The effect of total sleep deprivation on autonomic nervous system and cortisol responses to acute stressors in healthy individuals: A systematic review.

OBJECTIVE: To synthesise the literature examining the autonomic nervous system (ANS) and cortisol responses to an acute stressor following total sleep deprivation (TSD) in healthy adult subjects. METHODS: We conducted a systematic review (CRD42022293857) following the latest PRISMA statement. We searched Medline (via Ovid), Embase (via Ovid), PsycINFO (via Ovid), CINAHL complete and Scopus databases, without year restriction, using search terms related to "sleep deprivation", "stress", "autonomic nervous system" and "cortisol". Two independent team members used pre-defined inclusion/exclusion criteria to assess eligibility and extract data. We used RoB 2 to assess the risk of bias in randomised controlled trials, and ROBINS-I for non-randomised studies. RESULTS: Sixteen studies, with 581 participants (mean age = 29 ± 12 years), were eligible for inclusion in the descriptive syntheses. Half of the studies (n = 8) were conducted in the United States of America. The most commonly used study designs were randomised crossover studies (n = 7) and randomised controlled trials (n = 5). Most studies used a single night of TSD (n = 13) which was followed by a psychological (n = 6), physical (n = 5) or psychological and physical (n = 5) acute stressor event. Heart rate (n = 8), cortisol (n = 7) and blood pressure (n =6) were the most reported outcomes, while only a single study used forearm vascular conductance and forearm blood flow. Ten studies found that TSD changed, at least, one marker of ANS or cortisol response. TSD compared with a sleep control condition increased cortisol level (n=1), systolic blood pressure (n=3), diastolic blood pressure (n=2), mean arterial pressure (n=1), and electrodermal activity (n=1) after acute stress. Also, compared with a sleep control, TSD blunted cortisol (n=2), heart rate (n=1) and systolic blood pressure (n=2) responses after acute stress. However, TSD did not change ANS or cortisol responses to acute stressors in 73 % of the total reported outcomes. Furthermore, 10 RCT studies (62.5 %) were assigned as "some concerns" and two RCT studies (12.5 %) were attributed "high" risk of bias. Additionally, one non-randomised trial was classified as "moderate" and three non-randomised trials as "serious" risk of bias. CONCLUSION: The markers of ANS and cortisol responses to acute stress after TSD in healthy individuals reveal a scarcity of consistent evidence. The included studies present enough evidence that TSD induces either blunted or exaggerated ANS or cortisol responses to laboratory stresses supporting the "bidirectional multi-system reactivity hypothesis.". It appears that a comprehensive understanding of this phenomenon still lacks robust evidence, and further research is needed to clarify these relationships.

Computational and Complex Network Modeling for Analysis of Sprinter Athletes' Performance in Track Field Tests.

Sports and exercise today are popular for both amateurs and athletes. However, we continue to seek the best ways to analyze best athlete performances and develop specific tools that may help scientists and people in general to analyze athletic achievement. Standard statistics and cause-and-effect research, when applied in isolation, typically do not answer most scientific questions. The human body is a complex holistic system exchanging data during activities, as has been shown in the emerging field of network physiology. However, the literature lacks studies regarding sports performance, running, exercise, and more specifically, sprinter athletes analyzed mathematically through complex network modeling. Here, we propose complex models to jointly analyze distinct tests and variables from track sprinter athletes in an untargeted manner. Through complex propositions, we have incorporated mathematical and computational modeling to analyze anthropometric, biomechanics, and physiological interactions in running exercise conditions. Exercise testing associated with complex network and mathematical outputs make it possible to identify which responses may be critical during running. The physiological basis, aerobic, and biomechanics variables together may play a crucial role in performance. Coaches, trainers, and runners can focus on improving specific outputs that together help toward individuals' goals. Moreover, our type of analysis can inspire the study and analysis of other complex sport scenarios.