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This study aimed to investigate the relationship between Leg Dynamometer (LD) with squat and deadlift one repetition maximum (1RM) test scores. Participant of this study involved 50 male university athletes who were recreationally active (aged between 21-25 years old). Participants performed LD and one repetition maximum for squat and deadlift test. The score of LD were tested for relationship with the 1RM squat and deadlift. Pearson correlation were conducted to test the relationship. Results showed that there was a significant correlation at high level (r = 0.88, p = 0.000, p <0.001) between the LD test score and the squat while a very high correlation was obtained between the LD test score and the deadlift (r = 0.98, p = 0.000, p <0.001). As the conclusion, LD test that is simple and easy to be conducted produce similar result as the squat and deadlift 1RM test and can be used as a simple assessment of muscular strength among university athletes.

Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing of multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, thereby identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber regeneration differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.

Objectives(1) To develop reference values for health-related fitness in European children and adolescents aged 6–18 years that are the foundation for the web-based, open-access and multilanguage fitness platform (FitBack); (2) to provide comparisons across European countries.MethodsThis study builds on a previous large fitness reference study in European youth by (1) widening the age demographic, (2) identifying the most recent and representative country-level data and (3) including national data from existing fitness surveillance and monitoring systems. We used the Assessing Levels of PHysical Activity and fitness at population level (ALPHA) test battery as it comprises tests with the highest test–retest reliability, criterion/construct validity and health-related predictive validity: the 20 m shuttle run (cardiorespiratory fitness); handgrip strength and standing long jump (muscular strength); and body height, body mass, body mass index and waist circumference (anthropometry). Percentile values were obtained using the generalised additive models for location, scale and shape method.ResultsA total of 7 966 693 test results from 34 countries (106 datasets) were used to develop sex-specific and age-specific percentile values. In addition, country-level rankings based on mean percentiles are provided for each fitness test, as well as an overall fitness ranking. Finally, an interactive fitness platform, including individual and group reporting and European fitness maps, is provided and freely available online (www.fitbackeurope.eu).ConclusionThis study discusses the major implications of fitness assessment in youth from health, educational and sport perspectives, and how the FitBack reference values and interactive web-based platform contribute to it. Fitness testing can be conducted in school and/or sport settings, and the interpreted results be integrated in the healthcare systems across Europe.

Whereas a variety of pre-exercise activities have been incorporated as part of a “warm-up” prior to work, combat, and athletic activities for millennia, the inclusion of static stretching (SS) within a warm-up has lost favor in the last 25 years. Research emphasized the possibility of SS-induced impairments in subsequent performance following prolonged stretching without proper dynamic warm-up activities. Proposed mechanisms underlying stretch-induced deficits include both neural (i.e., decreased voluntary activation, persistent inward current effects on motoneuron excitability) and morphological (i.e., changes in the force–length relationship, decreased Ca2+ sensitivity, alterations in parallel elastic component) factors. Psychological influences such as a mental energy deficit and nocebo effects could also adversely affect performance. However, significant practical limitations exist within published studies, e.g., long-stretching durations, stretching exercises with little task specificity, lack of warm-up before/after stretching, testing performed immediately after stretch completion, and risk of investigator and participant bias. Recent research indicates that appropriate durations of static stretching performed within a full warm-up (i.e., aerobic activities before and task-specific dynamic stretching and intense physical activities after SS) have trivial effects on subsequent performance with some evidence of improved force output at longer muscle lengths. For conditions in which muscular force production is compromised by stretching, knowledge of the underlying mechanisms would aid development of mitigation strategies. However, these mechanisms are yet to be perfectly defined. More information is needed to better understand both the warm-up components and mechanisms that contribute to performance enhancements or impairments when SS is incorporated within a pre-activity warm-up.

A suit-type wearable robot (STWR) is a new type of soft wearable robot (SWR) that can be worn easily anywhere and anytime to assist the muscular strength of wearers because it can be worn like normal clothes and is comfortable to wear even with no power supply. This paper proposes an STWR, in which a shape-memory-alloy-based fabric muscle (SFM) is used as the actuator. The STWR, which weighs less than 1 kg, has a simple structure, with the following components: SFMs, wire encoders for measuring the contraction length of the SFMs, and BOA that fix the actuators on the forearms. In this study, a position controller for the SFM using the wire encoder was developed, and a prototype STWR was fabricated using this position controller. Moreover, by putting the STWR on a mannequin, step-response experiments were performed in which the arms of the mannequin lifted barbells weighing 2 kg and 4 kg to a certain target position. A fast response of moving to the target position in less than 1 s was observed in all steps except for the initial heating step for the 2 kg barbell. The response speed of the SFM was noticeably slower for the 4 kg barbell compared to that for the 2 kg barbell; it moved to the target position in approximately 3 s in all the steps except for the initial heating step. The SFM-applied STWR could overcome the limitations of conventional robots in terms of weight and inconvenience, thereby demonstrating the application potential of STWRs.

Estimation of muscle forces during motion involves solving an indeterminate problem (more unknown muscle forces than joint moment constraints), frequently via optimization methods. When the dynamics of muscle activation and contraction are modeled for consistency with muscle physiology, the resulting optimization problem is dynamic and challenging to solve. This study sought to identify a robust and computationally efficient formulation for solving these dynamic optimization problems using direct collocation optimal control methods. Four problem formulations were investigated for walking based on both a two and three dimensional model. Formulations differed in the use of either an explicit or implicit representation of contraction dynamics with either muscle length or tendon force as a state variable. The implicit representations introduced additional controls defined as the time derivatives of the states, allowing the nonlinear equations describing contraction dynamics to be imposed as algebraic path constraints, simplifying their evaluation. Problem formulation affected computational speed and robustness to the initial guess. The formulation that used explicit contraction dynamics with muscle length as a state failed to converge in most cases. In contrast, the two formulations that used implicit contraction dynamics converged to an optimal solution in all cases for all initial guesses, with tendon force as a state generally being the fastest. Future work should focus on comparing the present approach to other approaches for computing muscle forces. The present approach lacks some of the major limitations of established methods such as static optimization and computed muscle control while remaining computationally efficient.

No previous study has investigated the prevalence and risk factors for primary sarcopenia in outpatient setting. This study aims to evaluate the prevalence and factors associated with primary sarcopenia in outpatient elderly. Additionally, we compared the severity of sarcopenia based on the 2014 and 2019 Asian Working Group for Sarcopenia (AWGS) criteria. This cross-sectional study was performed in 330 subjects aged over 60 years in an outpatient setting. The muscle strength, muscle performance and muscle mass were assessed using the handheld dynamometer, 6-m gait speed, and bioelectrical impedance analysis, respectively. The prevalence of sarcopenia was 10% as per the 2014 and 2019 AWGS criteria. The development of sarcopenia was positively correlated with the age with an odds ratio (OR) of 6.87 [95% confidence interval (CI) 1.63–28.88] in the middle-old group (70–79 years), and 13.71 (95%CI 3.66–51.41; p  = 0.009) in the very old group (≥ 80 years). Prefrailty and low physical activity were significantly associated with sarcopenia with an OR of 4.75 (95%CI 1.90—11.89) in prefrailty, 15.35 (95%CI 1.69–139.47) in the middle activity group, and 17.99 (95%CI 1.95–165.73) in the lowest activity group. In conclusion, primary sarcopenia was found in one-tenth of outpatient elderly. Age, prefrailty, and low activity were independent factors associated with sarcopenia.

This study aimed to assess the validity and functional relevance of a standardized procedure to assess lower limb muscle power by means of the 30-s sit-to-stand (STS) test when compared to leg extension power (LEP), traditional STS performance and handgrip strength. A total of 628 community-dwelling older subjects (60–93 years) from the Copenhagen Sarcopenia Study were included. Physical performance was assessed by the 30-s STS and 10-m maximal gait speed tests. Handgrip strength and LEP were recorded by a hand-held dynamometer and the Nottingham power rig, respectively. STS muscle power was calculated using the subjects’ body mass and height, chair height and the number of repetitions completed in the 30-s STS test. We found a small albeit significant difference between LEP and unilateral STS power in older men (245.5 ± 88.8 vs. 223.4 ± 81.4 W; ES = 0.26; p  < 0.05), but not in older women (135.9 ± 51.9 vs. 138.5 ± 49.6 W; ES = 0.05; p  > 0.05). Notably, a large positive correlation was observed between both measures (r = 0.75; p  < 0.001). Relative STS power was more strongly related with maximal gait speed than handgrip strength, repetition-based STS performance and relative LEP after adjusting for age (r = 0.53 vs 0.35–0.45; p  < 0.05). In conclusion, STS power obtained from the 30-s STS test appeared to provide a valid measure of bilateral lower limb power and was more strongly related with physical performance than maximal handgrip strength, repetition-based STS performance and LEP.

Understanding how and to what extent forces applied to the mandible by the masticatory muscles influence its form, is of considerable importance from clinical, anthropological and evolutionary perspectives. This study investigates these questions. Head CT scans of 382 adults were utilized to measure masseter and temporalis muscle cross-sectional areas (CSA) as a surrogate for muscle force, and 17 mandibular anthropometric measurements. Sixty-two mandibles of young individuals (20–40 years) whose scans were without artefacts (e.g., due to tooth filling) were segmented and landmarked for geometric morphometric analysis. The association between shape and muscle CSA (controlled for size) was assessed using two-block partial least squares analysis. Correlations were computed between mandibular variables and muscle CSAs (all controlled for size). A significant association was found between mandibular shape and muscle CSAs, i.e. larger CSAs are associated with a wider more trapezoidal ramus, more massive coronoid, more rectangular body and a more curved basal arch. Linear measurements yielded low correlations with muscle CSAs. In conclusion, this study demonstrates an association between mandibular muscle force and mandibular shape, which is not as readily identified from linear measurements. Retrodiction of masticatory muscle force and so of mandibular loading is therefore best based on overall mandibular shape.

Static optimization is commonly employed in musculoskeletal modeling to estimate muscle and joint loading; however, the ability of this approach to predict antagonist muscle activity at the shoulder is poorly understood. Antagonist muscles, which contribute negatively to a net joint moment, are known to be important for maintaining glenohumeral joint stability. This study aimed to compare muscle and joint force predictions from a subject-specific neuromusculoskeletal model of the shoulder driven entirely by measured muscle electromyography (EMG) data with those from a musculoskeletal model employing static optimization. Four healthy adults performed six sub-maximal upper-limb contractions including shoulder abduction, adduction, flexion, extension, internal rotation and external rotation. EMG data were simultaneously measured from 16 shoulder muscles using surface and intramuscular electrodes, and joint motion evaluated using video motion analysis. Muscle and joint forces were calculated using both a calibrated EMG-driven neuromusculoskeletal modeling framework, and musculoskeletal model simulations that employed static optimization. The EMG-driven model predicted antagonistic muscle function for pectoralis major, latissimus dorsi and teres major during abduction and flexion; supraspinatus during adduction; middle deltoid during extension; and subscapularis, pectoralis major and latissimus dorsi during external rotation. In contrast, static optimization neural solutions showed little or no recruitment of these muscles, and preferentially activated agonistic prime movers with large moment arms. As a consequence, glenohumeral joint force calculations varied substantially between models. The findings suggest that static optimization may under-estimate the activity of muscle antagonists, and therefore, their contribution to glenohumeral joint stability.