The Impact of Pressure Training on the Performance of Semi-Professional Female Rugby League Players.

BACKGROUND: The aim of this study was to examine the effect of pressure training on the performance of semi-professional female rugby league athletes. METHODS: Using a within-subjects design, 16 female athletes (19.9 ± 3.4 years) performed a passing accuracy task under three conditions; (1) a control condition; (2) a physiological fatigue condition; and (3) a threat of consequence condition. Passing performance, perceived pressure, rate of perceived exertion (RPE), and self-confidence were assessed. RESULTS: A significant main effect of conditions was found for rate of perceived exertion (p < 0.001), self-confidence (p < 0.028), and perceived pressure (p = 0.011). There was no main effect of condition on passing performance. Post hoc comparisons revealed that RPE was significantly higher in the physiological fatigue condition when compared to the control (p = 0.009) and threat of consequence conditions (p < 0.001). Perceived pressure was significantly higher in the threat of consequence condition compared to the control condition (p = 0.037). CONCLUSIONS: The main findings of this study are that (1) passing performance was not impacted by pressure training conditions, and (2) threats of consequences are an effective manipulation to generate pressure in female semi-professional rugby league players. These results offer nuanced insights into the impact of pressure generation in training environments for female semi-professional rugby league athletes.

The Night-Time Sleep and Autonomic Activity of Male and Female Professional Road Cyclists Competing in the Tour de France and Tour de France Femmes.

BACKGROUND: Sleep is a critical component of recovery, but it can be disrupted following prolonged endurance exercise. The objective of this study was to examine the capacity of male and female professional cyclists to recover between daily race stages while competing in the 2022 Tour de France and the 2022 Tour de France Femmes, respectively. The 17 participating cyclists (8 males from a single team and 9 females from two teams) wore a fitness tracker (WHOOP 4.0) to capture recovery metrics related to night-time sleep and autonomic activity for the entirety of the events and for 7 days of baseline before the events. The primary analyses tested for a main effect of 'stage classification'-i.e., rest, flat, hilly, mountain or time trial for males and flat, hilly or mountain for females-on the various recovery metrics. RESULTS: During baseline, total sleep time was 7.2 ± 0.3 h for male cyclists (mean ± 95% confidence interval) and 7.7 ± 0.3 h for female cyclists, sleep efficiency was 87.0 ± 4.4% for males and 88.8 ± 2.6% for females, resting HR was 41.8 ± 4.5 beats·min-1 for males and 45.8 ± 4.9 beats·min-1 for females, and heart rate variability during sleep was 108.5 ± 17.0 ms for males and 119.8 ± 26.4 ms for females. During their respective events, total sleep time was 7.2 ± 0.1 h for males and 7.5 ± 0.3 h for females, sleep efficiency was 86.4 ± 1.2% for males and 89.6 ± 1.2% for females, resting HR was 44.5 ± 1.2 beats·min-1 for males and 50.2 ± 2.0 beats·min-1 for females, and heart rate variability during sleep was 99.1 ± 4.2 ms for males and 114.3 ± 11.2 ms for females. For male cyclists, there was a main effect of 'stage classification' on recovery, such that heart rate variability during sleep was lowest after mountain stages. For female cyclists, there was a main effect of 'stage classification' on recovery, such that the percentage of light sleep (i.e., lower-quality sleep) was highest after mountain stages. CONCLUSIONS: Some aspects of recovery were compromised after the most demanding days of racing, i.e., mountain stages. Overall however, the cyclists obtained a reasonable amount of good-quality sleep while competing in these physiologically demanding endurance events. This study demonstrates that it is now feasible to assess recovery in professional athletes during multiple-day endurance events using validated fitness trackers.

Long-Term Robustness and Failure Mechanisms of Electrochemical Stripping for Wastewater Ammonia Recovery.

Nitrogen in wastewater has negative environmental, human health, and economic impacts but can be recovered to reduce the costs and environmental impacts of wastewater treatment and chemical production. To recover ammonia/ammonium (total ammonia nitrogen, TAN) from urine, we operated electrochemical stripping (ECS) for over a month, achieving 83.4 ± 1.5% TAN removal and 73.0 ± 2.9% TAN recovery. With two reactors, we recovered sixteen 500-mL batches (8 L total) of ammonium sulfate (20.9 g/L TAN) approaching commercial fertilizer concentrations (28.4 g/L TAN) and often having >95% purity. While evaluating the operation and maintenance needs, we identified pH, full-cell voltage, product volume, and water flux into the product as informative process monitoring parameters that can be inexpensively and rapidly measured. Characterization of fouled cation exchange and omniphobic membranes informs cleaning and reactor modifications to reduce fouling with organics and calcium/magnesium salts. To evaluate the impact of urine collection and storage on ECS, we conducted experiments with urine at different levels of dilution with flush water, extents of divalent cation precipitation, and degrees of hydrolysis. ECS effectively treated urine under all conditions, but minimizing flush water and ensuring storage until complete hydrolysis would enable energy-efficient TAN recovery. Our experimental results and cost analysis motivate a multifaceted approach to improving ECS's technical and economic viability by extending component lifetimes, decreasing component costs, and reducing energy consumption through material, reactor, and process engineering. In summary, we demonstrated urine treatment as a foothold for electrochemical nutrient recovery from wastewater while supporting the applicability of ECS to seven other wastewaters with widely varying characteristics. Our findings will facilitate the scale-up and deployment of electrochemical nutrient recovery technologies, enabling a circular nitrogen economy that fosters sanitation provision, efficient chemical production, and water resource protection.

Electrochemical Wastewater Refining: A Vision for Circular Chemical Manufacturing.

Wastewater is an underleveraged resource; it contains pollutants that can be transformed into valuable high-purity products. Innovations in chemistry and chemical engineering will play critical roles in valorizing wastewater to remediate environmental pollution, provide equitable access to chemical resources and services, and secure critical materials from diminishing feedstock availability. This perspective envisions electrochemical wastewater refining─the use of electrochemical processes to tune and recover specific products from wastewaters─as the necessary framework to accelerate wastewater-based electrochemistry to widespread practice. We define and prescribe a use-informed approach that simultaneously serves specific wastewater-pollutant-product triads and uncovers a mechanistic understanding generalizable to broad use cases. We use this approach to evaluate research needs in specific case studies of electrocatalysis, stoichiometric electrochemical conversions, and electrochemical separations. Finally, we provide rationale and guidance for intentionally expanding the electrochemical wastewater refining product portfolio. Wastewater refining will require a coordinated effort from multiple expertise areas to meet the urgent need of extracting maximal value from complex, variable, diverse, and abundant wastewater resources.

Hit the gym or hit the hay: can evening exercise characteristics predict compromised sleep in healthy adults?

Introduction: Recent sleep guidelines regarding evening exercise have shifted from a conservative (i.e., do not exercise in the evening) to a more nuanced approach (i.e., exercise may not be detrimental to sleep in circumstances). With the increasing popularity of wearable technology, information regarding exercise and sleep are readily available to the general public. There is potential for these data to aid sleep recommendations within and across different population cohorts. Therefore, the aim of this study was to examine if sleep, exercise, and individual characteristics can be used to predict whether evening exercise will compromise sleep. Methods: Data regarding evening exercise and the subsequent night's sleep were obtained from 5,250 participants (1,321F, 3,929M, aged 30.1 ± 5.2 yrs) using a wearable device (WHOOP 3.0). Data for females and males were analysed separately. The female and male datasets were both randomly split into subsets of training and testing data (training:testing = 75:25). Algorithms were trained to identify compromised sleep (i.e., sleep efficiency <90%) for females and males based on factors including the intensity, duration and timing of evening exercise. Results: When subsequently evaluated using the independent testing datasets, the algorithms had sensitivity for compromised sleep of 87% for females and 90% for males, specificity of 29% for females and 20% for males, positive predictive value of 32% for females and 36% for males, and negative predictive value of 85% for females and 79% for males. If these results generalise, applying the current algorithms would allow females to exercise on ~ 25% of evenings with ~ 15% of those sleeps being compromised and allow males to exercise on ~ 17% of evenings with ~ 21% of those sleeps being compromised. Discussion: The main finding of this study was that the models were able to predict a high percentage of nights with compromised sleep based on individual characteristics, exercise characteristics and habitual sleep characteristics. If the benefits of exercising in the evening outweigh the costs of compromising sleep on some of the nights when exercise is undertaken, then the application of the current algorithms could be considered a viable alternative to generalised sleep hygiene guidelines.

Evaluating the Typical Day-to-Day Variability of WHOOP-Derived Heart Rate Variability in Olympic Water Polo Athletes.

Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform exercise in athletic populations. Advancements in the photoplethysmography technology of wearable devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV, and therefore enhanced application in athletes. However, it is important that the reliability of such technology is acceptable prior to its application in practical settings. Eleven elite male water polo players (age 28.8 ± 5.3 years [mean ± standard deviation]; height 190.3 ± 3.8 cm; body mass 95.0 ± 6.9 kg; international matches 117.9 ± 92.1) collected their HR and HRV daily via a WHOOP strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSDCV) and HR (HRCV) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and presented as a mean of the entire recording period. The mean weekly lnRMSSDCV and HRCV over the 16-week period was 5.4 ± 0.7% (mean ± 95% confidence intervals) and 7.6 ± 1.3%, respectively. The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of day-to-day variability in alternative lnRMSSD (~3-13%) and HR (~10-11%) assessment protocols, indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability than that which is naturally present in these variables.

A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults.

The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00-08:00 h). Participants were fitted with all six wearable devices-and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen's kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen's kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address-in some way, we could consider that the whole world is becoming a sleep laboratory.

Running on Empty: Self-Reported Sleep/Wake Behaviour during Ultra-Marathon Events Exceeding 100 Miles.

The aim of this study was to examine sleep/wake behaviour and sleep strategies before, during and after ultra-marathon running events exceeding 100 miles (161 km). A total of 119 athletes completed a web-based questionnaire regarding their habitual sleep/wake behaviour before, during, and after ultra-marathon participation. Event-specific data were grouped by race distance categories; 100-149 miles (161-240 km), 150-199 miles (241-321 km), and ≥200 miles (322 km). Athletes commonly reported not sleeping throughout the duration of their races (74%). However, for events that were ≥200 miles, athletes reported more sleep opportunities, longer sleep duration, and more total sleep when compared to events that were 100-149 miles in distance (p ≤ 0.001). This suggests that for races of shorter distances, the benefit of continuous racing outweighs the negative impact of continuous wakefulness/sleep deprivation. However, for longer races (≥200 miles), there is an apparent tradeoff between sleep deprivation and race strategy, whereby athletes cannot sustain a desired level of performance without obtaining sleep. This is consistent with established sleep/wake behaviour models suggesting that sleep need increases as wakefulness increases, or in this case, as race duration increases. For athletes participating in ultra-marathons, sleep management education and/or consultation with a sleep scientist prior to racing may be beneficial. Future research should examine the optimal strategies concerning the frequency and duration of sleep during ultra-marathons and the subsequent impact on performance.

Sleep and sexual satisfaction in couples with matched and mismatched chronotypes: A dyadic cross-sectional study.

Chronotype can be defined as an overt expression of circadian rhythmicity in an individual that dictates tendencies towards being a morning or evening person - also referred to as 'morningness' or 'eveningness.' Chronotypes generally impact preferred bed and wake times, in addition to a range of personal and social factors. This study examined how matching/mismatching chronotypes within relationships impact sexual satisfaction and sleep quality. A sample of 32 couples (52% females, 38.3 ± 11.7 years) each completed an online survey that assessed chronotype (reduced Morningness Eveningness Questionnaire), sleep (Pittsburgh Sleep Quality Index), and sexual satisfaction (Index of Sexual Satisfaction). Partner surveys were matched to identify whether chronotypes were matching or mismatching. Couples with matched chronotypes reported greater sexual satisfaction than those with mismatched chronotypes, F(1, 58) = 19.57, p < .001. Matched couples also reported better sleep quality than couples whose chronotypes were mismatched, F(1,62) = 48.02, p < .001. The individual chronotype did not seem to impact on sleep quality or sexual satisfaction. To improve sleep quality and sexual satisfaction, strategies (e.g., circadian phase advance or delay) could be used to increase circadian alignment between members of a couple.

The Impact of Sleep Inertia on Physical, Cognitive, and Subjective Performance Following a 1- or 2-Hour Afternoon Nap in Semiprofessional Athletes.

PURPOSE: This study examined the impact of sleep inertia on physical, cognitive, and subjective performance immediately after a 1- or 2-hour afternoon nap opportunity. METHODS: Twelve well-trained male athletes completed 3 conditions in a randomized, counterbalanced order-9 hours in bed overnight without a nap opportunity the next day (9 + 0), 8 hours in bed overnight with a 1-hour nap opportunity the next day (8 + 1), and 7 hours in bed overnight with a 2-hour nap opportunity the next day (7 + 2). Nap opportunities ended at 4:00 PM. Sleep was assessed using polysomnography. Following each condition, participants completed four 30-minute test batteries beginning at 4:15, 4:45, 5:15, and 5:45 PM. Test batteries included a warm-up, self-ratings of readiness to perform, motivation to perform and expected performance, two 10-m sprints, 2 agility tests, a 90-second response-time task, and 5 minutes of seated rest. RESULTS: Total sleep time was not different between conditions (P = .920). There was an effect of condition on readiness (P < .001), motivation (P = .001), and expected performance (P = .004)-all 3 were lower in the 8 + 1 and 7 + 2 conditions compared with the 9 + 0 condition. There was no effect of condition on response time (P = .958), sprint time (P = .204), or agility (P = .240), but a large effect size was observed for agility. CONCLUSIONS: After waking from a nap opportunity, agility may be reduced, and athletes may feel sleepy and not ready or motivated to perform. Athletes should schedule sufficient time (∼1 h) after waking from a nap opportunity to avoid the effects of sleep inertia on performance.

Correction: Bellenger et al. Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP. Sensors 2021, 21, 3571.

The authors wish to correct the following errors in the original paper [...].

The influence of training and competition on sleep behaviour of soccer referees.

OBJECTIVES: The aims of the present study were to (1) quantify sleep behaviours of soccer referees and (2) compare sleep behaviours between nights before training, before matches, and after matches. METHODS: Fourteen professional soccer referees from the A-League (mean±SD; age 34 ± 4 years; sex: 11 males, 3 females) participated in this observational study. Referees' sleep behaviours were examined using sleep diaries and wrist activity monitors for 31 consecutive nights during the 2018-2019 A-League season. Separate linear mixed models were conducted to assess differences in sleep behaviours between nights before training, before matches, and after matches. RESULTS: On average, referees did not obtain recommended sleep durations across the in-season (mean±SD sleep duration: 6.4 h ± 0.7 h). Referees went to bed later, spent less time in bed, and slept significantly less post-matches compared to pre-training and pre-match nights (p< 0.05). CONCLUSIONS: Referees were particularly susceptible to inadequate sleep on nights following training and matches. The findings related to poor sleep behaviours highlight the importance of implementing monitoring systems to understand the sleep behaviours of referees, with further research encouraged to ascertain the efficacy of various sleep hygiene practices to optimise sleep in this population.

Sleep-Wake Behaviour of 200-Mile Ultra-Marathon Competitors: A Case Study.

The aim of this study was to examine the sleep-wake behaviour of 200-mile ultra-marathon runners before, during, and after a competition. A longitudinal, observational study was conducted to collect the sleep data of four (two females; mean age: 45.5 ± 3.1 years) runners competing in a 200-mile ultra-marathon (N = 4). Wrist-worn activity monitors, in conjunction with self-report sleep diaries, were used to measure sleep, beginning seven days prior to the race and concluding seven days following the race (2-19 June 2021). Descriptive analysis of runners' subjective and objective sleep data was conducted. All runners completed the 200-mile event in an average of 82.5 ± 7.1 h. On average, runners obtained 4.7 ± 3.0 h of sleep from 4.8 ± 2.4 sleep episodes, averaging 59.9 ± 49.2 min of sleep per episode. Runners averaged 6.0 ± 1.3 h of sleep per night in the week before the competition and 6.3 ± 1.3 h per night in the week following the competition. Runners in the 200-mile (326 km) ultra-marathon drastically restricted their sleep. However, obtained sleep, the number of sleep episodes, and sleep episode length were greater than those previously reported with 100-mile (161 km) runners. In-race sleep data suggest an increased need for sleep as race duration increases. Interestingly, runners obtained less than the recommended ~8 h of sleep per night, in both pre-race and post-race phases of the competition.

The Impact of Chronotype on the Sleep and Training Responses of Elite Female Australian Footballers.

The primary aims of the present study were to examine the impact of chronotype on sleep/wake behaviour, perceived exertion, and training load among professional footballers. Thirty-six elite female professional football player's (mean ± SD: age, 25 ± 4 y; weight, 68 ± 7 kg) sleep and training behaviours were examined for 10 consecutive nights during a pre-season period using a self-report online player-management system and wrist activity monitors. All athletes completed the Morningness-Eveningness Questionnaire (rMEQ) on the first day of data collection. Eleven participants were morning types, seventeen participants were intermediate types, and three participants were evening types. Separate linear mixed models were conducted to assess differences in sleep, perceived exertion, and training behaviours between chronotype groups. Morning types woke up earlier (wake time: 07:19 ± 01:16 vs. 07:53 ± 01:01, p = 0.04) and reported higher ratings of perceived exertion compared to intermediate types (6.7 ± 1.1 vs. 5.9 ± 1.2, p = 0.01). No differences were observed between chronotype groups for bedtime, time in bed, total sleep time, sleep efficiency, training duration, or training load. In circumstances where professional female football players are required to train at a time opposing their natural circadian preference (e.g., morning type training in the evening), their perceived exertion during training may be higher than that of players that are training at a time that aligns with their natural circadian preference (e.g., evening type training in the evening). It is important for practitioners to monitor individual trends in training variables (e.g., rating of perceived exertion, training load) with relation to athlete chronotype and training time. Future research should examine the relationship between chronotype, training time, and rating of perceived exertion across different training durations.

Changes in health promoting behavior during COVID-19 physical distancing: Utilizing wearable technology to examine trends in sleep, activity, and cardiovascular indicators of health.

The COVID-19 pandemic incited unprecedented restrictions on the behavior of society. The aims of this study were to quantify changes to sleep/wake behavior and exercise behavior, as well as changes in physiological markers of health during COVID-19 physical distancing. A retrospective analysis of 5,436 US-based subscribers to the WHOOP platform (mean age = 40.25 ± 11.33; 1,536 females, 3,900 males) was conducted covering the period from January 1st, 2020 through May 15th, 2020. This time period was separated into a 68-day baseline period and a 67-day physical distancing period. To provide context and allow for potential confounders (e.g., change of season), data were also extracted from the corresponding time periods in 2019. As compared to baseline, during physical distancing, all subjects fell asleep earlier (-0.15 hours), woke up later (0.29 hours), obtained more sleep (+0.21 hours) and reduced social jet lag (-0.13 hours). Contrasting sleep behavior was seen in 2019, with subjects falling asleep and waking up at a similar time (-0.01 hours; -0.03 hours), obtaining less sleep (-0.14 hours) and maintaining social jet lag (+0.06 hours) in corresponding periods. Individuals exercised more intensely during physical distancing by increasing the time spent in high heart rate zones. In 2020, resting heart rate decreased (-0.90 beats per minute) and heart rate variability increased (+0.98 milliseconds) during physical distancing when compared to baseline. However, similar changes were seen in 2019 for RHR (-0.51 beats per minute) and HRV (+2.97 milliseconds), suggesting the variation may not be related to the introduction of physical distancing mandates. The findings suggest that individuals improved health related behavior (i.e., increased exercise intensity and longer sleep duration) during physical distancing restrictions. While positive changes were seen to cardiovascular indicators of health, it is unclear whether these changes were a direct consequence of behavior change.

A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep.

The aims of this study were to: (1) compare actigraphy (ACTICAL) and a commercially available sleep wearable (i.e., WHOOP) under two functionalities (i.e., sleep auto-detection (WHOOP-AUTO) and manual adjustment of sleep (WHOOP-MANUAL)) for two-stage categorisation of sleep (sleep or wake) against polysomnography, and; (2) compare WHOOP-AUTO and WHOOP-MANUAL for four-stage categorisation of sleep (wake, light sleep, slow wave sleep (SWS), or rapid eye movement sleep (REM)) against polysomnography. Six healthy adults (male: n = 3; female: n = 3; age: 23.0 ± 2.2 yr) participated in the nine-night protocol. Fifty-four sleeps assessed by ACTICAL, WHOOP-AUTO and WHOOP-MANUAL were compared to polysomnography using difference testing, Bland-Altman comparisons, and 30-s epoch-by-epoch comparisons. Compared to polysomnography, ACTICAL overestimated total sleep time (37.6 min) and underestimated wake (-37.6 min); WHOOP-AUTO underestimated SWS (-15.5 min); and WHOOP-MANUAL underestimated wake (-16.7 min). For ACTICAL, sensitivity for sleep, specificity for wake and overall agreement were 98%, 60% and 89%, respectively. For WHOOP-AUTO, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 90%, 60%, 86% and 63%, respectively. For WHOOP-MANUAL, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 97%, 45%, 90% and 62%, respectively. WHOOP-AUTO and WHOOP-MANUAL have a similar sensitivity and specificity to actigraphy for two-stage categorisation of sleep and can be used as a practical alternative to polysomnography for two-stage categorisation of sleep and four-stage categorisation of sleep.

Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP.

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP's PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP and ECG over 15 opportunities. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP's proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10-11%) and SWC (5-5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP's proprietary filter, which approached or exceeded the CV (3-13%) and SWC (1.5-6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.

Analyzing changes in respiratory rate to predict the risk of COVID-19 infection.

COVID-19, the disease caused by the SARS-CoV-2 virus, can cause shortness of breath, lung damage, and impaired respiratory function. Containing the virus has proven difficult, in large part due to its high transmissibility during the pre-symptomatic incubation. The study's aim was to determine if changes in respiratory rate could serve as a leading indicator of SARS-CoV-2 infections. A total of 271 individuals (age = 37.3 ± 9.5, 190 male, 81 female) who experienced symptoms consistent with COVID-19 were included- 81 tested positive for SARS-CoV-2 and 190 tested negative; these 271 individuals collectively contributed 2672 samples (days) of data (1856 healthy days, 231 while infected with COVID-19 and 585 while negative for COVID-19 but experiencing symptoms). To train a novel algorithm, individuals were segmented as follows; (1) a training dataset of individuals who tested positive for COVID-19 (n = 57 people, 537 samples); (2) a validation dataset of individuals who tested positive for COVID-19 (n = 24 people, 320 samples); (3) a validation dataset of individuals who tested negative for COVID-19 (n = 190 people, 1815 samples). All data was extracted from the WHOOP system, which uses data from a wrist-worn strap to produce validated estimates of respiratory rate and other physiological measures. Using the training dataset, a model was developed to estimate the probability of SARS-CoV-2 infection based on changes in respiratory rate during night-time sleep. The model's ability to identify COVID-positive individuals not used in training and robustness against COVID-negative individuals with similar symptoms were examined for a critical six-day period spanning the onset of symptoms. The model identified 20% of COVID-19 positive individuals in the validation dataset in the two days prior to symptom onset, and 80% of COVID-19 positive cases by the third day of symptoms.

Evaluation of the CERTUS Environmental Listeria Species Detection Kit for the Detection of Listeria Species on Environmental Surfaces: AOAC Performance Tested MethodSM 101802.

BACKGROUND: CERTUS Environmental Listeria species Detection Kit (CERTUS EL Detection Kit) is a real-time, bio-contained assay designed to accurately detect Listeria species (L. grayi, L. innocua, L. ivanovii, L. marthii, L. monocytogenes, L. seeligeri, and L. welshimeri) from environmental surface matrixes using an antibody-coupled magnetic microparticle with a Surface Enhanced Raman Spectroscopy (SERS) nanoparticle technology test system paired with proprietary CERTUS EL Selective Growth Media and CERTUS Detection Unit. OBJECTIVE: The method was evaluated for AOAC®Performance Tested MethodSM certification. METHODS: Inclusivity and exclusivity, matrix studies, product consistency and stability were conducted to evaluate the CERTUS EL Detection Kit. RESULTS: In the matrix studies, stainless steel, ceramic tile, plastic (polystyrene) and sealed concrete environmental surfaces (4 × 4" test areas) were tested. No statistically significant differences were found by Probability of Detection analysis (POD) in any of the matrixes when results were compared to the U.S. Food and Drug Administration cultural microbiology reference method for Listeria. The CERTUS EL Detection Kit correctly identified all 50 target Listeria isolates and correctly excluded all 30 non-target strains that were analyzed. Probability of Detection analysis of CERTUS EL Detection Kit robustness, product consistency (lot-to-lot) and stability studies demonstrated no statistically significant differences, and no variation was observed between instruments. CONCLUSIONS: The data collected in these studies demonstrate that the CERTUS EL Detection Kit is a reliable method for the rapid and specific detection of Listeria from stainless steel, ceramic tile, plastic (polystyrene) and sealed concrete environmental surfaces.