BackgroundHand 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.AimsTo compare the contribution of age, sex, height, weight, and forearm circumference (FCF) to variations in HGS based on a large-scale sample.MethodsWe 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.ResultsAnalysis 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).ConclusionHGS 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 registrationhttp://www.chictr.org.cn/showproj.aspx?proj=165914, identifier ChiCTR2200058586.
This paper presents a novel scissoring composite actuator which can successfully degenerate longitudinal vibration into scissoring vibration at actuator tips for potential medical applications. The proposed actuator consists of back mass, multilayer piezoceramic stack, front mass with netted pre-stress structure and beam. The actuator is driven by only a small axially poled multilayer piezoceramic stack. Moreover, a special symmetrical grooved structure is designed at the beam end to convert longitudinal driving vibration into opposite bending vibrations at the beam tip, resulting in scissoring-type composite vibration. The converted scissoring vibration concentrates on the beam tip without any deflection along other parts, which is highly desirable for narrow-spaced medical operations. The proposed design principle is demonstrated by structural analysis and verified by different types of finite element modeling (FEM) simulations, including Eigen frequency analysis, harmonic analysis, and transient analysis. The results reveal the design effectiveness of the actuator’s structure on scissoring-type mode excitation. Finally, a prototype of the proposed piezoelectric actuator is fabricated and tested, rendering superior performance and highly reliable mode conversion. The proposed actuator exhibits potential for advanced medical applications.
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.
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