Linking Multi-Night Sleep Heart Rate to Vigilance: Insights from Antarctic Field Research

Linking Multi-Night Sleep Heart Rate to Vigilance: Insights from Antarctic Field Research | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Linking Multi-Night Sleep Heart Rate to Vigilance: Insights from Antarctic Field Research Lucie Ráčková, Marek Brabec, Michael Klinka, Miroslav Králík, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7768957/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigated the associations between sleep heart rate, sleep duration, and Psychomotor Vigilance Task performance using repeated measures data collected during an Antarctic expedition. Data were collected without inducing sleep restriction, reflecting naturalistic sleep patterns. Results revealed that a higher mean sleep heart rate on the preceding night was significantly associated with slower reaction time on the PVT. In contrast, association with sleep duration did not reach statistical significance. Interestingly, being a first-time participant was a significant predictor of faster reaction times, whereas age was not, suggesting that the observed performance differences are more likely attributable to an adaptive response to environmental novelty rather than age-related cognitive variation. Night-to-night variations in sleep parameters were not significant predictors. These findings suggest that mean sleep heart rate may serve as a more effective metric than sleep duration for predicting vigilant performance in real-life contexts without induced sleep deprivation. Despite limitations of a modest sample size and data from a single expedition, this study offers novel insights into optimizing human performance in extreme environments and informing risk mitigation strategies. Future research should replicate these findings across diverse contexts and integrate longitudinal physiological and psychological assessments for a comprehensive understanding of performance variability. Biological sciences/Neuroscience Biological sciences/Physiology Biological sciences/Psychology Social science/Psychology sleep heart rate sleep duration vigilance performance Antarctica reaction time Figures Figure 1 Figure 2 Introduction Vigilant attention refers to the capacity to stay focused and react promptly to stimuli over extended periods 1 – 3 . It is vital in numerous real-world situations, especially when interacting with advanced automated systems that require operators to monitor displays for long durations. Vigilant attention is also critical in high-stakes environments where a timely and precise response is essential (e.g., in all transportation modes, sports, medical monitoring and surgery, military, remote operation of unmanned vehicles, astronautics, nuclear plants, security-related tasks, and a wide range of industrial tasks) 4 – 7 . Examining the factors that affect and predict vigilance may contribute to the creation of practical tools designed to improve performance, enhance safety, minimize errors, support resource management, select appropriate personnel, and elevate overall results in critical tasks that demand sustained attention 4 , 8 . Previous studies have used various psychometric assessments for vigilance performance prediction 4 , but as Matthews et al. reported, they generally account for less than 10% of performance variance 5 . Sleep duration or sleep latency were also used 9 , yielding a prediction accuracy of about 70% 10 in sleep deprivation studies. Researching physiological indicators could elucidate part of the so far unexplained individual variance in prediction models 11 . Heart rate (HR) and heart rate variability (HRV) are well-established markers of autonomic nervous system activity and overall cardiovascular health, and have been linked to cognitive performance, including attention and reaction time 12 , 13 . Hansen et al. demonstrated that being in a relatively higher HRV group (specifically, greater root mean square of successive differences, rMSSD) correlated with faster and more accurate responses during sustained attention tasks 14 . However, real-world applications require tools that can predict cognitive performance beyond the immediate pre-task period. Sleep-based HR monitoring offers a promising alternative, as it minimizes daytime confounds such as physical activity, environmental stimuli, and behavioral variability. Sleep with sufficient duration, good quality, timing, regularity, and without disturbances is critical for our well-being 15 . Sustained attention is particularly vulnerable to sleep loss, likely due to the sensitivity of frontal brain regions to sleep deprivation. Sleep architecture typically comprises multiple cycles, with 75–80% spent in non-rapid eye movement (NREM) sleep and 20–25% in rapid eye movement (REM) sleep 16 . Across these stages, autonomic balance shifts: vagal (parasympathetic) activity progressively lowers HR from wakefulness to deep NREM sleep, while sympathetic activity rises during REM, driving HR back toward waking levels 17 – 20 . Furthermore, sleep disruptions and prior wake behaviors, such as sleep deprivation, elevate sympathetic activity, contributing to increased HR and reduced restfulness 15 ,2021 . Consequently, average sleep HR reflects the integrated influence of autonomic regulation, hormonal fluctuations, and reflexive cardiorespiratory and baroreceptor control. Some studies have explored whether HR or HRV measured during sleep serve as predictors of vigilant performance. In a sleep deprivation study, Chua et al. 22 used frequency-domain HRV and power spectral density analyses of ECG measured during the psychomotor vigilance task (PVT) to predict PVT performance. They reported that the R–R interval power in the 0.02–0.08 Hz band was a strong predictor of PVT outcomes, performing comparably to established physiological measures of sleepiness such as ocular indicators and EEG recordings. Another study by Hietakoste et al. 23 found that lower short-term HRV metrics (NN intervals, rMSSD, and high-frequency power) were associated with longer reaction times. While these findings are promising, several challenges limit their broader applicability. First, sleep deprivation studies consistently show that vigilance declines with cumulative sleep debt 24 – 26 . Therefore, it is not clear whether relationships demonstrated under acute sleep deprivation generalize to well-rested conditions. Second, in the study by Hietakoste et al., it remains unclear how many participants were affected by conditions such as sleep apnea versus being otherwise healthy, making it difficult to interpret the results in a general population context. Lastly, although laboratory experiments are crucial for highly controlled evaluations, they fail to account for environmental influences and the complexities of real-life situations, resulting in reduced ecological validity, generalizability, and relevance of the findings 27 – 29 . This study aims to examine the effects of mean sleep HR and sleep duration over the two preceding nights on vigilant performance during an 83-day summer Antarctic expedition. This unique research setting offers a high degree of environmental control due to its isolation, while avoiding the artificial constraints of traditional laboratory studies 30 – 34 . Using a longitudinal (or repeated measures) design, we collected data from six PVT assessments conducted at biweekly intervals, and from sport trackers that participants wore daily during sleep for HR measurement. The data were analyzed using Generalized Additive Mixed Models (GAMM 54, 55 ) to capture potentially nonlinear relationships. Predictors included: (1) mean sleep HR and sleep duration from the first night (immediately preceding PVT assessment), and (2) changes in these metrics between the first and second night to account for short-term variability. Beyond enhancing our understanding of the interplay between sleep-related physiological markers and cognitive performance, as well as broader insights into the physiological regulation of sleep homeostasis and circadian rhythm functioning, this study offers practical implications for fields of occupational health and human factors. Results Data were collected from 16 participants (details reported in the Participants section of Methods). PVT performance was assessed six times; 13 participants completed all six assessments, while the remaining three completed five, resulting in a total of n = 111 PVT assessments. The average proportion of missing values per participant was 22.08% (range: 0.0%–50.00%) for HR the night before PVT measurement, and 23.13% (range: 0.0%–66.68%) for HR two nights before PVT measurement, leading to a total of n = 57 datapoints used in the analysis. The first GAMM modeled linear effect of mean sleep heart rate and mean sleep duration from the first and second night prior to the PVT measurement, as well as sex, first-time participation, and laptop type. Participant ID was included as a random effect to account for repeated measures. We found a statistically significant relationship between mean sleep HR and mean RT from the night before PVT measurement (β = 1.51 ± 0.54; p = 0.008). According to this (linear) model estimate, one BPM increase in mean sleep heart rate was associated with a 1.51 ms increase in RT. First-time participants at the expedition showed significantly faster RT (β = −39.24 ± 15.85; p = 0.02). Laptop type also showed a statistically significant effect (β = 26.74 ± 4.26; p < 0.001). In contrast, sex and sleep duration the night before testing were not significantly associated with RT. Similarly, neither the difference in mean sleep heart rate nor the difference in sleep duration over the two preceding nights showed a significant relationship with mean RT. The random effect for participant was highly significant (p < 0.001), indicating substantial inter-individual variability in PVT performance. The model accounted for a substantial proportion of variance in RT (adjusted R² = 0.88; deviance explained = 92.0%). Our second model examined the non-linear smooth effect of mean sleep heart rate from the night preceding the PVT measurement, while retaining linear effects for all other predictors listed in the paragraph above. The association between RT and mean sleep HR was positive and statistically significant (estimated degrees of freedom = 1.001, F = 7.853, p = 0.008). However, Supplementary Figure S4- 1 shows that the non-parametrically estimated as part of a GAMM model estimated effect is not non-linear. This is also supported by Akaike information criterion (AIC), which was comparable between the linear (AIC = 469.78) and non-linear (AIC = 469.83) models. First-time participants exhibited a statistically significant increase in mean RT (β = -39.24 ± 15.85, p = 0.018). Laptop type had a strong effect on RT (β = 26.74 ± 4.60; p < 0.001). No statistically significant effects were found for sex, sleep duration the night before testing, or for the differences in sleep duration or heart rate across the two preceding nights. The random effect for participant was highly significant (p < 0.001), indicating substantial inter-individual variability in PVT performance. The comprehensive GAMM model (with random individual intercepts, sex, first timer, laptop type, lag 1 day and lag 2 days sleep duration, lag 1 day and lag 2 mean HR) explained a larger proportion of variance in (adjusted R² = 0.882; deviance explained = 92.0%). Discussion To the best of our knowledge, this is the first study that explored the influence of mean sleep HR, measured over multiple nights, on vigilance performance. The results yielded key findings with significant scientific and practical implications. We identified a significant linear effect of mean sleep HR from the night before PVT measurement (one BPM increase in mean sleep heart rate was associated with a 1.513 ± 0.540 ms increase in RT). However, we failed to confirm a significant effect of sleep duration, a finding that contrasts with the majority of existing studies 9 , 35 – 38 . This discrepancy may arise from the fact that previous studies often employ sleep deprivation protocols that induce substantial effects, whereas our study sampled participants under real-life conditions without induced sleep restriction. Mean sleep duration from the night before PVT measurement was 6.77 hours (SD = 1.72, range 1.81 to 8.98). Consequently, it is plausible that in real-life contexts without a sleep deprivation protocol, sleep heart rate may serve as a more effective metric than sleep duration in vigilant performance prediction. In both models, previous experience with expedition emerged initially as a statistically significant factor, with first-time participants exhibiting faster reaction time by − 39.243 ± 15.852 ms. Additionally, inclusion of age in the model didn’t yield a significant improvement, further supporting the importance of previous experience as a factor. Association with previous experience would align with findings from our previous study, where expedition first-timers demonstrated significantly elevated mean sleep heart rates by 16.46 (SE = 2.76) BPM, independent of both perceived stress and chronological age 39 . These data collectively could suggest that novelty of the environment may elicit a measurable physiological stress response, likely involving autonomic nervous system activation, which occurs irrespective of subjective stress appraisals. Such a response may have reflected an adaptation process to novel and demanding environmental conditions, consistent with prior literature on allostatic load in unfamiliar settings. In contrast, night-to-night changes in mean sleep heart rate and sleep duration were not significantly associated with subsequent mean RT. While this finding may suggest that short-term fluctuations in sleep physiology do not have a measurable impact on cognitive alertness in this context, the absence of significant associations should be interpreted with caution. The limited sample size and non-negligible missing data proportion likely reduced the statistical power to detect subtle or delayed effects. It is also possible that individual differences in vulnerability to sleep-related cognitive impairment may have masked group-level associations. Furthermore, it is plausible that meaningful effects would emerge with longer observation periods over multiple nights, as prior studies have demonstrated that the cumulative effects of sleep disruption and elevated physiological arousal may become more evident over extended durations or under higher operational demands 24 – 26 . Similarly, neither model showed a statistically significant effect of sex on mean RT, which is somewhat contrary to other previous studies that report sex differences in response speed. However, effect sizes that are reported in those studies are generally small, and it may be that our sample size was insufficient to detect them. These observations underscored several limitations of the present study, which are addressed below. Beyond limitations in sample size mentioned above, it needs to be noted that utilizing data from only one expedition in one specific Antarctic station prevents us from generalizing results to other polar expeditions or the general population. The lack of variability in context may limit ecological validity, particularly given known differences between expedition settings 40 – 42 . Moreover, we were unable to include potentially important moderating variables such as personality traits, perceived stress, subjective sleep quality, and fatigue—factors that may exert substantial influence on cognitive performance and reaction time 43 . Methodological constraints also warrant consideration. Although mean heart rate was estimated using wrist-worn photoplethysmography, which has demonstrated acceptable reliability in prior research (see Technology specifications in our previous study using the same device 39 ), data may still be subject to measurement error due to device-specific limitations or participant behavior (see review by de Zambotti et al. 44 ). Additionally, psychomotor vigilance was assessed using identical software installed on two different laptops. We accounted for potential differences in hardware performance by including laptop type as a covariate in all models. In conclusion, this study provides novel evidence that mean sleep heart rate was significantly associated with subsequent vigilance performance under real-world expedition conditions, while sleep duration was not. Specifically, a higher mean sleep heart rate on the preceding night predicted slower reaction time. Prior expedition experience also emerged as a significant factor, with first-time participants demonstrating faster reaction times. In contrast, short-term fluctuations in sleep parameters did not significantly predict performance, potentially due to limited statistical power, inter-individual variability, and the absence of more notable sleep deprivation or cumulative effects. Although these findings have both theoretical and practical relevance, the generalizability of results is constrained by methodological limitations, including a single expedition setting, modest sample size, and absence of key moderating variables. Notwithstanding, this study provides important insights to a growing body of research aimed at optimizing human performance in extreme environments and informing risk mitigation strategies for expeditionary and operational settings. Future research should replicate our study in various expeditions and occupational contexts to confirm our findings and enhance generalizability. Additionally, future studies should integrate longitudinal physiological monitoring with psychological assessments and other well-known covariates (e.g., personality, perceived stress, subjective sleep metrics, actigraphy-derived sleep stages) to provide a more comprehensive understanding of the mechanisms underlying performance variability in operational and extreme environments. Lastly, we recommend that future researchers extend monitoring duration to capture potential cumulative and temporal trends in adaptation. Materials and methods Participants This research forms part of a broader study examining stress trajectories during the 2021/2022 summer Antarctic expedition. Ethical approval was granted by the Masaryk University Ethics Committee, and all procedures adhered to applicable guidelines and regulations. Participant recruitment took place during a pre-expedition meeting in November 2021 and was coordinated by the Czech Antarctic Research Programme (CARP) at Masaryk University. Selected expeditioners were presented with the study’s objectives, procedures, and potential risks and benefits. Informed consent was then obtained from those willing to participate, and this voluntary agreement served as the primary inclusion criterion. Since all expeditioners underwent a medical screening, the only exclusion criterion was the withdrawal of consent. The study population included 16 participants (5 women; mean age = 35.41 years, SD = 10.51) undergoing an Antarctic expedition, half of them for the first time. Twelve were Czech nationals, three were Slovak citizens living and working in the Czech Republic, and one was British. Nine had completed university degrees, four held postgraduate qualifications, two had completed secondary education, and one had finished only elementary school. The average body mass index was 24.18 (SD 3.30) kg/m 2 for men, and 21.93 (SD 3.04) kg/m 2 for women. Other participant characteristics and assessment of potential confounders are detailed in Supplementary Section S2 , following guidelines by Nelson et al. 45 . Expedition context The expedition began with the team’s departure from the Czech Republic on December 16, 2021. Travel to Chile took three days, followed by a mandatory 10-day quarantine due to COVID-19, during which the team was isolated in a hotel in Punta Arenas starting December 18. On December 30, the group flew to King George Island and continued by boat to James Ross Island, arriving on December 31. They remained stationed at the Johann Gregor Mendel Czech Antarctic Station, located on James Ross Island (63°48′02″ S, 57°52′54″ W, elevation 10 m), until March 2. Afterward, the team traveled back to King George Island and stayed at the General Artigas Uruguayan scientific station until March 6. The expedition concluded with their return to the Czech Republic or the United Kingdom on March 8, 2022. In total, the expedition had 83 days, out of which 68 were spent in Antarctica. Study protocol At the beginning of the expedition, participants were given wrist-worn wearables and instructed to start using them daily (see details in our previous study 39 ). Furthermore, every other week (± 2 days), participants were scheduled for PVT assessment, resulting in 6 measurements per subject. First measurements were conducted between 21st and 26th December, during a 10-day quarantine in Chile. Last measurements were conducted between 28th February and 4th March, during a stay at General Artigas Uruguayan scientific station. Psychomotor Vigilance Task The 5-minute version of the PVT was executed with the use of a precise computer software developed at Masaryk University, which was programmed to work offline and following the criteria reported in Basner and Dinges 6 . Software is deposited In GitHub (link located in the Supplementary Section S3 ). The test was administered on laptop Lenovo ThinkPad T14 laptop. However, due to technical issues, part of the administration had to be delivered on an alternative laptop Dell Latitude 3410. Participants were instructed to monitor the white screen and press the spacebar as soon as a red solid circle appeared on the screen. This stopped the counter and displayed the reaction time (RT) in milliseconds for a 1-s period. The inter-stimulus interval varied randomly from 2 to 10 seconds. Participants goal was to react as soon as possible when the stimulus appeared to keep the RT as low as possible, but not to press the button too soon. Responses were regarded as valid if RT was \(\:\ge\:\) 100 ms and \(\:\le\:\) 500 ms 46 . We also reciprocally transformed reaction times ( \(\:1/(RT⁄1000\:)\) ), as this transformation was shown to emphasize slowing in the optimum and intermediate response domain 6 . In this paper, we used only mean RT, although we also performed analysis with the 1/RT response (leading to more pronounced nonlinearities in the mean HR effect following from the mathematical nature of the reciprocal transform, but otherwise reaching exactly the same conclusions qualitatively). Heart rate measurement Technology specifications The design and protocol for heart rate data collection using wrist-worn wearables followed standardized guidelines outlined by Nelson et al. 47 . We used a Garmin 55 Forerunner, with no software updates performed during the study period. The device is equipped with the Garmin Elevate V3 optical sensor and utilizes Activity Tracking mode, which records data at a 1 Hz sampling rate. Technical factors related to the reliability and replicability of the measurements are detailed in the Supplementary Section S2 , following guidelines by Nelson et al. 45 . Data pre-processing procedures are described in the “Data Analysis” subsection below. Instructions for participants Before using the sport testers, participants received guidance through a pre-expedition manual. These instructions were based on official recommendations provided by Garmin. Participants were informed about the importance of proper watch placement to ensure accurate data collection. They were advised to wear the devices, so they remained in close contact with the skin without causing discomfort or restricting blood flow. Given natural fluctuations in arm size throughout the day, participants were instructed to adjust the strap accordingly. They were also reminded to maintain wrist hygiene and to clean the watches with pure water as needed. Full instructions are available in Supplementary Section S1. Data analysis Sport tracker data processing for heart rate and sleep duration analysis Data were downloaded from Garmin watches through weekly back-ups using a USB cable. The data were in FIT format, and we used the FITfileR package 48 from R software version 4.1.1. to analyze them. Data were manually controlled and cleaned. Time series HR data for each night were plotted and visually inspected. At the beginning of each recording, we checked for a distinct drop in heart rate (e.g., from 90 BPM to 60 BPM), indicating a transition from general activity to lying down and initiating sleep. We marked the start of the sleep period at the point where HR stabilized at lower values. Similarly, at the end of the recording, we identified a rise of similar magnitude in heart rate, indicating awakening and potential movement. We excluded this post-sleep wakeful period by trimming the data at the point where heart rate started to increase and showed a sustained upward trend. An example is shown in Fig. 3 and Fig. 4. Periods of disturbed sleep within the sleep period were not excluded. If there were two recordings for one day (e.g., one beginning at 0:30 and the other at 23:45), the latter was considered as belonging to the following day. In rare cases, some recordings had to be excluded from the analysis, such as recordings of heart rate during naps, recordings longer than 20 hours, or short recordings with a mean value above 100 BPM. Moreover, some days also had missing data (approx. one day per week). After elimination of data unsuitable for analysis, mean, minimal, and maximal heart rate during sleep, variance, and length of the sleep were calculated for each recording from available data for each day. The mean sleep duration per night was 6.77 hours (SD = 1.72, range 1.81 to 8.98). Statistical procedures All analyses were performed using R Statistical Software (version 4.3.3) 49 . Formal analysis was conducted with generalized additive modelling (GAM) using the gam function in the mgcv R package 50 , with the assumption of a Gaussian distribution. GAMs are similar to generalized linear models, but due to relaxing the linearity assumption, they allow for revealing non-linear relationships and potentially important structures in data, which would be missed otherwise. Outputs from R can be found in Supplementary Section S4. Models were compared using AIC or model-adjusted R 2 (see Table S4-1). Results from the analysis are interpreted with the estimate of the coefficient beta (β) and standard error (SE). All tests were performed with a significance level alpha set to 0.05. Declarations Funding declaration This project was financed by the Ministry of Education, Youth and Sports (No LM2018121), and Operational Programme Research, Development and Innovation - project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632). Marek Brabec was partially supported by the long-term strategic development funding of the Institute of Computer Science CAS (RVO 67985807). Additional Information The authors declare no competing interests. Author Contribution LR wrote the main manuscript, conceptualized the study, developed the methodology, and performed the investigation. MaBr created statistical models and performed formal analysis as well as prepared graphs for data presentation. MiKr supervised the formal analysis, creation of statistical models and commented on the manuscript. MiKl developed PVT software. JBV participated in conceptualization, development of design, and methodology, provided resources for the study, and supervised the study. MiBr provided resources for the study. All co-authors approved the submitted version and agreed to be accountable for their contributions. Acknowledgement The authors would like to thank the participants who volunteered for this study. We would also like to thank the Czech Antarctic Research Programme for their support. We further thank Prof. Mathias Basner and Dr. Christopher W. Jones for their valuable feedback and insightful discussions during the preparation of this manuscript. Dr. Daniela Kuruczová and Dr. Veronika Eclerová helped in the early project phases with feedback on study design. Moreover, Dr. Daniela Kuruczová performed a preliminary descriptive analysis of questionnaires. ChatGPT and Grammarly software were used for language and grammar checks. Authors thank to Research Infrastructure RECETOX RI (No LM2018121), financed by the Ministry of Education, Youth and Sports, and Operational Programme Research, Development and Innovation - project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632) for the supportive background. Marek Brabec was partially supported by the long-term strategic development funding of the Institute of Computer Science CAS (RVO 67985807). 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The diversity of overwintering groups in Antarctica and the limitations of psychological studies. Polar J. 15 , 172–182 (2025). Ráčková, L., Kapler, P. & Bienertová-Vašků, J. Summer Antarctic expeditions in seasonal stations as analogs for long-duration space exploration missions: A critical review. Czech Polar Rep 14 , (2024). Tu, D. et al. Dynamic ensemble prediction of cognitive performance in spaceflight. Sci. Rep. 12 , 11032 (2022). de Zambotti, M. et al. State of the science and recommendations for using wearable technology in sleep and circadian research. Sleep 47 , (2024). Nelson, B. W. et al. Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research. NPJ Digit. Med. 3 , 90 (2020). Basner, M., Mollicone, D. & Dinges, D. F. Validity and sensitivity of a brief psychomotor vigilance test (PVT-B) to total and partial sleep deprivation. Acta Astronaut. 69 , 949–959 (2011). Nelson, B. W. et al. Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research. NPJ Digit. Med. 3 , 90 (2020). Smith, M. & FITfileR Read FIT files using only native R code. Preprint at (2022). R Core Team. R: A Language and Environment for Statistical Computing. Preprint at. (2025). Wood, S. N. Generalized Additive Models . (Chapman and Hall/CRC, (2017). 10.1201/9781315370279 Additional Declarations No competing interests reported. Supplementary Files SupplementaryHRPVTAntarctica.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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01:18:38","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97824,"visible":true,"origin":"","legend":"","description":"","filename":"b57e575732234241a37f48d5c5afd5801structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7768957/v1/e3bb8139bd67c7414839603c.xml"},{"id":95223535,"identity":"a3601959-2c78-4339-a4e1-21387be30ddf","added_by":"auto","created_at":"2025-11-05 16:22:24","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108361,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7768957/v1/7e2e373e4f4a1f25fe64ce0d.html"},{"id":95222991,"identity":"2e7a2c2e-c448-4da5-8614-5880bb3d9e24","added_by":"auto","created_at":"2025-11-05 16:21:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":369994,"visible":true,"origin":"","legend":"\u003cp\u003eObserved vs. Fitted Mean Reaction Times per Individual with GAM Prediction.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7768957/v1/9f7c9d9a739775c33293c9f3.png"},{"id":95222818,"identity":"73c274e5-8630-4a0f-bee7-601a1983e049","added_by":"auto","created_at":"2025-11-05 16:21:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17147,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of HR estimates for the sleep period before cleaning. Red lines indicate cut-off points.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7768957/v1/01c525d56b0876e1740b4118.png"},{"id":102497710,"identity":"53b2c1fd-1ed1-4970-bbf4-3e756228e401","added_by":"auto","created_at":"2026-02-12 09:56:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":805394,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7768957/v1/80bb17a5-978f-40e5-8d6c-7a5b8f672a91.pdf"},{"id":95063709,"identity":"9f3d540b-cf97-480b-b50e-4cb945b0a718","added_by":"auto","created_at":"2025-11-04 01:18:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15006624,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryHRPVTAntarctica.docx","url":"https://assets-eu.researchsquare.com/files/rs-7768957/v1/dfae6174a7d5436c20ac8ba4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linking Multi-Night Sleep Heart Rate to Vigilance: Insights from Antarctic Field Research","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVigilant attention refers to the capacity to stay focused and react promptly to stimuli over extended periods\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. It is vital in numerous real-world situations, especially when interacting with advanced automated systems that require operators to monitor displays for long durations. Vigilant attention is also critical in high-stakes environments where a timely and precise response is essential (e.g., in all transportation modes, sports, medical monitoring and surgery, military, remote operation of unmanned vehicles, astronautics, nuclear plants, security-related tasks, and a wide range of industrial tasks)\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Examining the factors that affect and predict vigilance may contribute to the creation of practical tools designed to improve performance, enhance safety, minimize errors, support resource management, select appropriate personnel, and elevate overall results in critical tasks that demand sustained attention\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Previous studies have used various psychometric assessments for vigilance performance prediction\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, but as Matthews et al. reported, they generally account for less than 10% of performance variance\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Sleep duration or sleep latency were also used\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, yielding a prediction accuracy of about 70%\u003csup\u003e10\u003c/sup\u003e in sleep deprivation studies. Researching physiological indicators could elucidate part of the so far unexplained individual variance in prediction models\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHeart rate (HR) and heart rate variability (HRV) are well-established markers of autonomic nervous system activity and overall cardiovascular health, and have been linked to cognitive performance, including attention and reaction time\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Hansen et al. demonstrated that being in a relatively higher HRV group (specifically, greater root mean square of successive differences, rMSSD) correlated with faster and more accurate responses during sustained attention tasks\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, real-world applications require tools that can predict cognitive performance beyond the immediate pre-task period. Sleep-based HR monitoring offers a promising alternative, as it minimizes daytime confounds such as physical activity, environmental stimuli, and behavioral variability.\u003c/p\u003e\u003cp\u003eSleep with sufficient duration, good quality, timing, regularity, and without disturbances is critical for our well-being\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Sustained attention is particularly vulnerable to sleep loss, likely due to the sensitivity of frontal brain regions to sleep deprivation. Sleep architecture typically comprises multiple cycles, with 75\u0026ndash;80% spent in non-rapid eye movement (NREM) sleep and 20\u0026ndash;25% in rapid eye movement (REM) sleep\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Across these stages, autonomic balance shifts: vagal (parasympathetic) activity progressively lowers HR from wakefulness to deep NREM sleep, while sympathetic activity rises during REM, driving HR back toward waking levels\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Furthermore, sleep disruptions and prior wake behaviors, such as sleep deprivation, elevate sympathetic activity, contributing to increased HR and reduced restfulness\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,2021\u003c/sup\u003e. Consequently, average sleep HR reflects the integrated influence of autonomic regulation, hormonal fluctuations, and reflexive cardiorespiratory and baroreceptor control.\u003c/p\u003e\u003cp\u003eSome studies have explored whether HR or HRV measured during sleep serve as predictors of vigilant performance. In a sleep deprivation study, Chua et al. \u003csup\u003e22\u003c/sup\u003e used frequency-domain HRV and power spectral density analyses of ECG measured during the psychomotor vigilance task (PVT) to predict PVT performance. They reported that the R\u0026ndash;R interval power in the 0.02\u0026ndash;0.08 Hz band was a strong predictor of PVT outcomes, performing comparably to established physiological measures of sleepiness such as ocular indicators and EEG recordings. Another study by Hietakoste et al. \u003csup\u003e23\u003c/sup\u003e found that lower short-term HRV metrics (NN intervals, rMSSD, and high-frequency power) were associated with longer reaction times. While these findings are promising, several challenges limit their broader applicability. First, sleep deprivation studies consistently show that vigilance declines with cumulative sleep debt\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Therefore, it is not clear whether relationships demonstrated under acute sleep deprivation generalize to well-rested conditions. Second, in the study by Hietakoste et al., it remains unclear how many participants were affected by conditions such as sleep apnea versus being otherwise healthy, making it difficult to interpret the results in a general population context. Lastly, although laboratory experiments are crucial for highly controlled evaluations, they fail to account for environmental influences and the complexities of real-life situations, resulting in reduced ecological validity, generalizability, and relevance of the findings\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study aims to examine the effects of mean sleep HR and sleep duration over the two preceding nights on vigilant performance during an 83-day summer Antarctic expedition. This unique research setting offers a high degree of environmental control due to its isolation, while avoiding the artificial constraints of traditional laboratory studies\u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Using a longitudinal (or repeated measures) design, we collected data from six PVT assessments conducted at biweekly intervals, and from sport trackers that participants wore daily during sleep for HR measurement. The data were analyzed using Generalized Additive Mixed Models (GAMM\u003csup\u003e54, 55\u003c/sup\u003e) to capture potentially nonlinear relationships. Predictors included: (1) mean sleep HR and sleep duration from the first night (immediately preceding PVT assessment), and (2) changes in these metrics between the first and second night to account for short-term variability. Beyond enhancing our understanding of the interplay between sleep-related physiological markers and cognitive performance, as well as broader insights into the physiological regulation of sleep homeostasis and circadian rhythm functioning, this study offers practical implications for fields of occupational health and human factors.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eData were collected from 16 participants (details reported in the Participants section of Methods). PVT performance was assessed six times; 13 participants completed all six assessments, while the remaining three completed five, resulting in a total of n\u0026thinsp;=\u0026thinsp;111 PVT assessments. The average proportion of missing values per participant was 22.08% (range: 0.0%\u0026ndash;50.00%) for HR the night before PVT measurement, and 23.13% (range: 0.0%\u0026ndash;66.68%) for HR two nights before PVT measurement, leading to a total of n\u0026thinsp;=\u0026thinsp;57 datapoints used in the analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe first GAMM modeled linear effect of mean sleep heart rate and mean sleep duration from the first and second night prior to the PVT measurement, as well as sex, first-time participation, and laptop type. Participant ID was included as a random effect to account for repeated measures. We found a statistically significant relationship between mean sleep HR and mean RT from the night before PVT measurement (β\u0026thinsp;=\u0026thinsp;1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54; p\u0026thinsp;=\u0026thinsp;0.008). According to this (linear) model estimate, one BPM increase in mean sleep heart rate was associated with a 1.51 ms increase in RT. First-time participants at the expedition showed significantly faster RT (β = \u0026minus;39.24\u0026thinsp;\u0026plusmn;\u0026thinsp;15.85; p\u0026thinsp;=\u0026thinsp;0.02). Laptop type also showed a statistically significant effect (β\u0026thinsp;=\u0026thinsp;26.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, sex and sleep duration the night before testing were not significantly associated with RT. Similarly, neither the difference in mean sleep heart rate nor the difference in sleep duration over the two preceding nights showed a significant relationship with mean RT. The random effect for participant was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating substantial inter-individual variability in PVT performance. The model accounted for a substantial proportion of variance in RT (adjusted R\u0026sup2; = 0.88; deviance explained\u0026thinsp;=\u0026thinsp;92.0%).\u003c/p\u003e\u003cp\u003eOur second model examined the non-linear smooth effect of mean sleep heart rate from the night preceding the PVT measurement, while retaining linear effects for all other predictors listed in the paragraph above. The association between RT and mean sleep HR was positive and statistically significant (estimated degrees of freedom\u0026thinsp;=\u0026thinsp;1.001, F\u0026thinsp;=\u0026thinsp;7.853, p\u0026thinsp;=\u0026thinsp;0.008). However, \u003cem\u003eSupplementary Figure S4-\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/em\u003e shows that the non-parametrically estimated as part of a GAMM model estimated effect is not non-linear. This is also supported by Akaike information criterion (AIC), which was comparable between the linear (AIC\u0026thinsp;=\u0026thinsp;469.78) and non-linear (AIC\u0026thinsp;=\u0026thinsp;469.83) models. First-time participants exhibited a statistically significant increase in mean RT (β = -39.24\u0026thinsp;\u0026plusmn;\u0026thinsp;15.85, p\u0026thinsp;=\u0026thinsp;0.018). Laptop type had a strong effect on RT (β\u0026thinsp;=\u0026thinsp;26.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.60; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No statistically significant effects were found for sex, sleep duration the night before testing, or for the differences in sleep duration or heart rate across the two preceding nights. The random effect for participant was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating substantial inter-individual variability in PVT performance. The comprehensive GAMM model (with random individual intercepts, sex, first timer, laptop type, lag 1 day and lag 2 days sleep duration, lag 1 day and lag 2 mean HR) explained a larger proportion of variance in (adjusted R\u0026sup2; = 0.882; deviance explained\u0026thinsp;=\u0026thinsp;92.0%).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study that explored the influence of mean sleep HR, measured over multiple nights, on vigilance performance. The results yielded key findings with significant scientific and practical implications. We identified a significant linear effect of mean sleep HR from the night before PVT measurement (one BPM increase in mean sleep heart rate was associated with a 1.513\u0026thinsp;\u0026plusmn;\u0026thinsp;0.540 ms increase in RT). However, we failed to confirm a significant effect of sleep duration, a finding that contrasts with the majority of existing studies\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. This discrepancy may arise from the fact that previous studies often employ sleep deprivation protocols that induce substantial effects, whereas our study sampled participants under real-life conditions without induced sleep restriction. Mean sleep duration from the night before PVT measurement was 6.77 hours (SD\u0026thinsp;=\u0026thinsp;1.72, range 1.81 to 8.98). Consequently, it is plausible that in real-life contexts without a sleep deprivation protocol, sleep heart rate may serve as a more effective metric than sleep duration in vigilant performance prediction.\u003c/p\u003e\u003cp\u003eIn both models, previous experience with expedition emerged initially as a statistically significant factor, with first-time participants exhibiting faster reaction time by \u0026minus;\u0026thinsp;39.243\u0026thinsp;\u0026plusmn;\u0026thinsp;15.852 ms. Additionally, inclusion of age in the model didn\u0026rsquo;t yield a significant improvement, further supporting the importance of previous experience as a factor. Association with previous experience would align with findings from our previous study, where expedition first-timers demonstrated significantly elevated mean sleep heart rates by 16.46 (SE\u0026thinsp;=\u0026thinsp;2.76) BPM, independent of both perceived stress and chronological age\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These data collectively could suggest that novelty of the environment may elicit a measurable physiological stress response, likely involving autonomic nervous system activation, which occurs irrespective of subjective stress appraisals. Such a response may have reflected an adaptation process to novel and demanding environmental conditions, consistent with prior literature on allostatic load in unfamiliar settings.\u003c/p\u003e\u003cp\u003eIn contrast, night-to-night changes in mean sleep heart rate and sleep duration were not significantly associated with subsequent mean RT. While this finding may suggest that short-term fluctuations in sleep physiology do not have a measurable impact on cognitive alertness in this context, the absence of significant associations should be interpreted with caution. The limited sample size and non-negligible missing data proportion likely reduced the statistical power to detect subtle or delayed effects. It is also possible that individual differences in vulnerability to sleep-related cognitive impairment may have masked group-level associations. Furthermore, it is plausible that meaningful effects would emerge with longer observation periods over multiple nights, as prior studies have demonstrated that the cumulative effects of sleep disruption and elevated physiological arousal may become more evident over extended durations or under higher operational demands \u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Similarly, neither model showed a statistically significant effect of sex on mean RT, which is somewhat contrary to other previous studies that report sex differences in response speed. However, effect sizes that are reported in those studies are generally small, and it may be that our sample size was insufficient to detect them. These observations underscored several limitations of the present study, which are addressed below.\u003c/p\u003e\u003cp\u003eBeyond limitations in sample size mentioned above, it needs to be noted that utilizing data from only one expedition in one specific Antarctic station prevents us from generalizing results to other polar expeditions or the general population. The lack of variability in context may limit ecological validity, particularly given known differences between expedition settings\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Moreover, we were unable to include potentially important moderating variables such as personality traits, perceived stress, subjective sleep quality, and fatigue\u0026mdash;factors that may exert substantial influence on cognitive performance and reaction time\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Methodological constraints also warrant consideration. Although mean heart rate was estimated using wrist-worn photoplethysmography, which has demonstrated acceptable reliability in prior research (see Technology specifications in our previous study using the same device\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e), data may still be subject to measurement error due to device-specific limitations or participant behavior (see review by de Zambotti et al.\u003csup\u003e44\u003c/sup\u003e). Additionally, psychomotor vigilance was assessed using identical software installed on two different laptops. We accounted for potential differences in hardware performance by including laptop type as a covariate in all models.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides novel evidence that mean sleep heart rate was significantly associated with subsequent vigilance performance under real-world expedition conditions, while sleep duration was not. Specifically, a higher mean sleep heart rate on the preceding night predicted slower reaction time. Prior expedition experience also emerged as a significant factor, with first-time participants demonstrating faster reaction times. In contrast, short-term fluctuations in sleep parameters did not significantly predict performance, potentially due to limited statistical power, inter-individual variability, and the absence of more notable sleep deprivation or cumulative effects. Although these findings have both theoretical and practical relevance, the generalizability of results is constrained by methodological limitations, including a single expedition setting, modest sample size, and absence of key moderating variables. Notwithstanding, this study provides important insights to a growing body of research aimed at optimizing human performance in extreme environments and informing risk mitigation strategies for expeditionary and operational settings.\u003c/p\u003e\u003cp\u003eFuture research should replicate our study in various expeditions and occupational contexts to confirm our findings and enhance generalizability. Additionally, future studies should integrate longitudinal physiological monitoring with psychological assessments and other well-known covariates (e.g., personality, perceived stress, subjective sleep metrics, actigraphy-derived sleep stages) to provide a more comprehensive understanding of the mechanisms underlying performance variability in operational and extreme environments. Lastly, we recommend that future researchers extend monitoring duration to capture potential cumulative and temporal trends in adaptation.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eThis research forms part of a broader study examining stress trajectories during the 2021/2022 summer Antarctic expedition. Ethical approval was granted by the Masaryk University Ethics Committee, and all procedures adhered to applicable guidelines and regulations. Participant recruitment took place during a pre-expedition meeting in November 2021 and was coordinated by the Czech Antarctic Research Programme (CARP) at Masaryk University. Selected expeditioners were presented with the study\u0026rsquo;s objectives, procedures, and potential risks and benefits. Informed consent was then obtained from those willing to participate, and this voluntary agreement served as the primary inclusion criterion. Since all expeditioners underwent a medical screening, the only exclusion criterion was the withdrawal of consent.\u003c/p\u003e\u003cp\u003eThe study population included 16 participants (5 women; mean age\u0026thinsp;=\u0026thinsp;35.41 years, SD\u0026thinsp;=\u0026thinsp;10.51) undergoing an Antarctic expedition, half of them for the first time. Twelve were Czech nationals, three were Slovak citizens living and working in the Czech Republic, and one was British. Nine had completed university degrees, four held postgraduate qualifications, two had completed secondary education, and one had finished only elementary school. The average body mass index was 24.18 (SD 3.30) kg/m\u003csup\u003e2\u003c/sup\u003e for men, and 21.93 (SD 3.04) kg/m\u003csup\u003e2\u003c/sup\u003e for women. Other participant characteristics and assessment of potential confounders are detailed in \u003cem\u003eSupplementary Section S2\u003c/em\u003e, following guidelines by Nelson et al.\u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExpedition context\u003c/p\u003e\u003cp\u003eThe expedition began with the team\u0026rsquo;s departure from the Czech Republic on December 16, 2021. Travel to Chile took three days, followed by a mandatory 10-day quarantine due to COVID-19, during which the team was isolated in a hotel in Punta Arenas starting December 18. On December 30, the group flew to King George Island and continued by boat to James Ross Island, arriving on December 31. They remained stationed at the Johann Gregor Mendel Czech Antarctic Station, located on James Ross Island (63\u0026deg;48\u0026prime;02\u0026Prime; S, 57\u0026deg;52\u0026prime;54\u0026Prime; W, elevation 10 m), until March 2. Afterward, the team traveled back to King George Island and stayed at the General Artigas Uruguayan scientific station until March 6. The expedition concluded with their return to the Czech Republic or the United Kingdom on March 8, 2022. In total, the expedition had 83 days, out of which 68 were spent in Antarctica.\u003c/p\u003e\u003cp\u003eStudy protocol\u003c/p\u003e\u003cp\u003eAt the beginning of the expedition, participants were given wrist-worn wearables and instructed to start using them daily (see details in our previous study\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e). Furthermore, every other week (\u0026plusmn;\u0026thinsp;2 days), participants were scheduled for PVT assessment, resulting in 6 measurements per subject. First measurements were conducted between 21st and 26th December, during a 10-day quarantine in Chile. Last measurements were conducted between 28th February and 4th March, during a stay at General Artigas Uruguayan scientific station.\u003c/p\u003e\u003cp\u003ePsychomotor Vigilance Task\u003c/p\u003e\u003cp\u003eThe 5-minute version of the PVT was executed with the use of a precise computer software developed at Masaryk University, which was programmed to work offline and following the criteria reported in Basner and Dinges\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Software is deposited In GitHub (link located in the \u003cem\u003eSupplementary Section S3\u003c/em\u003e). The test was administered on laptop Lenovo ThinkPad T14 laptop. However, due to technical issues, part of the administration had to be delivered on an alternative laptop Dell Latitude 3410. Participants were instructed to monitor the white screen and press the spacebar as soon as a red solid circle appeared on the screen. This stopped the counter and displayed the reaction time (RT) in milliseconds for a 1-s period. The inter-stimulus interval varied randomly from 2 to 10 seconds. Participants goal was to react as soon as possible when the stimulus appeared to keep the RT as low as possible, but not to press the button too soon.\u003c/p\u003e\u003cp\u003eResponses were regarded as valid if RT was \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e100 ms and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 500 ms\u003csup\u003e46\u003c/sup\u003e. We also reciprocally transformed reaction times (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1/(RT\u0026frasl;1000\\:)\\)\u003c/span\u003e\u003c/span\u003e), as this transformation was shown to emphasize slowing in the optimum and intermediate response domain\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In this paper, we used only mean RT, although we also performed analysis with the 1/RT response (leading to more pronounced nonlinearities in the mean HR effect following from the mathematical nature of the reciprocal transform, but otherwise reaching exactly the same conclusions qualitatively).\u003c/p\u003e\u003cp\u003eHeart rate measurement\u003c/p\u003e\u003cp\u003eTechnology specifications\u003c/p\u003e\u003cp\u003eThe design and protocol for heart rate data collection using wrist-worn wearables followed standardized guidelines outlined by Nelson et al.\u003csup\u003e47\u003c/sup\u003e. We used a Garmin 55 Forerunner, with no software updates performed during the study period. The device is equipped with the Garmin Elevate V3 optical sensor and utilizes Activity Tracking mode, which records data at a 1 Hz sampling rate. Technical factors related to the reliability and replicability of the measurements are detailed in the \u003cem\u003eSupplementary Section S2\u003c/em\u003e, following guidelines by Nelson et al.\u003csup\u003e45\u003c/sup\u003e. Data pre-processing procedures are described in the \u0026ldquo;Data Analysis\u0026rdquo; subsection below.\u003c/p\u003e\u003cp\u003eInstructions for participants\u003c/p\u003e\u003cp\u003eBefore using the sport testers, participants received guidance through a pre-expedition manual. These instructions were based on official recommendations provided by Garmin. Participants were informed about the importance of proper watch placement to ensure accurate data collection. They were advised to wear the devices, so they remained in close contact with the skin without causing discomfort or restricting blood flow. Given natural fluctuations in arm size throughout the day, participants were instructed to adjust the strap accordingly. They were also reminded to maintain wrist hygiene and to clean the watches with pure water as needed. \u003cem\u003eFull instructions are available in Supplementary Section S1.\u003c/em\u003e\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eSport tracker data processing for heart rate and sleep duration analysis\u003c/p\u003e\u003cp\u003eData were downloaded from Garmin watches through weekly back-ups using a USB cable. The data were in FIT format, and we used the FITfileR package \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e from R software version 4.1.1. to analyze them. Data were manually controlled and cleaned. Time series HR data for each night were plotted and visually inspected. At the beginning of each recording, we checked for a distinct drop in heart rate (e.g., from 90 BPM to 60 BPM), indicating a transition from general activity to lying down and initiating sleep. We marked the start of the sleep period at the point where HR stabilized at lower values. Similarly, at the end of the recording, we identified a rise of similar magnitude in heart rate, indicating awakening and potential movement. We excluded this post-sleep wakeful period by trimming the data at the point where heart rate started to increase and showed a sustained upward trend. An example is shown in Fig.\u0026nbsp;3 and Fig.\u0026nbsp;4. Periods of disturbed sleep within the sleep period were not excluded. If there were two recordings for one day (e.g., one beginning at 0:30 and the other at 23:45), the latter was considered as belonging to the following day. In rare cases, some recordings had to be excluded from the analysis, such as recordings of heart rate during naps, recordings longer than 20 hours, or short recordings with a mean value above 100 BPM. Moreover, some days also had missing data (approx. one day per week). After elimination of data unsuitable for analysis, mean, minimal, and maximal heart rate during sleep, variance, and length of the sleep were calculated for each recording from available data for each day. The mean sleep duration per night was 6.77 hours (SD\u0026thinsp;=\u0026thinsp;1.72, range 1.81 to 8.98).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStatistical procedures\u003c/p\u003e\u003cp\u003eAll analyses were performed using R Statistical Software (version 4.3.3)\u003csup\u003e49\u003c/sup\u003e. Formal analysis was conducted with generalized additive modelling (GAM) using the gam function in the mgcv R package\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, with the assumption of a Gaussian distribution. GAMs are similar to generalized linear models, but due to relaxing the linearity assumption, they allow for revealing non-linear relationships and potentially important structures in data, which would be missed otherwise. Outputs from R can be found in \u003cem\u003eSupplementary Section S4.\u003c/em\u003e Models were compared using AIC or model-adjusted R\u003csup\u003e2\u003c/sup\u003e (see Table S4-1). Results from the analysis are interpreted with the estimate of the coefficient beta (β) and standard error (SE). All tests were performed with a significance level alpha set to 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding declaration\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis project was financed by the Ministry of Education, Youth and Sports (No LM2018121), and Operational Programme Research, Development and Innovation - project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632). Marek Brabec was partially supported by the long-term strategic development funding of the Institute of Computer Science CAS (RVO 67985807).\u003c/p\u003e\u003cp\u003eAdditional Information\u003c/p\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLR wrote the main manuscript, conceptualized the study, developed the methodology, and performed the investigation. MaBr created statistical models and performed formal analysis as well as prepared graphs for data presentation. MiKr supervised the formal analysis, creation of statistical models and commented on the manuscript. MiKl developed PVT software. JBV participated in conceptualization, development of design, and methodology, provided resources for the study, and supervised the study. MiBr provided resources for the study. All co-authors approved the submitted version and agreed to be accountable for their contributions.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the participants who volunteered for this study. We would also like to thank the Czech Antarctic Research Programme for their support. We further thank Prof. Mathias Basner and Dr. Christopher W. Jones for their valuable feedback and insightful discussions during the preparation of this manuscript. Dr. Daniela Kuruczov\u0026aacute; and Dr. Veronika Eclerov\u0026aacute; helped in the early project phases with feedback on study design. Moreover, Dr. Daniela Kuruczov\u0026aacute; performed a preliminary descriptive analysis of questionnaires. ChatGPT and Grammarly software were used for language and grammar checks. Authors thank to Research Infrastructure RECETOX RI (No LM2018121), financed by the Ministry of Education, Youth and Sports, and Operational Programme Research, Development and Innovation - project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632) for the supportive background. Marek Brabec was partially supported by the long-term strategic development funding of the Institute of Computer Science CAS (RVO 67985807).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available due to ethical considerations and participants' protection, but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKl\u0026ouml;sch, G., Zeitlhofer, J. \u0026amp; Ipsiroglu, O. Revisiting the concept of vigilance. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 874757 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOken, B. S., Salinsky, M. C. \u0026amp; Elsas, S. M. Vigilance, alertness, or sustained attention: physiological basis and measurement. \u003cem\u003eClin. 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(Chapman and Hall/CRC, (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1201/9781315370279\u003c/span\u003e\u003cspan address=\"10.1201/9781315370279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sleep heart rate, sleep duration, vigilance, performance, Antarctica, reaction time","lastPublishedDoi":"10.21203/rs.3.rs-7768957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7768957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the associations between sleep heart rate, sleep duration, and Psychomotor Vigilance Task performance using repeated measures data collected during an Antarctic expedition. Data were collected without inducing sleep restriction, reflecting naturalistic sleep patterns. Results revealed that a higher mean sleep heart rate on the preceding night was significantly associated with slower reaction time on the PVT. In contrast, association with sleep duration did not reach statistical significance. Interestingly, being a first-time participant was a significant predictor of faster reaction times, whereas age was not, suggesting that the observed performance differences are more likely attributable to an adaptive response to environmental novelty rather than age-related cognitive variation. Night-to-night variations in sleep parameters were not significant predictors. These findings suggest that mean sleep heart rate may serve as a more effective metric than sleep duration for predicting vigilant performance in real-life contexts without induced sleep deprivation. Despite limitations of a modest sample size and data from a single expedition, this study offers novel insights into optimizing human performance in extreme environments and informing risk mitigation strategies. Future research should replicate these findings across diverse contexts and integrate longitudinal physiological and psychological assessments for a comprehensive understanding of performance variability.\u003c/p\u003e","manuscriptTitle":"Linking Multi-Night Sleep Heart Rate to Vigilance: Insights from Antarctic Field Research","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 01:18:34","doi":"10.21203/rs.3.rs-7768957/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08f896a8-9407-42fb-8a21-9483b9a62b5b","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57260891,"name":"Biological sciences/Neuroscience"},{"id":57260892,"name":"Biological sciences/Physiology"},{"id":57260893,"name":"Biological sciences/Psychology"},{"id":57260894,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-02-12T09:53:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 01:18:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7768957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7768957","identity":"rs-7768957","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}