Context Anterior cruciate ligament reconstruction (ACLR) and gait speed are risk factors for developing knee osteoarthritis (OA). Measuring minute-level cadence during free-living activities may aid in identifying individuals at elevated risk of developing slow habitual gait speed and, in the long term, OA. Objective To assess differences in peak 1-minute cadence and weekly time in different cadence intensities between individuals with and without ACLR. Design Cross-sectional study. Setting Short-term, free-living conditions. Patients or Other Participants A total of 57 participants with ACLR (34 women, 23 men; age = 20.9 ± 3.2 years, time since surgery = 28.7 ± 17.7 months) and 42 healthy control participants (22 women, 20 men; age = 20.7 ± 1.7 years). Main Outcome Measure(s) Each participants wore a physical activity monitor for 7 days. Data were collected at 30 Hz, processed in 60-second epochs, and included in the analyses if the activity monitor was worn for at least 10 hours per day over 4 days. Mean daily steps, peak 1-minute cadence, and weekly minutes spent at 60 to 79 (slow walking), 80 to 99 (medium walking), 100 to 119 (brisk walking), ≥100 (moderate- to vigorous-intensity ambulation), and ≥130 (vigorous-intensity ambulation) steps per minute were calculated. One-way analyses of covariance were conducted to determine differences between groups, controlling for height and activity-monitor wear time. Results Those with ACLR took fewer daily steps (8422 ± 2663 versus 10 033 ± 3046 steps; P = .005) and spent fewer weekly minutes in moderate- to vigorous-intensity cadence (175.8 ± 116.5 minutes versus 218.5 ± 137.1 minutes; P = .048) than participants without ACLR. We observed no differences in minutes spent at slow (ACLR = 77.4 ± 40.5 minutes versus control = 83.9 ± 34.3 minutes; P = .88), medium (ACLR = 71.6 ± 40.2 minutes versus control = 82.9 ± 46.8 minutes; P = .56), brisk (ACLR = 115.3 ± 70.3 minutes versus control = 138.3 ± 73.3 minutes; P = .18), or vigorous-intensity (ACLR = 24.3 ± 36.5 minutes versus control = 38.1 ± 60.9 minutes; P = .10) cadences per week. Conclusions Participants with ACLR walked approximately 40 minutes less per week in moderate- to vigorous-intensity cadence than participants without ACLR. Increasing the time spent at cadence ≥100 steps per minute and overall volume of physical activity may be useful as interventional targets to help reduce the risk of early development of OA after ACLR.

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Context: Sport specialization during adolescence may affect future injury risk. This association has been demonstrated in some professional sports athletes. Objective: Determine the association between adolescent sport specialization levels in high school and injuries sustained during collegiate club sports. Design: Cross-sectional study. Setting: Paper and online surveys. Patients: Collegiate club sport athletes. Intervention: An anonymous survey was administered from September 2019–May 2020. The survey included sport specialization classification via commonly used 3-point scale (Low, Moderate, High) for each high school year (9th–12th), high school sports participation, and collegiate club sport injury history. The number of years an individual was highly specialized in high school was calculated (0–4 years). Individuals who participated in the same sport in high school and college were compared to individuals who were playing a different sport in college than their high school sports. Main Outcome Measures: An injury related to sport club activities and classified as arising from a contact, non-contact, or overuse mechanism that required the individual to seek medical treatment or diagnosis. Injuries were classified into overuse and acute injury mechanisms for the upper and lower extremity, (UE and LE respectively) and head/neck. Results: Single sport participation or number of years highly specialized in high school sport were not associated with college club sport injuries (p>0.1). Individuals who played a different collegiate club sport than their high school sports were more likely to report a LE and head/neck acute injury compared to athletes who played the same collegiate and high school sport (LE=20% vs 8%, χ2=7.4 p=0.006; head/neck=16% vs 3%, χ2=19.4 p<0.001). Conclusions: Adolescent sport specialization was not associated with reported club sport injuries in collegiate club sport athletes. Collegiate club sport athletic trainers should be aware that incoming students exploring a new sport may be at risk for LE and head/neck acute injuries.

Context: Sport specialization among youth athletes has been associated with increased risk of overuse injuries. Previous research demonstrates that children perceive specialization to be beneficial in making their high school team and receiving athletic college scholarships. Previous research demonstrates that parents play a significant role in their child’s sport experience. However, it is unknown if parents and children answer questions related to specialization factors in a similar manner. Objective: To evaluate the beliefs of youth athletes and parents on factors related to sport specialization and evaluate the level of agreement between dyads on sports specialization. Design: Cross-sectional. Setting: Online and paper surveys. Patients or Other Participants: Aim 1: 1998 participants (993 children and 1005 parents). Aim 2: 77 paired parent–child dyads. Interventions: Self-administered survey. Main Outcome Measures: The responses were summarized via frequency and proportions (%). Chi-squares were calculated between parent and child responses. Kappa coefficients were calculated for dyads to determine level of agreement. Sport specialization was classified using a common 3-point scale. Results: The parents were more concerned about risk of injury in sports compared with children (P < .001, χ2 = 231.4; parent: extremely: 7.1%; child: extremely: 3.7%). However, children were more likely to believe that specialization was associated with their chances of obtaining an athletic college scholarship compared with parents (P < .001, χ2 = 201.6; parent: very/extremely likely: 13.7%; child: very/extremely likely: 15.8%). Dyad subanalysis indicated a moderate level of agreement for “quitting other sports to focus on one sport” (κ = .50) and a low level of agreement for “identifying a primary sport” (κ = .30) and “training >8 months per year in primary sport” (κ = .32). Conclusions: Parents and youth athletes had differing beliefs on the factors related to sport specialization. Dyad analysis shows that parents and children answer sport specialization classification questions differently. Health care providers should be aware of these differences, and messaging should be individualized to the audience.

Secondary school ATs (SSATs) are uniquely positioned healthcare providers at an optimal public health intersection where they can provide equitable healthcare to low socioeconomic status (SES) adolescents. SSATs face similar challenges to physicians in treating low SES patients, but their strategies may be different compared to other medical professions. However, the consequences of low SES population healthcare delivery by SSATs have not been explored. SSATs were asked to share what challenges, if any, they encounter with providing care for their low SES patients and what strategies they find most effective to overcome these challenges. Data were collected via semi-structured interviews and reflective field notes and analyzed using a four-step, interpretative phenomenological analysis (IPA) guided theme development. Data saturation was met, and the sample size aligned with other IPA studies. Trustworthiness was established with research triangulation and Yardley’s four principles. Three interrelated themes emerged: (a) mechanisms for identifying SES, (b) the impact of SES on care, and (c) navigating SES challenges. SSATs described many strategies that were gained through their clinical experiences to overcome healthcare barriers. SSATs have the potential to decrease health disparities through their role as a liaison and advocates for their low SES patients.