew, dipping, or pinching) during the previous year. We assessed binge alcohol drinking with the 3-item Alcohol Use Disorders Identification Test–Concise. We coded alcohol binge-drinking dichotomously by applying the recommended sex-specific cutoff scores (≥4 drinks for men and ≥3 drinks for women). 23,24
Sleep behavior
We assessed sleep health by using a modified version of the 2-item version of the Pittsburgh Insomnia Rating Scale, which asked respondents about their sleep satisfaction and sleep-related fatigue. In this modified version, answers were scored on a 4-point Likert-type scale, ranging from 0 to 3, with >2 indicating poor sleep. 25,26
Depression
We controlled for depression by using respondent scores on the traditional Personal Health Questionnaire Depression Scale. 27 -29 Answers to each of the 8 questions were scored on a Likert scale ranging from 0 to 3, with a maximum total score of 24. Consistent with previous reports, we chose a total score cutoff of ≥5 to indicate that a respondent had at least mild depression. 30
Physical injury
We also controlled for physical activity injury, which we defined as sustaining any physical activity–related injury during the previous 6 months, and we coded it as a dichotomous variable.
Physical activity
We assessed physical activity by using the self-reported frequency of high-intensity interval training, resistance training, and vigorous physical activity during the previous month. We classified high-intensity interval training as none, moderate (1-4 days per week), or high (5-7 days per week). We classified resistance training as minimal (≤1 day per week), low-moderate (2-4 days per week), or high (5-7 days per week). We classified vigorous physical activity as low (<75 minutes per week), moderate (75-150 minutes per week), or high (>150 minutes per week). 31 We calculated the average number of exercise minutes per week during the previous month by multiplying self-reported days of vigorous activity per week by minutes of activity on those days.
Statistical Methods
We compared the proportions of active-duty and reserve soldiers with demographic characteristics, physical test results, and health behaviors (including physical activity) by calculating the active-to-reserve odds ratio (OR) and 95% confidence interval (CI) for each variable. To identify group differences, we used logistic regression models in which we treated active-duty vs reserve as the outcome variable and the demographic, health behavior, physical test, and physical activity measures as the independent variables. For variables with >2 categories (age, high-intensity interval training, resistance training, and vigorous physical activity), we assigned a single category as a reference (age 25-34, none, 2-4 days per week, and <75 minutes per week, respectively).
We also used logistic regression models to determine which demographic and health behaviors (including physical activity) were associated with soldiers failing the BCT and/or the APFT. We used separate multiple logistic regression models for active-duty soldiers and reserve soldiers. For each variable, we calculated both crude ORs and adjusted ORs (aORs). We based the aOR results on multiple logistic regressions that controlled for all other variables that were part of the study. For variables with >2 categories, we used the same references as noted previously.
We examined the interactions between nondemographic predictors by splitting the groups in half and using forward stepwise entry with all possible interactions. We retained the 2-way interactions that were significant in both samples and that altered the main and/or combined effects by at least 20%. We used this procedure to reduce findings that may have occurred by chance, given that there were no predicted interactions and considering the large sample size and number of potential interactions.
To determine whether the GAT-PDE matched sample was representative of all GAT respondents, we compared frequencies of health behaviors between this GAT-PDE matched sample and a sample of those with GAT data but without PDE data. This revealed that the health behavior frequencies of both samples were nearly identical.
Results
Of the 239 329 respondents included in the study, most were male (84.3%), non-Hispanic white (73.8%), married (52.5%), and aged 25-34 (42.1%) (Table 1). A greater percentage of reservists than active-duty soldiers failed the BCT (7.4% vs 5.7%) and the APFT (17.9% vs 6.4%).
Demographic, physical-test, and health-behavior measures of US Army soldiers (N = 239 329), by soldier type, from the US Army Global Assessment Tool (GAT)a and Person-Event Data Environment (PDE),b September 2013 through March 2015
a The US Army GAT is a confidential 105-item survey that soldiers are required to complete annually. It collects data on demographic characteristics, physical and psychological health, nutritional habits, daily physical activities, and other health metrics. 17 The study included only the most recent set of data for soldiers who took the GAT more than once during the study period. Data from US Army civilian employees were excluded.
b The PDE is a computer system that pulls data from multiple US Department of Defense databases. Researchers can access this system using an encrypted virtual desktop environment interface to find data from multiple military information systems.
cActive-duty and reserve soldiers were compared by calculating the active-to-reserve odds ratio and 95% CI for each variable, using logistic regression models in which active-duty vs reserve was treated as the outcome variable, and demographic, health behavior, and physical test measures were treated as independent variables.
d Other includes American Indian/Alaska Native, Asian, black or African American, Native Hawaiian/other Pacific Islander, and multiracial/multiethnic.
e The US Army Body Composition Test (BCT) involves soldiers having height and weight measured. Results are based on US Army BCT standards. 19
f The US Army Physical Fitness Test assesses muscular strength, local muscular endurance, and cardiorespiratory fitness based on 3 activities: push-ups, sit-ups, and a 3.2-km run. Each activity is scored from 0 to 100, depending on age and sex; a minimum score of 60 for each activity is required to pass the test. 21
g Comparison of unhealthy eaters (scored in the lowest quartile of composite score of healthy food intake calculated by using the frequency of consumption of fruit, vegetables, whole grains, dairy products, and fish) vs non-unhealthy eaters (reference group, scored above the lowest quartile). 22
h Based on consumption of breakfast ≥6 days per week vs <6 days per week (reference group).
i Based on consumption of recovery snacks within 60 minutes after exercise “often” or “most of the time” vs consumption of recovery snacks “sometimes,” “rarely,” or “never” (reference group).
j Based on consumption of supplements at least once during previous year.
k The reference group is nonusers of supplements.
l Based on use of any tobacco products (cigarettes, cigars, smokeless tobacco, chew, dipping, pinching) during previous year.
m Based on 3-item Alcohol Use Disorders Identification Test–Concise (AUDIT-C) and application of recommended sex-specific cutoff scores (≥4 drinks per day for men and ≥3 drinks per day for women) to code for binge drinking. 23,24
n Based on 2-item modified version of the Pittsburgh Insomnia Rating Scale (PIRS2), which asks about satisfaction with sleep and sleep-related fatigue. 25,26 A score >2 indicates insomnia. The reference group is participants with a score ≤2 (no insomnia).
o Based on Personal Health Questionnaire Depression Scale (PHQ-8). 27 -30 A score ≥5 indicates mild or severe depression. The reference group is participants with a score <5 (no mild or severe depression).
p Sustaining any physical activity–related injury during the previous 6 months.
Active-duty soldiers had higher odds than reservists of regularly eating breakfast (OR = 1.12; 95% CI, 1.10-1.14) and using dietary supplements (OR = 1.10; 95% CI, 1.09-1.13), particularly weight-loss supplements (OR = 1.37; 95% CI, 1.33-1.41) and performance-enhancing supplements (OR = 1.34; 95% CI, 1.31-1.37) (Table 1). Active-duty soldiers also had higher odds than reservists of eating unhealthy foods (OR = 1.12; 95% CI, 1.10-1.14), using tobacco (OR = 1.36; 95% CI, 1.33-1.41), binge-drinking alcohol (OR = 1.11; 95% CI, 1.09-1.13), and having insomnia (OR = 1.46; 95% CI, 1.43-1.48), mild depression (OR = 1.50; 95% CI, 1.48-1.53), and a physical activity–related injury (OR = 2.52; 95% CI, 2.47-2.57).
The odds of reporting high levels of high-intensity interval training (OR = 1.47; 95% CI, 1.42-1.51), resistance training (OR = 1.45; 95% CI, 1.42-1.48), and vigorous physical activity (OR = 2.92; 95% CI, 2.86-2.98) were all higher among active-duty soldiers than among reservists (Table 2).
Physical activity measure results of US Army soldiers (N = 239 329), by soldier type, from the US Army Global Assessment Tool (GAT),a September 2013 through March 2015
a The GAT is a confidential 105-item survey that soldiers are required to complete annually. It collects data on demographic characteristics, physical and psychological health, nutritional habits, daily physical activities, and other health metrics. 17 The study includes only the most recent set of data for soldiers who took the GAT more than once during the study period. Data from US Army civilian employees were excluded.
bActive-duty and reserve soldier groups were compared by calculating the active-to-reserve odds ratio and 95% CI for each variable, by using logistic regression models in which active-duty vs reserve was treated as the outcome variable and physical activity measures were treated as independent variables.
c Raw data from the GAT were used to calculate levels of activity during previous month.
d The GAT data set included summarized data about moderate activity levels, classified as low, medium, or high.
BCT Failure
The overall pattern of demographic predictors of BCT failure was similar for active-duty soldiers and reserve soldiers (Table 3). For age, the highest odds of failing the BCT occurred in the youngest age group (17-24), and those odds (relative to soldiers aged 25-34) were significantly higher for active-duty soldiers (aOR = 1.49; 95% CI, 1.41-1.58) than for reservists (aOR = 1.21; 95% CI, 1.15-1.28). The odds of failing the BCT were significantly lower for officers (compared with enlisted soldiers) for both active-duty soldiers (aOR = 0.35; 95% CI, 0.31-0.38) and reservists (aOR = 0.45; 95% CI, 0.41-0.49).
Odds of Body Composition Test (BCT)a failure of US Army soldiers (N = 239 329), by demographic, physical-test, and health-behavior measures, from the US Army Global Assessment Tool (GAT)b and Person-Event Data Environment (PDE),c September 2013 through March 2015
Abbreviations: aOR, adjusted odds ratio; OR, odds ratio.
a The US Army BCT involves soldiers having height and weight measured, and results are based on US Army BCT standards. 19
b The US Army GAT is a confidential 105-item survey that soldiers are required to complete annually. It collects data on demographic characteristics, physical and psychological health, nutritional habits, daily physical activities, and other health metrics. 17 This study includes only the most recent set of data for soldiers who took the GAT more than once during the study period. Data from US Army civilian employees were excluded.
c PDE is a computer system that pulls data from multiple US Department of Defense databases. Researchers can access this system using an encrypted virtual desktop environment interface to find data from multiple military information systems.
d aORs were based on a multiple logistic regression model controlling for all other measures listed, except moderate physical activity.
e Other includes American Indian/Alaska Native, Asian, black or African American, Native Hawaiian/other Pacific Islander, and multiracial/multiethnic.
f Based on lowest quartile of composite score of healthy food intake based on the frequency of consumption of fruit, vegetables, whole grains, dairy products, and fish. 22 Reference group is those not categorized as unhealthy eaters.
g Based on consumption of breakfast ≥6 days per week. Reference group is those who eat breakfast <6 days per week.
h Based on consumption of recovery snacks within 60 minutes after exercise often or most of the time. Reference group is those who consumer recovery snacks “sometimes,” “rarely,” or “never.”
i Based on consumption of supplements at least once during previous year. The reference group is nonusers.
j Based on use of any tobacco products (cigarettes, cigars, smokeless tobacco, chew, dipping, pinching) during previous year. The reference group is nonusers.
k Based on 3-item Alcohol Use Disorders Identification Test–Concise (AUDIT-C) and application of recommended sex-specific cutoff scores (≥4 drinks per day for men and ≥3 drinks per day for women) to code for binge drinking. 23,24 The reference group is non–binge drinkers.
l Based on 2-item modified version of the Pittsburgh Insomnia Rating Scale (PIRS2), which asks about satisfaction with sleep and sleep-related fatigue. 25,26 A score >2 indicates insomnia. The reference group is participants with a score ≤2 (no insomnia).
m Based on Personal Health Questionnaire Depression Scale (PHQ-8). 27 -30 A score ≥5 indicates mild or severe depression. The reference group is participants with a score <5 (no mild or severe depression).
n Based on sustaining any physical activity–related injury during previous 6 months.
o Raw data from GAT were used to calculate levels of activity during previous month.
p The GAT data set included summarized data about moderate activity levels, classified as low, medium, or high. When the moderate physical activity variable was included in logistic regression models, it generated multicollinearity and instability, likely because of high correlation with vigorous physical activity results. As such, it was not included in adjusted regression models for physical test failure.
The odds of BCT failure for active-duty and reserve soldiers were significantly higher for those with mild depression (aOR = 1.39; 95% CI, 1.31-1.47; and aOR = 1.29; 95% CI, 1.22-1.36, respectively) and a recent physical activity–related injury (aOR = 1.45; 95% CI, 1.38-1.53; and aOR = 1.41; 95% CI, 1.34-1.49, respectively) (Table 3). Conversely, the odds of BCT failure were significantly lower for soldiers who regularly ate breakfast (aOR = 0.91; 95% CI, 0.86-0.96; and aOR = 0.88; 95% CI, 0.84-0.93, respectively) and recovery snacks after exercise (aOR = 0.75; 95% CI, 0.71-0.79; and aOR = 0.68; 95% CI, 0.64-0.71, respectively).
Active-duty and reserve soldiers who did high levels of resistance training had lower odds of failing the BCT than soldiers who did low-to-moderate levels of resistance training (aOR = 0.94; 95% CI, 0.90-0.99; aOR = 0.81; 95% CI, 0.76-0.86, respectively) (Table 3). However, the odds of BCT failure for both active-duty and reserve soldiers did not differ significantly by level of high-intensity interval training or vigorous physical activity.
APFT Failure
With some exceptions, the overall pattern of demographic predictors of APFT failure was similar for active-duty and reserve soldiers (Table 4). As with failure of the BCT, soldiers aged 17-24 had the highest odds of failing the APFT, and those odds (relative to soldiers aged 25-34) were significantly higher for active-duty soldiers (aOR = 1.93; 95% CI, 1.83-2.04) than for reservists (aOR = 1.70; 95% CI, 1.64-1.77). The odds of failing the APFT were also significantly lower for officers (compared with enlisted soldiers) for both active-duty soldiers (aOR = 0.25; 95% CI, 0.22-0.29) and reservists (aOR = 0.28; 95% CI, 0.26-0.30).
Odds of Army Physical Fitness Test (APFT)a failure of US Army soldiers (N = 239 329), by demographic, physical-test, and health-behavior measures, from US Army Global Assessment Tool (GAT)b and Person-Event Data Environment (PDE),c September 2013 through March 2015
Abbreviations: aOR, adjusted odds ratio; OR, odds ratio.
a The US Army APFT assesses muscular strength, local muscular endurance, and cardiorespiratory fitness based on 3 activities: push-ups, sit-ups, and a 3.2-km run. Each activity is scored from 0 to 100, depending on age and sex; a minimum score of 60 for each activity is required to pass the test. 21
b The US Army GAT is a confidential 105-item survey that soldiers are required to complete annually. It collects data on demographic characteristics, physical and psychological health, nutritional habits, daily physical activities, and other health metrics. 17 This study includes only the most recent set of data for soldiers who took the GAT more than once during the study period. Data from US Army civilian employees were excluded.
c The PDE is a computer system that pulls data from multiple US Department of Defense databases. Researchers can access this system using an encrypted virtual desktop environment interface to find data from multiple military information systems.
d aORs are based on multiple logistic regression model controlling for all other measures listed, except moderate physical activity.
e Other includes American Indian/Alaska Native, Asian, black or African American, Native Hawaiian/other Pacific Islander, and multiracial/multiethnic.
f Based on lowest quartile of composite score of healthy food intake based on the frequency of consumption of fruit, vegetables, whole grains, dairy products, and fish. 22 The reference group is those not categorized as unhealthy eaters.
g Based on consumption of breakfast ≥6 days per week. Reference group is those who eat breakfast <6 days per week.
h Based on consumption of recovery snacks within 60 minutes after exercise often or most of the time. Reference group is those who consume recovery snacks “sometimes,” “rarely,” or “never.”
i Based on consumption of supplements at least once during previous year. The reference group is nonusers.
j Based on use of any tobacco products (cigarettes, cigars, smokeless tobacco, chew, dipping, pinching) during previous year. The reference group is nonusers.
k Based on 3-item Alcohol Use Disorders Identification Test–Concise (AUDIT-C) and application of recommended sex-specific cutoff scores (≥4 drinks per day for men and ≥3 drinks per day for women) to code for binge drinking. 23,24 The reference group is non–binge drinkers.
l Based on 2-item modified version of the Pittsburgh Insomnia Rating Scale (PIRS2), which asks about satisfaction with sleep and sleep-related fatigue. 25,26 A score >2 indicates insomnia. The reference group is participants with a score ≤2 (no insomnia).
m Based on Personal Health Questionnaire Depression Scale (PHQ-8). 27 -30 A score ≥5 indicates mild or severe depression. The reference group is participants with a score <5 (no mild or severe depression).
n Based on sustaining any physical activity–related injury during previous 6 months.
o Raw data from GAT were used to calculate levels of activity during previous month.
p The GAT data set included summarized data on moderate activity levels, classified as low, medium, or high. When the moderate physical activity variable was included in logistic regression models, it generated multicollinearity and instability, likely because of high correlation with vigorous physical activity results. As such, it was not included in adjusted regression models for physical test failure.
The odds of APFT failure for active-duty and reserve soldiers were significantly higher for those who used tobacco (aOR = 1.28; 95% CI, 1.22-1.35; and aOR = 1.46; 95% CI, 1.40-1.51, respectively), had mild depression (aOR = 1.30; 95% CI, 1.23-1.37; and aOR = 1.21; 95% CI, 1.17-1.26, respectively), and had a recent physical activity–related injury (aOR = 1.76; 95% CI, 1.68-1.85; and aOR = 1.48; 95% CI, 1.42-1.54, respectively) compared with those who did not (Table 4).
Conversely, the odds of APFT failure were significantly lower for soldiers who ate breakfast regularly (aOR = 0.82; 95% CI, 0.78-0.87; and aOR = 0.77; 95% CI, 0.74-0.80, respectively), ate recovery snacks regularly (aOR = 0.73; 95% CI, 0.69-0.77; and aOR = 0.70; 95% CI, 0.67-0.72, respectively), took dietary supplements (aOR = 0.92; 95% CI, 0.87-0.98; and aOR = 0.92; 95% CI, 0.88-0.96, respectively), and reported binge-drinking alcohol (aOR = 0.81; 95% CI, 0.77-0.86; and aOR = 0.82; 95% CI, 0.79-0.85, respectively) compared with those who did not.
Active-duty and reserve soldiers who did moderate (aOR = 0.90; 95% CI, 0.85-0.95; and aOR = 0.88; 95% CI, 0.85-0.92, respectively) and high (aOR = 0.83; 95% CI, 0.76-0.92; and aOR = 0.84; 95% CI, 0.78-0.91, respectively) levels of interval training had significantly lower odds of APFT failure than soldiers who did no high-intensity interval training (Table 4). Similarly, soldiers who did high levels of resistance training had significantly lower odds of APFT failure (aOR = 0.90; 95% CI, 0.86-0.96; and aOR = 0.83; 95% CI, 0.79-0.86, respectively) than soldiers who did minimal levels of resistance training. Reservists who reported moderate (aOR = 0.85; 95% CI, 0.82-0.89) and high (aOR = 0.73; 95% CI, 0.70-0.77) levels of vigorous physical activity also had significantly lower odds of APFT failure than reservists who reported low levels of vigorous physical activity, but this was not the case for active-duty soldiers.
Discussion
The finding that reservists had higher odds of BCT and APFT failure than active-duty soldiers may in part reflect the difference between the 2 populations in everyday job demands. 14 Although both groups are required to adhere to the same military standards, active-duty soldiers are routinely afforded time at work to maintain APFT preparedness, whereas reservists are less likely to receive the same accommodations from their civilian employers. A growing body of research highlights that reservists fare worse than active-duty soldiers postdeployment, particularly in the area of health behaviors. 32 The findings on physical test performance in this study suggest that reservists may struggle more than active-duty soldiers not just after deployment but also in their everyday health and well-being. 33
The finding that reservists had higher odds of BCT and APFT failure than active-duty soldiers also raises concerns about the military readiness of reservists compared with active-duty soldiers. Given these concerns and that reservists are understudied compared with active-duty soldiers, efforts should be made to understand the different organizational and cultural environments to which active-duty and reserve soldiers are exposed. 6,7 Research exploring stressor-outcomes relationships in reservists might also be beneficial. Using this information, military policy makers may be better able to target health screenings and tailor health-promotion, intervention, and treatment programs that address the profiles and needs of both reserve soldiers and active-duty soldiers. 10,15
This study showed that for both populations, the factors associated with passing either physical test were older age (≥25), being an officer, regularly eating breakfast and recovery snacks, and doing high levels of resistance training. Conversely, the factors associated with failing the tests were younger age (<25), mild depression, and having a recent physical activity–related injury. This information may be particularly useful in the development of programs and policies focused on soldier health and well-being. For example, regular screening of soldiers for depression would allow early identification of soldiers who are more likely to fail these physical tests and would potentially benefit from treatment before taking the tests. 2,33
This study demonstrated that active-duty soldiers performed better than reservists not only on physical tests but also on other important health behavior measures, including taking dietary supplements and doing high levels of high-intensity interval training, resistance training, and vigorous physical activity. On the other hand, despite having worse BCT and APFT outcomes, reservists appeared to perform better than active-duty soldiers on other health behavior measures. They had lower odds of unhealthy eating, tobacco use, alcohol binge drinking, insomnia, mild depression, and physical activity–related injuries. These findings are consistent with previous research and may reflect that the behaviors of reservists are more in line with the general US population. 34 This similarity suggests that health-promotion, intervention, and treatment programs that have worked well in the general population may apply to reservists.
Our study found that health behaviors that protected reservists against either BCT failure or APFT failure included regularly eating breakfast and recovery snacks, regularly taking dietary supplements, and doing high levels of resistance training, moderate-to-high levels of interval training, and moderate-to-high levels of vigorous exercise. This information could be used by policy makers and program leaders to develop guidelines and design interventions to bolster the future performance of reservists on the BCT and APFT and improve their military readiness. 31
Our study findings also suggest that although physical fitness and body composition tests can provide useful indicators of overall health, they may need to be tailored to active-duty and reserve populations. It is reassuring to note that the testing used for soldiers is always evolving. The US Army announced that it will replace the APFT with the Army Combat Readiness Test (ACRT) in 2020. 35 The ACRT is age- and sex-neutral, and it is designed to assess how soldiers will perform in combat environments. It involves 6 timed events: deadlifts, standing power throw, T-pushups, sprint/drag/carry maneuver, leg-tucks, and the 3.2-km run—the only event retained from the APFT. The ACRT is intended to help improve overall combat readiness while also reducing the high prevalence of musculoskeletal injuries. 36 Policy makers are considering whether different occupational specialties should have different test score requirements. The results of our study suggest that policy makers should also consider whether active-duty and reserve soldiers should have different test score requirements.
Limitations
This study had several limitations. First, GAT data are self-reported and, thus, are subject to recall bias, social desirability effects, and potential biases associated with the personal nature of the questions. Nevertheless, this potential limitation may be offset by the study’s use of validated measures, which had excellent specificity and moderate sensitivity, and by the fact that the data self-reported by military populations have been shown to be reliable when compared with objective electronic medical record data. 37,38 Second, the reliance on cross-sectional data in this study cannot establish causality between health behavior measures and physical test outcomes. It may be that failing a test is perceived as discouraging and, thus, may lead to unhealthy behaviors rather than healthy behaviors. Finally, because the findings focused on US Army soldiers, they may not be generalizable to other US service branches or other militaries.
Conclusions
Policy makers and military leaders should consider using this information to implement targeted health screenings and tailored health-promotion, intervention, and treatment programs directed at the needs and environmental realities of both active-duty and reserve soldiers.
Footnotes
Acknowledgments
The authors acknowledge the assistance of the Research Facilitation Laboratory in providing a data analysis platform and technical support. The views expressed in this article are those of the authors and do not reflect the official policy or position of the Uniformed Services University of the Health Sciences, the US Department of Defense, or the US government.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the US Army’s Comprehensive Soldier and Family Fitness program (HT9404-12-1-0017; F191GJ).
