ECSS Paris 2023: OP-AP38
INTRODUCTION: Heat strain is a significant external factor that should not be underestimated when it comes to optimizing physical performance in sports. Studies have shown that better acclimatization, along with improved thermoregulation, is positively correlated with enhanced performance [1]. In monitoring core body temperature, telemetric digestible pills represent the gold standard of measurement systems. However, the expenses and limited reusability are major drawbacks. New measurement methods, such as wearable sensors, offer a non-invasive, and cost-effective alternative. Nevertheless, the accuracy of these sensors requires further investigation in different exercise settings. METHODS: Twelve endurance-trained participants [6=male; 6=female; age=24.83J (±2.17); bodyweight=67.87kg (±8.01); height=173.75cm (±6.06); maximal oxygen uptake: 52.1 mL/min/kg (±7.45)] were enrolled in the study. Each participant performed two running bouts on a treadmill (46 min; at 85% of the individual anaerobic threshold) on a treadmill (Saturn, h/p/cosmos, Germany). The external load was determined by percentage of individual anaerobic threshold (IAT) via cardiopulmonary exercise testing. Core temperature (°C) was measured continuously with the wearable CORE sensor (greenTEG AG, Switzerland) and the telemetric pills eCelsius-Performance (BodyCAP, France). The tests were conducted in a thermally controlled environment (24.03°C±1.28). For statistical analysis, we performed spearman correlation analysis to detect the agreement between both measurement systems. Significance level was set to p<0.05. RESULTS: A strong correlation was found between pill and CORE sensor in both protocols (T1: r = 0.86, p < 0.001; T2: r = 0.79 p < 0.001). In later stages of both protocols, the sensor slightly overestimated the core body temperature compared to the pill (+0.3°C). CONCLUSION: The wearable sensor seems to be a cost-effective alternative to the digestive pill when operating in a thermally controlled environment. Due to the slight overestimation of temperature in the later stages of the investigation, the risk of overheating seems to be minimized when relying on the sensor data. However, field studies involving additional stress conditions and different thermal environments will be necessary to further assess the reliability of the sensors in the future.
Read CV Severin ZentgrafECSS Paris 2023: OP-AP38
INTRODUCTION: Sleep deprivation and heat stress impair human performance and may disrupt thermoregulation, leading to increased physiological strain. As both conditions are prevalent in basic military training, this underscores the need for continuous, real-life monitoring. Wearable technologies provide a non-invasive solution, with upper-arm wearables offering a well-tolerated option. However, their validity across various activity intensities and environmental conditions remains underexplored. This research aimed to (1) validate the upper-arm-worn devices, the Calera Research for core body temperature (CBT) and the Polar Verity Sense for heart rate (HR), as HR is integral to both the CBT estimation and sleep detection algorithms, and (2) evaluate the performance of a self-developed sleep detection algorithm using acceleration (ACC) and HR data from the upper-arm. METHODS: In a semi-controlled validation study, 16 healthy participants (age 27.4±5.8 years; height 173.5±9.2cm; weight 69.9±9.4kg) completed two test sessions under two heat stress conditions, performing nine activities from rest to vigorous intensity. HR and CBT were validated against reference measures (Polar H10 for HR, rectal sensor for CBT) using mean absolute error (MAE) and agreement analysis. In a 15-week military field study, continuous HR (1 Hz) and ACC (52Hz) data from 46 participants (age 19.8±1.4 years; height 177.5±6.9cm; weight 73.9±10.8 kg) were collected to develop an upper-arm-based sleep detection algorithm. Adapted from the Hypnospy framework [1], the algorithm integrates a customized cleaning process, physiological and movement-based thresholds, smoothing, and volatility analysis. Daily self-reported sleep windows served as a reference for comparison. RESULTS: The Polar Verity Sense demonstrated strong accuracy across most activities (bias -0.05 bpm; MAE 1.43 bpm). The Calera Research showed moderate accuracy, with a systematic overestimation and temporal shift (bias 0.21°C; MAE 0.39°C). The sleep detection algorithm provided estimations comparable to self-reports for each participant. CONCLUSION: These findings highlight the performance of the Polar Verity Sense and Calera Research for continuous monitoring across rest and various activity intensities, providing reliable and actionable data. The sleep detection algorithm provided reasonable estimates but showed some discrepancies compared to self-reports, likely due to inaccuracies in participant responses rather than algorithmic limitations. While these findings indicate potential for automated sleep and physiological monitoring in real-world conditions, further validation against objective sleep measures is needed. Overall, this research supports the potential applicability of upper-arm-worn wearables for 24-hour monitoring. REFERENCES: Perez-Pozuelo, I., Posa, M., Spathis, D. et al. Detecting sleep outside the clinic using wearable heart rate devices. Sci Rep 12, 7956 (2022). https://doi.org/10.1038/s41598-022-11792-7.
Read CV Theresa SchweizerECSS Paris 2023: OP-AP38
INTRODUCTION: Real-time heart rate (HR) and body core temperature (Tc) monitoring is key for mitigating heat-related injury risk and optimising training in hot and/or humid environments. However, validated methods for monitoring Tc are impractical for use in the field due to their invasiveness (e.g. rectal probes) and prohibitive cost (e.g. telemetric capsules). Wearable technology enables non-invasive estimation of HR and Tc to facilitate real-time physiological strain monitoring. Yet, their validity for personalised physiological monitoring during field training is unclear. Hence, we selected and validated consumer wearable devices for physiological strain monitoring during real-world occupational training. METHODS: We reviewed the existing literature and selected four wearables for an initial laboratory validation based on four criteria: safety, suitability, accuracy, and data accessibility. Fitbit Sense (FB, Coolbit Tc algorithm) CALERAresearch (CA, CALERAresearch arm Tc algorithm) and Equivital Lifemonitor (EQ, ECTemp) were selected for further validation in the field. Eighty healthy firefighters (age = 30 ± 5 years, female = 5) completed up to three firefighting training activities based on their individual training requirements. All training activities, namely breathing apparatus proficiency test (BAPT), confined space rescue (CSR), and marine firefighting scenario (MFS), were conducted in warm and humid conditions (Tdb = 29.5 ± 1.2℃, RH = 78 ± 10%). Criterion for HR validity was set at a mean absolute percentage error (MAPE) <10% and Intraclass Correlation Coefficient (ICC) ≥0.9. Validity criterion for prediction of group and individual Tc responses were set at a mean bias error (MBE) <±0.27℃ and 95% prediction errors within ±0.40℃ (PTA ± 0.40℃) respectively. RESULTS: HR and Tgi ranged between 41 to 203bpm (6359 data points) and 36.5 to 40.6℃ (6341 data points) respectively. EQ displayed excellent HR accuracy across all training activities (MAPE = 5 ± 14%, ICC = 0.900 to 0.956). FB exhibited poor-to-moderate accuracy (ICC = 0.215 to 0.559) with larger underestimations at higher HR values. Group prediction errors were acceptable (MBE<±0.27℃) in FB (-0.07 ± 0.68℃) and CA (-0.25 ± 0.35℃) during BAPT. While FB and CA had a similar MBE and PTA ± 0.40℃ (all p > 0.05), FB exhibited larger 95% CIs across all training activities (FB = ±2.45℃, CA = ±0.86℃). Percentage of individual Tc prediction errors within ±0.40℃ was poor in all devices (FB = 41%, CA = 55%, EQ = 18%). CONCLUSION: The EQ had the highest accuracy for HR monitoring. While FB and CA were valid for estimating group-based Tc responses during BAPT, both devices failed to meet our validity criterion in other firefighting training activities. All wearable Tc algorithms displayed individual prediction errors that were too large for personalised physiological strain monitoring during field training. Further refinement of selected consumer wearables is necessary to enable their deployment in real-world physiological strain monitoring.
Read CV Shawn TanECSS Paris 2023: OP-AP38