Mental health, physical health, and health-related behaviors of U.S. Army Special Forces

Abstract

Objectives

To prospectively examine the health and health-related behaviors of Army Special Forces personnel in comparison with two distinct, but functionally similar Army groups.

Methods

Special Forces, Ranger Qualified, and General Purposes Forces enrolled in the Millennium Cohort Study were identified using data from the Defense Manpower Data Center. Using prospective survey data (2001–2014), we estimated the association of Army specialization with mental health, social support, physical health, and health-related behaviors with multivariable regression models.

Results

Among the 5,392 eligible participants (84.4% General Purposes Forces, 10.0% Special Forces, 5.6% Ranger Qualified), Special Forces personnel reported the lowest prevalence of mental disorders, physical health problems, and unhealthy behaviors. In the multivariable models, Special Forces personnel were less likely to report mental health problems, multiple somatic symptoms, and unhealthy behaviors compared with General Purpose Forces infantrymen (odds ratios [OR]: 0.20–0.54, p-values < .01). Overall, Special Forces personnel were similar in terms of mental and physical health compared with Ranger Qualified infantrymen, but were less likely to sleep < 5 hours/night (OR: 0.60, 95% confidence intervals: 0.40, 0.92) and have 5 or more multiple somatic symptoms (OR: 0.69, 95% CI: 0.49, 0.98). Both Special Forces personnel and Ranger Qualified infantrymen engaged in more healthy behaviors compared with General Purpose Forces infantrymen (OR: 2.57–6.22, p-values<0.05). Engagement in more healthy behaviors reduced the odds of subsequent adverse health outcomes, regardless of specialization.

Conclusions

Army Special Forces personnel were found to be mentally and physically healthier than General Purpose Forces infantrymen, which may in part be due to their tendency to engage in healthy behaviors. Findings indicate that engagement in a greater number of healthy behaviors may reduce odds for subsequent adverse outcomes.

About the Authors:

Adam D. Cooper

Roles Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliations Deployment Health Research Department, Naval Health Research Center, San Diego, CA, United States of America, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, United States of America, Innovative Employee Solutions, San Diego, CA, United States of America

Steven G. Warner

Roles Formal analysis, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliations Deployment Health Research Department, Naval Health Research Center, San Diego, CA, United States of America, Leidos, Reston, VA, United States of America

Anna C. Rivera

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

Affiliations Deployment Health Research Department, Naval Health Research Center, San Diego, CA, United States of America, Leidos, Reston, VA, United States of America

Rudolph P. Rull

Roles Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Writing – review & editing

Affiliation: Deployment Health Research Department, Naval Health Research Center, San Diego, CA, United States of America

Amy B. Adler

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliation: Walter Reed Army Institute of Research, Silver Spring, MD, United States of America

Dennis J. Faix

Roles Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Writing – review & editing

Affiliation: Naval Medical Research Unit Two, Phnom Penh, Cambodia

Rob Neff

Roles Conceptualization, Investigation, Methodology, Project administration, Writing – review & editing

Affiliation: Preservation of the Force and Family, Headquarters United States Special Operation Command, MacDill Air Force Base, Tampa, FL, United States of America

Edwin A. Deagle

Roles Conceptualization, Investigation, Methodology, Writing – review & editing

Affiliation: Preservation of the Force and Family, Headquarters United States Special Operation Command, MacDill Air Force Base, Tampa, FL, United States of America

Ryan J. Caserta

Roles Conceptualization, Investigation, Methodology, Writing – review & editing

Affiliation: Preservation of the Force and Family, Headquarters United States Special Operation Command, MacDill Air Force Base, Tampa, FL, United States of America

Cynthia A. LeardMann

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Deployment Health Research Department, Naval Health Research Center, San Diego, CA, United States of America, Leidos, Reston, VA, United States of America

ORCID logo http://orcid.org/0000-0002-3683-6016

for the Millennium Cohort Study Team

¶A complete list of the Millennium Cohort Study Team can be found in the Acknowledgments.

Introduction

The U.S. Army Special Forces (SF) are elite cohesive units expected to perform in high-risk operational contexts. Because missions carried out by these individuals often involve extreme levels of stress and fierce combat spanning months at a time, SF personnel must pass multi-faceted screenings and undergo rigorous training to ensure they are qualified and adequately prepared. SF personnel are often the first service members deployed to remote and dangerous locations and thus experience more frequent and intense combat deployments than non-SF personnel [1–4]. They may thus be predisposed to a higher likelihood of mental and physical health problems [5]. The majority of previous studies, however, suggested that SF personnel were less likely to exhibit symptoms or seek medical care for mental and physical health problems compared with those from other military occupational populations, including general infantry, combat artillery, and mechanized infantry soldiers [6–10].

Numerous factors likely contribute to the individuals, who are selected and serve as SF personnel, being healthier and more resilient. They complete intense screening and training prior to and between deployments and serve in tight-knit cohesive groups that can take pride in their expertise and elite status [11]. While each of these factors may play a role in the resilience and hardiness of SF individuals, SF personnel may also engage in more healthy behaviors such as sleeping 7 to 9 hours most nights and higher levels of physical activity [9, 12]. In civilian populations, engagement in healthy behaviors (e.g., adequate sleep, regular exercise) have been associated with better mental health [13–19], a reduction of chronic pain [20–22], and overall well-being [23–25].

Despite previous studies reporting that SF personnel were healthier than non-SF personnel, these analyses were limited by cross-sectional designs [6,7], unavailability of data on potential confounding factors such as combat history [7, 8, 9], and/or an inability to differentiate the roles of personnel serving in SF units (e.g., SF, support, medical) [6, 8, 9]. Furthermore, previous investigations have not identified the specific factors that explain why SF personnel may exhibit more positive mental and physical health outcomes than non-SF personnel.

To our knowledge, the current study is the first to evaluate and compare mental health, social support, physical health, and health-related behaviors among SF personnel and other similar occupational specializations using data from a longitudinal study. This approach may provide critical insights toward understanding the strengthening aspects of selection, training, experience, unit culture, and health-related behaviors.

The first aim of this study was to compare the mental health, physical health, social support, and health-related behaviors of SF personnel with two functionally similar Army groups (General Purpose Forces (GPF) and Ranger Qualified infantrymen (RQ)), while adjusting for baseline health, demographics, and military factors. The second aim compared the number of healthy behaviors between specializations. Finally, we examined the association of number of healthy behaviors with subsequent health outcomes, and whether this association varied by military specialization.

Methods

Study population

The Millennium Cohort Study was established in 2001 to prospectively investigate and examine the long-term impact of military service. Invited personnel were randomly sampled from U.S. military rosters, representing all service branches and components, with oversampling of selected subgroups, as previously described [26]. Participants were enrolled and completed their first survey in four separate phases between 2001 and 2014. Participants were then requested to complete additional surveys approximately every 3–4 years, including after their separation from military service. The surveys assessed participants’ mental health, physical health, and health-related behaviors, as well as military and non-military experiences [27–28]. The Millennium Cohort procedures were approved by the Institutional Review Board at the Naval Health Research Center (NHRC 2000.0007). All participants provided written voluntary informed consent.

For this study, SF personnel were compared with two other types of Army soldiers who also serve in combat roles: RQ and GPF infantrymen. SF personnel consisted of those who had passed the 24-day Special Forces Assessment and Selection Course followed by the 12–18 months Special Forces Qualification Course. Many of these individuals have also completed Ranger school and usually serve in small units with other SF personnel. RQ infantrymen consisted of infantry soldiers who volunteered for and completed Ranger school, the U.S. military’s 9-week premier leadership course. After completing this leadership course, most serve in conventional Army units and are not considered to be members of the Special Operations Forces. GPF infantrymen included Soldiers who met Army requirements for this military occupational specialty in terms of mandatory training, cognitive scores, and physical fitness standards.

Type of Army specialization was assessed using Military Occupational Specialty (MOS) and identifiers obtained from the Defense Manpower Data Center (DMDC) between 2001 and 2014. SF personnel were defined as those who had an MOS code beginning with 18B, 18C, 18D, 18E, 18F, or 18Z (Weapons Sergeant, Engineer Sergeant, Medical Sergeant, etc.) for enlisted personnel and 180A or 18A for commissioned and warrant officers, respectively. RQ infantrymen were defined as individuals who had completed Ranger School and were awarded the ‘Ranger Tab.’ Enlisted RQ infantrymen had an MOS beginning with 11B, 11C, 11H, 11M, or 11Z (Infantryman–Ranger Parachutist, Indirect Fire Infantryman–Ranger, Infantry Senior Sergeant–Ranger, etc.) and a Special Qualification Identifier of G or V. RQ officers had an MOS beginning with 11A and an additional skills identifier of 5R or 5S. GPF infantrymen had an MOS beginning with 11A, 11B, 11C, 11H, 11M, or 11Z (Infantry Officer (Commissioned), Infantryman, Indirect Fire Infantryman, Heavy Antiarmor Weapons Infantryman, etc.), but did not have the additional required identifiers to be categorized as RQ. To ensure homogenous groups, Soldiers identified as ever serving in the 75th Ranger Regiment unit, a Special Operations Force, were excluded from all analyses.

The sample for this study was restricted to Millennium Cohort male participants from any of the four enrollment cycles who were SF personnel, RQ infantrymen, or GPF infantrymen (n = 10,116). In order to conduct a prospective analysis, participants must have completed two consecutive surveys, with at least one completed while serving in the military in their specialization (i.e., SF personnel, RQ infantrymen, or GPF infantrymen) (excluded n = 4,586). The first of these two consecutive surveys was referred to as “baseline” and the second as “follow-up.” Of the 5,530 eligible participants, those who did not complete demographic, military, and baseline health data were excluded (n = 138), thus yielding a final study population of 5,392 soldiers.

Measures

Mental health, physical health, social support, and health-related behaviors were assessed using data from the follow-up survey. The four mental health outcomes included posttraumatic stress disorder (PTSD), depression, panic/anxiety, and an aggregated outcome of the four disorders referred to as “any mental disorder.” PTSD was assessed using the standardized PTSD Checklist–Civilian Version (PCL-C) (Cronbach’s α = 0.97). Based on the Diagnostic and Statistical manual of Mental Disorders, 4th edition (DSM-IV) diagnostic criteria (i.e.,endorsing “moderately” or higher on one or more intrusion items, two or more hyper-arousal items, and three or more avoidance items in the past month), which has demonstrated strong psychometric properties, participants were classified as screening positive for PTSD [29]. Standardized items and scoring of the Primary Care Evaluation of Mental Disorders Patient Health Questionnaire (PHQ) were used to assess depression, panic syndrome, and other anxiety syndromes. Previous studies observed good agreement between PHQ diagnoses and those of mental health professionals [30]. Using the DSM-IV major depressive disorder criteria [31], individuals who endorsed five of the eight PHQ items (i.e., “more than half the days” or higher), including anhedonia or depressed mood were identified as screening positive for depression (Cronbach’s α = 0.92). Participants were identified as having panic syndrome if they positively responded to specific psychosocial questions and had four or more symptoms of an anxiety attack. Other anxiety syndrome was measured using six items from the PHQ. Participants were classified as screening positive for panic/anxiety if they were positive for panic syndrome and/or other anxiety syndrome [32].

Social support was assessed using one PHQ item. Individuals reporting being “bothered a lot” or “bothered a little” by “having no one to turn to when you have a problem” in the last 4 weeks were defined as having a lack of social support.

The two physical health outcomes were bodily pain and somatic symptoms. Bodily pain was based on the two pain items from the Medical Outcomes Short Form 36-Item Health Survey for Veterans (SF-36V). Using the standardized scoring algorithm of the SF-36V scales, the responses were summed and scored with a range from 0 to 100. Bodily pain was further categorized into tertiles as low, medium, and high bodily pain [33–35]. Somatic symptoms were based on 14 PHQ items that assess physical health symptoms (e.g., headaches, dizziness, nausea) in the last 4 weeks (Cronbach’s α = 0.85). The total number of symptoms were summed based on standardized scoring and then categorized as 0–4 or 5+ symptoms [32].

Health-related behaviors consisted of alcohol-related problems, smoking, sleep hours, trouble sleeping, physical activity, and fast food consumption. Alcohol-related problems was defined as reporting at least one of the five alcohol-related PHQ items in the last 12 months (Cronbach’s α = 0.68) [30]. Smoking behavior was categorized as non-smokers (<100 cigarettes/lifetime), former smokers (≥100 cigarettes/lifetime, successfully quit), and current smokers (≥100 cigarettes/lifetime, had not successfully quit). Sleep hours was assessed using the number of hours of sleep in an average 24-hour period in the last month. Trouble sleeping was defined as reporting “trouble falling asleep or staying asleep” in the last month on either the PCL-C (“moderately” or more) or the PHQ (“several days” or more) item [36]. Physical activity level was ascertained from the self-reported number of minutes engaged in activity per week and categorized as low (cannot physically do, or <150 minutes), medium (150–299 minutes), medium-high (300–449 minutes per week), and high (≥450 minutes) [37]. Fast food consumption was categorized as “None,” “Once per week,” “2–3 times per week,” and “4+ times per week.”

For the second aim, the number of healthy behaviors was based on the total number of healthy behaviors endorsed at baseline. Using the same algorithm as previous studies, each of the six behaviors was categorized as “healthy,” and then summed (i.e., score 0–6) [38]. The healthy behaviors included lower alcohol consumption (1–14 drinks/week), not smoking (i.e., non-smoker), adequate sleep (7–9 hrs/day), physical activity (≥150 minutes of weekly physical activity), low sedentary lifestyle factors (less than 8 hours/day), and low fast food consumption (<1 time/week) [38]. Based on low frequencies for the lowest and highest number of healthy behaviors, this variable was categorized as 0–2, 3, 4, and 5–6 healthy behaviors.

Covariates

Covariates were assessed at baseline using data obtained from the survey and the DMDC. Educational level and marital status were assessed using self-reported survey data. Deployment experience was based on deploying prior to baseline, either self-reported or based on an electronic record in the Contingency Tracking System, in conjunction with baseline items that assessed combat experiences. Participants who had at least one deployment prior to baseline were classified as “deployed without combat” or “deployed with combat,” based on endorsement of one of more combat items. Participants who did not deploy prior to baseline were categorized as “not deployed.” Baseline mental component scores and physical component scores were assessed and calculated using the standard items and scoring from the SF-36V [33–35], where higher scores indicate better health. All other demographic (e.g., race/ethnicity, age) and military service characteristics (e.g., service component, pay grades) were determined using DMDC records.

Statistical analysis

Descriptive statistics assessed the distributions of demographic characteristics, military factors, baseline health, and outcomes by type of specialization. For the primary aim, multivariable logistic regression was used to estimate the association between specialization and each mental health, social support, physical health, and health-related behavior outcome, with separate models run for each outcome. Models were adjusted for demographics, military factors, and baseline health. Analyses where SF personnel were the occupational category of interest utilized distinct reference groups, GPF infantrymen and RQ infantrymen, in separate models.

The second aim examined the number of healthy behaviors. Because three of the behaviors (fast food, sedentary lifestyle, and physical activity) were not assessed at baseline for the first two enrollment panels, those participants who completed baseline surveys without these items (n = 3,000), along with those missing any of the six assessed behaviors (n = 120), were excluded, thus reducing the study population for these analyses to 2,272 participants. The relationship between specialization and baseline number of healthy behaviors was assessed using multivariable logistic regression.

For the third aim, we estimated the association between the baseline number of healthy behaviors and subsequent health outcomes (e.g., any mental disorder, bodily pain). Limiting the health outcomes of interest was necessary due to the inclusion of some of the behaviors in the healthy behaviors predictor variable. Logistic regression models were adjusted for demographics, military factors, and baseline health. To assess whether the association between the number of healthy behaviors and health outcomes was modified by type of specialization, we included an interaction term (specialization * healthy behaviors).

Since behaviors and health status may change upon leaving military service, we conducted sub-analyses in which we excluded those who were no longer serving in the military at the time of their follow-up survey, resulting in the removal of 23% of the study population. All models in Tables 3, 4 and 5 were re-analyzed with this restricted population, with the exception of the specific mental health disorders, PTSD, depression, panic/anxiety, due to low numbers. In addition, we conducted analyses where we adjusted for time between surveys.

For all models, collinearity was assessed and defined as a value >4 using the variance inflation factor. Data management and all statistical analyses were conducted using SAS (version 9.4, SAS Institute, Inc., Cary, NC, USA).

Results

Most of the 5,392 study participants were GPF infantrymen (84.4%), while 10.0% were SF personnel, and 5.6% were RQ infantrymen. SF personnel were proportionally more likely to be non-Hispanic white, higher educated, and older compared with GPF and RQ infantrymen (Table 1). In addition, SF personnel were proportionally more likely to be married, serving on active duty, and an officer than GPF personnel. At baseline, 70.8% of SF personnel had been previously deployed and experienced combat compared with 66.0% for RQ infantrymen and 58.4% for GPF infantrymen. SF personnel were proportionally more likely to report higher levels of physical and mental functioning at baseline than GPF and RQ infantrymen (Table 1). On average, GPF infantrymen had served the longest in their specialization at baseline (71.1 months, SD: 30.0), followed by RQ (52.2 months, SD: 25.5) and SF personnel (47.7 months, SD: 28.4). The mean number of months between baseline and follow-up surveys for all groups was similar (35.4–37.5 months).

[Figure omitted. See PDF.]

Table 1. Baseline characteristics of soldiers by Army specialization, the Millennium Cohort Study*.

https://doi.org/10.1371/journal.pone.0233560.t001

With the exception of depression, SF personnel reported the lowest frequencies of mental and physical health problems at follow-up, followed by RQ infantrymen, and then GPF infantrymen (Table 2). Depression frequencies were similar for SF personnel and RQ infantrymen, but higher for GPF infantrymen. SF personnel (40.3%) were proportionally more likely to report 5–6 healthy behaviors compared with RQ infantrymen (26.9%) and GPF infantrymen (19.1%).

[Figure omitted. See PDF.]

Table 2. Frequencies of mental health, social support, physical health, health-related behaviors, and number of healthy behaviors by Army specialization among soldiers, the Millennium Cohort Study*.

https://doi.org/10.1371/journal.pone.0233560.t002

Effect estimates for mental health, social support, physical health, and health-related behaviors are listed in Table 3. In models adjusting for demographics, military factors, and baseline health, SF personnel were approximately four times less likely to screen positive for any mental disorder than GPF infantrymen (AOR = 0.25, 95% CI: 0.15, 0.42), while no significant difference was observed when compared with RQ infantrymen. RQ infantrymen were approximately two to three times less likely to screen positive for any mental disorder compared with GPF infantrymen (AOR = 0.38, 95% CI: 0.22, 0.65). Similar associations were observed for the three individual mental health outcomes.

[Figure omitted. See PDF.]

Table 3. Adjusted odds ratios of mental health, social support, physical health, and health-related behaviors outcomes by Army specialization among soldiers, the Millennium Cohort Study.

https://doi.org/10.1371/journal.pone.0233560.t003

With respect to physical health, SF personnel were significantly less likely to report 5+ somatic symptoms compared with GPF infantrymen (AOR = 0.47, 95% CI: 0.37, 0.59) and RQ personnel (AOR = 0.69, 95% CI: 0.49, 0.98). RQ infantrymen were less likely to report 5+ somatic symptoms (AOR = 0.68, 95% CI: 0.51, 0.90) compared with GPF. There were no statistically significant associations for bodily pain between the groups (Table 3).

For social support and health-related behaviors, SF personnel were significantly less likely than GPF infantrymen to report lack of social support (AOR = 0.50, 95% CI: 0.37, 0.69), alcohol-related problems (AOR = 0.54, 95% CI: 0.38, 0.77), current smoking (AOR = 0.35, 95% CI: 0.23, 0.54), sleeping 5 or less hours (AOR = 0.42, 95% CI: 0.31, 0.56), trouble sleeping (AOR = 0.39, 95% CI: 0.31, 0.50), and low levels of physical activity (AOR = 0.47, 95% CI: 0.35, 0.65) (Table 3). There was only one statistically significant difference observed between SF personnel and RQ infantrymen. Specifically, SF personnel were less likely to report sleeping 5 or less hours (AOR = 0.60, 95% CI: 0.40, 0.92) compared with RQ personnel. RQ infantrymen were significantly less likely to report current smoking (AOR = 0.47, 95% CI: 0.29, 0.76), sleeping 5 or less hours (AOR = 0.69, 95% CI: 0.50, 0.96), trouble sleeping (AOR = 0.54, 95% CI: 0.40, 0.72), and low levels of physical activity (AOR = 0.55, 95% CI: 0.38, 0.80) compared with GPF infantrymen. No differences between specializations were observed for the frequency of fast food consumption (Table 3).

The mean number of healthy behaviors was highest for SF personnel (4.2), followed by RQ infantrymen (3.9) and then GPF infantrymen (3.5). Both SF personnel (AOR range: 3.17–6.22) and RQ infantrymen (AOR range = 2.26–2.57) were more likely to report a higher number of healthy behaviors compared with GPF infantrymen, adjusting for baseline health, demographics, and military characteristics (Table 4). However, for SF personnel these associations were statistically significant across all levels, which was not the case for RQ personnel. No significant difference in scores were observed between SF personnel and RQ infantrymen.

[Figure omitted. See PDF.]

Table 4. Adjusted odds ratios of composite health score at baseline by Army specialization among soldiers, the Millennium Cohort Study.

https://doi.org/10.1371/journal.pone.0233560.t004

Further examination of the relationship between the number of heathly behaviors and the selected outcomes, where all specializations were combined, revealed that those reporting fewer healthy behaviors at baseline had an increased risk for subsequent adverse outcomes (e.g., high bodily pain) (Table 5). Specifically, compared with those reporting 5–6 healthy behaviors at baseline, the risk for a positive screen for any mental disorder increased with each decrement in healthy behaviors (AOR range: 1.73 for 4 behaviors to 2.27 for 0–2 behaviors). A similar pattern was observed for a high level of bodily pain (AOR range: 1.42 for 4 behaviors to 2.26 for 0–2 behaviors) and somatic symptoms (AOR range: 1.31 for 4 behaviors to 1.88 for 0–2 behaviors). Those who reported 0 to 2 baseline healthy behaviors also had an elevated risk for perceived lack of social support (AOR: 2.08, 95% CI: 1.45, 3.00). Tests for heterogeneity did not suggest any interactions between specialization and the composite score for healthy behaviors (p-values: 0.45 to 0.77; results not shown in tables).

[Figure omitted. See PDF.]

Table 5. Adjusted odds ratios of outcomes by number of healthy behaviors, all Army specializations combined among soldiers in the Millennium Cohort Study.

https://doi.org/10.1371/journal.pone.0233560.t005

There were only a few differences in effect estimates when we conducted analyses restricted to those who were still serving in the military at the time of their follow-up survey (n~4,000) (results not shown). Specifically, the magnitude and significance of the association between specialization and all eleven outcomes, including number of healthy behaviors, remained consistent with results from the main analysis (Tables 3 and 4) when comparing SF personnel with GPF infantrymen. Comparing RQ and GPF infantrymen, two associations (somatic symptoms and number of healthy behaviors) were reduced in magnitude and were no longer significant. In comparisons between SF personnel and RQ infantrymen, SF were less likely to report trouble sleeping (OR: 0.66, 95% CI: 0.45–0.97) and lack of social support (OR: 0.59, 95% CI 0.36, 0.95), while the magnitude and significance of the associations for the other outcomes did not appear to change. In the analyses of the association between the number of healthy behaviors and the four outcomes of interest with all specializations combined, the magnitude of association increased for all levels of behavior, except those who reported four healthy behaviors were no longer significantly more likely to report high levels of bodily pain. Adjustment for the time between surveys did not significantly change the effect estimates.

Discussion

These findings suggest that while SF personnel are known to experience combat deployments of greater intensity, they also appear to be the healthiest of the three groups examined. SF personnel were less likely than GPF infantrymen to report psychological health disorders, somatic symptoms, unhealthy behaviors (e.g., alcohol-related problems, smoking, trouble sleeping), and more likely to report social support and healthy behaviors (e.g., sleeping 7–9 hours, high levels of physical activity). Both SF personnel and RQ infantrymen reported engaging in a higher number of healthy behaviors compared with GPF infantrymen. Engagement in more healthy behaviors was associated with decreased risk for subsequent adverse health outcomes, regardless of Army specialization.

These results corroborate and expand upon previous findings indicating that SF personnel have better mental health and physical health compared with other soldiers [7–9]. One novel finding was of similarities in mental health and physical health between SF personnel and RQ infantrymen. For these select groups, these lower frequencies of adverse mental health outcomes and somatic symptoms in relation to GPF infantrymen may be attributable to innate characteristics of those who strive for and successfully complete these elite assignments and trainings.

Our results suggest that differences observed in mental and physical health outcomes between SF personnel and RQ infantrymen compared with GPF infantrymen may also be partly attributable to engagement in healthy behaviors. We found that SF personnel and RQ infantrymen were more likely to engage in a greater number of healthy behaviors and less likely to engage in unhealthy behaviors compared with GPF infantrymen. Moreover, the number of healthy behaviors at baseline was associated with social support and health outcomes at follow-up. These observed links between healthy behaviors and better mental and physical health are consistent with results from other populations [13–22].

SF personnel reported better sleep than both GPF and RQ infantrymen. Unlike GPF and RQ infantrymen, SF personnel serve together under one command (United States Army Special Operations Command) and therefore may receive more consistent guidance on the importance of healthy sleep patterns [11]. In contrast, GPF and RQ infantrymen are assigned to a wide range of Army commands. These various commands may provide inconsistent guidance and unit culture regarding healthy behaviors, with some placing greater importance on sleep than others, potentially due to differing mission demands, requirements, leadership emphasis, or unit norms.

While the findings from the current study suggest engagement of healthy behaviors and reduction of unhealthy behaviors may benefit all soldiers regardless of specialization, a pertinent question for future research remains how to facilitate the widespread implementation and adoption of these behaviors. The two general approaches are either to treat the already established unhealthy behaviors with therapy, education, and/or pharmacological programs, or to develop a proactive approach to prevent them from occurring in the first place. Treatments to address tobacco use and insomnia, for example, have had measureable success in modifying behaviors [39–43]. However, a proactive approach would be more efficient and potentially impactful [44–46]. The Army’s Comprehensive Soldier and Family Fitness (CSF2) program, for example, focuses on building resilience, performance, and readiness through “dimensions of strength” that include exercise, nutrition, training, and self-control [47–48]. While there is limited scientific evidence available on this positive psychology approach in a military population, future research may identify targeted areas for refinement for ensuring optimal implementation [49–50].

While key strengths of this analysis included the longitudinal design for examining personnel in three different Army specializations and the ability to mutually adjust for multiple covariates, there were some notable limitations. It is possible that our sample may not be representative of all SF personnel, RQ infantrymen, and GPF infantrymen. However, the Millennium Cohort Study is based on a random sampling of all U.S. military members and investigation of potential sources of selection bias revealed a representative military population [51–54]. Since we relied on self-administered surveys, the information collected may be susceptible to recall errors. SF personnel and RQ infantrymen may be less inclined to report mental health issues for fear of stigma or that they will be removed from duty, although evidence suggests that all three groups may be similar in this regard [55–56]. In addition, we assessed other health outcomes, which may be less susceptible to stigma and reporting bias, and these outcomes demonstrated similar patterns. As with any prospective cohort study, loss to follow-up may potentially bias the results, although analyses on weighting for nonresponses have not identified changes in metrics for PTSD, depression, and disordered eating [57].

In summary, we found that SF personnel and RQ infantrymen were less likely to report mental health problems, physical health problems, and unhealthy behaviors than GPF infantrymen. Engaging in more healthy behaviors at baseline was associated with decreased odds for mental health and physical health problems at follow-up, regardless of Army specialization. This specific finding is of great importance to the military community as it indicates that engagement in modifiable healthy behaviors, independent of baseline health status, may significantly impact health outcomes. These findings suggest that further individual, unit, and leader encouragement of the adoption of healthy behaviors may be an efficient and cost-effective approach for preventing adverse health outcomes in military populations.

Acknowledgments

In addition to the authors, Millennium Cohort Study team includes Satbir Boparai, MBA; Felicia Carey, PhD; Toni Geronimo-Hara, MPH; Isabel Jacobson*, MPH; Claire Kolaja, MPH; Rayna Matsuno, PhD; Deanne Millard; Chiping Nieh, PhD; Ben Porter, PhD; Teresa Powell, MS; Beverly Sheppard; Daniel Trone, PhD; and Jennifer Walstrom. The authors also appreciate contributions from the Deployment Health Research Department, Millennium Cohort Family Study, the Birth and Infant Health Research Team, and the Henry M. Jackson Foundation. We thank the Millennium Cohort Study participants.

*Lead author of this team ([email protected])

Disclaimer: I am a military service member or employee of the U.S. Government. This work was prepared as part of my official duties. Title 17, U.S.C. §105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S.C. §101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties. The study protocol was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable Federal regulations governing the protection of human subjects. Research data were derived from an approved Naval Health Research Center, Institutional Review Board protocol number NHRC.2000.0007.

Citation: Cooper AD, Warner SG, Rivera AC, Rull RP, Adler AB, Faix DJ, et al. (2020) Mental health, physical health, and health-related behaviors of U.S. Army Special Forces. PLoS ONE 15(6): e0233560. https://doi.org/10.1371/journal.pone.0233560

References

1. Malvesti ML. To serve the nation: US Special Operations Forces in an era of persistent conflict. Washington, DC: Center for New American Society; 2010.

2. Searle TR. Tribal engagement in anbar province: The critical role of Special Operations Forces. National Defense University Washington DC Institute for National Strategic Studies; 2008.

3. Simons A, & Tucker D. United States special operations forces and the war on terrorism. Small Wars and Insurgencies. 2003;14(1):77–91.

4. Robinson L. Masters of Chaos: The Secret History of the Special Forces. New York, Public Affairs. 2004.

5. Hartman RT., Wolfson J, & Yevich SJ. Military deployment health surveillance policy and its application to Special Operations Forces. Mil Med. 2004;169(1):1–6. pmid:14964493

6. Espinoza JM. Posttraumatic stress disorder and the perceived consequences of seeking therapy among US Army Special Forces personnel exposed to combat. J Psychol Issues Organ Cult. 2010;1(1):6–28.

7. Manning FJ, & Fullerton TD. Health and Well‐Being in Highly Cohesive Units of the US Army. J Appl Soc Psychol. 1988;18(6):503–519.

8. Morgan CA III, Hazlett G, Wang S, Richardson EG Jr., Schnurr P, & Southwick SM. Symptoms of dissociation in humans experiencing acute, uncontrollable stress: a prospective investigation. Am J Psychiatry. 2001;158(8):1239–1247. pmid:11481157

9. Reynolds K, Cosio-Lima L, Bovill M, Tharion W, Williams J, & Hodges T. A comparison of injuries, limited-duty days, and injury risk factors in infantry, artillery, construction engineers, and special forces soldiers. Mil Med. 2009;174(7):702–708. pmid:19685841

10. Bartone PT, Roland RR, Picano JJ, & Williams TJ. Psychological hardiness predicts success in US Army Special Forces candidates. Int. J. Sel. Assess. 2008;16(1):78–81.

11. U.S. Department of the Army. Army Special Operations Forces. Field Manual 3–05. Washington DC: U.S. Department of the Army, September 20, 2006.

12. Maupin D, Wills T, Orr R, & Schram BEN. Fitness Profiles in Elite Tactical Units: A Critical Review. Int J Exerc Sci. 2018;11(3):1041. pmid:30147824

13. Amminger GP, Schafer MR, Papageorgiou K, Klier CM, Cotton SM, Harrigan SM, et al. Long-chain omega-3 fatty acids for indicated prevention of psychotic disorders: A randomized, placebo-controlled trial. Arch Gen Psychiatry. 2010;67:146–154. pmid:20124114

14. Daley AJ. Exercise therapy and mental health in clinical populations: Is exercise therapy a worthwhile intervention? Adv Psychiatr Treat. 2002;8(4):262–270.

15. Deslandes A, Moraes H, Ferreira C, Veiga H, Silveria H, Mouta , et al. Exercise and mental health: Many reasons to move. Neuropsychobiology. 2009;59:191–198. pmid:19521110

16. Dowd S, Vickers K, Krahn D. Exercise for depression: How to get patients moving. Curr Psychol. 2004;3(6):10–20.

17. Gómez-Pinilla F. Brainfoods: The effect of nutrients on brain function. Nat Rev Neurosci. 2008;9(7):568–578. pmid:18568016

18. Stathopoulou G, Powers MB, Berry AC, Smits JA, Otto MW. Exercise interventions for mental health: a quantitative and qualitative review. Clin. Psychol Sci Pract. 2006;13(2):179–193.

19. Sidhu KS, Vandana P, Balon R. Exercise prescription: A practical effective therapy for depression. Curr Psychiatr. 2009;8(6):39–51.

20. Hayden JA, Van Tulder MW, Malmivaara AV, Koes BW. Meta-analysis: exercise therapy for nonspecific low back pain. Ann Intern Med. 2005;142(9):765–775. pmid:15867409

21. Mior S. Exercise in the treatment of chronic pain. Clin J Pain. 2001;17(4):S77–S85.

22. Moldofsky H. Sleep and pain. Sleep Med Rev. 2001;5(5):385–396. pmid:12531004

23. Haack M, Mullington JM. Sustained sleep restriction reduces emotional and physical well-being. Pain. 2005;119(1–3):56–64. pmid:16297554

24. Pilcher JJ, Ginter DR, Sadowsky B. Sleep quality versus sleep quantity: relationships between sleep and measures of health, well-being and sleepiness in college students. J Psychosom Res. 1997;42(6):583–596. pmid:9226606

25. Walsh R. Lifestyle and mental health. Am Psychol. 2011;66(7):579–592. pmid:21244124

26. Smith TC, & Millennium Cohort Study Team. (2009). The US Department of Defense Millennium Cohort Study: career span and beyond longitudinal follow-up. J Occup Environ Med. 2009;51(10):1193–1201. pmid:19786902

27. Gray GC, Chesbrough KB, Ryan MA, Amoroso P, Boyko EJ, Gackstetter GD, et al. The Millennium Cohort Study: a 21-year prospective cohort study of 140,000 military personnel. Mil Med. 2002;167(6):483–488. pmid:12099084

28. Ryan MA, Smith TC, Smith B, Amoroso P, Boyko EJ, Gray GC. Millennium Cohort: enrollment begins a 21-year contribution to understanding the impact of military service. J Clin Epidemiol. 2007;60(2):181–191. pmid:17208125

29. Weathers F, Litz B, Herman D, Huska JA, Keane T. The PTSD checklist (PCL): reliability, validity, and diagnostic utility. San Antonio: Paper presented at: Annual Meeting of International Society for Traumatic Stress Studies; 1993.

30. Spitzer RL, Kroenke K, Williams JB, Patient Health Questionnaire Primary Care Study Group. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA. 1999;282(18):1737–1744. pmid:10568646

31. Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114(1–3):163–173. pmid:18752852

32. Kroenke K, Spitzer RL, Williams JB, Löwe B. The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gen Hosp Psychiatry. 2010;32(4):345–359. pmid:20633738

33. Kazis LE, Lee A, Avron Spiro WR III, Ren XS, Miller DR, Selim A, et al. Measurement comparisons of the medical outcomes study and veterans SF-36® health survey. Health Care Financ Rev. 2004;25(4):43–58. pmid:15493443

34. Kazis LE, Miller DR, Clark JA, Skinner KM, Lee A, Ren XS, et al. Improving the response choices on the veterans SF-36 health survey role functioning scales: results from the Veterans Health Study. J Ambul Care Manage. 2004;27(3):263–280. pmid:15287216

35. Kazis LE, Miller DR, Skinner KM, Lee A, Ren XS, Clark JA, et al. Patient-reported measures of health: the Veterans Health Study. J Ambul Care Manage. 2004;27(1):70–83. pmid:14717468

36. Seelig AD, Jacobson IG, Donoho CJ, Trone DW, Crum-Cianflone NF, Balkin TJ. Sleep and health resilience metrics in a large military cohort. Sleep. 2016;39(5):1111–1120. pmid:26951391

37. Thomas CL, Nieh C, Hooper TI, Gackstetter GD, LeardMann CA, Porter B, et al; for the Millennium Cohort Study Team. Sexual harassment, sexual assault, and physical activity among U.S. military service members in the Millennium Cohort Study. J Interpers Violence. In press.

38. Bookwalter DB, Porter B, Jacobson IG, Kong SY, Littman AJ, Rull RP, et al. Healthy behaviors and incidence of overweight and obesity in military Veterans. Annals of Epidemiol. In Press.

39. Earles J, Folen RA, Ma M, Kellar M, Geralde R, Dydek G. Clinical effectiveness of sustained-release bupropion and behavior therapy for tobacco dependence in a clinical setting. Mil Med. 2002;167(11):923–925. pmid:12448619

40. Klesges RC, Ebbert JO, Talcott GW, Thomas F, Richey PA, Womack C, et al. Efficacy of a tobacco quitline in active duty military and TRICARE beneficiaries: a randomized trial. Mil Med. 2015;180(8):917–925. pmid:26226536

41. Morgan BJ. Evaluation of an educational intervention for military tobacco users. Mil Med. 2001;166(12):1094–1098. pmid:11778412

42. Taylor DJ, Peterson AL, Pruiksma KE, Young-McCaughan S, Nicholson K, Mintz J, et al. Internet and in-person cognitive behavioral therapy for insomnia in military personnel: a randomized clinical trial. Sleep. 2017;40(6):zsx075.

43. Taylor DJ, Peterson AL, Pruiksma KE, Hale WJ, Young-McCaughan S, Wilkerson A, et al. Impact of cognitive behavioral therapy for insomnia disorder on sleep and comorbid symptoms in military personnel: a randomized clinical trial. Sleep. 2018;41(6):zsy069.

44. Crowley, SK. The association of physical fitness with psychological health outcomes in soldiers during Army basic combat training. (Doctoral dissertation). 2013.

45. Jaeschke AMC. Psychosocial predictors of resilience in a military sample. West Virginia University. 2016.

46. Williams J, Brown JM, Bray RM, Anderson Goodell EM, Rae Olmsted K, Adler AB. Unit cohesion, resilience, and mental health of soldiers in basic combat training. Mil Psychol. 2016;28(4):241–250.

47. Cornum R, Matthews MD, Seligman ME. Comprehensive soldier fitness: building resilience in a challenging institutional context. Am Psychol. 2011;66(1):4. pmid:21219042

48. Harms, PD, Herian, M, Krasikova, DV, Vanhove, AJ, Lester, PB. The Comprehensive Soldier and Family Fitness Program Evaluation. Report# 4: Evaluation of Resilience Training and Mental and Behavioral Health Outcomes. 2013.

49. Brown, NJL. A critical examination of the US Army’s Comprehensive Soldier Fitness program. https://thewinnower.com/papers/49-a-critical-examination-of-the-u-s-army-s-comprehensive-soldier-fitness-program. Accessed 20 May 2019. 2016.

50. Eidelson R, Pilisuk M, Soldz S. The dark side of comprehensive soldier fitness. Am Psychol, 2011;66(7):643–644. pmid:21967209

51. Smith B, Wingard DL, Ryan MA, Macera CA, Patterson TL, Slymen DJ. US military deployment during 2001–2006: comparison of subjective and objective data sources in a large prospective health study. Ann Epidemiol. 2007;17(12):976–982. pmid:17923418

52. Smith TC, Jacobson IG, Smith B, Hooper TI, Ryan MA, Millennium Cohort Study Team. The occupational role of women in military service: validation of occupation and prevalence of exposures in the Millennium Cohort Study. Int J Environ Health Res. 2007;17(4):271–284. pmid:17613091

53. Smith TC, Smith B, Jacobson IG, Corbeil TE, Ryan MA, Millennium Cohort Study Team. Reliability of standard health assessment instruments in a large, population-based cohort study. Ann Epidemiol. 2007;17(7):525–532. pmid:17433714

54. Wells TS, Jacobson IG, Smith TC, Spooner CN, Smith B, Reed RJ, et al. Prior health care utilization as a potential determinant of enrollment in a 21-year prospective study, the Millennium Cohort Study. Eur J Epidemiol. 2008;23(2):79–87. pmid:18193482

55. Acosta JD, Becker A, Cerully JL, Fisher MP, Martin LT, Vardavas R, et al. Mental health stigma in the military. RAND National Defense Research Institute Santa Monica, CA. 2014.

56. Greene-Shortridge TM, Britt TW, Castro CA. The stigma of mental health problems in the military. Mil Med. 2007;172(2):157–161. pmid:17357770

57. Littman AJ, Boyko EJ, Jacobson IG, Horton J, Gackstetter GD, Smith B, et al. Assessing nonresponse bias at follow-up in a large prospective cohort of relatively young and mobile military service members. BMC Med Res Methodol. 2010;10(1):99.