Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test

Turner, Jeffrey A.; Wagner, Torrey; Langhals, Brent T.

doi:10.3390/sports10040054

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Countermovement jump (CMJ) variations are most commonly implemented because they are familiar movements, simple and quick to administer, yield low injury risk, exhibit good reliability (4)(5)(6)27,29), and are ecologically valid for many sport and occupational environments (3,14,32,37,40). The tests are generally used as measures of maximal lower-body power output, countermovement strategies, and asymmetries for monitoring neuromuscular performance adaptation or maladaptation (29).…”

Section: Introductionmentioning

confidence: 99%

Countermovement Jump Force-Time Curve Analyses: Reliability and Comparability Across Force Plate Systems

Merrigan,

Strang,

Eckerle

et al. 2023

Journal of Strength and Conditioning Research

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Merrigan, JJ, Strang, A, Eckerle, J, Mackowski, N, Hierholzer, K, Ray, NT, Smith, R, Hagen, JA, and Briggs, RA. Countermovement jump force-time curve analyses: reliability and comparability across force plate systems. J Strength Cond Res XX(X): 000–000, 2023—Considering the growing prevalence of commercial force plates providing automated force-time analyses, understanding levels of agreement across force plate systems is warranted. Countermovement jump (CMJ) metrics across Vald ForceDecks (FD), Hawkin Dynamics (HD), and Sparta Science (SS) force plate systems were compared. Twenty-two subjects completed CMJ testing (∼128 comparisons) on each force plate system separately with rest between jumps. Baseline testing occurred 3 times and demonstrated poor test-retest reliability for modified reactive strength index (mRSI) and rate of force development (RFD). ForceDecks and HD comparisons yielded acceptable agreement for concentric/propulsive relative force and net impulse, jump height, eccentric/braking RFD, and mRSI, but systematic and proportionate bias existed for RFD. Sparta Science jump height and reactive strength index (RSI) demonstrated systematic overestimations compared with HD and FD, but jump height had acceptable agreement according to concordance correlation coefficients (CCC = 0.92–0.95). Agreement between SS load (eccentric RFD) and HD braking RFD was acceptable (CCC = 0.91), whereas agreement between SS load and FD deceleration RFD was considered acceptable (CCC = 0.81–0.87) but demonstrated systematic and proportionate bias. ForceDecks (CCC = 0.89) and HD (CCC = 0.85) average relative concentric/propulsive force yielded acceptable agreement with SS explode (average relative concentric force), but SS explode demonstrated systematically lower values than FD and HD. Sparta Science drive (concentric impulse) yielded acceptable agreement with HD relative propulsive impulse (CCC = 0.85), but not FD concentric impulse. Human performance practitioners need to be aware of inconsistencies among testing procedures and analyses across force plate systems, such as differences in metric definitions and units of measurement, before making comparisons across systems.

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Section: Introductionmentioning

confidence: 99%

Countermovement Jump Force-Time Curve Analyses: Reliability and Comparability Across Force Plate Systems

Merrigan,

Strang,

Eckerle

et al. 2023

Journal of Strength and Conditioning Research

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Predictors of combat training attrition in Israel Defense Forces soldiers

Gendler

Talmy

Shapiro

et al. 2023

Occupational Medicine

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Background Attrition from combat service carries significant organizational and personal ramifications, but predicting factors associated with attrition remains challenging. Aims To evaluate medical and psychosocial factors associated with attrition from basic combat training (BCT) in the Israel Defense Forces (IDF). In addition, we identify subsets of the recruit population which exhibit certain trends in terms of medical corresponding with a high risk of attrition. Methods A cross-sectional study of IDF combat trainees undergoing infantry BCT between 2012 and 2017. Data were collected from the soldiers’ electronic medical and administrative records. We used multivariable logistic regression and the SAS® decision-tree tool to analyse key predictive factors for attrition. Results A total of 46 472 soldiers enlisted to BCT during the research period. The mean body mass index (BMI) was 21.8 (SD 3.54). The overall attrition rate was 10%. The following factors were associated with attrition from BCT: ethnicity (P < 0.01), BMI (P < 0.01), pre-enlisting motivation score (P < 0.01) and the number of mental health officer visits (P < 0.01). Using a decision-tree model, we found a high attrition rate among soldiers who had >5.2 to primary care physician visits (11% attrition rate versus 3%) or more than 11 sick leave days (59% versus 19%). Conclusions This study sheds light on unique measures relating to attrition. Attrition is associated with several demographic and psychosocial factors. Early prediction of motivation and monitoring of healthcare utilization may enable early identification and focused interventions targeting soldiers at high risk for attrition. These findings need to be further translated into actionable directives and further investigations.

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Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test

Turner

Wagner

Langhals

2022

Sports

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Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a 21-feature dataset was analyzed related to 223 active-duty Airmen who participated in a comprehensive mental and social health survey, body composition assessment, and physical performance battery. Graphical analysis revealed pass/fail trends related to body composition and obesity. Logistic regression and limited-capacity neural network algorithms were then applied to predict fitness test performance using these biomechanical and psychological variables. The logistic regression model achieved a high level of significance (p < 0.01) with an accuracy of 0.84 and AUC of 0.89 on the holdout dataset. This model yielded important inferences that Airmen with poor sleep quality, recent history of an injury, higher BMI, and low fitness satisfaction tend to be at greater risk for fitness test failure. The neural network model demonstrated the best performance with 0.93 accuracy and 0.97 AUC on the holdout dataset. This study is the first application of psychological features and neural networks to predict fitness test performance and obtained higher predictive accuracy than prior work. Accurate prediction of Airmen at risk of failing the USAF fitness test can enable early intervention and prevent workplace injury, absenteeism, inability to deploy, and attrition.

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Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test

Cited by 3 publications

References 23 publications

Countermovement Jump Force-Time Curve Analyses: Reliability and Comparability Across Force Plate Systems

Countermovement Jump Force-Time Curve Analyses: Reliability and Comparability Across Force Plate Systems

Predictors of combat training attrition in Israel Defense Forces soldiers

Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test

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