Collegiate athletes and CrossFit participants both follow structured exercise programs that incorporate strength, endurance, and cardiovascular components. However, each environment has a unique profile of physiological stressors, which may lead to characteristic differences in skeletal muscle development. PURPOSE: To examine muscular differences in body composition between male Division-1 collegiate athletes and consistent CrossFit participants. METHODS: We performed body composition testing on 82 Division-1 athletes and 206 CrossFit exercisers with the InBody 770 Bioelectrical Impedance Analyzer. All subjects in both groups were male. We matched samples for age using coarsened exact matching. This resulted in a new sample of 27 athletes and 27 CrossFit participants. Anthropometric variables exported were height, bodyweight, lean body mass, dry lean mass, skeletal muscle mass, single leg lean mass (mean value of both legs), single arm lean mass (mean value of both arms), lean trunk mass, and skeletal muscle index. We compared samples in all dependent variables using two-tailed independent-samples t-tests; 95% confidence intervals of all differences are reported. RESULTS: Among collegiate athletes, height was 72.9 ± 3.1 in, bodyweight was 185.1 ± 18.3 lb, lean body mass was 164.7 ± 16.7 lb, dry lean mass was 44.7 ± 4.7 lb, skeletal muscle mass was 94.9 ± 9.9 lb, lean leg mass was 24.9 ± 2.8 lb, lean arm mass was 9.6 ± 1.8 lb, lean trunk mass was 71.6 ± 6.8 lb, and skeletal muscle index was 9.1 ± 0.6. Athlete values were significantly higher than CrossFit exercisers in height (p < 0.001; 95% CI: 4.1, 7.3), dry lean mass (p < 0.001; 95% CI: 4.3, 9.3), lean body mass (p < 0.001; 95% CI: 14.6, 32.6), skeletal muscle mass (p < 0.001; 95% CI: 8.4, 19.2), lean leg mass (p < 0.001; 95% CI: 3.3, 6.2), lean arm mass (p = 0.001; 95% CI: 0.5, 1.8), and lean trunk mass (p < 0.001; 95% CI: 3.2, 10.9). Significance was not found in bodyweight (p = 0.342; 95% CI: -6.3, 17.7) or skeletal muscle index (p = 0.298; 95% CI: -0.2, 0.6). All comparisons met the assumption of equality of variances (p > 0.120). CONCLUSION: Collegiate athletes had more lean body mass than age-matched CrossFit exercisers. This finding was significant in whole-body comparisons as well as individual assessments of the upper limbs, lower limbs, and trunk.

Patients with Myalgic Encephalomyelitis (ME) have diminished exercise performance, work output, and lower oxygen consumption in the post-exertional state. PURPOSE: To test whether reductions in ventilatory responses contribute to impaired exercise performance. METHODS: Maximal exercise tests were performed on sequential days using a cycle ergometer. Ventilatory responses in 15 ME patients were compared with 18 control subjects. Values for minute ventilation (VE), respiratory rate (RR), breathing reserve (BR), and minute ventilation/carbon dioxide production (VE/VCO2) were collected. Independent-samples t-tests compared ME and control groups at rest. Mixed ANOVA with repeated measures compared each dependent variable between test 1 and test 2, between ME and control groups, and between values collected in the resting state to those collected during maximal exercise. RESULTS: Subject age was 39.3 ± 9.6 yr, height was 166.1 cm, and weight was 68.8 kg. ME subjects were 6.6 yr older (p = 0.05); there were no differences in height or weight (p > 0.20). Across the total sample at rest during test 1, VE was 11.5 ± 4.6, RR was 16.0 ± 6.0, BR was 90.6 ± 3.5, and VE/CO2 was 31.1 ± 4.9. Between control and ME groups, RR exhibited a trending difference (p = 0.06); no other differences were observed (p > 0.25). When measuring peak values (control vs. ME), several differences emerged on test 2. During test 1, VE was similar (78.2 ± 5.3 vs 69.0 ± 6.0; p = 0.27) but differences were detected during test 2 (87.6 ± 6.1 vs. 63.9 ± 6.7; p = 0.01). BR values were also similar during test 1 (38.2 ± 3.8 vs. 38.7 ± 4.0; p = 0.92) and differed during test 2 (31.7 ± 3.4 vs 43.7 ± 3.6; p = 0.02). RR was similar during test 1 (38.5 ± 2.4 vs. 40.4 ± 2.7; p = 0.60) and exhibited a trending difference during test 2 (44.3 ± 2.4 vs. 37.2 ± 2.6; p = 0.06). VE/VCO2 was similar during test 1 (29.9 ± 1.0 vs. 32.2 ± 1.1; p = 0.14) and the difference observed during test 2 was a weak trend (32.1 ± 0.89 vs 29.6 ± 0.97; p = 0.08). CONCLUSION: Patients with ME display normal ventilatory response to exercise during initial testing but have post-exertional blunting of ventilatory responses in subsequent tests. Because multiple body systems must be activated to produce a robust exercise response, small dysfunction across several systems may contribute to post-exertional malaise in ME patients.

Most universities offer fitness and recreational opportunities. Few prospective studies have measured the effect of those services on academic outcomes. PURPOSE: To evaluate the effect of exercise behavior and recreational sport participation on student success. METHODS: We tracked 1,506 students at a private D1 university for 4 years. Upon completion of the 2017-2018 academic year, we exported a registry of every undergraduate student who accessed the university fitness center. We documented whether they participated in club sports (CS), intramural sports (IS), group exercise classes (GEC), or special activity classes (SAC), as well as the frequency of visits to the facility; these served as independent variables. We also recorded grade point average (GPA) and whether the students had graduated upon completion of the 2021-2022 academic year; these served as dependent variables. Independent-samples t-tests and chi-squared tests measured group differences in academic outcomes. Linear and logistic regressions tested the effects of combinations of independent variables on GPA and graduation respectively. RESULTS: 9.6% of students participated in CS, 7.4% participated in IS, 8.9% participated in GEC, and 3.3% participated in SAC. Mean GPA was 3.1 ± 0.6 and 75.7% successfully graduated. Significant and trending elevations of GPA were observed in students who participated in GEC (p < 0.001; 95% CI: 0.2 to 0.4) and CS (p = 0.072; 95% CI: -0.0 to 0.2). Graduation rate was 10.8 percentage points higher in students who participated in IS (p = 0.010) and 23.1 points higher in SAC participants (p < 0.001). Linear regression predicting GPA included 4 significant and trending predictors: sex (β = 0.163; p < 0.001), CS participation (β = 0.093; p = 0.063), GEC participation (β = 0.187; p < 0.001), and number of visits to the fitness facility (β = 0.004; p < 0.001). Logistic regression for graduation included 4 significant and trending predictors: sex (OR = 1.253; p = 0.070), IS participation (OR = 2.131; p = 0.007), SAC participation (OR = 15.491; p = 0.007), and number of facility visits (OR = 1.008; p = 0.020). CONCLUSIONS: Increased involvement in exercise and recreational sports associated with a higher GPA and increased odds of graduation. Administrative emphasis of fitness programming may be an effective way to enhance student success.

Autonomic dysfunction contributes to post-exertional malaise (PEM) in Myalgic Encephalomyelitis (ME). Heart rate variability (HRV) may be useful for examining measures of sympathetic and parasympathetic tonus. PURPOSE: To continuously monitor HRV from rest to maximal exertion during serial exercise tests. METHODS: Two patients with ME performed two CPETs 24 hours apart. Raw interbeat interval data collected through ECG were analyzed with Kubios software. HRV values for high and low frequency power (HFP and LFP) during a 5-minute rest period prior to the CPET and during the 5-min epoch leading up to the subject’s maximum HR during the CPET were extracted for both days. Frequency-domain HRV measures were then log transformed (lnHFP and lnLFP), and values were compared from day 1 to day 2 during the rest period and the maximum HR period. Data are reported as (resting period, max exercise period) for each variable. HFP and LFP were then compared to the maximum HR for each day. RESULTS: In subject 1, both lnHFP (4.84, 6.38) and lnLFP (6.42, 7.33) were higher at rest on day 2, as compared to day 1. In the maximum HR period, lnHFP decreased on day 2 (2.34, 1.66) and lnLFP increased (1.42, 1.53). These values were coupled with a decrease in 13 bpm in the max HR on day 2 (172, 159 bpm). In contrast, for subject 2; both lnHFP (6.30, 6.13) and lnLFP (7.48, 6.71) were lower at rest on day 2. In the maximum HR period, lnHFP increased on day 2 (1.17, 3.42) but lnLFP decreased (2.78, 2.47). This was coupled with an increase of 8 bpm for this subject in the max HR on day 2. DISCUSSION: While HFP is a measure of parasympathetic tone, LFP captures both sympathetic (SNS) and parasympathetic (PNS) activity. Increased HFP would suggest increased PNS tone and a blunting of max HR, with diminished HFP suggesting vagal withdrawal and greater SNS contribution. However, in subject 1, decreased HFP was associated with a lower max HR; and in subject 2, higher HFP was associated with a higher max HR. The direction of change for both HFP and LFP, along with the change in max HR, from day 1 to day 2 in these two subjects was opposite despite engaging in the same series of CPETs. CONCLUSIONS: The results of this case series suggest that the autonomic response to exercise in those with ME differs from person to person. Future research may explore additional factors interacting during PEM.

Field hockey involves considerable aerobic and anaerobic stress. Some of the training load and all of the on-field movement can be captured using heart rate monitors with global positioning systems. PURPOSE: To evaluate differences in exercise demand between practice and competition settings in collegiate field hockey. METHODS: We monitored 19 Division-1 female field hockey players for 88 consecutive days of their competitive season, comprising 51 practices and 20 games. All players wore Polar Team Pro devices (Polar Electro Inc., Bethpage, NY) during each practice and game. Dependent variables exported were mean heart rate (HR), mean HR percentage (HR%), duration spent at 90-100% of maximum HR, average speed, maximum speed, total distance covered, and training load score. Independent-samples t-test were conducted to compare game and practice metrics; where Levene's test for equality of variances was not met, we used Mann-Whitney U tests. RESULTS: In practice settings, mean HR was 138.1 ± 15.7 bpm, mean HR% was 69.4 ± 7.9%, the duration spent at 90-100% of maximum HR was 2.9 ± 2.7 min, average speed was 2.9 ± 0.7 km/h, maximum speed was 23.7 ± 4.2 km/h, total distance covered was 3,866.4 ± 1,583.5 m, and training load score was 117.4 ± 63.6. In game settings, mean HR was 9.2 ± 1.1 bpm higher (P < 0.001; 95% CI: 7.2, 11.3), mean HR% was 4.6 ± 0.5% higher (P < 0.001; 95% CI: 3.6, 5.6), duration spent at 90-100% of maximum HR was 0.5 ± 0.2 min longer (P = 0.008; 95% CI: 0.1, 0.8), average speed was 3.8 ± 0.1 km/h faster (P < 0.001; 95% CI: 3.4, 4.2), maximum speed was 2.9 ± 0.3 km/h faster (P < 0.001; 95% CI: 2.4, 3.4), total distance covered was 1,694.8 ± 117.3 m farther (P < 0.001; 95% CI: 1,418.7, 1,971.0), and training load score was 43.1 ± 4.0 higher (P < 0.001; 95% CI: 35.6, 50.5). Mann-Whitney U tests were performed for comparisons without equal variances: average speed (U = 35,103.5; P < 0.001), total distance (U = 79,733.5; P < 0.001), and training load score (U = 83,201.5; P < 0.001). CONCLUSION: Athletes experienced greater physical demand in games compared to practices. Games had higher HR values, covered more distance, spent more time near maximum HR, reached higher speeds, and had higher training load scores. More intense practices that stimulate match settings should be considered to better prepare field hockey players for competition.

Establishing normative data specific to sex and sport participation can improve performance appraisal, and may aid in identification of limitations. PURPOSE: To generate kinematic sequencing norms specific to each sex in various sports. METHODS: We tested 543 D1 athletes representing 15 sports using SpartaTrac technology. Subjects executed 6 vertical jumps on a force plate to generate a “Movement Signature” consisting of Load (eccentric force development during the downward phase), Explode (force output during the transitional phase), and Drive (magnitude and duration of concentric force during the upward phase). Athletes were stratified by sex and sport, and normative values were calculated. Multivariate tests estimated differences in these values between groups. RESULTS: Across the total sample, Load was 49.8 ± 10.2, Explode was 47.0 ± 9.9, and Drive was 55.5 ± 10.2; there was wide variance between different men’s sports (p<0.001) and women’s sports (p<0.001) sports. Among men, Load was highest in baseball (53.3 ± 10.3), basketball (52.6 ± 8.9), and soccer (52.5 ± 10.0); it was lowest in tennis (49.0 ± 10.7), water polo (49.1 ± 6.4), and swimming (50.4 ± 9.7) Explode was highest in basketball (53.2 ± 10.2), baseball (52.4 ± 8.6), and soccer (50.8 ± 8.3); it was lowest in water polo (44.0 ± 6.9), tennis (44.9 ± 5.9), and swimming (46.5 ± 8.5). Drive was highest in swimming (60.0 ± 9.9), water polo (54.6 ± 9.5), and baseball (54.0 ± 9.2); it was lowest in tennis (51.4 ± 14.0), basketball (52.8 ± 9.1), and soccer (53.2 ± 7.9). Among women, Load was highest in basketball (55.4 ± 17.1), volleyball (51.0 ± 8.7), and field hockey (48.0 ± 9.0); it was lowest in cross country (40.9 ± 5.8), soccer (45.9 ± 7.1), and water polo (46.1 ± 6.6). Explode was highest in basketball (53.0 ± 12.3), volleyball (48.1 ± 6.6), and field hockey (47.3 ± 8.8); it was lowest in water polo (34.9 ± 5.4), cross country (41.6 ± 8.8), and swimming (41.9 ± 6.6). Drive was highest in water polo (62.9 ± 10.4), volleyball (59.6 ± 7.4), and swimming (57.9 ± 9.9); it was lowest in basketball (51.3 ± 11.2), field hockey (53.0 ± 7.8), and soccer (54.6 ± 8.8). CONCLUSIONS: Ground reaction forces vary between sport populations. Normative values may aid in the customization of training programs for athletes whose signatures differ markedly from expected performances.

Practice structures that closely resemble the characteristics of competition are likely to elicit better sport preparation. PURPOSE: To compare the exercise profiles experienced by collegiate soccer players during practice and game settings. METHODS: We evaluated cardiovascular and movement parameters of 30 D1 female soccer players during 6 twice-daily practices (12 sessions), 14 once-daily practices, and 7 games. Polar Team Pro devices (Polar Electro, Inc.) captured exercise duration, distance traveled, number of sprints, average and maximum running speed, average and maximum heart rate (HR), and Polar-generated metrics for cardio load and training load. Means for each of these variables were calculated for every player across the 4 exercise conditions: first twice-daily practice (P1), second twice-daily practice (P2), single practice session (P3), and game. Differences in workload parameters between these settings were assessed with repeated measures ANOVA. RESULTS: Exercise duration was longer in games (116.3 ± 21.0 min) than all practice types (p < 0.001 for each comparison). Players covered more distance in games (4,462.3 ± 2,555.7 m) than practices (p < 0.005). Average running speed was higher in practice than games, but the largest difference was a trend (p = 0.064). Maximum speed achieved was highest in games (24.4 ± 4.9 kph); only the comparisons with P1 (p < 0.001) and P3 (p = 0.001) were significant. Athletes recorded more sprints in games (14.1 ± 9.8) than all practice types (p < 0.001). The lowest mean HR (128.8 ± 19.8 bpm) and percentage of HR max (64.4 ± 10.2%) occurred in games; both comparisons with P2 reached significance (p < 0.005). Maximum HR was highest in games (188.5 ± 19.8 bpm); only the comparison with P1 reached significance (p = 0.001). The largest cardio load (143.9 ± 66.7) and training load (126.5 ± 71.4) were achieved in games; comparisons with P1, P2, and P3 were significant (p < 0.01). CONCLUSIONS: Practices and games exhibited differences in exercise stress. Practices were shorter and had higher mean values for HR and running speed. In games, players performed more sprints, achieved higher speeds, and recorded higher maximum HR values. Coaching staff may consider incorporating additional anaerobic activity in soccer training to better simulate competition settings.

Athletes born earlier in the year may experience developmental advantages owing to eligibility cutoff dates in youth sports, typically January 1. Known as relative age effects (RAE), this phenomenon has been described in numerous athletic contexts; however, the proportional contributions of physical development and skill acquisition remain unknown. PURPOSE: To investigate the occurrence and anthropometric characteristics of RAE in collegiate athletes. METHODS: We tested 114 athletes (82 men, 32 women) representing 13 sports in a Division 1 athletics program in Northern California. Each subject was tested using the InBody 770 analyzer. We recorded height, weight, BMI, lean body mass, skeletal muscle mass, body fat mass, body fat percentage, lean leg mass, arm circumference, and estimated basal metabolic rate. We tabulated birth months and assigned subjects to their designated quarters (January-March as the first quarter). Multivariate tests including sex as a between-subjects factor were used to identify differences in InBody outcomes based on birth month for the entire sample. Coarsened exact matching was conducted to create subset containing two groups; consisting of subjects born in first three (n = 21) and last three (n = 21) months, matched by sex and age. Independent samples t-tests were conducted to examine differences in anthropometric measurements between the two groups in the subset. RESULTS: Across the total sample, 30.7% of athletes were born between January and March; there was a significant difference between sports (p = 0.027) and a trending difference between sexes (p = 0.071). Males and females exhibited differences (p < 0.001) in every anthropometric outcome except BMI (p = 0.123). There were no differences observed in any variable by birth quarter (p > 0.100), but peak physical characteristics appeared to exist in the middle months. Independent samples t-tests on the matched subset identified no difference between athletes born in the first three months and those born in the last three months for all anthropometric measures (p > 0.300). CONCLUSIONS: Among a diverse set of collegiate sports, our results suggest the existence of RAE corresponding to a January 1 eligibility cutoff may be related more to additional skill acquisition than physical maturation.

More than 130 million Americans visit emergency departments each year. Between 1996 and 2009, there was a 13-fold increase in documentation of obesity as the principal diagnosis. Fewer than 25% of these patients meet ACSM recommendations for aerobic and resistance exercise, and no more than a third are counseled on exercise behavior. For exercise counselling to become standard practice, we must improve the nature of reporting. PURPOSE: To examine how obesity is documented in a clinical setting and estimate its effect on patient outcomes. METHODS: We conducted a chart review of two patient samples over a 4-year period (2012-2015). Both samples were drawn from a single institution; 768 were treated at the trauma center (TC) and 2,106 were treated at the emergency department (ED). All patients in both samples were between 15 and 85 years of age and had a Glasgow Coma Scale score ≥ 14. We evaluated obesity reporting and the consequences of obesity on patient outcomes using logistic, linear, and negative binomial regressions as appropriate. RESULTS: In both samples, documenting of obesity increased each year (p < 0.001). In the TC group, 3.3% of patients were documented as obese in 2012, 9.4% in 2013, 30.0% in 2014, and 24.3% in 2015. In the ED sample, 1.1% of patients were documented as obese in 2012, 7.0% in 2013, 29.6% in 2014, and 34.0% in 2015. In 2014 and 2015, when reporting was sufficient, obese patients had lower oximetry (p = 0.020), higher heart rate (p = 0.010), higher systolic (p = 0.006) and diastolic (p = 0.027) blood pressure, more myocardial infarctions (p = 0.014), and higher rates of hypertension (p = 0.007) and diabetes (p < 0.001). Controlling or age, sex, and injury severity, patients categorized as obese cost $31 k more to the patient (p = 0.005) and $16 k more to the hospital (p = 0.002). Holding sex and age constant, the odds of experiencing a myocardial infarction were 3.1-fold higher in obese patients (p = 0.006) and the odds of being diagnosed with diabetes were 3.0-fold higher (p < 0.001). CONCLUSIONS: Obesity is a strong predictor of patient outcomes in both trauma and emergency medicine. These findings delineate the obesity trends in patient samples and emphasize the importance for obesity interventions using information from clinical settings. Physicians are well-positioned to emphasize exercise guidelines to patients.

Depression affects approximately 1.5% of American adults and incidence increases with age (5% of adults over 60). Incidence of dementia and psychiatric diseases also increase with age. Deeper understanding of contributing factors can aid in the prevention and treatment of these disorders. PURPOSE: To identify cardiovascular abnormalities that may underlie these illnesses. METHODS: 2,306 hospital patients were evaluated for cardiovascular and cognitive health. Demographic information, anthropometric values, clinical tests, and diagnostic history were collected. Independent variables were heart rate, blood pressure, and diagnosis of hypertension. Dependent variables were depression, dementia, cerebrovascular accidents, and psychiatric disorders. Descriptive statistics characterized the sample. Logistic regressions tested the effect of the cardiovascular predictors on cerebral and psychological outcomes. Significance was set at P < 0.05. RESULTS: 23 patients had depression, 115 were diagnosed with dementia, 92 experienced a cerebrovascular accident, and 161 had a psychiatric illness. Patients with hypertension were diagnosed with depression 120% more frequently (P = 0.045); 56% of depressed patients were hypertensive. Among patients with depression, there was a 264% increase in the odds of a dementia diagnosis (P = 0.006). In patients with dementia, systolic blood pressure (SBP) was 13 mmHg (9%) higher (P < 0.001), pulse pressure was 13 mmHg (23%) higher (P < 0.001), and heart rate was 7 bpm (8%) lower (P < 0.001). Patients with hypertension were diagnosed with dementia 379% more frequently than normotensive patients (P < 0.001). A diagnosis of hypertension also corresponded to 436% higher incidence of cerebrovascular accidents (P < 0.001). Controlling for age, there was a 2.2-fold increase in the odds of an adverse event in patients with dementia (P = 0.005). However, patients with psychiatric disorders had SBP that was 5 mmHg (4%) lower (P = 0.018); similarly, pulse pressure was 5 mmHg (7%) lower (P = 0.007). CONCLUSIONS: These findings support the hypothesis that cardiovascular deterioration coincides with increased risk for depression and neurocognitive issues. Aerobic exercise training oriented toward improved cardiovascular health likely reduces adverse events and psychological decline.

The health and harm of alcohol consumption has been long debated. In recent decades, multiple epidemiological analyses have demonstrated a strong correlation between moderate consumption and a reduced risk of myocardial infarction, peripheral vascular disease, and ischemic stroke. While moderation may confer a cardiovascular protective effect, the same cannot be said of alcohol abuse, cigarette smoking, and illicit drug use. PURPOSE: To test the predictive power of moderate alcohol use on alcohol abuse and the use of cigarettes and illicit drugs. METHODS: We analyzed the registry of a U.S. hospital in an urban-suburban setting, consisting of 2,306 patients admitted over a 5-year period. At intake, demographic and health data were recorded, including alcohol, tobacco, and illicit drug use, and previous histories thereof. Blood alcohol content (BAC) and toxicology screens were administered to patients suspected of current alcohol or drug use. Logistic regressions tested the effects of alcohol consumption on alcohol abuse, smoking status, and use of illicit drugs. RESULTS: Patients were 52.1 ± 22.4 years of age, 56.0% were men, 11.8% were currently using alcohol (BAC of 0.05 ± 0.10), 5.7% had a history of alcohol abuse, 25.8% reported regular smoking, 5.0% had a history of illicit drug use, and 27.1% had a positive toxicology screen. Holding potential confounders constant, logistic regression found current alcohol use to predict a 13.5-fold increase in the odds of alcohol abuse (p < 0.001; 95% CI of odds ratio: 8.96 to 20.27), a 213% increase in the odds of smoking (p < 0.001; 95% CI of odds ratio: 1.63 to 2.79), and a 209% increase in the odds of illicit drug use (p < 0.001; 95% CI of odds ratio: 1.34 to 3.27). A history of alcohol abuse predicted a 428% increase in the odds of smoking (p < 0.001; 95% CI of odds ratio: 2.95 to 6.22) and a 783% increase in the odds of illicit drug use (p < 0.001; 95% CI of odds ratio: 4.82 to 12.72). CONCLUSIONS: While some cardiovascular benefits correlate with moderate alcohol consumption, there may be a paradoxical effect whereby its association with high-risk behaviors (i.e., smoking and illicit drug use) leads to health detriments in a large subset of drinkers. Accordingly, it might be prudent to consider more than cigarette exposure in pre-exercise health screening practices.

In college athletics programs, individual sports differ widely in resources, attendance, and publicity. Few investigations have examined whether these inter-sport differences influence the individual attention athletes receive by strength trainers and coaching staff. PURPOSE: To examine if athletes across diverse sports in a Division 1 athletics program experience comparable fitness testing. METHODS: We tracked comprehensive fitness assessments undergone by 114 Division 1 collegiate athletes representing 6 men's sports (baseball, basketball, soccer, swimming, tennis, and water polo) and 7 women's sports (basketball, field hockey, soccer, swimming, track and field, volleyball, and water polo). Independent-samples t-tests, chi-squared tests, logistic regression, and one-way ANOVA were used, as appropriate, to compare testing frequencies between men and women and across sports. RESULTS: Men (n = 82) were 19.9 ± 1.4 years old; women (n = 32) were 20.1 ± 1.7 years old (p = 0.696); age was unrelated to testing frequency (p = 0.569). Most fitness assessments (71.9%) occurred during fall semester; there was no difference between sexes in time of testing (p = 0.351); a difference was observed between sports (p < 0.001) with testing dates tracking each sport's season of participation. A single fitness evaluation was experienced by 50.0% of all athletes, the maximum number of testing dates by a single athlete was 32, and the mean number was 3.4 ± 4.6. Men were tested 3.9 ± 5.1 times; women were tested 2.1 ± 2.6 times (p = 0.014). 55.4% of men underwent multiple testing dates compared to 35.5% of women (p = 0.058). Logistic regression, holding sex and sport constant, found each additional inch of height to predict a 12.4% increase in the odds of undergoing multiple tests (p = 0.044; 95% CI of OR: 1.003 to 1.259). One-way ANOVA revealed a difference in testing frequencies between sports (p < 0.001): men's tennis (10.4 ± 8.6), men's basketball (5.0 ± 2.2), and women's volleyball (4.1 ± 8.3) had the most frequently evaluated athletes. CONCLUSIONS: The variance in attention received by collegiate athletes is multifactorial. Some factors that emerged in this study were sex of the athlete and sport the athlete plays. Inconsistency in team fitness assessment may demonstrate performance priorities in collegiate athletic programs.

An effective pitcher is both consistent and powerful; appropriate training optimizes these characteristics. In baseball, the overload principle is commonly employed with the use of cable devices and weighted balls. While this may elicit increases in velocity, the alteration of throwing mechanics is not well understood. PURPOSE: To evaluate acute performance and biomechanical responses to applied resistance in pitching. METHODS: 10 Division 1 collegiate baseball pitchers were tested using Proteus technology (Proteus Motion, USA). After a standardized warm-up, they completed 5 sets of 5 pitches against varying electromagnetic loads. Each successive set increased in resistance by 1 lb, ranging from 1 to 5 lbs. Repeated measures ANOVA examined the effect of load on throwing power (w), acceleration (m/s2), explosiveness (w/s), velocity (m/s2), deceleration (m/s2), endurance (score of power maintenance in serial repetitions), range of motion in three-dimensional space, and consistency (score of how well throw mechanics were replicated across all repetitions in a set). Power, acceleration, explosiveness, velocity, and deceleration were considered acute performance metrics. Endurance, range of motion, and consistency were considered biomechanical responses to increased load. Significance was set at p < 0.05. RESULTS: Pitchers were 73.0 ± 2.8 inches tall, were mostly right-handed (88%), and had a fastball velocity of 84.6 ± 3.9 mph. Repeated measures ANOVA detected differences in power (F = 306.443; p < 0.001), acceleration (F = 103.327; p < 0.001), explosiveness (F = 92.782; p < 0.001), velocity (F = 8.186; p < 0.001), and deceleration (F = 129.861; p < 0.001) in response to incremental load changes. However, increasing load did not affect consistency (F = 1.023; p = 0.415), endurance (F = 1.914, p = 0.111), or range of motion (F = 2.840, p = 0.100). CONCLUSIONS: Adjustments in load produced acute performance changes in pitching power, acceleration, explosiveness, velocity, and deceleration without influencing consistency, endurance, or range of motion. These findings provide preliminary evidence that pitch training against three-dimensional isotonic resistance may enhance throw velocity without significant compromise to kinematic parameters.

Americans over the age of 65 are at a higher risk of falls. After falling, they commonly experience decreases in functionality, independence, and quality of life. In ACSM's Guidelines of Exercise Testing and Prescription, older adults are recommended to participate in aerobic exercise 5 days a week, supplemented with 2 days of flexibility and resistance training. We propose additional considerations may be required for adults who have previously experienced a fall. PURPOSE: To evaluate the effect of past falls on likelihood and incidence of future falls among older adults. METHODS: We evaluated 615 patients consecutively admitted in a single year to a Level 1 trauma center for a fall-related injury. All patients were ≥ 65 years of age. We conducted a retrospective analysis to determine the number of previous admissions for fall-related injuries over a 5-year period, and we tracked patients prospectively, recording the number of additional falls experienced for 8 months. We estimated the odds that a patient would experience a future fall using logistic regression and the number of future falls experienced with negative binomial regression. The primary predictor was number of previous falls; we held constant admission month, cognitive decline, and medication use associated with compromised balance. RESULTS: Patients were 80.0 ± 9.1 years old, 71.9% were female, they had 1.9 ± 1.3 previous fall-related injuries, and they sustained 0.5 ± 0.9 falls during the tracking period. With confounders held constant, each additional previous fall predicted a 3.9-fold increase in the odds of experiencing a future fall (p < 0.001; 95% CI of OR: 3.131 to 4.961); the overall model was significant (p < 0.001; pseudo R2 = 0.460). Age (p = 0.351) and sex (p = 0.236) were not significant predictors. Holding the same confounders constant, negative binomial regression found each additional previous fall to predict a 94.9% increase in the number of future falls (p < 0.001; 95% CI of IRR: 1.728 to 2.198); age (p = 0.283) and sex (p = 0.163) were not significant. CONCLUSIONS: Our findings highlight the importance of screening older adults for a history of falls prior to exercise prescription. For clients and patients who report experiencing a fall, it may be prudent to incorporate safe forms of balance and stability training.

Maintenance of an optimal body composition is an important component of physical functioning and longevity. Comprehensive anthropometric analyses provide patients and clients with objective assessments of their current fitness. It is important to understand the potential consequences of this information on an individual's motivation to continue health monitoring. PURPOSE: To explore which factors of body composition analysis influence future testing behavior. METHODS: We tested 209 men and 219 women from two exercise facilities (a commercial gym and a CrossFit facility) using the InBody 770 bioelectrical impedance analyzer. We documented age, height, weight, BMI, lean body mass, skeletal muscle mass, lean leg mass, arm circumference, body fat mass, trunk fat mass, and body fat percentage. All subjects were eligible for repeated testing on a voluntary basis. We used negative binomial regression to evaluate the effect of anthropometric variables on the number of repeat tests. RESULTS: Subjects were 35.5 ± 10.3 years old, weighed 187.5 ± 50.2 lb, had a BMI of 29.7 ± 6.5 kg/m2, 130.0 ± 31.1 lb lean body mass, 73.4 ± 18.7 lb skeletal muscle mass, 37.6 ± 9.0 lb lean leg mass, 14.4 ± 3.8in arm circumference, 57.6 ± 33.6 lb body fat mass, 29.8 ± 14.0 lb trunk fat mass, and 29.6 ± 11.0% body fat. Subjects were screened 2.9 ± 3.6 times (range: 1 to 33). Holding constant sex (p = 0.004) and duration following the initial test date (p = 0.003), the only anthropometric factor that emerged as a significant predictor of serial testing was skeletal muscle mass (p = 0.001). Each additional pound corresponded to a 1.7% increase in the number of follow-up tests (95% CI of IRR: 1.007 to 1.027). In this model, females were screened 69.3% more times (95% CI of IRR: 1.181 to 2.426). Higher bodyweight (p = 0.089), body fat mass (p = 0.105) and body fat percentage (p = 0.139) exhibited non-significant patterns of reduction in the number of subsequent tests; they were not included in the model. No other predictor was related to testing behavior (p > 0.250). CONCLUSIONS: Understanding which components of body composition analysis affect motivation to continue testing provides health practitioners insight into which populations may benefit from additional encouragement. Our results indicate males with lower muscle mass are more susceptible to attrition.

In collegiate and professional baseball, fastball velocity is inversely correlated with opposing batting average. Although the importance of velocity is widely accepted, there is no consensus on the exercises that most accurately predict it. PURPOSE: To examine relationships between fastball velocity and isotonic power output in diverse exercise motions. METHODS: We recorded fastball velocity in 13 collegiate baseball pitchers using Rapsodo (Rapsodo Inc., USA) and conducted comprehensive biomechanical testing with Proteus (Proteus Motion Inc., USA). Players underwent baseline testing, followed by a 6-week training intervention, and were then retested. At both assessments, players completed 6 repetitions at 12 lb of magnetic resistance on 8 upper limb exercises (unilateral and bilateral biceps curl, triceps extension, horizontal row, and horizontal press) and 3 trunk and lower limb exercises (straight-arm trunk rotation, lateral bound, and vertical jump). Mean peak power (watts) across all repetitions was tabulated for each movement. Simple linear regressions evaluated associations between fastball velocity and power output for each movement at both time points. RESULTS: Subjects were 20.3 ± 1.3 years of age and had a mean fastball velocity of 85.4 ± 4.2 mph. At baseline, the only exercise that significantly predicted velocity was lateral bound (p = 0.033). Mean lateral bound power was 165.1 ± 20.0 watts, and each additional watt predicted a 0.1 mph increase in velocity (R2 = 0.164; 95% CI of β: 0.008 to 0.171). At follow-up, other positive relationships emerged. Each additional watt of power in lateral bound (p = 0.003; R2 = 0.313; 95% CI of β = 0.016 to 0.071), two-handed triceps extension (p = 0.012; R2 = 0.453; 95% CI of β = 0.013 to 0.082), two-handed horizontal press (p = 0.029; R2 = 0.363; 95% CI of β = 0.009 to 0.140), and straight-arm trunk rotation (p = 0.013; R2 = 0.232; β = 0.008 to 0.060) associated with increased fastball velocity. CONCLUSIONS: At baseline, lateral leg power appeared to be the dominant contributor to pitching velocity. Following a training intervention, variability in upper limb power output exhibited relationships with performance. Coaches and training staff may consider focusing on these exercises in training prescriptions for collegiate pitchers.

Soft tissue mobilization (STM) is commonly performed by athletic trainers in collegiate sports; it can be implemented in a variety of ways. How different STM techniques influence subsequent biomechanical function is unexamined. PURPOSE: To evaluate the effect of various STM techniques on windmill pitching kinematics in collegiate softball. METHODS: 10 Division 1 softball pitchers underwent 4 testing periods involving an STM session followed by mechanical evaluation of a windmill pitch using a Proteus device (Proteus Motion, USA). The different STM options were: active release technique (ART), cupping therapy (CT), instrumented-assisted mobilization (IAM), and a comparison group receiving no treatment (control). The STM methods were conducted in a random order with 48 hours separating each test. Immediately after STM, players performed 8 maximal-effort windmill pitches on Proteus (3 acclimation repetitions followed by 5 analyzed pitches). Proteus calculated power (watts), explosiveness (watts/sec), endurance (percent maintenance of power in serial repetitions), range of motion (ROM; distance traveled in 3D space), and consistency (replication of range of motion). Repeated measures ANOVAs evaluated differences in Proteus outputs in the different STM trials. RESULTS: Subjects were 19.4 ± 1.1 years old, height was 64.5 ± 5.5in, weight was 150.4 ± 15.3 lb, arm length was 67.1 ± 3.4in, and mean pitching experience was 5.2 ± 4.3 years. Proteus performances following the control treatment were: power of 30.8 ± 2.9, explosiveness of 27.3 ± 8.3, endurance of 90.4 ± 4.7, ROM of 6.4 ± 1.0, and consistency of 80.3 ± 10.5. ANOVA demonstrated non-significant patterns (p > 0.150 compared to control) in which the ART testing had the highest subsequent power (32.5 ± 4.2), explosiveness (29.2 ± 7.9), and consistency (81.0 ± 12.8), and IAM had the highest endurance (92.9 ± 3.7). CONCLUSIONS: In a small, pilot sample of 10 softball pitchers, no STM method emerged as superior to the others or to the control group. Being the first investigation of its kind, further analyses on larger samples and in diverse athletic contexts are warranted to identify possible performance aids available to athletic trainers.