Overarm Training Tolerance: A Pilot Study on the Use of Muscle Oxygen Saturation as a Biomarker.

Ulnar collateral ligament (UCL) tears occur due to the prolonged exposure and overworking of joint stresses, resulting in decreased strength in the flexion and extension of the elbow. Current rehabilitation approaches for UCL tears involve subjective assessments (pain scales) and objective measures such as monitoring joint angles and range of motion. The main goal of this study is to find out if using wearable near-infrared spectroscopy technology can help measure digital biomarkers like muscle oxygen levels and heart rate. These measurements could then be applied to athletes who have been injured. Specifically, measuring muscle oxygen levels will help us understand how well the muscles are using oxygen. This can indicate improvements in how the muscles are healing and growing new blood vessels after reconstructive surgery. Previous research studies demonstrated that there remains an unmet clinical need to measure biomarkers to provide continuous, internal data on muscle physiology during the rehabilitation process. This study's findings can benefit team physicians, sports scientists, athletic trainers, and athletes in the identification of biomarkers to assist in clinical decisions for optimizing training regimens for athletes that perform overarm movements; the research suggests pathways for possible earlier detection, and thus earlier intervention for injury prevention.

Wearable Devices and Digital Biomarkers for Optimizing Training Tolerances and Athlete Performance: A Case Study of a National Collegiate Athletic Association Division III Soccer Team over a One-Year Period.

Wearable devices in sports have been used at the professional and higher collegiate levels, but not much research has been conducted at lower collegiate division levels. The objective of this retrospective study was to gather big data using the Catapult wearable technology, develop an algorithm for musculoskeletal modeling, and longitudinally determine the workloads of male college soccer (football) athletes at the Division III (DIII) level over the course of a 12-week season. The results showed that over the course of a season, (1) the average match workload (432 ± 47.7) was 1.5× greater than the average training workload (252.9 ± 23.3) for all positions, (2) the forward position showed the lowest workloads throughout the season, and (3) the highest mean workload was in week 8 (370.1 ± 177.2), while the lowest was in week 4 (219.1 ± 26.4). These results provide the impetus to enable the interoperability of data gathered from wearable devices into data management systems for optimizing performance and health.

MODELING REALITY: REVISITING CALVERT'S FITNESS SIMULATION.

Wearable sensors have gained mainstream acceptance for health and fitness monitoring despite the absence of clinically validated analytic models for clinical decision support. Individual sensors measuring, say, EKG signal and heart rate can provide insight on cardiovascular response, but a more complete picture of health and fitness requires a more complete portfolio of sensors and data. This paper outlines the research underway to revisit and reconfigure the 1976 Calvert systems model of the effect of training on physical performance. Specifically, we use wearable sensor data from clinical trials to supplement a hybrid model created by nesting Perl's Performance-Potential model within Calvert's transfer function approach to system simulation. Contemporary simulation tools combined with wearables clinical trial data is the foundation for a more agile platform for simulation of fitness and exploration of causality between training and physical performance. This platform offers the opportunity to strategically integrate data from various wearable sensors in a fashion enabling improved support for post-injury and return to sport decision-making.