The coronavirus disease 2019 (COVID-19) pandemic resulted in physical isolation measures in many parts of the world. In Australia, nationwide restrictions included staying at home, unless seeking medical care, providing care, purchasing food, undertaking exercise, or attending work in an essential service. All undergraduate university classes transitioned to online, mostly home-based learning. We, therefore, examined the effect of isolation measures during the early phase of the COVID-19 pandemic in Australia (March/April) on diet (24-h recall) and physical activity (Active Australia Survey) patterns in third-year biomedical students. Findings were compared with students enrolled in the same course in the previous two years. In females, but not males, energy intake was ~20% greater during the pandemic, and snacking frequency and energy density of consumed snacks also increased compared with 2018 and 2019. Physical activity was impacted for both sexes during the pandemic with ~30% fewer students achieving “sufficient” levels of activity, defined by at least 150 min over at least five sessions, compared with the previous two years. In a follow-up study six to eight weeks later (14–18% response rate), during gradual easing of nationwide restrictions albeit continued gym closures and online learning, higher energy intake in females and reduced physical activity levels in both sexes persisted. These data demonstrate the health impacts of isolation measures, with the potential to affect long-term diet and activity behaviours.

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The impact of perceived wellness on a range of external load parameters, rating of perceived exertion (RPE) and external load:RPE ratios, was explored during skill-based training in Australian footballers. Fifteen training sessions involving 36 participants were analysed. Each morning before any physical training, players completed a customised perceived wellness questionnaire (sleep quality, fatigue, stress, mood and muscle soreness). Microtechnology devices provided external load (average speed, high-speed running distance, player load and player load slow). Players provided RPE using the modified Borg category-ratio 10 RPE scale. Mixed-effect linear models revealed significant effects of wellness Z-score on player load and player load slow. Effects are reported with 95% confidence limits. A wellness Z-score of -1 corresponded to a -4.9 ± 3.1 and -8.6 ± 3.9% reduction in player load and player load slow, respectively, compared to those without reduced wellness. Small significant effects were also seen in the average speed:RPE and player load slow:RPE models. A wellness Z-score of -1 corresponded to a 0.43 ± 0.38 m·min(-1) and -0.02 ± 0.01 au·min(-1) change in the average speed:RPE and player load slow:RPE ratios, respectively. Magnitude-based analysis revealed that the practical size of the effect of a pre-training perceived wellness Z-score of -1 would have on player load slow was likely negative. The results of this study suggests that monitoring pre-training perceived wellness may provide coaches with information about the intensity of output that can be expected from individual players during a training session.

The relationship between external training load and session rating of perceived exertion (s-RPE) training load and the impact that playing experience, playing position and 2-km time-trial performance had on s-RPE training load were explored. From 39 Australian Football players, 6.9 ± 4.6 training sessions were analysed, resulting in 270 samples. Microtechnology devices provided external training load (distance, average speed, high-speed running distance, player load (PL) and player loadslow (PLslow)). The external training load measures had moderate to very large associations (r, 95% CI) with s-RPE training load, average speed (0.45, 0.35-0.54), high-speed running distance (0.51, 0.42-0.59), PLslow (0.80, 0.75-0.84), PL (0.86, 0.83-0.89) and distance (0.88, 0.85-0.90). Differences were described using effect sizes (d ±95% CL). When controlling for external training load, the 4- to 5-year players had higher s-RPE training load than the 0- to 1- (0.44 ± 0.33) and 2- to 3-year players (0.51 ± 0.30), ruckmen had moderately higher s-RPE training load than midfielders (0.82 ± 0.58), and there was a 0.2% increase in s-RPE training load per 1 s increase in time-trial (95% CI: 0.07-0.34). Experience, position and time-trial performance impacted the relationship between external training load and s-RPE training load. This suggests that a given external training load may result in different internal responses between athletes, potentially leaving individuals at risk of overtraining or failing to elicit positive adaptation. It is therefore vital that coaches and trainers give consideration to these mediators of s-RPE training load.