Physiological recovery among workers in long-distance sleddog race: a case study on female veterinarians in Finnmarksløpet.

During Finnmarksløpet (FL, one of the longest distance sleddog races in the world), veterinarians are exposed to extreme environmental conditions and tight working schedules, with little and fragmented sleep. OBJECTIVE: The aim of this case study was to examine cardiovascular parameters and sleep-wake patterns among veterinarians working within FL, during and after (for a month) the end of the race. METHODS: Six female veterinarians volunteered for the study. The participants wore a wrist device for a total of eight weeks in order to passively and semi-continuously record physiological responses throughout the day (i.e., heart rate, heart rate variability, number of steps, and sleep quality). Moreover, perceived sleep quality was assessed by Pittsburgh Sleep Quality Index (PSQI). RESULTS: During and for one month after completion of the FL, most veterinarians presented an alteration of cardiovascular parameters and sleep quality. The heart rate circadian rhythm returned to pre-race values within about two weeks. CONCLUSIONS: The long-lasting alteration of the veterinarians’ cardiovascular parameters and sleep-wake patterns might have negative consequences for their health in the long-term, especially if similar experiences are repeated more times though the course of a year or season. More research is needed in order to understand the health risks, as well as how to prevent them, among veterinarians in long-distance sleddog races or other similar events.acceptedVersio

Missing data imputation techniques for wireless continuous vital signs monitoring

Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This study explored the performance of various imputation techniques for continuous vital signs measurements. Wireless vital signs measurements (heart rate, respiratory rate, blood oxygen saturation, axillary temperature) from surgical ward patients were used for repeated random simulation of missing data periods (gaps) of 5–60 min in two-hour windows. Gaps were imputed using linear interpolation, spline interpolation, last observation- and mean carried forwards technique, and cluster-based prognosis. Imputation performance was evaluated using the mean absolute error (MAE) between original and imputed gap samples. Besides, effects on signal features (window’s slope, mean) and early warning scores (EWS) were explored. Gaps were simulated in 1743 data windows, obtained from 52 patients. Although MAE ranges overlapped, median MAE was structurally lowest for linear interpolation (heart rate: 0.9–2.6 beats/min, respiratory rate: 0.8–1.8 breaths/min, temperature: 0.04–0.17 °C, oxygen saturation: 0.3–0.7% for 5–60 min gaps) but up to twice as high for other techniques. Three techniques resulted in larger ranges of signal feature bias compared to no imputation. Imputation led to EWS misclassification in 1–8% of all simulations. Imputation error ranges vary between imputation techniques and increase with gap length. Imputation may result in larger signal feature bias compared to performing no imputation, and can affect patient risk assessment as illustrated by the EWS. Accordingly, careful implementation and selection of imputation techniques is warranted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10877-023-00975-w

Effects of immersive virtual reality on sensory overload in a random sample of critically ill patients.

BACKGROUND Sensory overload and sensory deprivation have both been associated with negative health outcomes in critically ill patients. While there is a lack of any clear treatment or prevention strategies, immersive virtual reality is a promising tool for addressing such problems, but which has not been repetitively tested in random samples. Therefore, this study aimed to determine how critically ill patients react to repeated sessions of immersive virtual reality. METHODS This exploratory study was conducted in the mixed medical-surgical intermediate care unit of the University Hospital of Bern (Inselspital). Participants (N = 45; 20 women, 25 men; age = 57.73 ± 15.92 years) received two immersive virtual reality sessions via a head-mounted display and noise-canceling headphones within 24 h during their stay in the unit. Each session lasted 30-min and showed a 360-degree nature landscape. Physiological data were collected as part of the participants' standard care, while environmental awareness, cybersickness, and general acceptance were assessed using a questionnaire designed by our team (1 = not at all, 10 = extremely). RESULTS During both virtual reality sessions, there was a significant negative linear relationship found between the heart rate and stimulation duration [first session: r(43) = -0.78, p < 0.001; second session: r(38) = -0.81, p < 0.001] and between the blood pressure and stimulation duration [first session: r(39) = -0.78, p < 0.001; second session: r(30) = -0.78, p < 0.001]. The participants had a high comfort score [median (interquartile range {IQR}) = 8 (7, 10); mean = 8.06 ± 2.31], did not report being unwell [median (IQR) = 1 (1, 1); mean = 1.11 ± 0.62], and were not aware of their real-world surroundings [median (IQR) = 1 (1, 5); mean = 2.99 ± 3.22]. CONCLUSION The subjectively reported decrease in environmental awareness as well as the decrease in the heart rate and blood pressure over time highlights the ability of immersive virtual reality to help critically ill patients overcome sensory overload and sensory deprivation. Immersive virtual reality can successfully and repetitively be provided to a randomly selected sample of critically ill patients over a prolonged duration

The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography

Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.</p

The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography

Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.</p

Analysis of the impact of interpolation methods of missing RR-intervals caused by motion artifacts on HRV features estimations

Wearable physiological monitors have become increasingly popular, often worn during people's daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors

Autonomic factors do not underlie the elevated self-disgust levels in Parkinson’s disease

Introduction Parkinson’s disease (PD) is manifested along with non-motor symptoms such as impairments in basic emotion regulation, recognition and expression. Yet, self-conscious emotion (SCEs) such as self-disgust, guilt and shame are under-investigated. Our previous research indicated that Parkinson patients have elevated levels of self-reported and induced self-disgust. However, the cause of that elevation–whether lower level biophysiological factors, or higher level cognitive factors, is unknown. Methods To explore the former, we analysed Skin Conductance Response (SCR, measuring sympathetic activity) amplitude and high frequency Heart Rate Variability (HRV, measuring parasympathetic activity) across two emotion induction paradigms, one involving narrations of personal experiences of self-disgust, shame and guilt, and one targeting self-disgust selectively via images of the self. Both paradigms had a neutral condition. Results Photo paradigm elicited significant changes in physiological responses in patients relative to controls—higher percentages of HRV in the high frequency range but lower SCR amplitudes, with patients to present lower responses compared to controls. In the narration paradigm, only guilt condition elicited significant SCR differences between groups. Conclusions Consequently, lower level biophysiological factors are unlikely to cause elevated self-disgust levels in Parkinson’s disease, which by implication suggests that higher level cognitive factors may be responsible

Heart Rate Variability in Individuals with Down Syndrome: A Scoping Review with Methodological Considerations

This is the final version. Available on open access from MDPI via the DOI in this recordIndividuals with Down syndrome (DS) present similar heart rate variability (HRV) parameters at rest but different responses to selected movement maneuvers in comparison to individuals without DS, which indicates reduced vagal regulation. The present study undertakes a scoping review of research on HRV in individuals with DS, with special attention paid to the compliance of the studies with standards and methodological paper guidelines for HRV assessment and interpretation. A review was performed using PubMed, Web of Science and CINAHL databases to search for English language publications from 1996 to 2020 with the MESH terms “heart rate variability” and “down syndrome”, with the additional inclusion criteria of including only human participants and empirical investigations. From 74 studies, 15 were included in the review. None of the reviewed studies met the recommendations laid out by the standards and guidelines for providing the acquisition of RR intervals and necessary details on HRV analysis. Since authors publishing papers on this research topic do not adhere to the prescribed standards and guidelines when constructing the methodology, results of the research papers on the topic are not directly comparable. Authors need to design the study methodology more robustly by following the aforementioned standards, guidelines and recommendations

The Use of Heart Rate Variability in Esports: A Systematic Review

Heart rate variability (HRV) is a psychophysiological measure of particular interest in esports due to its potential to monitor player self-regulation. This study aimed to systematically review the utilisation of HRV in esports. Consideration was given to the methodological and theoretical underpinnings of previous works to provide recommendations for future research. The protocol was made available on the Open Science Framework. Inclusion criteria were empirical studies, examining HRV in esports, using esports players, published in English. Exclusion criteria were non-peer-reviewed studies, populations with pre-existing clinical illness other than Internet Gaming Disorder (IGD), opinion pieces or review papers. In November 2022 a search of Web of Science, PubMed, and EBSCOHost identified seven studies using HRV in esports. Risk of bias was assessed using the Mixed Methods Appraisal Tool. Narrative review identified two primary uses of HRV in esports; stress response and IGD. A lack of theoretical and methodological underpinning was identified as a major limitation of current literature. Further investigation is necessary before making recommendations regarding the use of HRV in esports. Future research should employ sound theoretical underpinning such as the use of vagally mediated HRV and the robust application of supporting methodological guidelines when investigating HRV in esports