Hand grip strength should be normalized by weight not height for eliminating the influence of individual differences: Findings from a cross-sectional study of 1,511 healthy undergraduates.

Background: Hand grip strength (HGS) is a fast, useful, and inexpensive outcome predictor of nutritional status and muscular function assessment. Numerous demographic and anthropometric factors were reported to be associated with HGS, while which one or several factors produce greater variations in HGS has not been discussed in detail. This is important for answering how should HGS be normalized for eliminating the influence of individual differences in clinical practice. Aims: To compare the contribution of age, sex, height, weight, and forearm circumference (FCF) to variations in HGS based on a large-scale sample. Methods: We enrolled 1,511 healthy undergraduate students aged 18-23 years. Age, weight, height, and sex were obtained. HGS was measured using a digital hand dynamometer, and FCF was measured at the point of greatest circumference using a soft ruler in both hands. Pearson's or Spearman's correlation coefficients were calculated with data of women and men separated and mixed for comparison. Partial correlation analysis and multivariate linear regression were used to compare the effect of variables on HGS. Results: Analysis results confirmed the correlates of higher HGS include higher height, heavier weight, being men and dominant hand, and larger FCF. The correlation between HGS and FCF was the highest, and the bivariate correlation coefficient between weight and HGS was largerata of women and men were mixed, than that between height and HGS. When data of women and men were mixed, there were moderate correlations between HGS and height and weight (r = 0.633∼0.682). However, when data were separated, there were weak correlations (r = 0.246∼0.391). Notably, partial correlation analysis revealed no significant correlation between height and HGS after eliminating the weight effect, while the correlation between weight and HGS was still significant after eliminating the height effect. Multivariate linear regression analyses revealed sex was the most significant contributor to the variation in HGS (Beta = -0.541 and -0.527), followed by weight (Beta = 0.243 and 0.261) and height (Beta = 0.102 and 0.103). Conclusion: HGS and FCF reference values of healthy college students were provided. Weight was more correlate with hand grip strength, at least among the healthy undergraduates. Clinical trial registration: http://www.chictr.org.cn/showproj.aspx?proj=165914, identifier ChiCTR2200058586.

Monitoring the training dose and acute fatigue response during elbow flexor resistance training using a custom-made resistance band.

BACKGROUND: Home-based resistance training offers an alternative to traditional, hospital-based or rehabilitation center-based resistance training and has attracted much attention recently. However, without the supervision of a therapist or the assistance of an exercise monitoring system, one of the biggest challenges of home-based resistance training is that the therapist may not know if the patient has performed the exercise as prescribed. A lack of objective measurements limits the ability of researchers to evaluate the outcome of exercise interventions and choose suitable training doses. OBJECTIVE: To create an automated and objective method for segmenting resistance force data into contraction phase-specific segments and calculate the repetition number and time-under-tension (TUT) during elbow flexor resistance training. A pilot study was conducted to evaluate the performance of the segmentation algorithm and to show the capability of the system in monitoring the compliance of patients to a prescribed training program in a practical resistance training setting. METHODS: Six subjects (three male and three female) volunteered to participate in a fatigue and recovery experiment (5 min intermittent submaximal contraction (ISC); 1 min rest; 2 min ISC). A custom-made resistance band was used to help subjects perform biceps curl resistance exercises and the resistance was recorded through a load cell. The maximum and minimum values of the force-derivative were obtained as distinguishing features and a segmentation algorithm was proposed to divide the biceps curl cycle into concentric, eccentric and isometric contraction, and rest phases. Two assessors, who were unfamiliar with the study, were recruited to manually pick the visually observed cut-off point between two contraction phases and the TUT was calculated and compared to evaluate performance of the segmentation algorithm. RESULTS: The segmentation algorithm was programmatically implemented and the repetition number and contraction-phase specific TUT were calculated. During isometric, the average TUT (3.75 ± 0.62 s) was longer than the prescribed 3 s, indicating that most subjects did not perform the exercise as prescribed. There was a good TUT agreement and contraction segment agreement between the proposed algorithm and the assessors. CONCLUSION: The good agreement in TUT between the proposed algorithm and the assessors indicates that the proposed algorithm can correctly segment the contraction into contraction phase-specific parts, thereby providing clinicians and researchers with an automated and objective method for quantifying home-based elbow flexor resistance training. The instrument is easy to use and cheap, and the segmentation algorithm is programmatically implemented, indicating good application prospect of the method in a practical setting.

Integrated System for Monitoring Muscular States during Elbow Flexor Resistance Training in Bedridden Patients.

To improve or maintain the physical function of bedridden patients, appropriate and effective exercises are required during the patient's bed rest. Resistance training (RT) is an effective exercise for improving the physical function of bedridden patients, and the improvement of the physical function is caused by mechanical stimuli associated with RT. Currently, the measured mechanical stimuli are external variables which represent the synthetic effect of multiple muscles and body movements. Important features of stimuli experienced by muscles are of crucial importance in explaining muscular strength and power adaptation. This study describes an integrated system for assessing muscular states during elbow flexor resistance training in bedridden patients, and some experiments were carried out to test and evaluate this system. The integrated system incorporates an elbow joint angle estimation model (EJAEM), a musculoskeletal model (MSM), and a muscle-tendon model. The EJAEM enables real-time interaction between patient and MSM. The MSM is a three-dimensional model of the upper extremity, including major muscles that make up the elbow flexor and extensor, and was built based on public data. One set of concentric and eccentric contraction was performed by a healthy subject, and the results of the calculations were analyzed to show important features of mechanical stimuli experienced by muscles during the training. The integrated system provides a considerable method to monitor the body-level and muscle-level mechanical stimuli during elbow flexor resistance training in bedridden patients.