Myotonometry is a relatively novel method used to quantify the biomechanical and viscoelastic properties (stiffness, compliance, tone, elasticity, creep, mechanical relaxation) of palpable musculotendinous structures with portable mechanical devices called myotonometers. Myotonometers obtain these measures by recording the magnitude of radial tissue deformation that occurs in response to the amount of force that is perpendicularly applied to the tissue through a device's probe. Myotonometric parameters such as stiffness and compliance have repeatedly demonstrated strong correlations with force production and muscle activation. Paradoxically, individual muscle stiffness measures have been associated with both superior athletic performance and higher incidence of injury. This suggest there may be optimal stiffness levels that promotes athletic performance while too much or too little may lead to an increased risk of injury. Numerous studies suggest that myotonometry may assist practitioners in the development of performance and rehabilitation programs that improves athletic performance, mitigates injury risk, guides therapeutic interventions, and optimizes return to activity decision making. Thus, the purpose of this narrative review is to summarize the potential utility of myotonometry as a clinical tool that assists musculoskeletal clinicians with the diagnosis, rehabilitation, and prevention of athletic injuries.

Myotonometry is a relatively novel method used to quantify the biomechanical and viscoelastic properties (stiffness, compliance, tone, elasticity, creep, and mechanical relaxation) of palpable musculotendinous structures with portable mechanical devices called myotonometers. Myotonometers obtain these measures by recording the magnitude of radial tissue deformation that occurs in response to the amount of force that is perpendicularly applied to the tissue through a device’s probe. Myotonometric parameters such as stiffness and compliance have repeatedly demonstrated strong correlations with force production and muscle activation. Paradoxically, individual muscle stiffness measures have been associated with both superior athletic performance and a higher incidence of injury. This indicates optimal stiffness levels may promote athletic performance, whereas too much or too little may lead to an increased risk of injury. Authors of numerous studies suggested that myotonometry may assist practitioners in the development of performance and rehabilitation programs that improve athletic performance, mitigate injury risk, guide therapeutic interventions, and optimize return-to-activity decision-making. Thus, the purpose of our narrative review was to summarize the potential utility of myotonometry as a clinical tool that assists musculoskeletal clinicians with the diagnosis, rehabilitation, and prevention of athletic injuries.

Introduction Low back and lower extremity injuries are responsible for the highest percentage of musculoskeletal injuries in U.S. Army soldiers. Execution of common soldier tasks as well as army combat fitness test events such as the three-repetition maximum deadlift depends on healthy functioning trunk and lower extremity musculature to minimize the risk of injury. To assist with appropriate return to duty decisions following an injury, reliable and valid tests and measures must be applied by military health care providers. Myotonometry is a noninvasive method to assess muscle stiffness, which has demonstrated significant associations with physical performance and musculoskeletal injury. The aim of this study is to determine the test–retest reliability of myotonometry in lumbar spine and thigh musculature across postures (standing and squatting) that are relevant to common soldier tasks and the maximum deadlift. Materials and Methods Repeat muscle stiffness measures were collected in 30 Baylor University Army Cadets with 1 week between each measurement. Measures were collected in the vastus lateralis (VL), biceps femoris (BF), lumbar multifidus (LM), and longissimus thoracis (LT) muscles with participants in standing and squatting positions. Intraclass correlation coefficients (ICCs3,2) were estimated, and their 95% CIs were calculated based on a mean rating, mixed-effects model. Results The test–retest reliability (ICC3,2) of the stiffness measures was good to excellent in all muscles across the standing position (ICCs: VL = 0.94 [0.87–0.97], BF = 0.97 [0.93–0.98], LM = 0.96 [0.91–0.98], LT = 0.81 [0.59–0.91]) and was excellent in all muscles across the squatting position (ICCs: VL = 0.95 [0.89–0.98], BF = 0.94 [0.87–0.97], LM = 0.96 [0.92–0.98], LT = 0.93 [0.86–0.97]). Conclusion Myotonometry can reliably acquire stiffness measures in trunk and lower extremity muscles of healthy individuals in standing and squatting postures. These results may expand the research and clinical applications of myotonometry to identify muscular deficits and track intervention effectiveness. Myotonometry should be used in future studies to investigate muscle stiffness in these body positions in populations with musculoskeletal injuries and in research investigating the performance and rehabilitative intervention effectiveness.

Urine epidermal growth factor (uEGF) is a novel biomarker utilized in assessing renal health in various renal diseases, specifically chronic kidney disease (CKD). uEGF promotes multiple intracellular pathways, stimulating renal cell growth, survival, and replication. uEGF production is activated by multiple agonists that bind to the uEGF receptor. Aerobic exercise initiates the upregulation of several of these agonists to increase the production of uEGF. Depending on the mode and intensity of aerobic exercise, uEGF agonists may activate differently in CKD populations. PURPOSE: To determine the influence of an acute bout of steady-state exercise (SSE) and high-intensity interval exercise (HIIE) on concentrations of uEGF agonists (serum insulin-like growth factor 1 (IGF-1), angiotensin II receptor type 1 (AGTR-1), and transforming growth factor beta 1 (TGF-β1)) in mid-spectrum CKD. METHODS: Twenty participants (n = 6 men; n = 14 women; age 62.0 + 9.9 yr; weight 80.9 + 16.2 kg; body fat 37.3 + 8.5% of weight; VO2max 19.4 + 4.7 ml/kg/min) completed 30 min of SSE at 65% VO2reserve or HIIE by treadmill walking (90% and 20% of VO2reserve in 3:2 min ratio) in a randomized crossover design. Both exercise conditions averaged ~ 65% VO2reserve. Blood and urine samples were obtained under standardized conditions just before, 1hr, and 24hrs after exercise. uEGF (ng/mL), serum IGF-1 (ng/mL), AGTR-1 (ng/mL), and TGF-β1 (pg/mL) responses were analyzed using 2 (condition) by 3 (sample point) repeated measures ANOVAs and Pearson Correlations. RESULTS: Serum IGF-1 and AGTR-1 increased 1hr and 24hr post-exercise in both exercise conditions; however, statistical significance was not achieved (p = 0.28 and p = 0.09). Similarly, serum TGF-β1 decreased at 24hrs in both exercise conditions but statistically remained unaltered (p = 0.42). IGF-1 was significantly correlated to uEGF in both conditions at all three-time points (p = 0.03), while AGTR-1 was significantly correlated to uEGF at 1hr in HIIE. uEGF findings were previously reported in ACSM abstract

METHODS:VO2max data from a marathon training class at the University of Minnesota collected before and after training was analyzed. Participants were healthy college aged males and females (n=55). The following averaging methods were compared: unaveraged, mid 5-of-7, 8-breath mean, and 30-second rolling average. Pairwise comparisons were done to test for differences between averaging methods. RESULTS: Pre-training means and standard deviations of VO2max (in ml.kg-1.min-1) for the respective data averaging techniques were as follows: Mid 5-of-7 (47.54±7.66), 8-breath mean (46.95±7.65), 30-second rolling (44.66±7.31) and unaveraged (57.60±10.71). Post-training VO2max values for the respective data averaging techniques were: Mid 5-of-7 (50.90±8.15), 8breath mean (50.40±7.995), 30-second rolling (48.57±7.993) and unaveraged (59.88±10.74). There were statistically significant differences between all averaging methods tested (P<0.05) in both pre-and post-training data. CONCLUSION: Studies reporting VO2max data should include the averaging method used in order to allow for interpretation and comparison between studies.