Browse

Purpose: To investigate the indirect measurement of 1-repetition-maximum (1RM) free-weight half-squat in high-level sprinters using the load–velocity relationship. Methods: Half-squat load and velocity data from 11 elite sprinters were collected in 2 separate testing sessions. Approximately 24 hours prior to the first testing session, sprinters completed a fatiguing high-intensity training session consisting of running intervals, staircase exercises, and body-weight exercises. Prior to the second testing session, sprinters had rested at least 48 hours. Two different prediction models (multiple-point method, 2-point method) were used to estimate 1RM based on the load and either mean or peak concentric velocity data of submaximal lifts (40%–90% 1RM). The criterion validity of all methods was examined through intraclass correlation coefficients, coefficient of variation (CV%), Bland–Altman plots, and the SEM. Results: None of the estimations were significantly different from the actual 1RM. The multiple-point method showed higher intraclass correlation coefficients (.91 to .97), with CVs from 3.6% to 11.7% and SEMs from 5.4% to 10.6%. The 2-point method showed slightly lower intraclass correlation coefficients (.76 to .95), with CVs 1.4% to 17.5% and SEMs from 9.8% to 26.1%. Bland–Altman plots revealed a mean random bias in estimation of 1RM for both methods (mean and peak velocity) ranging from 1.06 to 13.79 kg. Conclusion: Velocity-based methods can be used to roughly estimate 1RM in elite sprinters in the rested and fatigued conditions. However, all methods showed variations that limit their applicability for accurate load prescription for individual athletes.

Background: Laboratory assessment of maximal oxygen uptake ( ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ ) is physically and mentally draining for the athlete and requires expensive laboratory equipment. Indirect measurement of ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ could provide a practical alternative to laboratory testing. Purpose: To examine the relationship between the maximal power output (MPO) in an individualized 7 × 2-minute incremental test (INCR-test) and ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ and to develop a regression equation to predict ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ from MPO in female rowers. Methods: Twenty female club and Olympic rowers (development group) performed the INCR-test on a Concept2 rowing ergometer to determine ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ and MPO. A linear regression analysis was used to develop a prediction of ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ from MPO. Cross-validation analysis of the prediction equation was performed using an independent sample of 10 female rowers (validation group). Results: A high correlation coefficient (r = .94) was found between MPO and ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ . The following prediction equation was developed: ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ (mL·min⁻¹) = 9.58 × MPO (W) + 958. No difference was found between the mean predicted ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ in the INCR-test (3480 mL·min⁻¹) and the measured ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ (3530 mL·min⁻¹). The standard error of estimate was 162 mL·min⁻¹, and the percentage standard error of estimate was 4.6%. The prediction model only including MPO, determined during the INCR-test, explained 89% of the variability in ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ . Conclusion: The INCR-test is a practical and accessible alternative to laboratory testing of ${\dot{\mathrm{V}}\mathrm{O}}_2\text{max}$ .

Background: There is currently a nationwide effort to bring parks and green spaces within a 10-minute walk of the home. We examined the association between park area within 1 km of a child’s residence and self-reported park-specific physical activity (PA) along with accelerometer-derived moderate to vigorous physical activity (MVPA). Methods: A subsample of K through eighth-grade youth (n = 493) from the Healthy Communities Study reported whether they engaged in park-specific PA during the last 24 hours and wore an accelerometer for up to 7 days. Park area was defined as the percentage of park land in a 1 km Euclidean buffer around the participant’s residence, categorized into quintiles. Analysis consisted of logistic and linear regression modeling with interaction effects that controlled for clustering within communities. Results: Regression models estimated greater park-specific PA for participants in the fourth and fifth quintiles of park land. Age, sex, race ethnicity, and family income were unrelated to park-specific PA. Accelerometer analysis indicated that total MVPA was unrelated to park area. Older children (β = −8.73, P < .001) and girls (β = −13.44, P < .001) engaged in less MVPA. Seasonality significantly predicted both park-specific PA and total MVPA. Conclusion: Increasing park area is likely to improve youth PA patterns, lending support for the 10-minute walk initiative.

Background: Prescription medication usage has been used as a predictor of disease prevalence and overall health status. Evidence suggests an inverse relationship exists between polypharmacy, which is the use of 5 or more medications, and physical activity participation. However, there is limited evidence examining the relationship between sedentary time and polypharmacy in adults. The aim of this study was to examine the associations between sedentary time and polypharmacy in a large nationally representative sample of US adults. Methods: Study sample (N = 2879) included nonpregnant adult (≥20 y old) participants from the 2017–2018 National Health and Nutrition Examination Survey. Self-reported minutes per day of sedentary time were converted to hours per day. The dependent variable was polypharmacy (≥5 medications). Results: Analysis revealed that for every hour of sedentary time, there was 4% greater odds of polypharmacy (odds ratio, 1.04; 95% confidence interval, 1.00–1.07, P = .04) after adjusting for age, race/ethnicity, education, waist circumference, and the interaction term between race/ethnicity and education. Conclusion: Our findings suggest increased sedentary time is associated with an increased risk of polypharmacy among a large nationally representative sample of US adults.

Purpose: To determine whether competitive performance, as defined by International Biathlon Union (IBU) and International Ski Federation (FIS) points in biathlon and cross-country (XC) skiing, respectively, can be projected using a combination of anthropometric and physiological metrics. Shooting accuracy was also included in the biathlon models. Methods: Data were analyzed using multivariate methods from 45 (23 female and 22 male) biathletes and 202 (86 female and 116 male) XC skiers who were all members of senior national teams, national development teams, or ski-university or high school invite-only programs (age range: 16–36 y). Anthropometric and physiological characteristics were assessed via dual-energy X-ray absorptiometry and incremental roller-ski treadmill tests, respectively. Shooting accuracy was assessed via an outdoor standardized testing protocol. Results: Valid projective models were identified for female biathletes’ IBU points (R ² = .80/Q ² = .65) and female XC skiers’ FIS distance (R ² = .81/Q ² = .74) and sprint (R ² = .81/Q ² = .70) points. No valid models were identified for the men. The most important variables for the projection of IBU points were shooting accuracy, speeds at blood lactate concentrations of 4 and 2 mmol·L⁻¹, peak aerobic power, and lean mass. The most important variables for the projection of FIS distance and sprint points were speeds at blood lactate concentrations of 4 and 2 mmol·L⁻¹ and peak aerobic power. Conclusions: This study highlights the relative importance of specific anthropometric, physiological, and shooting-accuracy metrics in female biathletes and XC skiers. The data can help to identify the specific metrics that should be targeted when monitoring athletes’ progression and designing training plans.

Context: Early identification of incoming military personnel at elevated odds for bone stress injury (BSI) is important for the health and readiness of the US military. Design: Prospective cohort study. Methods: Knee kinematic data of the incoming US Military Academy cadets were collected while performing a jump-landing task (The Landing Error Scoring System) using a markerless motion capture system and depth camera. Data on incidence of lower-extremity injury, including BSI, were collected throughout the study period. Results: A total of 1905 participants (452 females, 23.7%) were examined for knee valgus and BSI status. A total of 50 BSI occurred during the study period (incidence proportion = 2.6%). The unadjusted odds ratio for BSI at initial contact was 1.03 (95% confidence interval [CI], 0.94–1.14; P = .49). Adjusted for sex, the odds ratio for BSI at initial contact was 0.97 (95% CI, 0.87–1.06; P = .47). At the instant of maximum knee-flexion angle, the unadjusted odds ratio was 1.06 (95% CI, 1.02–1.10; P = .01), and the odds ratio was 1.02 (95% CI, 0.98–1.07; P = .29) after adjusting for sex. This suggests that there was not a significant enough association for an increase in the odds of BSI based on either degree of knee valgus. Conclusions: Our results did not demonstrate an association between knee valgus angle data during a jump-landing task and future increased odds of BSI in a military training population. Further analysis is warranted, but the results suggests the association between kinematics and BSI cannot be effectively screened by knee valgus angle data in isolation.

Context: Exertional heat stroke (EHS) is the most deadly form the exertional heat illness with a higher incidence among active duty US military members than in the general population. Current guidelines on EHS recovery timelines and return to duty vary among the military branches. In some cases, individuals experience prolonged heat and exercise intolerance with repeat exertional heat illness events, which can complicate the recovery process. Management and rehabilitation of such individuals is unclear. Case Presentation: This manuscript addresses the case and management of a US Air Force Special Warfare trainee who experienced 2 episodes of EHS, despite early recognition, gold standard treatment, and undergoing 4 weeks of a stepwise recovery after an initial EHS. Management and Outcomes: After the second episode, a 3-step process was utilized, consisting of a prolonged and personalized recovery period, heat tolerance testing using Israeli Defense Force advanced modeling, and stepwise reacclimatization. This process allowed the trainee to successfully recover from repeat EHS and return to duty, and set a framework for future repeat EHS treatment guidelines. Conclusions: In individuals with repeat EHS, a prolonged recovery period followed by heat tolerance testing can be used to demonstrate appropriate thermotolerance and safely clear an individual to begin stepwise reacclimatization. Overall, patient care and military readiness may be improved by unified Department of Defense guidelines for return to duty after EHS.

Context: Long-lever shoulder strength tests may aid clinical decision-making regarding return to sport after a shoulder injury. The Athletic Shoulder Test (AST) was developed to measure force production in 3 positions of shoulder abduction (90°, 135°, and 180°) using force plates. However, handheld dynamometers (HHDs) are more portable, affordable, and may provide valid and reliable results which would increase the clinical utility of long-lever tests. HHDs vary in shape, design, and their capacity to report parameters such as rate of force production and require further investigation. The aim of this study was to examine the intrarater reliability of the Kinvent HHD and assess its validity against Kinvent force plates in the AST. Peak force (in kilograms), torque (in Newton meters), and normalized torque (in Newton meters per kilogram) were reported. Design: Validity and reliability study. Methods: Twenty-seven participants with no history of upper limb injury performed the test in a randomized order using the Kinvent HHD and force plates. Each condition was assessed 3 times, and peak force was recorded. Arm length was measured to calculate peak torque. Normalized peak torque was calculated by dividing torque by bodyweight (in kilograms). Results: The Kinvent HHD is reliable when measuring force (intraclass correlation coefficient [ICC] ≥ .80), torque (ICC ≥ .84), and normalized torque (ICC ≥ .64) during the AST. The Kinvent HHD is also valid when compared with the Kinvent force plates for force (ICC ≥ .79; r ≥ .82), torque (ICC ≥ .82; r ≥ .76), and normalized torque (ICC ≥ .71; r ≥ .61). There were no statistically significant differences across the 3 trials on analyses of variance (P > .05). Conclusions: The Kinvent HHD is a reliable tool when used to measure force, torque, and normalized torque in the AST. Furthermore, given the lack of significant difference between trials, clinicians can use one test to accurately report relative peak force/torque/normalized torque rather than average 3 separate trials. Finally, the Kinvent HHD is valid when compared with Kinvent force plates.