Reliability and Validity of the CORE Sensor to Assess Core Body Temperature during Cycling Exercise

Validity and reproducibility of the CALERA Research Sensor to estimate core temperature at different intensities of a cycling exercise in the heat

Recent heatwaves have highlighted the importance of accurate and continuous core temperature (TCORE) monitoring in sports settings. For example, accentuated rises in TCORE caused by physical exercises under environmental heat stress increase the risk of heat illnesses. Thus, using valid and reproducible devices is essential to ensure safe sports practice. In this study, we assessed the validity and reproducibility of the Calera Research Sensor (CRS) in estimating the TCORE of male and female participants during cycling exercise in a hot environment. Seven male (age: 36.2 ± 10.1 years) and eight female cyclists (age: 30.1 ± 5.0 years) underwent two identical cycling trials in a dry-bulb temperature of 32 °C and relative humidity of 60%. The protocol consisted of an initial 10-min rest followed by a 60-min exercise comprising 10 min at 20%, 25 min at 55%, and 25 min at 75% of maximal aerobic power, and an additional 25 min of post-exercise recovery. TCORE was recorded simultaneously every minute using a gastrointestinal capsule (TGi) and the CRS (TSENSOR). Bland-Altman analysis was performed to calculate bias, upper (LCS) and lower (LCI) concordance limits, and the 95% confidence interval (95%CI). The maximum acceptable difference between the two devices was predetermined at ±0.4 °C. A mixed linear model was used to assess the paired differences between the two measurement systems, considering the participants, trials, and environmental conditions as random effects and the cycling stages as fixed effects. An intra-class correlation coefficient (ICC) of 0.98 was recorded when analyzing data from the entire experiment. A non-significant bias value of 0.01 °C, LCS of 0.38 °C, LCI of -0.35 °C, and CI95% of ±0.36 °C were found. When analyzing data according to the participants' sex, CRS reproducibility was high in both sexes: ICC values of 0.98 and 0.99 were reported for males and females, respectively. CI95% was 0.35 °C in experiments with males and 0.37 °C with females, thereby falling within the acceptable margin of difference. Therefore, CRS was considered valid (compared to TGi) and reproducible in estimating TCORE in both sexes at various intensities of cycling exercise in the heat.

External auricle temperature enhances ear-based wearable accuracy during physiological strain monitoring in the heat

Body core temperature (Tc) monitoring is crucial for minimizing heat injury risk. However, validated strategies are invasive and expensive. Although promising, aural canal temperature (Tac) is susceptible to environmental influences. This study investigated whether incorporation of external auricle temperature (Tea) into an ear-based Tc algorithm enhances its accuracy during multiple heat stress conditions. Twenty males (mean ± SD; age = 25 ± 3 years, BMI = 21.7 ± 1.8, body fat = 12 ± 3%, maximal aerobic capacity (VO2max) = 64 ± 7 ml/kg/min) donned an ear-based wearable and performed a passive heating (PAH), running (RUN) and brisk walking trial (WALK). PAH comprised of immersion in hot water (42.0 ± 0.3 °C). RUN (70 ± 3%VO2max) and WALK (50 ± 10%VO2max) were conducted in an environmental chamber (Tdb = 30.0 ± 0.2 °C, RH = 71 ± 2%). Several Tc models, developed using Tac, Tea and heart rate, were validated against gastrointestinal temperature. Inclusion of Tea as a model input improved the accuracy of the ear-based Tc algorithm. Our best performing model (Trf3) displayed good group prediction errors (mean bias error = - 0.02 ± 0.26 °C) but exhibited individual prediction errors (percentage target attainment ± 0.40 °C = 88%) that marginally exceeded our validity criterion. Therefore, Trf3 demonstrates potential utility for group-based Tc monitoring, with additional refinement needed to extend its applicability to personalized heat strain monitoring.

The Effect of Menthol Mouth Rinsing and Fluid Temperature on Male Cycling Performance in Thermoneutral Conditions

PURPOSE

The purpose of this study was to determine the effect of a menthol (MEN) mouth rinse (MR) on cycling time trial (TT) performance in thermoneutral conditions and to explore the impact of fluid temperature (cold water [CW] or thermoneutral water [TNW]) on MEN's effect on performance.

METHODS

Twelve trained male cyclists (VO2 peak, 61.4 ± 12.1 mL/kg/min) completed a cycling TT in thermoneutral conditions (21 ± 0.2 °C, 40 ± 0.6% relative humidity) with four different mouth rinses: (1) MEN + CW; (2) MEN + TNW; (3) CW; and (4) TNW. The time to complete the TT and the power output (W) were recorded. The ratings of perceived exertion (RPE, Borg 6-20), thermal sensation (TS), and thermal comfort (TC) were recorded prior to and throughout the TT. The core body temperature (Tc) and heart rate (HR) were recorded throughout.

RESULTS

The TT duration was not significantly different between trials (MEN + TNW: 38:11 ± 12:48, MEN + CW: 37:21 ± 13:00, CW: 38:12 ± 13:54, TNW: 36:06 ± 14:12 mins:secs, p < 0.05). The mean trial power output did not significantly differ between conditions (>0.05). The Tc, HR, RPE, TS, and TC were not significantly different between trials (p > 0.05).

CONCLUSION

The results suggest that a MEN MR with either CW or TNW does not significantly improve cycling TT performance in trained male cyclists compared to a CW or TNW MR in thermoneutral conditions.

Validity of the CALERA Research Sensor to Assess Body Core Temperature during Maximum Exercise in Patients with Heart Failure

(1) Background: It is important to monitor the body core temperature (Tc) of individuals with chronic heart failure (CHF) during rest or exercise, as they are susceptible to complications. Gastrointestinal capsules are a robust indicator of the Tc at rest and during exercise. A practical and non-invasive sensor called CALERA Research was recently introduced, promising accuracy, sensitivity, continuous real-time analysis, repeatability, and reproducibility. This study aimed to assess the validity of the CALERA Research sensor when monitoring patients with CHF during periods of rest, throughout brief cardiopulmonary exercise testing, and during their subsequent recovery. (2) Methods: Twelve male CHF patients volunteered to participate in a 70-min protocol in a laboratory at 28 °C and 39% relative humidity. After remaining calm for 20 min, they underwent a symptom-limited stress test combined with ergospirometry on a treadmill, followed by 40 min of seated recovery. The Tc was continuously monitored by both Tc devices. (3) Results: The Tc values from the CALERA Research sensor and the gastrointestinal sensor showed no associations at rest (r = 0.056, p = 0.154) and during exercise (r = -0.015, p = 0.829) and a weak association during recovery (r = 0.292, p < 0.001). The Cohen's effect size of the differences between the two Tc assessment methods for rest, exercise, and recovery was 1.04 (large), 0.18 (none), and 0.45 (small), respectively. The 95% limit of agreement for the CALERA Research sensor was -0.057 ± 1.03 °C. (4) Conclusions: The CALERA sensor is a practical and, potentially, promising device, but it does not provide an accurate Tc estimation in CHF patients at rest, during brief exercise testing, and during recovery.

Educating the healthcare workforce of the future: lessons learned from the development and implementation of a 'Wearables in Healthcare' course

Digital health technologies will play an ever-increasing role in the future of healthcare. It is crucial that the people who will help make that transformation possible have the evidence-based and hands-on training necessary to address the many challenges ahead. To better prepare the future health workforce with the knowledge necessary to support the re-engineering of healthcare in an equitable, person-centric manner, we developed an experiential learning course-Wearables in Healthcare-for advanced undergraduate and graduate university students. Here we describe the components of that course and the lessons learned to help guide others interested in developing similar courses.

Sustained exercise load by young adult females while wearing surgical mask raises core body temperature measured with zero-heat-flux thermometer

When a pandemic such as that caused by the novel coronavirus disease termed COVID-19 emerges, it is recommended to wear a mask when in public situations, with information regarding the impact on thermoregulation essential, especially during exercise or hard physical labor. The present study investigated changes in core body temperature (CBT) while wearing a surgical mask (SM) during exercise (T_CBT) using a non-invasive zero-heat-flux (ZHF) thermometer. Nine young adult females performed ergometer exercise for 30 min at 60 W with (mask group) and without (control) a SM under a non-hot condition, shown by wet bulb globe temperature (WBGT) findings. T_CBT, mean skin temperature (T_MST), heart rate (HR), and humidity in the perioral region of the face (%RH) were determined. Each of those markers showed increased values during exercise, with the increases in T_CBT, HR, and %RH, but not T_MST, during exercise found to be significantly greater in the mask group. HR reserve (%HRR), derived as load intensity during exercise, was also significantly higher in the mask group. Each subject completed all of the experimental protocols without noting pain or discomfort. These results suggest that wearing a SM while performing mild exercise contributes to increased T_CBT associated with increased exercise intensity, expressed as %HRR in a non-heated condition. Furthermore, the ZHF thermometer was shown to be safe and is considered useful for conducting such studies_. Additional examinations will be necessary to examine gender and age group differences, as well as the use of different exercise methods and intensity and ambient conditions.

CORE™ wearable sensor: Comparison against gastrointestinal temperature during cold water ingestion and a 5 km running time-trial

Five km running time-trials (TT) are associated with rapid and significant increases in core body temperature (T_C). For such races, real-time feedback from pre-exercise and exercise T_C may be helpful in the design of an optimal pacing strategy aimed at limiting the possibility of developing heat-related illnesses. This study compared measurements of T_C obtained with a wearable device, the CORE™, to those of a gastrointestinal pill (GI pill), during cold water ingestion and a 5 km running TT. Twelve participants (25 ± 4 yrs) ingested 7.5 mL/kg fat-free mass of 4 °C water over the first 5 min of a 120 min sitting period, after which they completed a 5 km running TT at 30 °C, 50% relative humidity. A T_C difference > ± 0.25 °C between sensors was deemed clinically unacceptable. Prior to water ingestion, the CORE-derived T_C was 0.49 ± 0.25 °C lower than the GI pill. The CORE was irresponsive to the 0.26 ± 0.22 °C peak decline in T_C captured with the GI pill 40 min following water ingestion. Prior to the TT, T_C was 0.30 ± 0.25 °C lower with the CORE than the GI pill. During the TT, the CORE underestimated the rate of increase in T_C by 0.0125 ± 0.019 °C/min compared with the GI pill, and mean absolute difference in T_C between sensors was of 0.47 ± 0.34 °C. In conclusion, the CORE does not capture the cooling effect of cold water ingestion and provides a clinically relevant underestimation of T_C during a 5 km running TT in the heat.

Prospective Validation of 2B-Cool : Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries

INTRODUCTION

An uncontrollably rising core body temperature (T C ) is an indicator of an impending exertional heat illness. However, measuring T C invasively in field settings is challenging. By contrast, wearable sensors combined with machine-learning algorithms can continuously monitor T C nonintrusively. Here, we prospectively validated 2B-Cool , a hardware/software system that automatically learns how individuals respond to heat stress and provides individualized estimates of T C , 20-min ahead predictions, and early warning of a rising T C .

METHODS

We performed a crossover heat stress study in an environmental chamber, involving 11 men and 11 women (mean ± SD age = 20 ± 2 yr) who performed three bouts of varying physical activities on a treadmill over a 7.5-h trial, each under four different clothing and environmental conditions. Subjects wore the 2B-Cool system, consisting of a smartwatch, which collected vital signs, and a paired smartphone, which housed machine-learning algorithms and used the vital sign data to make individualized real-time forecasts. Subjects also wore a chest strap heart rate sensor and a rectal probe for comparison purposes.

RESULTS

We observed very good agreement between the 2B-Cool forecasts and the measured T C , with a mean bias of 0.16°C for T C estimates and nearly 75% of measurements falling within the 95% prediction intervals of ±0.62°C for the 20-min predictions. The early-warning system results for a 38.50°C threshold yielded a 98% sensitivity, an 81% specificity, a prediction horizon of 35 min, and a false alarm rate of 0.12 events per hour. We observed no sex differences in the measured or predicted peak T C .

CONCLUSION

2B-Cool provides early warning of a rising T C with a sufficient lead time to enable clinical interventions and to help reduce the risk of exertional heat illness.

Concurrent validity of the CORE wearable sensor with BodyCap temperature pill to assess core body temperature during an elite women's field hockey heat training camp

AbstractWearable temperature sensors offer the potential to overcome several limitations associated with current laboratory- and field-based methods for core temperature assessment; however, their ability to provide accurate data at elevated core temperatures (Tc) has been questioned. Therefore, this investigation aimed to determine the concurrent validity of a wearable temperature sensor (CORE) compared to a reference telemetric temperature pill (BodyCAP) during a team-sport heat training camp prior to the 2020 Olympic Games. Female field hockey players (n = 19) in the Australian national squad completed 4 sessions in hot conditions where their temperature was monitored via CORE and BodyCAP. Concurrent validity of the wearable CORE device was determined with reference to the ingested BodyCAP pill. Lin's Concordance Correlation Coefficients determined there was 'poor' agreement between devices during all sessions. Mean bias demonstrated that CORE underestimated Tc in all sessions (-0.06°C to -0.34°C), with wide mean 95% confidence intervals (±0.35°C to ±0.56°C). Locally estimated scatterplot smoothing regression lines illustrated a non-linearity of error, with greater underestimation of Tc by the CORE device, as Tc increased. The two devices disagreed more than ±0.3°C for 41-60% of all data samples in each session. Our findings do not support the use of the CORE device as a valid alternative to telemetric temperature pills for Tc assessment, particularly during exercise in hot conditions where elevated Tc are expected.

Free-living core body temperature monitoring using a wrist-worn sensor after COVID-19 booster vaccination: a pilot study

Core body temperature (CBT) is a key vital sign and fever is an important indicator of disease. In the past decade, there has been growing interest for vital sign monitoring technology that may be embedded in wearable devices, and the COVID-19 pandemic has highlighted the need for remote patient monitoring systems. While wrist-worn sensors allow continuous assessment of heart rate and oxygen saturation, reliable measurement of CBT at the wrist remains challenging. In this study, CBT was measured continuously in a free-living setting using a novel technology worn at the wrist and compared to reference core body temperature measurements, i.e., CBT values acquired with an ingestible temperature-sensing pill. Fifty individuals who received the COVID-19 booster vaccination were included. The datasets of 33 individuals were used to develop the CBT prediction algorithm, and the algorithm was then validated on the datasets of 17 participants. Mean observation time was 26.4 h and CBT > 38.0 °C occurred in 66% of the participants. CBT predicted by the wrist-worn sensor showed good correlation to the reference CBT (r = 0.72). Bland-Altman statistics showed an average bias of 0.11 °C of CBT predicted by the wrist-worn device compared to reference CBT, and limits of agreement were - 0.67 to + 0.93 °C, which is comparable to the bias and limits of agreement of commonly used tympanic membrane thermometers. The small size of the components needed for this technology would allow its integration into a variety of wearable monitoring systems assessing other vital signs and at the same time allowing maximal freedom of movement to the user.

Heat flux systems for body core temperature assessment during exercise

Heat flux systems are increasingly used to assess core body temperature. However, validation of multiple systems is scarce. Therefore, an experiment was performed in which three commercially available heat flux systems (3 M, Medisim and Core) were compared to rectal temperature (T_re). Five females and four males performed exercise in a climate chamber set at 18 °C/50% relative humidity until exhaustion. Exercise duration was 36.3 ± 5.6 min (mean ± standard deviation). T_re in rest was 37.2 ± 0.3 °C. Medisim's-values were lower than T_re (36.9 ± 0.4 °C, p < 0.05); 3 M (37.2 ± 0.1 °C) and Core's (37.4 ± 0.3 °C) did not differ from T_re. Maximal temperatures after exercise were 38.4 ± 0.2 °C (T_re), 38.0 ± 0.4 °C (3 M), 38.8 ± 0.3 °C (Medisim) and 38.6 ± 0.3 °C (Core); Medisim was significantly higher than T_re (p < 0.05). The temperature profiles of the heat flux systems during exercise differed to varying degree from the rectal profiles; the Medisim system showed a faster increase during exercise than T_re (0.48 ± 0.25 °C in 20 min, p < 0.05), the Core system tended to show a systematic overestimation during the entire exercise period and the 3 M system showed large errors at the end of exercise, likely due to sweat entering the sensor. Therefore, the interpretation of heat flux sensor values as core body temperature estimates should be done with care; more research is required to elucidate the physiological significance of the generated temperature values.

Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages.

An Advanced Internet of Things System for Heatstroke Prevention with a Noninvasive Dual-Heat-Flux Thermometer

Heatstroke is a concern during sudden heat waves. We designed and prototyped an Internet of Things system for heatstroke prevention, which integrates physiological information, including deep body temperature (DBT), based on the dual-heat-flux method. A dual-heat-flux thermometer developed to monitor DBT in real-time was also evaluated. Real-time readings from the thermometer are stored on a cloud platform and processed by a decision rule, which can alert the user to heatstroke. Although the validation of the system is ongoing, its feasibility is demonstrated in a preliminary experiment.

Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review

Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers.

A Novel Non-Invasive Thermometer for Continuous Core Body Temperature: Comparison with Tympanic Temperature in an Acute Stroke Clinical Setting

There is a growing research interest in wireless non-invasive solutions for core temperature estimation and their application in clinical settings. This study aimed to investigate the use of a novel wireless non-invasive heat flux-based thermometer in acute stroke patients admitted to a stroke unit and compare the measurements with the currently used infrared (IR) tympanic temperature readings. The study encompassed 30 acute ischemic stroke patients who underwent continuous measurement (Tcore) with the novel wearable non-invasive CORE device. Paired measurements of Tcore and tympanic temperature (Ttym) by using a standard IR-device were performed 3−5 times/day, yielding a total of 305 measurements. The predicted core temperatures (Tcore) were significantly correlated with Ttym (r = 0.89, p < 0.001). The comparison of the Tcore and Ttym measurements by Bland−Altman analysis showed a good agreement between them, with a low mean difference of 0.11 ± 0.34 °C, and no proportional bias was observed (B = −0.003, p = 0.923). The Tcore measurements correctly predicted the presence or absence of Ttym hyperthermia or fever in 94.1% and 97.4% of cases, respectively. Temperature monitoring with a novel wireless non-invasive heat flux-based thermometer could be a reliable alternative to the Ttym method for assessing core temperature in acute ischemic stroke patients.

Road to Tokyo 2020 Olympic Games: Training Characteristics of a World Class Male Triathlete

There is a growing interest in the scientific literature for reporting top-class endurance athletes training programs. This case study reports on the training program of a world-class male triathlete preparing to compete in the Tokyo 2020 Olympic Games. A macrocycle of 43 weeks is presented. The triathlete performed 14.74 ± 3.01 h of weekly endurance training volume. Training intensity distribution (TID) was 81.93% ± 6.74%/7.16% ± 2.03%/10.91% ± 6.90% for zones 1 (low intensity, VT2) respectively. Pyramidal TID model is observed during the initial stages of the periodization and Polarized TID model is observed at the end of the macrocycle. The triathlete's peak ⩒O2 was increased by 20% on cycling and by 14% on running. Peak power was increased by 3.13% on cycling test and peak speed by 9.71% on running test. Finally, the triathlete placed 12th in Olympic distance and 10th in Mixed Relay in Tokyo 2020 Olympic games.

Accuracy of Algorithm to Non-Invasively Predict Core Body Temperature Using the Kenzen Wearable Device

With climate change increasing global temperatures, more workers are exposed to hotter ambient temperatures that exacerbate risk for heat injury and illness. Continuously monitoring core body temperature (T_C) can help workers avoid reaching unsafe T_C. However, continuous T_C measurements are currently cost-prohibitive or invasive for daily use. Here, we show that Kenzen's wearable device can accurately predict T_C compared to gold standard T_C measurements (rectal probe or gastrointestinal pill). Data from four different studies (n = 52 trials; 27 unique subjects; >4000 min data) were used to develop and validate Kenzen's machine learning T_C algorithm, which uses subject's real-time physiological data combined with baseline anthropometric data. We show Kenzen's T_C algorithm meets pre-established accuracy criteria compared to gold standard T_C: mean absolute error = 0.25 °C, root mean squared error = 0.30 °C, Pearson r correlation = 0.94, standard error of the measurement = 0.18 °C, and mean bias = 0.07 °C. Overall, the Kenzen T_C algorithm is accurate for a wide range of T_C, environmental temperatures (13-43 °C), light to vigorous heart rate zones, and both biological sexes. To our knowledge, this is the first study demonstrating a wearable device can accurately predict T_C in real-time, thus offering workers protection from heat injuries and illnesses.