Context: Swimmers are known for high training loads and overuse injuries, but few studies have investigated the relationship between the two at the college level. Objective: To determine the association between workload and non-contact musculoskeletal injury in college swimmers. We hypothesized (1) higher overall workload (kilometers swum throughout the season) would be associated with increased odds of injury and (2) the ratio of the current week's (acute) workload divided by the current month's (chronic) workload (i.e., the acute:chronic workload ratio (ACWR)) would be more strongly associated with odds of injury than acute or chronic workloads alone. Design: Prospective cohort study. Setting: College natatorium. Patients or Other Participants: Thirty-seven Division III college swimmers. Main Outcome Measures: Logistic regression using generalized estimating equations was used to calculate odds ratios (OR) with 95% confidence intervals (CI) for injury relative to high workloads and high ACWRs. Injury rates for several ranges of workloads and ACWRs were also calculated. Results: Eleven participants (29.7%) sustained 12 injuries, with seven injuries occurring during the participants' winter training trip. Injury was associated with high acute workloads (OR = 27.1 [95% CI = 8.2, 89.8]) and high ACWRs (OR = 25.1 [95% CI = 7.7, 81.4]) but not high chronic workloads (OR = 2.6 [95% CI = 0.3, 20.0]) or overall workloads (OR = 1.00 [95% CI = 0.99, 1.01]). High acute workloads (>37.2 km/week) and high ACWRs (>1.56) increased injury rate from ≤1% to 15% and 14%, respectively, compared to all lower acute workloads and ACWRs. Conclusions: College swimmers can tolerate high workloads spread out over the season. However, caution should be used when prescribing high acute workloads and high ACWRs (e.g., winter training trip) due to increased odds of injury.

Little is known about how students in rehabilitation sciences accept and use generative artificial intelligence (GenAI) in their educational endeavors. We explored student perceptions, acceptance, and utilization of GenAI in school and their beliefs about its usefulness and ease of use. A cross-sectional online survey was conducted of adult students enrolled in rehabilitation sciences programs within a private urban academic university. The survey was based on the Technology Acceptance Model with questions specific to the use of GenAI in school. A total of 196 responses were included in the analysis (32.9% response rate), with responses received across all rehabilitation sciences programs. Half the respondents (50%) reported using GenAI “some of the time” in school, and 6.1% indicated frequent usage of “most of” or “all” the time. Users reported using GenAI to explain and review concepts (n = 49), to generate content or ideas (n = 20), and for grammatical support (n = 21). Users perceived GenAI as more useful and easier to use than non-users. Over half of rehabilitation sciences students use GenAI for school; however, only 6.1% report routine usage. Given the expected growth and potential of GenAI, faculty should explore strategies to facilitate the acceptance and appropriate use of this innovative technology.

Context: Shoulder pain is pervasive in swimmers of all ages. However, given the limited number of prospective studies, injury risk factors in swimmers remain uncertain.Objective: To determine the extent to which the risk factors of previous injury, poor movement competency, erroneous freestyle swimming technique, and low perceived susceptibility to sport injury were associated with noncontact musculoskeletal injury in collegiate swimmers.Design: Prospective cohort study.Setting: College natatorium.Patients or Other Participants: Thirty-seven National College Athletic Association Division III swimmers (21 females, 16 males; median age = 19 years [interquartile range = 3 years], height = 175 ± 10 cm; mass = 70.0 ± 10.9 kg).Main Outcome Measure(s): Participants completed preseason questionnaires on their previous injuries and perceived susceptibility to sport injury. At the beginning of the season, they completed the Movement System Screening Tool and the Freestyle Swimming Technique Assessment. Logistic regression was used to calculate odds ratios (ORs) with 95% CIs for the association between each risk factor and injury.Results: Eleven of the 37 participants (29.7%) sustained an injury. Univariate analyses identified 2 risk factors: previous injury (OR = 8.89 [95% CI = 1.78, 44.48]) and crossover hand positions during the freestyle entry phase (OR = 8.50 [95% CI = 1.50, 48.05]). After adjusting for previous injury, we found that a higher perceived percentage chance of injury (1 item from the Perceived Susceptibility to Sport Injury) decreased the injury odds (adjusted OR = 0.11 [95% CI = 0.02, 0.82]). Poor movement competency was not associated with injury (P > .05).Conclusions: Previous injury, a crossover hand-entry position in freestyle, and a low perceived percentage chance of injury were associated with increased injury odds. Ascertaining injury histories and assessing for crossover positions may help identify swimmers with an elevated injury risk and inform injury-prevention strategies.

Context: Swimming technique is widely believed to influence performance, but this relationship has rarely been tested objectively using a real-time poolside assessment. Objective: To determine the (1) test–retest reliability, interrater reliability, and criterion validity (live vs video) of real-time poolside assessment of upper limb (UL) errors in front crawl (FC) swimming technique and (2) the relationship between UL errors and FC swimming performance. Design: Cross-sectional reliability, validity, and correlational study. Setting: Swim team practice at a college natatorium. Participants: Thirty-nine Division III college swimmers (21 women and 18 men, age = 19 [1] y, swimming experience = 11 [3] y). Main Outcome Measures: Seven UL errors in FC swimming technique, many of which involved unnecessary vertical and mediolateral motions, were assessed in real time from outside the pool during swim practice. Test–retest reliability, interrater reliability, and criterion validity were calculated using Cohen kappa (κ) and weighted kappa (κ _w). Swimming performance was determined by the participants’ best FC events relative to the conference records. The correlation between total UL errors and FC swimming performance was assessed with Pearson r. Results: Cohen κ and κ _w were moderate for the majority of errors, with the following ranges: 0.46 to 0.90 (test–retest), −0.01 to 1.00 (interrater), and 0.36 to 0.66 (criterion validity). There was a significant correlation between total UL errors and FC swimming performance: r(24) = −.59 (P = .001, R ² = .35). Conclusions: Reliability and validity were moderate for the majority of errors. The fewer UL errors swimmers made while practicing FC, the faster their best FC race times tended to be relative to the conference record. UL errors in FC swimming technique explained 35% of the variance in performance.

Context: Decreased trunk neuromuscular control is a risk factor for both upper- and lower-extremity injuries, yet there are few reliable and valid clinical tests to identify deficits. Objective: The purpose of this study was to determine the reliability and known-groups validity of a novel clinical test, the seated trunk control test (STCT). Design: Cross-sectional reliability and known-groups validity study. Setting: Laboratory. Participants: 89 unique participants: 34 were 3 months postoperative anterior cruciate ligament reconstruction (ACLR) and 55 healthy controls. Methods: For the STCT, participants sat on a balance board with their eyes closed for three 30-second trials while investigators counted balance errors. Intraclass correlations (ICCs) were used to assess interrater reliability (N = 20) and test–retest reliability (N = 40). To assess known-groups validity, independent t tests were used to compare STCT errors at 3 months post-ACLR with healthy matched controls (N = 34/group). Area under a receiver operating characteristic curve identified an optimal cutoff for distinguishing between groups. Results: The STCT had perfect interrater reliability (ICC_2,3 = 1.00) and good test–retest reliability (ICC_3,3 = .79; 95% confidence interval = .61–.89). The ACLR group made significantly more errors on the STCT (mean [SD] = 15.5 [5.4]) than controls (mean [SD] = 8.2 [4.1]; P < .001, Cohen d = 1.52). The STCT’s ability to distinguish between groups was excellent (area under a ROC curve = 0.86). A cutoff of 12 errors maximized sensitivity (76%) and specificity (85%). Conclusions: The STCT is reliable between raters and across days. It also has excellent ability to distinguish between individuals with a recent ACLR and healthy matched controls, which provides initial evidence to suggest that the STCT may be clinically useful for identifying deficits in trunk neuromuscular control.