Outdoor exercise performance in ambient heat: time to overcome challenging factors?

Small changes in thermal conditions hinder marathon running performance in the tropics

We examined marathon performance of the same group of runners in relation to small changes in dry bulb temperature (T_db) and wet bulb temperature (T_wb) across 3 consecutive y, and investigated whether performance was poorer during an evening marathon compared with morning marathons. Marathon results were obtained from the 2017, 2018, and 2019 Standard Chartered Singapore Marathons. T_db, T_wb, T_d, relative humidity, and absolute humidity were gathered for each marathon. K-means clustering and linear regressions were performed on 610 runners who participated in all three marathons. Analysis of the 610 runners’ marathon performance was contrasted with T_db and T_wb. Linear regressions were also performed on 190 runners filtered by percentile, yielding similar results. For clusters with similar T_db from all runners K-means clustering, an increase in mean T_wb by 1.5°C coincided with an increase in finishing time by 559 s (9.3 min) (p < 0.033). T_wb hinders marathon performance more than T_db, with each percentage rise in T_db and T_wb resulting in an increase in net time by 7.6% and 39.1%, respectively (p < 0.025). Male and female runners’ response to T_db and T_wb changes were similar (overlap in 95% confidence intervals for the respective regression coefficients). In conclusion, small variations in environmental parameters affected marathon performance, with T_wb impairing marathon performance more than T_db. Marathon performance was likely better in the morning than evening, possibly due to time of day differences, along with unfavorable T_db that superseded training effects and the effects of lower T_wb.

An investigation into environmental variables influencing post-race exertional heat illness in thoroughbred racehorses in temperate eastern Australia

Exertional heat illness (EHI) is a syndrome that occurs when metabolic heat production from muscular contraction exceeds the rate at which it can be dissipated. Core body temperature rises to critical levels, causing hyperthermia and central nervous system dysfunction. Best practice for the prevention of EHI centres around early detection, rapid response and aggressive cooling. Advance planning enables risk mitigation measures. The more that is known about EHI in horses, the better prepared those in the positions of responsibility can be to anticipate events in which the risk of EHI may be elevated. This prospective, observational study investigated environmental risk factors associated with the occurrence of EHI. From 2014 to 2018, the number of horses exhibiting the symptoms of post-race EHI was recorded at 73 convenience sampled race meetings. Of the 4809 starters, the signs of EHI were recorded for 457. Thermal environmental data were measured and included ambient temperature, radiant heat, vapor pressure (humidity) and wind speed (WS). Mixed linear regression models were computed to assess the associations between the occurrence and incidence of post-race EHI and the four thermal environmental variables. The analysis showed that vapor pressure and WS had the largest effects on the occurrence of post-race EHI. The major limitations were that the race meetings selected were convenience sampled according to attendance by the first author and individual horse data were not available. EHI is influenced by a complex interaction of independently acting environmental variables, but warm, windless, humid days are most likely to result in the cases of EHI.

Extreme Terrestrial Environments: Life in Thermal Stress and Hypoxia. A Narrative Review

Living, working and exercising in extreme terrestrial environments are challenging tasks even for healthy humans of the modern new age. The issue is not just survival in remote environments but rather the achievement of optimal performance in everyday life, occupation, and sports. Various adaptive biological processes can take place to cope with the specific stressors of extreme terrestrial environments like cold, heat, and hypoxia (high altitude). This review provides an overview of the physiological and morphological aspects of adaptive responses in these environmental stressors at the level of organs, tissues, and cells. Furthermore, adjustments existing in native people living in such extreme conditions on the earth as well as acute adaptive responses in newcomers are discussed. These insights into general adaptability of humans are complemented by outcomes of specific acclimatization/acclimation studies adding important information how to cope appropriately with extreme environmental temperatures and hypoxia.

Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?

Among the tools proposed to assess the athlete's “fatigue,” the analysis of heart rate variability (HRV) provides an indirect evaluation of the settings of autonomic control of heart activity. HRV analysis is performed through assessment of time-domain indices, the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (RMSSD) measured during short (5 min) recordings in supine position upon awakening in the morning and particularly the logarithm of RMSSD (LnRMSSD) has been proposed as the most useful resting HRV indicator. However, if RMSSD can help the practitioner to identify a global “fatigue” level, it does not allow discriminating different types of fatigue. Recent results using spectral HRV analysis highlighted firstly that HRV profiles assessed in supine and standing positions are independent and complementary; and secondly that using these postural profiles allows the clustering of distinct sub-categories of “fatigue.” Since, cardiovascular control settings are different in standing and lying posture, using the HRV figures of both postures to cluster fatigue state embeds information on the dynamics of control responses. Such, HRV spectral analysis appears more sensitive and enlightening than time-domain HRV indices. The wealthier information provided by this spectral analysis should improve the monitoring of the adaptive training-recovery process in athletes.