Background: Inconsistent associations have been reported for impact-related ground reaction force variables and running injuries when grouping all injuries together. However, previous work has shown more consistent associations when focusing on specific injuries. Purpose: To compare ground reaction force variables between healthy and injured runners as a group and within specific common injuries. Study Design: Controlled laboratory study. Methods: A total of 125 runners presenting with patellofemoral pain, tibial bone stress injury, plantar fasciitis, Achilles tendinopathy, or iliotibial band syndrome and 65 healthy controls completed an instrumented treadmill assessment at a self-selected speed. Impact-related ground reaction force variables included vertical average (VALR) and instantaneous (VILR) load rates, posterior and medial/lateral instantaneous load rates, and vertical stiffness at initial loading (VSIL). Mean comparisons were made between the general and specific injury and control groups (α = .05). Cutoff thresholds were established and evaluated using several criteria. Results: VALR (+17.5%; P < .01), VILR (+15.8%; P < .01), and VSIL (+19.7%; P < .01) were significantly higher in the overall injured versus control groups. For individual injuries, VALR, VILR, and VSIL were significantly higher for patellofemoral pain (+23.4%-26.4%; P < .01) and plantar fasciitis (+17.5%-29.0%; P < .01), as well as VSIL for Achilles tendinopathy (+29.4%; P < .01). Cutoff thresholds showed better diagnostic criteria for individual versus grouped injuries. Conclusion: Impact variables (VALR, VILR, and VSIL) were significantly higher when assessing the injured group as a whole. However, these findings were driven by specific injury groups, highlighting the importance of taking an injury-specific approach to biomechanical risk factors for running injury. Clinical Relevance: These results suggest that practitioners may want to address impact loading in their treatment of injured runners, especially in those with patellofemoral pain and plantar fasciitis.

ObjectivesMusculoskeletal injuries (MSI) are an important concern in military populations. The purpose of this study was to describe the burden of MSI and associated financial cost, in a sample of US Air Force Special Operations Command Special Tactics Operators.MethodsIn this cross-sectional study, medical records of the Operators were reviewed during the years 2014–2015. MSI that occurred during a 1-year period prior to the date of review were described. MSI attributes described included incidence, anatomic location, cause, activity when MSI occurred, type and lifetime cost of MSI estimated using the Web-based Injury Statistics Query and Reporting System.ResultsA total of 130 Operators participated in the study (age: 29.1±5.2 years). The 1-year cumulative incidence of MSI was 49.2 injured Operators/100 Operators/year. The most frequent anatomic location and sublocation for MSI were the lower extremity (40.9% of MSI) and shoulder (20.9%), respectively. Lifting was a common cause of MSI (21.8%). A large per cent of MSI (55.5%) occurred while Operators were engaged in either physical or tactical training. Common MSI types were pain/spasm/ache (44.5%). Many MSI (41.8%) were classified as potentially preventable by an injury prevention training programme. The total lifetime cost of these MSI was estimated to be approximately US$1.2 million.ConclusionMSI are an important cause of morbidity and financial cost in this sample of Air Force Special Tactics Operators. There is a need to develop a customised injury prevention programme to reduce the burden and cost of MSI in this population.

Purpose Despite the health benefits of running, the prevalence of running-related injuries (RRI) remains high. The underlying risk factors between these injuries are still not well understood. Therefore, the aim of this study was to compare biomechanical, anthropometric, and demographic injury risk factors between different locations in injured recreational runners. Methods In this retrospective case–control analysis, 550 injured runners (49.6% female) with a medically diagnosed RRI were included. All runners had undergone an instrumented treadmill analysis to determine habitual footstrike pattern, vertical instantaneous load rate, peak vertical ground reaction force (vGRF) and cadence. Injuries were classified by location according to a recent consensus statement. A logistic regression model was used to determine the association between the biomechanical parameters and RRI locations. Because injuries can be associated with age, sex, and body mass index, these variables were also entered into the logistic regression. Results Strike pattern and peak vGRF were the only biomechanical variable distinguishing an injury from the group of injuries. A midfoot strike differentiated Achilles tendon injuries (odds ratio [OR], 2.27; 90% confidence interval [CI], 1.17–4.41) and a forefoot strike distinguished posterior lower leg injuries (OR, 2.59; 90% CI, 1.50–4.47) from the rest of the injured group. Peak vGRF was weakly associated with hip injuries (OR, 1.14; 90% CI, 1.05–1.24). Female sex was associated with injuries to the lower leg (OR, 2.65; 90% CI, 1.45–4.87) and hip/groin (OR, 2.22; 90% CI, 1.43–3.45). Male sex was associated with Achilles tendon injuries (OR, 1.923; 90% CI, 1.094–3.378). Conclusions Sex, foot strike pattern, and vGRF were the only factors that distinguished specific injury locations from the remaining injury locations.

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The purpose of this study was to identify and compare energy requirements specific to Special Operations Forces in field training, in both cool and hot environments. Three separate training sessions were evaluated, 2 in a hot environment (n = 21) and 1 in a cool environment (n = 8). Total energy expenditure was calculated using doubly labeled water. Dietary intake was assessed via self-report at the end of each training mission day, and macronutrient intakes were calculated. Across the 3 missions, mean energy expenditure (4618 ± 1350 kcal/day) exceeded mean energy intake (2429 ± 838 kcal/day) by an average of 2200 kcal/day. Macronutrient intakes (carbohydrates (g/(kg·day body weight (bw))) = 3.2 ± 1.2; protein (g/(kg·day bw)) = 1.3 ± 0.7; fat (g/(kg·day bw)) = 1.2 ± 0.7) showed inadequate carbohydrate and possibly protein intake across the study period, compared with common recommendations. Total energy expenditures were found to be similar between hot (4664 ± 1399 kcal/day) and cool (4549 ± 1221 kcal/day) environments. However, energy intake was found to be higher in the cool (3001 ± 900 kcal/day) compared with hot (2200 ± 711 kcal/day) environments. Based on the identified energy deficit, high variation in energy expenditures, and poor macronutrient intake, a greater attention to feeding practices during similar training scenarios for Special Operations Forces is needed to help maintain performance and health. The differences in environmental heat stress between the 2 climates/environments had no observed effect on energy expenditures, but may have influenced intakes.

Introduction Musculoskeletal injury rates in military personnel remain unacceptably high. Application of machine learning algorithms could be useful in multivariate models to predict injury in this population. The purpose of this study was to investigate if interaction between individual predictors, using a decision tree model, could be used to develop a population-specific algorithm of lower-extremity injury (LEI) risk. Methods One hundred forty Air Force Special Forces Operators (27.4 ± 5.0 yr, 177.6 ± 5.8 cm, 83.8 ± 8.4 kg) volunteered for this prospective cohort study. Baseline testing included body composition, isokinetic strength, flexibility, aerobic/anaerobic capacity, anaerobic power, and landing biomechanics. To evaluate unilateral landing patterns, subjects jumped off two-feet from a distance (40% of their height) over a hurdle and landing single-legged on a force plate. Medical chart reviews were conducted 365 d postbaseline. χ2 automatic interaction detection (CHAID) was used, which compares predictor variables to LEI and assigns a population-specific “cut-point” for the most relevant predictors. Results Twenty-seven percent of operators (n = 38) suffered LEI. A maximum knee flexion angle difference of 25.1% had the highest association with injury in this population (P = 0.006). Operators with >25.1% differences in max knee flexion angle (n = 13) suffered LEI at a 69.2% rate. Seven of the 13 Operators with >25.1% difference in max knee flexion angle weighed >81.8 kg, and 100% of those operators suffered LEI (P = 0.047; n = 7). Only 33% of operators with >25.1% difference in max knee flexion angle that weighed <81.8 kg suffered LEI. Conclusions This study demonstrated increased risk of LEI over a 365-d period in Operators with greater differences in single-leg landing strategies and higher body mass. The CHAID approach can be a powerful tool to analyze population-specific risk factors for injury, along with how those factors may interact to enhance risk.

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The degree to which standard laboratory gait assessments accurately reflect impact loading in an outdoor running environment is currently unknown. Purpose To compare tibial shock between treadmill and road marathon conditions. Methods One hundred ninety-two runners (men/women, 105/87; age, 44.9 ± 10.8 yr) completed a treadmill gait assessment while wearing a tibial-mounted inertial measurement unit, several days before completing a marathon race. Participants ran at 90% of their projected race speed and 30 s of tibial shock data were collected. Participants then wore the sensors during the race and tibial shock was averaged over the 12th, 23rd, and 40th kilometers. One-way analysis of covariance and correlation coefficients were used to compare vertical/resultant tibial shock between treadmill and marathon conditions. Analyses were adjusted for differences in running speed between conditions. Results A significant main effect of condition was found for mean vertical and resultant tibial shock (P < 0.001). Early in the marathon (12-km point), runners demonstrated higher mean tibial shock adjusted for speed compared with the treadmill data (vertical = +24.3% and resultant = +30.3%). Mean differences decreased across the course of the marathon. Vertical tibial shock at the 40th kilometer of the race was similar to treadmill data, and resultant shock remained higher. Vertical and resultant tibial shock were significantly correlated between treadmill and the 12th kilometer of the race (r s = 0.64–0.65, P < 0.001), with only 40% to 42% of the variance in outdoor tibial shock explained by treadmill measures. Correlations for tibial shock showed minimal changes across stages of the marathon. Conclusions These results demonstrate that measures of impact loading in an outdoor running environment are not fully captured on a treadmill.