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Margaret C. Morrissey, Michael R. Szymanski, Andrew J. Grundstein, and Douglas J. Casa

Intense exercise in extreme heat can increase the risk of developing exertional heat stroke (EHS). EHS is 100% survivable with appropriate care, and it is imperative that health care professionals recognize predisposing factors that may increase susceptibility to EHS. Understanding risk factors for EHS will enable clinicians to create effective prevention strategies to improve exercise heat tolerance and mitigate EHS risk. This review addresses new perspectives on risk factors for EHS that focus on hydration, heat acclimatization, medical conditions, climate change and policies, medications, and strength and conditioning sessions.

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Yasuki Sekiguchi, Courteney L. Benjamin, Samantha O. Dion, Ciara N. Manning, Jeb F. Struder, Erin E. Dierickx, Margaret C. Morrissey, Erica M. Filep, and Douglas J. Casa

The purpose of this study was to examine the effect of heat acclimation (HA) on thirst levels, sweat rate, and percentage of body mass loss (%BML), and changes in fluid intake factors throughout HA induction. Twenty-eight male endurance athletes (mean ± SD; age, 35 ± 12 years; body mass, 73.0 ± 8.9 kg; maximal oxygen consumption, 57.4 ± 6.8 ml·kg−1·min−1) completed 60 min of exercise in a euhydrated state at 58.9 ± 2.3% velocity of maximal oxygen consumption in the heat (ambient temperature, 35.0 ± 1.3 °C; relative humidity, 48.0 ± 1.3%) prior to and following HA where thirst levels, sweat rate, and %BML were measured. Then, participants performed 5 days of HA while held at hyperthermia (38.50–39.75 °C) for 60 min with fluid provided ad libitum. Sweat volume, %BML, thirst levels, and fluid intake were measured for each session. Thirst levels were significantly lower following HA (pre, 4 ± 1; post, 3 ± 1, p < .001). Sweat rate (pre, 1.76 ± 0.42 L/hr; post, 2.00 ± 0.60 L/hr, p = .039) and %BML (pre, 2.66 ± 0.53%; post, 2.98 ± 0.83%, p = .049) were significantly greater following HA. During HA, thirst levels decreased (Day 1, 4 ± 1; Day 2, 3 ± 2; Day 3, 3 ± 2; Day 4, 3 ± 1; Day 5, 3 ± 1; p < .001). However, sweat volume (Day 1, 2.34 ± 0.67 L; Day 2, 2.49 ± 0.58 L; Day 3, 2.67 ± 0.63 L; Day 4, 2.74 ± 0.61 L; Day 5, 2.74 ± 0.91 L; p = .010) and fluid intake (Day 1, 1.20 ± 0.45 L; Day 2, 1.52 ± 0.58 L; Day 3, 1.69 ± 0.63 L; Day 4, 1.65 ± 0.58 L; Day 5, 1.74 ± 0.51 L; p < .001) increased. In conclusion, thirst levels were lower following HA even though sweat rate and %BML were higher. Thirst levels decreased while sweat volume and fluid intake increased during HA induction. Thus, HA should be one of the factors to consider when planning hydration strategies.

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Jacob N. Kisiolek, Kyle A. Smith, Daniel A. Baur, Brandon D. Willingham, Margaret C. Morrissey, Samantha M. Leyh, Patrick G. Saracino, Cheri D. Mah, and Michael J. Ormsbee

The relationship between sleep duration, sleep quality, and race completion time during each stage of a 3-day ultra-endurance triathlon (stage 1: 10-km swim, 146-km cycle; stage 2: 276-km cycle; and stage 3: 84.4-km run) was investigated. Seventeen triathletes partook in sleep analysis throughout the ultra-endurance multiday triathlon using an actigraphy wristband. The participants wore the band to record objective sleep outcomes for approximately 4 days (1–2 d prerace, 3 race days, and 1 d postrace), except while racing. The total sleep time (TST; prerace: 414.1 [95.3] min, prestage 1: 392.2 [138.3] min, prestage 2: 355.6 [62.5] min, and prestage 3: 299.7 [107.0] min) significantly decreased over time (P < .05). Significant Pearson moment–product correlations were found between TST and subsequent race–day performance for race stage 1 (r = −.577; P = .019) and stage 3 (r = −.546; P = .035), with further analysis revealing that TST explained 33% and 30% of the variation in performance for stages 1 and 3, respectively. During a 3-day ultra-endurance triathlon, the TST was reduced and had a significant negative correlation to exercise performance, indicating that sleep loss was associated with slower performances. Sleep onset latency, wake episodes, and sleep efficiency did not significantly change over the course of this investigation, which may stem from the close proximity of exercise to sleep.