Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
Am J Sports Med. 2021 Jan;49(1):154-161. doi: 10.1177/0363546520969913. Epub 2020 Nov 19.
Knowledge of predictors for shoulder pain in swimmers can assist professionals working with the athlete in developing optimal prevention strategies. However, study methodology and limited available data have constrained a comprehensive understanding of which factors cause shoulder pain.
To investigate risk factors and develop and internally validate a multivariable prognostic model for the prediction of shoulder pain in swimmers.
Cohort study; Level of evidence, 2.
A total of 201 pain-free club- to international-level competitive swimmers were followed for 2 consecutive seasons. The cohort consisted of 96 male (mean ± SD age, 13.9 ± 2.2 years) and 105 female (13.9 ± 2.2 years) swimmers. Demographic, sport-specific, and musculoskeletal characteristics were assessed every 6 months. Swim-training exposure was observed prospectively. Shoulder pain interfering with training was the primary outcome. Multiple imputation was used to cope with missing data. The final model was estimated using multivariable logistic regression. We applied bootstrapping to internally validate the model and correct for overoptimism.
A total of 42 new cases of shoulder pain were recorded during the study. Average duration of follow-up was 1.1 years. Predictors included in the final model were acute:chronic workload ratio (odds ratio [OR], 4.31; 95% CI, 1.00-18.54), competitive level (OR, 0.19; 95% CI, 0.06-0.63), shoulder flexion range of motion, posterior shoulder muscle endurance (OR, 0.96; 95% CI, 0.92-0.99), and hand entry position error (OR, 0.37; 95% CI, 0.16-0.91). After internal validation, this model maintained good calibration and discriminative power (area under the receiver operating characteristic curve, 0.71; 95% CI, 0.60-0.94).
Our model consists of parameters that are readily measurable in a swimming setting, allowing the identification of swimmers at risk for shoulder pain. Multivariable logistic regression showed the strongest predictors for shoulder pain were regional competitive swimming level, acute:chronic workload ratio, posterior shoulder muscle endurance, and hand entry error.
了解游泳运动员肩部疼痛的预测因素有助于从事运动员工作的专业人员制定最佳预防策略。然而,研究方法和有限的可用数据限制了对导致肩部疼痛的因素的全面理解。
调查风险因素,并建立和内部验证一个多变量预测模型,以预测游泳运动员的肩部疼痛。
队列研究;证据水平,2 级。
对 201 名无肩部疼痛的俱乐部到国际级竞技游泳运动员进行了 2 个连续赛季的随访。该队列由 96 名男性(平均年龄±标准差,13.9±2.2 岁)和 105 名女性(13.9±2.2 岁)组成。每 6 个月评估一次人口统计学、运动专项和肌肉骨骼特征。前瞻性观察游泳训练暴露情况。肩部疼痛干扰训练是主要结局。采用多重插补法处理缺失数据。最终模型采用多变量逻辑回归进行估计。我们应用自举法对内验证模型,并对过度拟合进行校正。
研究期间共记录了 42 例新的肩部疼痛病例。平均随访时间为 1.1 年。纳入最终模型的预测因素包括急性:慢性工作量比(比值比[OR],4.31;95%置信区间[CI],1.00-18.54)、竞技水平(OR,0.19;95%CI,0.06-0.63)、肩关节活动度、肩后肌耐力(OR,0.96;95%CI,0.92-0.99)和手进入位置错误(OR,0.37;95%CI,0.16-0.91)。内部验证后,该模型仍具有良好的校准度和区分力(受试者工作特征曲线下面积,0.71;95%CI,0.60-0.94)。
我们的模型由游泳环境中易于测量的参数组成,可用于识别肩部疼痛风险的游泳运动员。多变量逻辑回归显示,肩部疼痛的最强预测因素是区域竞技游泳水平、急性:慢性工作量比、肩后肌耐力和手进入错误。