Skip to main content

Pre-neuromusculoskeletal injury Risk factor Evaluation and Post-neuromusculoskeletal injury Assessment for Return-to-duty/activity Enhancement (PREPARE) in military service members: a prospective, observational study protocol



Non-battle related musculoskeletal injuries (MSKI) are one of the primary medical issues diminishing Service member medical readiness. The MSKI problem is challenging because it is difficult to assess all of the factors that increase MSKI risk and influence post-MSKI outcomes. Currently, there are no high-throughput, clinically-feasible, and comprehensive assessments to generate patient-centric data for informing pre- and post-MSKI risk assessment and mitigation strategies. The objective of the “Pre-neuromusculoskeletal injury Risk factor Evaluation and Post-neuromusculoskeletal injury Assessment for Return-to-duty/activity Enhancement (PREPARE)” study is to develop a comprehensive suite of clinical assessments to identify the patient-specific factors contributing to MSKI risks and undesired post-MSKI outcomes.


This is a phased approach, multi-center prospective, observational study ( number: NCT05111925) to identify physical and psychosocial factors contributing to greater MSKI risk and undesired post-MSKI outcomes, and to identify and validate a minimal set of assessments to personalize risk mitigation and rehabilitation strategies. In Phase I, one cohort (n = 560) will identify the physical and psychosocial factors contributing to greater MSKI risks (single assessment), while a second cohort (n = 780) will identify the post-MSKI physical and psychosocial factors contributing to undesired post-MSKI outcomes (serial assessments at enrollment, 4 weeks post-enrollment, 12 weeks post-enrollment). All participants will complete comprehensive movement assessments captured via a semi-automated markerless motion capture system and instrumented walkway, joint range of motion assessments, psychosocial measures, and self-reported physical fitness performance and MSKI history. We will follow participants for 6 months. We will identify the minimum set of clinical assessments that provide requisite data to personalize MSKI risk mitigation and rehabilitation strategies, and in Phase II validate our optimized assessments in new cohorts.


The results of this investigation will provide clinically relevant data to efficiently inform MSKI risk mitigation and rehabilitation programs, thereby helping to advance medical care and retain Service members on active duty status.

Trial Registration: PREPARE was prospectively registered on (NCT05111925) on 5 NOV 2021, prior to study commencement.


Musculoskeletal injuries (MSKI) are endemic among military Service members and are the leading cause of limited/lost duty time [1, 2]. It is critical we identify the factors contributing to non-battle related MSKI risks and post-MSKI outcomes such that targeted MSKI risk mitigation and rehabilitation programs may be developed. A comprehensive biopsychosocial model is required to identify the pre- and post-MSKI physical and psychosocial factors that contribute to future MSKI risks and post-MSKI outcomes [3].

Service member specific MSKI risk factors and predictors of post-MSKI outcomes are multifaceted and interrelated. The primary predictor of future MSKI is previous MSKI [4, 5]. Additionally, aberrant movement patterns identify MSKI risks and may contribute to the development and/or recurrence of musculoskeletal conditions, such as low back pain [3, 6, 7]. Pre- and post-MSKI aberrant movement patterns may be exacerbated by low physical fitness [7], which in itself is an MSKI risk factor [4, 5, 7, 8]. Furthermore, sub-optimal joint mobility (e.g., restricted range of motion, capsular tightness) can contribute to aberrant movement patterns [9, 10] and potentially increase MSKI risks [11,12,13]. A number of potential post-MSKI physical [14,15,16,17] as well as deleterious psychosocial factors (e.g., fear of movement) may negatively impact sensorimotor function and movement quality, thereby increasing MSKI risks [18, 19] and exacerbating musculoskeletal conditions [3]. While the aforementioned factors are potential risk factors for subsequent MSKI, it is also possible the risk factors preceding the initial MSKI may not have been addressed or resolved with rehabilitation and therefore may still pose a risk for future MSKI.

Field-expedient movement assessments can efficiently identify individuals at greater MSKI risk within clinical settings [4,5,6, 20,21,22]. However, clinicians must utilize field-expedient functional assessments of varying difficulty and intensity to comprehensively identify movement related MSKI risks [23]. Common lower extremity movement assessments include the double and single leg squat [9, 10], jump-landings [20], and the triple hop for distance [24]. These assessments collectively evaluate lower extremity strength and neuromuscular control, and can identify inter-limb asymmetries; a key MSKI risk factor and predictor of post-MSKI outcomes [24]. Additionally, multivariate spatiotemporal gait assessments can quantify functional capabilities in physically active individuals, which is particularly relevant to Service members who often perform long distance ruck marches [16]. Common upper extremity movement assessments include the Closed Kinetic Chain Upper Extremity Stability Test [21, 22] and the Functional Movement Screen (FMS) Shoulder Clearing Test [4, 5]. Of note, individual FMS items are better MSKI risk predictors than cumulative FMS scores [4, 5, 23]. Similar to extremity MSKI risk factor evaluations, field-expedient assessments including the prone plank and the Active Hip Abduction [25] assessments are recommended for clinical use to evaluate low back pain risk [26].

Biopsychosocial approaches are required to comprehensively identify all of the factors that affect MSKI risks and outcomes [3]. The National Institutes of Health (NIH) Patient Reported Outcomes Measurement Information System (PROMIS) measures common domains (e.g., depression, physical function) associated with MSKI risk across populations [27]. Previous work [16], demonstrates PROMIS measures can detect changes in self-reported physical function following MSKI [27]. Furthermore, pain-related fear of movement (i.e., kinesiophobia) [28] is associated with limb asymmetries and stiffer movement patterns during activities, which may increase MSKI risks [19], and it is a determinant of whether individuals will successfully return-to-duty/activity (RTD/A) after MSKI [18, 19, 29].

The greatest limitations to previously proposed comprehensive MSKI assessments are clinical skills/expertise and time. Semi-automated motion capture systems can reduce the technical expertise required to successfully complete movement assessments and to help expedite the assessment process. Semi-automated motion capture systems have rigid objectivity that reduces inter-rater subjectivity [30] and improves the clinical utility of field-expedient functional assessments [23]. The semi-automated nature of these systems provides objective data and removes the significant time barriers (training and scoring) required for many field-expedient assessments when they are completed en masse (e.g., a company or battalion) [30]. Similarly, instrumented walkways efficiently provide valid measures of common gait variables [31] and can quantify triple hop distances [24]. Additionally, PROMIS computer adaptive testing (CAT) formats reduce question burden while increasing precision [27]. All of these systems require less than 15 min to train individuals how to operate and require no more time for data analyses than it takes for the individual to complete the assessments, making them ideal for clinical settings and novice raters.

The primary objective of the “Pre-neuromusculoskeletal injury Risk factor Evaluation and Post-neuromusculoskeletal injury Assessment for Return-to-duty/activity Enhancement (PREPARE)” study is to develop a high-throughput comprehensive suite of clinical assessments that collectively identify the Service member-specific factors that contribute to MSKI risks and undesired post-MSKI outcomes. Furthermore, we aim to develop and validate optimized versions of our pre- and post-MSKI assessments that efficiently identify the physical and psychosocial factors that inform MSKI risk mitigation and rehabilitation strategies. We hypothesize a set of field-expedient assessments can efficiently identify Service member-specific MSKI risk factors, post-MSKI deficits, and provide data to guide future patient-specific risk mitigation and rehabilitation programs.


Study design

This is a multi-center, prospective, observational study to create a suite of clinical assessments that collectively identify the patient-specific factors contributing to MSKI risks and undesired post-MSKI outcomes. We will utilize a phased approach to complete our objectives: Phase I will evaluate the physical and psychosocial factors contributing to greater MSKI risks and undesired post-MSKI outcomes, and identify the minimum set of assessments that provide the data needed to personalize MSKI risk mitigation and rehabilitation strategies; Phase II will validate these optimized MSKI clinical assessments in newly enrolled cohorts. Figure 1 provides the study flow of our investigation. This study design will allow us to answer our specific aims:

Fig. 1
figure 1

Study flow diagram

Specific Aim #1: Determine the physical and psychosocial factors that differentiate between Service members who do vs. do not sustain a MSKI within 6 months of study enrollment. Hypothesis: Semi-automated field-expedient assessments can identify the factors that differ between Service members who do vs. do not sustain a MSKI.

Specific Aim #2: Determine the post-MSKI physical and psychosocial factors that differentiate between Service members with conservatively managed MSKIs who do vs. do not develop undesired outcomes within 6 months of study enrollment. Hypothesis: Semi-automated field-expedient assessments can identify the factors that differ between Service members who do vs. do not develop undesired post-MSKI outcomes.

Specific Aim #3: Develop optimized versions of both pre- and post-MSKI assessments that efficiently identify the physical and psychosocial factors that inform MSKI risk mitigation and rehabilitation strategies. Hypothesis: A common set of semi-automated field-expedient assessments can inform clinical decision-making regarding which Service members are at the highest risk of sustaining MSKIs and undesired post-MSKI outcomes.

Specific Aim #4: Validate the optimized versions of both pre- and post-MSKI assessments by demonstrating their abilities to predict MSKI risks and outcomes in new Service member cohorts. Hypothesis: The optimized pre- and post-MSKI assessments will be able to accurately identify Service members at the highest risk for sustaining MSKIs and undesired post-MSKI outcomes across Service member populations.

Ethical aspects

This study was approved by the Walter Reed National Military Medical Center (WRNMMC) Institutional Review Board (protocol WRNMMC-2020-0283) and registered with (NCT05111925). The Fort Bragg performance site relied on the WRNMMC Institutional Review Board for study protocol review and regulatory oversight. Any changes to the study protocol will be requested through the WRNMMC Institutional Review Board and reflected on

Study participants will sign a site-specific written informed consent form, prior to completing study-related tasks. Upon enrollment in the study, study participants will be randomly assigned a unique participant identification number. Participants will only be referred to in study documentation by their unique identification number and the same number will be used across all data collection systems. Coded data will be stored on password-protected, encrypted networks and access to participant data will be limited to the study team and other authorized entities.

Study population

A total of 2,680 active duty Service members between 18 and 44 years of age will be enrolled in this study. In Phase I and Phase II we will recruit convenience samples of active duty Service members in-processing to new military units at Fort Bragg, NC. Participants will be identified during their in-processing medical screenings. Additionally, in Phase I we will recruit a convenience sample of active duty Service members receiving physical therapy for a MSKI at WRNMMC. In Phase II, we will recruit a convenience sample of active duty Service members receiving physical therapy for a MSKI at Fort Bragg, NC. In both Phase I and Phase II, participants with a MSKI will be identified by their physical therapists during their initial encounters.

Inclusion and exclusion criteria

The study inclusion and exclusion criteria will determine if potential study participants are eligible to participate in the study (Table 1). Inclusion and exclusion criteria will be self-reported by the potential participants, and study team members will not access the participant’s medical record or incorporate pregnancy testing to confirm inclusion/exclusion criteria.

Table 1 Participant inclusion and exclusion criteria

Data collection

Given the goal of this study to develop field-expedient and clinic-friendly solutions to quantify metrics from functional assessments, we will use a semi-automated commercially available markerless motion capture system and instrumented walkway. The HumanTrak (VALD Health, Charlotte, NC) markerless motion capture system will quantify/estimate joint kinematics during functional assessments (i.e., double leg squats, single-leg squats, jump-landings) [32]. The Zeno Walkway (ProtoKinetics LLS.; Havertown, PA) will efficiently provide valid measures of common gait variables [31] and quantify triple hop distances [24]. Range of motion data will be measured with standard clinical goniometers and digital inclinometers. Participant reported data will be collected electronically via a standardized research data collection platform.

Phase I approach

We will concurrently enroll cohorts for two separate observational studies: one at Fort Bragg, NC (n = 560) and one at WRNMMC (n = 780). The Fort Bragg cohort will identify the physical and psychosocial factors that contribute to greater MSKI risks (Specific Aim #1), while the WRNMMC cohort will identify the post-MSKI physical and psychosocial factors that contribute to undesired participant outcomes (Table 4, Specific Aim #2). Participants enrolled in Specific Aim #1 who go on to sustain an MSKI during the study follow-up period will be invited to participate into Specific Aim #2, and thus undergo post-MSKI tracking/assessments. Both cohorts will complete identical testing procedures. The study team will track study-related outcomes and pull relevant health related information through the Military Health System Data Repository (MDR) and participant self-report data. The MDR contains records on all healthcare events paid for by the Military Health System and is the best source of Military Health System data available for researchers.

Clinical assessments

For Specific Aim #1, all clinical assessments will be collected during a single testing session at the time of study enrollment. For Specific Aim #2, participants will undergo repeat (≤ 3 days of starting physical therapy [“initial”]; 4 weeks post-initial assessment, or at RTD/A clearance [i.e., cleared from physical therapy], if prior to 4 weeks; and 12 weeks post-initial assessment, or at RTD/A clearance, if prior to 12 weeks) clinical assessments.

Functional assessments

Lower extremity movement assessments will be recorded and automatically quantified using the markerless motion capture system or instrumented walkway. All participants will complete the assessments in the same order, progressing from less to more intense assessments. Upper extremity assessments will follow lower extremity assessments. Testing order will follow the order described in Table 2.

Table 2 Functional movement assessments and outcomes

Range of motion assessments

The joint will be moved through its range of motion (Table 3) to the point of first resistance or until the participant indicates discomfort. Three trials of each assessment will be recorded bilaterally, alternating limbs between measurements. To optimize efficiency, all participants will complete the range of motion assessments in the same order. Measurements will be averaged across trials for each limb and limb symmetry indices will be calculated [33]. We will establish our team’s reliability for all measures, prior to study commencement.

Table 3 Range of motion assessments

Participant reported outcomes

Participants will complete initial and follow-up (monthly) questionnaires. All questionnaires will be administered electronically, such that participants will be able to complete them from any location with internet access.

NIH PROMIS—we will utilize NIH PROMIS CAT measures for Physical Function, Pain Interference, Depression, and Anxiety. PROMIS measures will be converted to t-scores and scores that reach a floor or ceiling threshold (“outliers”) will be excluded from analyses.

Tampa Scale of Kinesiophobia [28]—we will utilize the abbreviated 11 question TSK-11. The total score will be calculated (summed) [28].

Musculoskeletal Injuriesparticipants will report previous MSKIs sustained within 1 year prior to study enrollment, any “severe” MSKIs they sustained that resulted in three or more consecutive weeks of missed activity, and any musculoskeletal surgeries. Participants will also report any new MSKIs they sustain throughout the study follow-up period. The questionnaire contains details regarding activity at the time of MSKI, the mechanism of injury, date of MSKI, and the geographic location.

Physical Fitness Test Performanceparticipants will report the scores from their most recent standard military physical fitness assessment. Raw scores will be converted to the standardized point system, based on Service specific requirements.

Outcome measures

All study outcomes will be tracked for up to 6 months. The primary outcomes are if a Service member sustains a MSKI or experiences an undesired post-MSKI outcome. A MSKI will be defined as a medical encounter that is associated with a relevant International Classification of Diseases (ICD)-10 code [34, 35]. We will abstract the following data for each MSKI: (1) body part; (2) activity at the time of MSKI; (3) mechanism of injury; (4) date; and (5) geographic location. MSKIs that result from a contact mechanism or enemy combatants will be excluded from analyses. Post-MSKI outcomes will be dichotomized as either “desired” or “undesired” as described in Table 4. Participants with any “undesired” outcome will be considered to have an overall undesired outcome. The secondary outcomes include: (1) time until the index MSKI relative to study enrollment; (2) time to RTD/A following the index MSKI; and (3) healthcare utilization (e.g., physical therapy encounters).

Table 4 Participant post-musculoskeletal injury outcome classifications

Phase I statistical and data analysis plan

Phase I analyses will extend a statistical approach previously shown to (1) identify distinct clusters of physical assessments representing meaningful domains of knee function, and (2) select optimal test batteries found to discriminate injured individuals versus healthy controls [36]. We will use a similar empirical strategy to reduce redundancy among our comprehensive assessments for MSKI-related risk. Descriptive analyses with traditional bivariate tests will evaluate score distributions overall and by incident MSKI and undesired outcomes. We will use hierarchical cluster analysis, a machine learning algorithm useful for representing natural groupings among observations, to next identify clusters of related continuous variables in the Phase I dataset. Distances between pairs of variables will be defined based on their correlations. Tree diagrams obtained from cluster analysis of the distance matrix will be used to identify variable clusters that will be interpreted based on current knowledge of underlying functional domains. Sensitivity analyses will assess the robustness of groupings to the choice of linkage method. Stratified analyses among the injured cohort will describe cluster reliability among subgroups defined by MSKI type (back pain vs extremity) at enrollment.

We will next define reduced, candidate test batteries as combinations of individual variables found to characterize distinct clusters representing meaningful functional domains [36]. Cluster representatives will be defined to maximize the clinical feasibility of the potential test battery, e.g., by minimizing estimated completion times. Candidate test batteries will be evaluated for each cohort in separate logistic regression models with component test scores as predictors and incident MSKI or undesired outcome (Table 4) as the dependent variable. Misclassification rates will be reported as the percent of participants where the outcome was incorrectly predicted after thresholding using clinically relevant sensitivities and specificities, with cross-validation to minimize bias in estimated probabilities. Models with the lowest model-based misclassification rates will be selected as optimal pre- and post-MSKI assessments and further evaluated in logistic regression models adjusted for age, sex, body mass index, physical fitness, history and duration of previous MSKI, and self-report of pain. Regression estimates and area under the curve (AUC) will be reported for adjusted and unadjusted models to describe predictive values of optimal assessments independent of traditional risk factors. Additional analyses will repeat final models defining MSKI by type (back pain vs extremity) to assess for changes in model performance with narrowed case definitions. Exploratory analyses will use regression-based variable selection methods (e.g., LASSO) to describe the potential predictive accuracy of available assessments while ignoring requirements for test feasibility or interpretation based on known functional domains.

Missing data

We expect infrequent (< 5%) missing functional measures at the baseline assessment based on completion rates in our previous work; however, multiple imputation will be considered if missingness exceeds 10% [37]. Because outcomes will be ascertained using the MDR, minimizing risk of loss to follow-up, no imputation of missing outcomes is planned.

Sample size

We assume event rates of 50% for incident MSKI (uninjured cohorts) [38] and 30% for undesired outcomes (injured cohorts) [39, 40], and 10% loss to follow over 6 months based on reported annual changes in beneficiary status in the MDR. We expect similar frequencies of back pain and extremity injuries based on previous ICD reporting of these MSKI types at our clinical sites. Targeted enrollment will be 560 Service members in the uninjured cohort (Specific Aim #1) and 780 Service members in the injured cohort (Specific Aim #2) to observe  > 100–125 outcomes in each cohort for each MSKI type. These expected event counts will yield  > 10 events per predictor variable in binary classification models with 10–12 predictors, allowing analyses to evaluate the predictive value of reduced assessments independent of traditional risk factors.

Phase II approach

We will enroll active duty Service members who are in-processing to a new unit at Fort Bragg, NC (n = 560) and active duty Service members receiving physical therapy for a MSKI within a military physical therapy clinic (n = 780) into two new observational cohorts. We will validate the abilities of our optimal pre- and post-MSKI assessments to correctly identify Service members who are at high risk for MSKI or undesired post-MSKI outcomes (Specific Aim #4). Identical to Phase I, participants who are enrolled into the uninjured cohort who go on to sustain a MSKI during the study follow-up period will be invited to participate in the injured cohort post-MSKI tracking/assessments.

Clinical assessments

We will administer our reduced battery of pre- and post-MSKI assessments (Specific Aim #3) among the newly enrolled validation cohorts. We will use the same data collection methods described for Phase I for the relevant assessments.

Phase II statistical and data analysis plan

Phase II analyses will evaluate the ability of the reduced batteries to correctly predict incident MSKI and undesired outcomes in validation cohorts. Misclassification rates for unadjusted and full models will be described with the AUC and sensitivities and specificities based on relevant thresholds. Calibration will be plotted using locally estimated scatterplot smoothing (LOESS) smoothing of the predicted versus observed probability of the outcome. Analyses will be repeated defining MSKI by type (back pain vs extremity) to evaluate predictive performance for narrowed case definitions.

Sample size

We will target equally sized cohorts for Phase I and Phase II. Sample size estimates for Phase II analyses are as described for classification analyses in Phase I cohorts. Based on these estimates we expect these validation cohort sizes to include  > 100 events with  > 100 non-events for each MSKI-specific model. These event counts have been shown to achieve sufficient power in validation studies of logistic regression prediction models [41].


Currently, Military Health System healthcare providers and human performance professionals have limited resources to efficiently and comprehensively identify the factors that influence MSKI risks and outcomes. The PREPARE study will deliver comprehensive clinical assessment batteries that efficiently identify the Service member specific factors that contribute to MSKI risk and undesired post-MSKI outcomes. In the near-term, this will provide contemporary evidenced-based military-relevant, field-expedient MSKI screening and prediction tools to inform Service members’ MSKI susceptibilities and ability to RTD/A following MSKI. In the long-term, this will result in personalized pre- and post-MSKI care, culminating in reduced rehabilitation time and costs and fewer days on limited duty status. Furthermore, the use of semi-automated field-expedient technologies will allow assessments to be immediately translated into practice. PREPARE will provide healthcare providers objective data across the spectrum of MSKI management (prevention to RTD/A) to guide MSKI risk mitigation strategies and make better informed RTD/A decisions; thereby, improving Service member health, medical readiness, and retention within the DoD.

The biggest challenge for completing the proposed work is enrolling a sufficient number of participants to power our analyses. This risk is minimal, as our potential participant population of in-processing active duty Service member at Fort Bragg, NC is 464% larger than is required to power our study. Furthermore, there is the risk that a sufficient number of participants will not sustain a MSKI during our follow-up period; this is unlikely, as MSKIs affect 54–58% of operational Service members [4, 38]. Similarly, we may not enroll a sufficient number of Service members with a MSKI of interest. This risk is also minimal, as our potential patient populations are 276% larger at WRNMMC and 657% larger at Fort Bragg, NC than is required to power our study. Participant attrition and loss to follow-up is mitigated, given our follow-up time points align with routine clinical encounters and outcomes will be collected through the MDR.

In the event the data in the automated reports of the field-expedient assessments provided by the markerless motion capture system and instrumented walkway cannot identify the factors affecting MSKI risks (Specific Aim #1) and post-MSKI outcomes (Specific Aim #2) we can export the raw kinematic and kinetic data. These data may identify minute neuromuscular control deficiencies that are unable to be detected by standard clinical assessments. Additionally, patient-specific demographic data (e.g., sex, age, tobacco use) may be used to increase the robustness of our models [5, 7, 8]. Finally, an alternative analytical approach will establish cut points for each assessment that dichotomize participants into a “at greater than normal risk” or “not greater than normal risk” category [4, 23]. We will then determine if Service members are at greater risk for MSKI or an undesired post-MSKI outcome based on the number of risk factors they possess (i.e., Service members with more risk factors are at greater risk for MSKIs and undesired post-MSKI outcomes) [4].

This study will deliver efficient, comprehensive clinical decision support aids that better enable clinicians to identify Service members at the highest risk of MSKI or post-MSKI undesired outcomes and inform risk mitigation and rehabilitation strategies. The use of semi-automated, field-expedient assessments that require minimal equipment and space will allow the findings of this study to be pushed immediately to clinicians. This will positively impact Service members and healthcare providers as they will have unprecedented access to objective data to guide in-theatre treatment and RTD/A decisions. Personalizing post-MSKI rehabilitative care and making more informed RTD/A decisions will expedite rehabilitation and reintegration and reduce secondary MSKI risk. These benefits will improve Service member health, reduce post-MSKI time to RTD/A, and improve Force medical readiness. Finally, the findings of this study may have translational relevance to other young, active, and otherwise healthy populations.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available because of data sharing restrictions on data generated within the United States Department of Defense; however, data may be available from the corresponding author on reasonable request, following approval from all required regulatory bodies.



Area under the curve


Computer adaptive testing


Department of defense


Functional movement screen


International classification of diseases


Locally estimated scatterplot smoothing


Military health system data repository


Musculoskeletal injuries


National institutes of health


Patient reported outcomes measurement information system


Pre-neuromusculoskeletal injury risk factor evaluation and Post-neuromusculoskeletal injury assessment for return-to-duty/activity enhancement




Tampa scale of kinesiophobia


Walter reed national military medical center


  1. Patzkowski JC, Rivera JC, Ficke JR, Wenke JC. The changing face of disability in the US Army: the operation enduring freedom and operation Iraqi Freedom effect. J Am Acad Orthop Surg. 2012;20(Suppl 1):S23-30.

    Article  Google Scholar 

  2. Branch AFS. Absolute and relative morbidity burdens attributable to various illnesses and injuries, active component, U.S. Armed Forces, 2022. MSMR. 2022;29(6):2–9.

    Google Scholar 

  3. Farrokhi S, Mazzone B, Schneider M, Gombatto S, Mayer J, Highsmith MJ, et al. Biopsychosocial risk factors associated with chronic low back pain after lower limb amputation. Med Hypotheses. 2017;108:1–9.

    Article  Google Scholar 

  4. Teyhen DS, Shaffer SW, Goffar SL, Kiesel K, Butler RJ, Rhon DI, et al. Identification of risk factors prospectively associated with musculoskeletal injury in a warrior athlete population. Sports health. 2020.

    Article  Google Scholar 

  5. Teyhen DS, Shaffer SW, Butler RJ, Goffar SL, Kiesel KB, Rhon DI, et al. What risk factors are associated with musculoskeletal injury in US army rangers? A prospective prognostic study. Clin Orthop Relat Res. 2015.

    Article  Google Scholar 

  6. Teyhen D, Bergeron MF, Deuster P, Baumgartner N, Beutler AI, de la Motte SJ, et al. Consortium for health and military performance and American College of Sports Medicine Summit: utility of functional movement assessment in identifying musculoskeletal injury risk. Curr Sports Med Rep. 2014;13(1):52–63.

    Article  Google Scholar 

  7. Lisman P, O’Connor FG, Deuster PA, Knapik JJ. Functional movement screen and aerobic fitness predict injuries in military training. Med Sci Sports Exerc. 2013;45(4):636–43.

    Article  Google Scholar 

  8. Jones BH, Cowan DN, Tomlinson JP, Robinson JR, Polly DW, Frykman PN. Epidemiology of injuries associated with physical training among young men in the army. Med Sci Sports Exerc. 1993;25(2):197–203.

    Article  CAS  Google Scholar 

  9. Mauntel TC, Begalle RL, Cram TR, Frank BS, Hirth CJ, Blackburn T, et al. The effects of lower extremity muscle activation and passive range of motion on single leg squat performance. J Strength Cond Res. 2013;27(7):1813–23.

    Article  Google Scholar 

  10. Mauntel TC, Post EG, Padua DA, Bell DR. Sex differences during an overhead squat assessment. J Appl Biomech. 2015;31(4):244–9.

    Article  Google Scholar 

  11. Gomes JL, de Castro JV, Becker R. Decreased hip range of motion and noncontact injuries of the anterior cruciate ligament. Arthroscopy. 2008;24(9):1034–7.

    Article  Google Scholar 

  12. McCann RS, Kosik KB, Terada M, Beard MQ, Buskirk GE, Gribble PA. Acute lateral ankle sprain prediction in collegiate women’s soccer players. Int J Sports Phys Ther. 2018;13(1):12–8.

    Article  Google Scholar 

  13. Maniar N, Shield AJ, Williams MD, Timmins RG, Opar DA. Hamstring strength and flexibility after hamstring strain injury: a systematic review and meta-analysis. Br J Sports Med. 2016;50(15):909–20.

    Article  Google Scholar 

  14. Sell TC, Clark NC, Abt JP, Lovalekar M, Lephart SM. Isokinetic strength of fully operational U.S. Navy Seals with a previous history of shoulder and knee injury. Isokin and Ex Sci. 2016;24(4):349–56.

    Google Scholar 

  15. Sell TC, Clark NC, Wood D, Abt JP, Lovalekar M, Lephart SM. Single-leg balance impairments persist in fully operational military special forces operators with a previous history of low back pain. Orthop J Sports Med. 2014;2(5):2325967114532780.

    Google Scholar 

  16. Papuga MO, Beck CA, Kates SL, Schwarz EM, Maloney MD. Validation of GAITRite and PROMIS as high-throughput physical function outcome measures following ACL reconstruction. J Orthop Res. 2014;32(6):793–801.

    Article  Google Scholar 

  17. de la Motte SJ, Lisman P, Sabatino M, Beutler AI, O’Connor FG, Deuster PA. The relationship between functional movement, balance deficits, and previous injury history in deploying marine warfighters. J strength cond Res. 2016;30(6):1619–25.

    Article  Google Scholar 

  18. Trigsted SM, Cook DB, Pickett KA, Cadmus-Bertram L, Dunn WR, Bell DR. Greater fear of reinjury is related to stiffened jump-landing biomechanics and muscle activation in women after ACL reconstruction. Knee Surg Sports Traumatol Arthrosc. 2018;26(12):3682–9.

    Article  Google Scholar 

  19. Paterno MV, Flynn K, Thomas S, Schmitt LC. Self-reported fear predicts functional performance and second ACL injury after ACL reconstruction and return to sport: a pilot study. Sports health. 2018;10(3):228–33.

    Article  Google Scholar 

  20. Padua DA, Marshall SW, Boling MC, Thigpen CA, Garrett WE Jr, Beutler AI. The Landing Error Scoring System (LESS) is a valid and reliable clinical assessment tool of jump-landing biomechanics: The JUMP-ACL study. Am J Sports Med. 2009;37(10):1996–2002.

    Article  Google Scholar 

  21. Goldbeck TG, Davies GJ. Test-retest reliability of the closed kinetic chain upper extremity stability test: a clinical field test. J Sport Rehabil. 2000;9:35–45.

    Article  Google Scholar 

  22. Tucci HT, Martins J, Sposito Gde C, Camarini PM, de Oliveira AS. Closed Kinetic Chain Upper Extremity Stability test (CKCUES test): a reliability study in persons with and without shoulder impingement syndrome. BMC Musculoskelet Disord. 2014;15:1.

    Article  Google Scholar 

  23. de la Motte SJ, Clifton DR, Gribbin TC, Beutler AI, Deuster PA. Functional movement assessments are not associated with risk of injury during military basic training. Military Med. 2019;184:e773.

    Article  Google Scholar 

  24. Cesar GM, Edwards HT, Hasenkamp RM, Burnfield JM. Prediction of athletic performance for male and female athletes measured by triple hop for distance. Trends Sport Sci. 2017;1(24):19–25.

    Google Scholar 

  25. Nelson-Wong E, Flynn T, Callaghan JP. Development of active hip abduction as a screening test for identifying occupational low back pain. J Orthop Sports Phys Ther. 2009;39(9):649–57.

    Article  Google Scholar 

  26. Abdelraouf OR, Abdel-Aziem AA. The relationship between core endurance and back dysfunction in collegiate male athletes with and without nonspecific low back pain. Int J Sports Phys Ther. 2016;11(3):337–44.

    Google Scholar 

  27. Giordano NA, Kent M, Buckenmaier CC, Mauntel TC, Dickens JF, Millington M, et al. A longitudinal comparison of patient-reported outcome measurement information system (PROMIS) to legacy scales in knee and shoulder arthroscopy patients. Arthroscopy. 2021;37(1):185–94.

    Article  Google Scholar 

  28. Woby SR, Roach NK, Urmston M, Watson PJ. Psychometric properties of the TSK-11: a shortened version of the Tampa Scale for Kinesiophobia. Pain. 2005;117(1–2):137–44.

    Article  Google Scholar 

  29. Watson PJ, Kerry Booker C, Main CJ. Evidence for the role of psychological factors in abnormal parspinal activity in patients with chronic low back pain. J Musculoskelet Pain. 1997;5:41–56.

    Article  Google Scholar 

  30. Mauntel TC, Padua DA, Stanley LE, Frank BS, DiStefano LJ, Peck KY, et al. Automated quantification of the landing error scoring system with a markerless motion-capture system. J Athl Train. 2017;52(11):1002–9.

    Article  Google Scholar 

  31. Vallabhajosula S, Humphrey SK, Cook AJ, Freund JE. Concurrent validity of the zeno walkway for measuring spatiotemporal gait parameters in older adults. J Geriatr Phys Ther. 2019;42(3):E42–50.

    Article  Google Scholar 

  32. Lenton GK, Lloyd DG, Barrett R, Bourne M. Validity and reliability of HumanTrak for measure 3D joint kinematics during functional movements. Queensland, Australia: Griffith Centre of Biomedical and Rehabiliation Engineering, Menzies Health Insitute Queensland, School of Allied Health Sciences, Griffith University; 2020.

  33. Barber SD, Noyes FR, Mangine RE, McCloskey JW, Hartman W. Quantitative assessment of functional limitations in normal and anterior cruciate ligament-deficient knees. Clin Orthop Relat Res. 1990;255:204–14.

    Article  Google Scholar 

  34. Hando BR, Scott WC, Bryant JF, Tchandja JN, Scott RM, Angadi SS. Association between markerless motion capture screenings and musculoskeletal injury risk for military trainees: a large cohort and reliability study. Orthop J Sports Med. 2021;9(10):23259671211041656.

    Google Scholar 

  35. Hauschild VD, Hauret K, Richardson M, Jones BH. A taxonomy of injuries for public health monitoring and reporting—Public Health Information Paper (PHIP) No.12-01-0717. In: Command APH, editor.: Defense Technical Information Center; 2018.

  36. Schelin L, Tengman E, Ryden P, Hager C. A statistically compiled test battery for feasible evaluation of knee function after rupture of the anterior cruciate ligament—derived from long-term follow-up data. PLoS ONE. 2017;12(5):e0176247.

    Article  Google Scholar 

  37. Dong Y, Peng CY. Principled missing data methods for researchers. Springerplus. 2013;2(1):222.

    Article  Google Scholar 

  38. Potter RN, Gardner JW, Deuster PA, Jenkins P, McKee K Jr, Jones BH. Musculoskeletal injuries in an Army airborne population. Mil Med. 2002;167(12):1033–40.

    Article  Google Scholar 

  39. Hoy D, Brooks P, Blyth F, Buchbinder R. The epidemiology of low back pain. Best Pract Res Clin Rheumatol. 2010;24(6):769–81.

    Article  CAS  Google Scholar 

  40. Waterman B, Owens B, Davey S, Zacchilli M, Belmont PJ. The epidemiology of ankle sprains in the United States. J Bone Joint Surg Am. 2010;92(13):2279–84.

    Article  Google Scholar 

  41. Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol. 2005;58(5):475–83.

    Article  Google Scholar 

  42. Committee on the Assessment of the Readjustment Needs of Military Personnel V, and thier Families, Populations BotHoS, Medicine Io. Returning home from Iraq and Afghanistan: assessment of readjustment needs of veterans, service members, and their families. Washington DC: National Academies Press; 2013.

    Google Scholar 

  43. Kardouni JR, Shing TL, McKinnon CJ, Scofield DE, Proctor SP. Risk for lower extremity injury after concussion: a matched cohort study in soldiers. J Orthop Sports Phys Ther. 2018.

    Article  Google Scholar 

  44. Bohannon RW, Wang YC, Gershon RC. Two-minute walk test performance by adults 18 to 85 years: normative values, reliability, and responsiveness. Arch Phys Med Rehabil. 2015;96(3):472–7.

    Article  Google Scholar 

  45. Strand SL, Hjelm J, Shoepe TC, Fajardo MA. Norms for an isometric muscle endurance test. J Hum Kinet. 2014;40:93–102.

    Article  Google Scholar 

  46. Dill KE, Begalle RL, Frank BS, Zinder SM, Padua DA. Altered knee and ankle kinematics during squatting in those with limited weight-bearing-lunge ankle-dorsiflexion range of motion. J Athl Train. 2014;49(6):723–32.

    Article  Google Scholar 

  47. Perrot S, Cohen M, Barke A, Korwisi B, Rief W, Treede RD, et al. The IASP classification of chronic pain for ICD-11: chronic secondary musculoskeletal pain. Pain. 2019;160(1):77–82.

    Article  Google Scholar 

Download references


The views expressed in this manuscript are those of the authors, and do not necessarily reflect the official policies of the U.S. Departments of the Army, Navy, or Air Force, the Department of Defense, nor the United States Government. Moreover, the opinions and assertions expressed herein are those of the authors and do not necessarily reflect the official policy or position of the Uniformed Services University. The identification of specific products or instrumentation is considered an integral part of the scientific endeavor and does not constitute endorsement or implied endorsement on the part of the authors nor by the U.S. Government.


This work is supported by the Defense Health Program Military Operational Medicine Research Program (W81XWH-22-MOMRP-D; Award: MO220177). The funding body had no role in the design of the study, and they will have no role in the data collection, interpretation of the data, or writing this or future manuscripts.

Author information

Authors and Affiliations



CB and BH are experts in assessing secondary health outcomes following MSKI and helped with the low back pain assessments and the overall design of the project. CB is also the primary contributor to writing the manuscript. NW and DB designed the statistical approach for the study, conducted the a priori sample size estimates, and drafted the statistical portions of the manuscript. DG, RW, AH, MH, JK, and MG are experts in military specific MSKI risk assessments and mitigation. They contributed heavily to the methodological approaches utilized in this study to ensure they were military relevant and feasible within a military medical environment. TM is the overall principal investigator of the study and has been involved in all aspects of project development and manuscript writing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Timothy C. Mauntel.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Walter Reed National Military Medical Center (WRNMMC) Institutional Review Board (protocol WRNMMC-2020-0283). Study participants will sign a site-specific written informed consent form, prior to completing study-related tasks.

Consent for publication

Not applicable.

competing interests

The authors declare they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Butowicz, C.M., Hendershot, B.D., Watson, N.L. et al. Pre-neuromusculoskeletal injury Risk factor Evaluation and Post-neuromusculoskeletal injury Assessment for Return-to-duty/activity Enhancement (PREPARE) in military service members: a prospective, observational study protocol. J Transl Med 20, 619 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: