Dual-energy X-ray absorptiometry (DEXA) was used to assess whole body and regional soft tissue mass, fat mass and lean body mass compositional changes in 68 female recruits (age 20.8 ± 1.14 years; body mass 59.5 ± 8.79 kg; stature 159.57 ± 5.53 cm) pre- and post 12-weeks of military basic training. A decrease in total body fat tissue mass (10.2%) and regional percent fat (10.9%) was measured with an increase in total lean body mass (8.7%). Of interest were the differences in the responses in the tissue composition of the arms (16.2% loss in fat mass with an 11.6% gain in lean mass), trunk (17.0% decrease in fat mass with a 10.4% increase in lean mass) and the legs (10.5% increase in lean mass but no change in fat mass). These findings show the importance of considering regional rather than whole body composition changes when assessing the effects of a training programme. Statement of Relevance: Female soldiers experienced a change in total body fat tissue (−10.2%) and lean body mass (+8.7%) after basic training; however, no significant fat mass decrease was evident in the leg region. Regional rather than whole body composition changes need to be considered when assessing the effects of a training programme.

Purpose: This study aimed to determine factors predictive of prolonged return to training (RTT) in athletes with recent SARS-CoV-2 infection. Methods: This is a cross-sectional descriptive study. Athletes not vaccinated against COVID-19 (n = 207) with confirmed SARS-CoV-2 infection (predominantly ancestral virus and beta-variant) completed an online survey detailing the following factors: demographics (age and sex), level of sport participation, type of sport, comorbidity history and preinfection training (training hours 7 d preinfection), SARS-CoV-2 symptoms (26 in 3 categories; “nose and throat,” “chest and neck,” and “whole body”), and days to RTT. Main outcomes were hazard ratios (HR, 95% confidence interval) for athletes with versus without a factor, explored in univariate and multiple models. HR < 1 was predictive of prolonged RTT (reduced % chance of RTT after symptom onset). Significance was P < 0.05. Results: Age, level of sport participation, type of sport, and history of comorbidities were not predictors of prolonged RTT. Significant predictors of prolonged RTT (univariate model) were as follows (HR, 95% confidence interval): female (0.6, 0.4–0.9; P = 0.01), reduced training in the 7 d preinfection (1.03, 1.01–1.06; P = 0.003), presence of symptoms by anatomical region (any “chest and neck” [0.6, 0.4–0.8; P = 0.004] and any “whole body” [0.6, 0.4–0.9; P = 0.025]), and several specific symptoms. Multiple models show that the greater number of symptoms in each anatomical region (adjusted for training hours in the 7 d preinfection) was associated with prolonged RTT (P < 0.05). Conclusions: Reduced preinfection training hours and the number of acute infection symptoms may predict prolonged RTT in athletes with recent SARS-CoV-2. These data can assist physicians as well as athletes/coaches in planning and guiding RTT. Future studies can explore whether these variables can be used to predict time to return to full performance and classify severity of acute respiratory infection in athletes.

Background: Personal health behaviours and lifestyle habits of health professionals influence their counselling practices related to non-communicable diseases (NCDs). There are limited data on the prevalence of unhealthy lifestyle habits among medical students and the impact of acquired health knowledge throughout the curriculum. The aim of this study was to determine and compare the prevalence of modifiable behavioural NCD risk factors of medical students in different academic years at a South African tertiary institution. Methods: A cross-sectional observational study of 532 consenting medical students was conducted. Participants completed five online questionnaires regarding lifestyle behaviours (physical activity, dietary habits, smoking, alcohol consumption and sleep). Results: Lifestyle-related risk factors with the highest prevalence were poor sleep quality (66.0%), low levels of habitual physical activity (55.8%) and low-to-moderate diet quality (54.5%). There were no differences between academic years for all risk factors measured. Over 60% of the cohort had two or more NCD risk factors and this prevalence did not differ across the degree program with the acquisition of more health knowledge. Conclusion: Medical students have a high prevalence of poor sleep quality, low levels of physical activity and low-to-moderate diet quality, which does not appear to change over the course of their academic career. Sleep hygiene, regular physical activity and healthy nutrition should be targeted in intervention programmes and be more prevalent in the medical curriculum.