Study design and ethical approval
This cross-sectional study protocol was approved by the institutional ethics committee of Shinshu University (approval number: 3722). This study was conducted in accordance with the Declaration of Helsinki and was revised in 2013. All participants were informed of the study’s aim, procedures, and potential risks and signed informed consent forms before their participation.
Healthy adults working as medical staff at Kakeyu-Misayama Rehabilitation Center, Kakeyu Hospital, Japan, were conveniently recruited via a displayed poster between July 2017 and November 2017. The inclusion criteria were as follows: 1) age ≥20 and <40 years; 2) no history of injury to the spine or lower limbs; 3) no history of neurological diseases; 4) no pain at rest or during exercise; 5) not pregnant or possibly pregnant; and 6) no cardiac pacemaker.
First the body composition was measured using BIA, and then the 1RM was measured. Both the assessments were conducted at a fixed time on the same day. Participants were instructed to refrain from eating or drinking large amounts of water 4 h before the measurement and consuming alcohol 8 h before the measurement. Participants were also required to not undertake any intense exercise for 8 h before the measurements.
BIA measurements were performed using a body composition analyzer (Inbody 430, Biospace, Korea) equipped with a terra-polar eight-point tactile electrode system. It used three multi-frequencies (5, 50, and 250 kHz) to measure the impedance of the subject’s appendicular muscles and trunk. The measurement by multi-frequencies was considered a better method for assessing muscle function than single-frequency measurement . After the participants wiped their soles off, they stood on the analyzer’s platform grasping the handles with both hands according to the manufacturer’s guidance. The measurements took approximately 40 s to complete. The analyzer calculated the values for absolute muscle and fat mass, body fat percentage, and segmental muscle mass (upper and lower limbs of both sides and trunk). We used dominant leg SMM and SMI that was the sum of appendicular SMM obtained by dividing the subjects’ squared height (kg/m2) for the analyses because SMI is reportedly correlated with muscle function in people with sarcopenia .
1RM measurement was performed using the subject’s dominant leg with an LP resistance training machine (HUR, Finland). This resistance training machine allowed the participants to lift the loads unilaterally. The 1RM procedure was performed according to the American College of Sports Medicine guidelines . All participants underwent a 5 min warm-up session using an ergo cycle bike before the measurements. The participants sat on the LP machine with their hip and knee joints fixed at approximately 90 °, and the pelvis was stabilized by the belt. The participants were also required to hold handgrips placed on the side of the machine seat with each hand. The familiarization session with LP with light resistance for 8–10 repetitions was performed using perceived 50%1RM. The measurements were started at loads of 80%1RM. The load in the measurement was progressively changed by 3–10 kg until the participants could not lift the loads. The goal was to complete a maximal lift in five attempts, and 3–5 min of rest were provided between sets. All tests were performed by the same evaluator in the same order.
The sample size analysis was conducted using G* Power software 220.127.116.11 (Heinrich Heine University, Dusseldorf, Germany). Because moderate to strong correlations between measurements obtained using BIA and isometric muscle strength of the lower limbs have been previously reported [17-19], we set the alpha to 0.05, power to 0.8, and effect size to 0.5 and calculated the required minimum sample size to be n=26. The participants’ characteristics are presented as mean ± standard deviation (SD). After confirming the normality of the obtained data using the Shapiro–Wilk test, we identified correlations between each of the variables obtained from the body composition analyzer and the 1RM for LP by calculating Pearson’s product-moment correlation coefficients. To create the 1RM prediction models, a simple linear regression analysis was performed using the variables obtained using BIA as independent variables. To evaluate the models’ accuracy, R2 and SEE parameters were considered. All analyses were performed using SPSS version 25 (International Business Machine Corp., Armonk, NY, USA). Any P-values <0.05 were considered statistically significant.