Longitudinal Study of Body Composition and Energy Expenditure in Overweight or Obese Young Adults

Physical Inactivity as an Early Sign of Frailty in Young Adult Survivors of Childhood Acute Lymphoblastic Leukemia

Young adult survivors of childhood leukemia have been reported with increased likelihood of age-related comorbidities compared with the general population. We compared the prevalence of frailty in young adult survivors of childhood acute lymphoblastic leukemia (n=58, median age=23 y, median survival time=18 y) with age-matched and sex-matched controls without history of cancer. Frailty phenotypes were determined using Fried frailty model. Association between frailty status and cardiometabolic conditions, systemic inflammation, and T-cell immunophenotype changes were also examined. Frailty and prefrailty were more common among survivors compared with controls (58.6% vs. 34.5%, P =0.019). Physical inactivity (39.7%) was the most frequently observed frailty criterion among the survivors. Prevalence of cardiometabolic conditions was comparable between the robust and frail/prefrail survivors. Robust survivors had a higher level of T-cell activation than the prefrail/frail survivors ( P <0.05), but no significant difference was observed for systemic inflammatory markers (IL-6 and C-reactive protein) and percentage of terminally differentiated T cells. Signs of frailty, especially physical inactivity, was detected in childhood acute lymphoblastic leukemia survivors early in their third decade of life. Survivors who were prefrail/frail also had altered T-cell activation; however, the role of such changes in T-cell phenotype in the etiology of frailty warrant further investigation.

Ultrastretchable Organogel/Silicone Fiber-Helical Sensors for Self-Powered Implantable Ligament Strain Monitoring

Implantable sensors with the abilities of real-time healthcare monitoring and auxiliary training are important for exercise-induced or disease-induced muscle and ligament injuries. However, some of these implantable sensors have some shortcomings, such as requiring an external power supply or poor flexibility and stability. Herein, an organogel/silicone fiber-helical sensor based on a triboelectric nanogenerator (OFS-TENG) is developed for power-free and sutureable implantation ligament strain monitoring. The OFS-TENG with high stability and ultrastretchability is composed of an organogel fiber and a silicone fiber intertwined with a double helix structure. The organogel fiber possesses the merits of rapid preparation (15 s), good transparency (>95%), high stretchability (600%), and favorable stability (over 6 months). The OFS-TENG is successfully implanted on the patellar ligament of the rabbit knee for the real-time monitoring of knee ligament stretch and muscle stress, which is expected to provide a solution for real-time diagnosis of muscle and ligament injuries. The prepared self-powered OFS-TENG can monitor data on human muscles and ligaments in real-time.

The Effect of Resistance Training in Healthy Adults on Body Fat Percentage, Fat Mass and Visceral Fat: A Systematic Review and Meta-Analysis

BACKGROUND

Resistance training is the gold standard exercise mode for accrual of lean muscle mass, but the isolated effect of resistance training on body fat is unknown.

OBJECTIVES

This systematic review and meta-analysis evaluated resistance training for body composition outcomes in healthy adults. Our primary outcome was body fat percentage; secondary outcomes were body fat mass and visceral fat.

DESIGN

Systematic review with meta-analysis.

DATA SOURCES

We searched five electronic databases up to January 2021.

ELIGIBILITY CRITERIA

We included randomised trials that compared full-body resistance training for at least 4 weeks to no-exercise control in healthy adults.

ANALYSIS

We assessed study quality with the TESTEX tool and conducted a random-effects meta-analysis, with a subgroup analysis based on measurement type (scan or non-scan) and sex (male or female), and a meta-regression for volume of resistance training and training components.

RESULTS

From 11,981 records, we included 58 studies in the review, with 54 providing data for a meta-analysis. Mean study quality was 9/15 (range 6-15). Compared to the control, resistance training reduced body fat percentage by - 1.46% (95% confidence interval - 1.78 to - 1.14, p < 0.0001), body fat mass by - 0.55 kg (95% confidence interval - 0.75 to - 0.34, p < 0.0001) and visceral fat by a standardised mean difference of - 0.49 (95% confidence interval - 0.87 to - 0.11, p = 0.0114). Measurement type was a significant moderator in body fat percentage and body fat mass, but sex was not. Training volume and training components were not associated with effect size. Resistance training reduces body fat percentage, body fat mass and visceral fat in healthy adults.

STUDY REGISTRATION

osf.io/hsk32.

Differences in Fat Mass Estimation Formulas in Physically Active Adult Population and Relationship with Sums of Skinfolds

Changes in body composition and specifically fat mass, has traditionally been used as a way to monitor the changes produced by nutrition and training. The objective of the present study was to analyse the differences between the formulas used to estimate fat mass and to establish the existing relationship with the body mass index and sums of skinfolds measurement in kinanthropometry. A total of 2458 active adults participated in the study. Body mass index (BMI) and skinfolds were measured, and the Kerr, Durnin-Womersley, Faulkner and Carter equations were used to assess fat mass. Significant differences were found between all the formulas for the percentage of fat mass, ranging from 10.70 ± 2.48 to 28.43 ± 5.99% (p < 0.001) and fat mass from 7.56 ± 2.13 to 19.89 ± 4.24 kg (p < 0.001). The correlations among sums of skinfolds and the different equations were positive, high and significant in all the cases (r from 0.705 to 0.926 p < 0.001), unlike in the case of BMI, were the correlation was lower and both positive or negative (r from -0.271 to 0.719; p < 0.001). In conclusion, there were differences between all the formulas used to estimate fat mass; thus, for the evaluation of fat mass with kinanthropometry of an active adult, the use of the same formula is recommended on all occasions when the results are going to be compared or when an athlete is compared with a reference.