Paola Wood

Incorporating tests into regular training sessions permits sport-specific and convenient monitoring of athletes without disrupting team preparation or adding to testing burden. However, few studies have investigated the measurement properties of training test sets for monitoring athletes. This study assessed the reliability and usefulness of a self-regulated 6-km rowing ergometer training-based test (6SRT) as a submaximal monitoring tool in elite rowers. In a repeated measures design, 43 (13 female) rowers at a national training centre were asked to perform the 6SRT according to a ‘firm-to-hard’/‘threshold’ session category goal at 22 strokes·min ⁻¹ in three consecutive weeks. Mean ± standard deviation 6SRT time, power output (PO), exercise heart rate (HR _ex ) and rating of perceived exertion (RPE) were 22:18.0 ± 1:21.6 min:s, 70 ± 4% 2-km time trial PO, 87 ± 3% maximal HR and 16 ± 1, respectively. There were no significant between-trial effects ( p ≥ 0.101, η ² _p ≤ 0.05) and differences between paired trials were trivial (Cohen's d < 0.2) for all variables. Intraclass correlation coefficients (ICC _3,1 : 0.62–0.98) were moderate to excellent for all responses except RPE (0.33). Coefficient of variation (CV: 0.6–3.5%) and typical error (TE: 0.7–4.1%) were low for all variables except HR recovery (HRR). Time, PO and HR _ex responses, and PO:HR _ex and PO:RPE intensity ratios showed TE ≤ smallest worthwhile change (SWC). The 6SRT shows acceptable reliability and usefulness under stable field conditions to be a valuable training test set for monitoring high-level rowers when maximal tests are undesirable or laboratory tests are inaccessible.

Background. The personal health behaviours and lifestyle habits of health professionals influence their counselling practices related to non-communicable diseases (NCDs). There is limited evidence on the prevalence of modifiable NCD risk factors among medical students in South Africa (SA). Objectives. To determine the prevalence of modifiable behavioural and metabolic NCD risk factors in first-year medical students at a SA university. Methods. This cross-sectional observational study included 256 first-year students. Participants completed five online questionnaires regarding lifestyle behaviours (physical activity, dietary habits, smoking, alcohol consumption and sleep quality). NCD-related metabolic markers including body composition, blood pressure, total cholesterol and random glucose concentrations, were measured. The prevalence of risk factors was calculated using internationally accepted criteria, and sex differences were reported using one-way ANOVA or χ2 test (p<0.05). Results. The prevalence of poor sleep quality was 79.7%, followed by low-to-moderate diet quality (66.8%), low levels of physical activity (64.1%), overweight/ obesity (26.2%), current/past smoking (13.3%) and harmful use of alcohol (12.5%). Most (88.3%) participants had two or more NCD-related risk factors. The prevalence of multiple NCD risk factors differed significantly between male and female participants, with more male participants having ≥2. Conclusions. This study found that first-year medical students at a South African university have a high prevalence of lifestyle-related risk factors, specifically poor sleep quality, low levels of physical activity and low-to-moderate diet quality. Sleep hygiene, regular activity and good nutrition should be promoted, and interventions focusing on nutrition, smoking cessation and alcohol consumption could be targeted by sex.

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.

Background: Gradual‐onset Achilles tendon injuries (GoATIs) in runners are common. Data show that chronic diseases are associated with GoATI.Objective: To determine risk factors associated with a history of GoATIs among long‐distance runners (21.1 and 56 km) entering a mass community‐based running event.Methods: Online pre‐race medical screening questionnaire data from 76,654 consenting Two Ocean Marathon race entrants (71.8% entrants) were collected prospectively over 4 years (2012–2015); this cross‐sectional study is a retrospective analysis of these data. A total of 617 entrants (0.8%) reported a GoATI in the last 12 months; 60,635 entrants reported no history of any running injury (controls). Categories of factors associated with GoATI were explored (univariate and multiple regression analyses): demographics (age group, sex, race, distance), training/racing history, and history of allergy, history of chronic disease, and Composite Chronic Disease Score. Prevalence and prevalence ratios (PRs; 95% CI) are reported.Results: Factors associated with a higher prevalence of a history of GoATI (univariate analysis vs. controls) were older age (>31 years) (p < .001), male sex (PR = 1.76; p < .001), and longer race distance (56 km vs. 21.1 km) (PR = 2.06; p < .001). Independent factors associated with a history of GoATI (multiple regression) were increased years of recreational running (PR = 1.17 for every 5‐year increase, p < .001), higher Composite Chronic Disease Score (PR = 2.07 for every 2‐unit increase, p < .001), and allergy history (PR = 1.98 p < .001).Conclusion: Novel independent factors associated with a history of GoATI in distance runners were increased years of recreational running, chronic disease history, and allergy history. Runners at risk for GoATI could be targeted for injury prevention interventions. Future studies should focus on establishing a causal relationship.

Context: SARS-CoV-2 infection can affect the exercise response in athletes. Factors associated with the exercise response have not been reported. Objective: To (1) describe heart rate (HR), systolic blood pressure (SBP), and rating of perceived exertion (RPE) responses to exercise in athletes with a recent SARS-CoV-2 infection and (2) identify factors affecting exercise responses. Design: Cross-sectional, experimental study. Patients or Other Participants: Male and female athletes (age = 24.2 ± 6.3 years) with a recent (<28 days) SARS-CoV-2 infection (n = 72). Setting: A COVID-19 Recovery Clinic for athletes. Main Outcome Measure(s): Heart rate, SBP, and RPE were measured during submaximal exercise (modified Bruce protocol) at 10 to 28 days after SARS-CoV-2 symptom onset. Selected factors (demographics, sport, comorbidities, preinfection training variables, and symptoms during the acute phase of the infection) affecting the exercise response were analyzed using random coefficient (linear mixed) models. Results: Heart rate, SBP, and RPE increased progressively from rest to stage 5 of the exercise test (P = .0001). At stage 5 (10.1 metabolic equivalents), a higher HR and a higher SBP during exercise were associated with younger age (P = .0007) and increased body mass index (BMI; P = .009), respectively. Higher RPE during exercise was significantly associated with a greater number of whole-body (P = .006) and total number (P = .004) of symptoms during the acute phase of infection. Conclusions: A greater number of symptoms during the acute infection was associated with a higher RPE during exercise in athletes at 10 to 28 days after SARS-CoV-2 infection. We recommend measuring RPE during the first exercise challenge after infection, as this may indicate disease severity and be valuable for tracking progress, recovery, and return to sport.

Higher exercise heart rate (HR) and prolonged return-to-sport in athletes with SARS-CoV-2 infection are described, but the cardiovascular response to exercise during recovery is not understood. This prospective, cohort, experimental study with repeated measures evaluated the cardiovascular response to exercise over 16 weeks in athletes recovering from SARS-CoV-2 infection. Athletes (n = 82) completed 2-5 repeat assessments at regulated intervals over 16 weeks post-SARS-CoV-2 infection. Data from 287 assessments (submaximal exercise tests; Modified Bruce protocol) are included. HR (bpm), systolic blood pressure (SBP) (mmHg) and rating of perceived exertion (RPE) (Borg scale 6-20) were measured. Rates of change in HR, SBP and RPE over time are reported. Submaximal exercise HR, SBP and RPE decreased significantly over 16 weeks (p < 0.01). There was a steeper rate of decline for HR and RPE ≤30 days compared to >30 days after SARS-CoV-2 infection: HR at Stage 3: ≤30 days -0.53 (0.01); >30 days -0.06 (0.02) and Stage 5: ≤30 days -0.77 (0.12); >30 days -0.12 (0.02); RPE at Stage 3: ≤30 days -0.09 (0.02); >30 days -0.01 (0.0002) and Stage 5: ≤30 days -0.13 (0.02); >30 days -0.02 (0.004). The findings provide clinical recommendation for exercise prescription and monitoring RPE in response to exercise post-SARS-CoV-2 infection and contribute to the clinical understanding of recovery which can help manage athlete expectations.

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.