Numerous reports state that drowsiness is one of the major factors affecting driving performance and resulting in traffic accidents. In the past, methods to detect driver drowsiness have been developed based on physiological, behavioral, and vehicular features. In this pilot study, we test the use of a new set of features for detecting driver drowsiness based on physiological changes related to thermoregulation. Nineteen participants successfully performed a driving simulation, while the temperature of the nose (T_nose) and wrist (T_wrist) as well as the heart rate (HR) were monitored. On average, an initial increase in temperature followed by a gradual decrease was observed in drivers who experienced drowsiness. For non-drowsy drivers, no such trends were observed. In addition, HR decreased on average in both groups, yet the decrease in the drowsy group was more distinct. Next, a classification based on each of these variables resulted in an accuracy of 68.4%, 88.9%, and 70.6% for T_nose, T_wrist, and HR, respectively. Combining the information of all variables resulted in an accuracy of 89.5%, meaning that ultimately the state of 17 out of 19 drivers was detected correctly. Hence, we conclude that the use of physiological features related to thermoregulation shows potential for future research in this field.

Purpose: The aim of this study was to identify the relationships between continuously measured internal and external load variables during volleyball competition. Methods: A total of 8 male elite volleyball athletes (Belgian Liga A and Liga B) were monitored during official competition matches. In total, 63 individual measurements are included in this study. The authors used heart-rate (HR) data as internal load and accelerometer-based activity as external load. Data were recorded at a sampling frequency of 1 Hz using wearable technology during official competition. Workload during continuous game play and individual jumps performed while serving and spiking (selected by means of video analysis) were studied using correlation analysis and dynamic time-series modeling. Results: Significant linear correlations were observed between peak acceleration and maximal HR of individual serves (ρ = .62; P = 1.6e⁻⁵) and spikes (ρ = .49; P = 1.2e⁻³) that were performed during the warm-up. These same actions performed during the match did not show significant correlations. The correlation between the mean HR and mean activity throughout the entire match was also found to be significant (ρ = .67; P = 2.0e⁻⁹). With respect to the time-series models, the mean value for the goodness of fit ($R_{\mathrm{T}}^2$) between HR and activity was equal to .83 and .67 for the individual actions and the entire matches, respectively. Conclusions: The results show that there are strong relationships between internal and external load during volleyball competition. Second-order transfer function models are capable of explaining the main dynamics of HR (internal load) in response to accelerometer-based activity (external load). Time-series analysis of continuously measured workload is proposed for use in practice.

Background: Commercial wearable devices allow for continuous heart rate (HR) monitoring in daily life. Their accuracy under ecologically valid conditions, however, remains insufficiently independently tested, especially during irregular activity, cognitive stress, and variable climates. Objective: This study evaluated the HR accuracy of 10 commercially available wearables under controlled variations in physical activity, cognitive stress, and temperature. We hypothesized that physical activity irregularity, cognitive stress, and thermal climate conditions would affect measurement accuracy. Methods: Forty-five healthy adults (21‐68, mean 34, SD 12 y) completed a standardized protocol in climate-controlled chambers simulating neutral (23 °C), hot (36 °C), and cold (10 °C) conditions. Tasks included rest, cognitive stress (Montreal Imaging Stress Task), steady walking, and intermittent walking. Each of the 10 devices (Fitbit Charge 6, Fitbit Inspire 3, Garmin Vivosmart 5, Garmin Vivoactive 5, Apple Watch SE, Google Pixel Watch 2, Polar Ignite 3, Polar Pacer, Xiaomi Watch 2, and Oura Ring Gen 3) was compared against electrocardiogram-derived HR from a Zephyr BioHarness chest strap. Accuracy was assessed using mean absolute error (MAE), mean absolute percentage error (MAPE), repeated-measures concordance correlation coefficient (CCC), and Bland-Altman analysis. Results: Significant variability across the devices was observed. Fitbit Charge 6 (MAE 4.5 bpm, MAPE 5.5%, CCC 0.93) and Google Pixel Watch 2 (MAE 4.9 bpm, MAPE 6.7%, CCC 0.87) showed strong agreement with the gold standard. In contrast, Fitbit Inspire 3, Polar Ignite 3, Polar Pacer, and Oura Ring displayed larger errors (MAE 9‐14 bpm, MAPE 11%‐16%) and lower CCC values (0.45‐0.66). The climate conditions did not significantly affect the measurement accuracy of the test devices. The activity type, however, did have a significant effect: intermittent walking increased errors for multiple devices. Conclusions: Wearable HR measurement accuracy is device-specific and context-dependent. Moderate climates did not impair performance, but irregular movement reduced accuracy. Fitbit Charge 6 and Google Pixel Watch 2 demonstrated the highest reliability, supporting their use in health and sports monitoring. Careful device selection and context-aware interpretation remain critical for applied and clinical applications.

With the development of power output sensors in the field of paddle sports and the ongoing advancements in dynamical analysis of exercise data, this study aims to model the measurements of external training intensity in relation to heart rate (HR) time‐series during flat‐water kayak sprint. Nine elite athletes performed a total of 47 interval training sessions with incremental intensity (light to (sub‐) maximal effort levels). The data of HR, speed and power output were measured continuously and rating of perceived exertion and blood lactate concentration ([BLa]) were sampled at the end of each interval stage. Different autoregressive‐exogenous (ARX) modelling configurations are tested, and we report on which combination of input (speed or power), model order (1st or 2nd), parameter estimation method (time‐(in)variant) and training conditions (ergometer or on‐water) is best suited for linking external to internal measures. Average model R² values varied between 0.60 and 0.97, with corresponding average root mean square error values of 15.6 and 3.2 bpm. 1st order models with time‐varying (TV) parameter estimates yield the best model performance (average R² = 0.94). At the level of the individual athlete, the TV modelling features (i.e., the model parameters and derivatives such as time constant values) show significant repeated measure correlations in relation to measures of exercise intensity. In conclusion, the study provides a comprehensive description of how the dynamic relationship between external load and HR for sprint kayaking training data can be modelled. Such models can be used as a basis for improving training evaluation and optimisation.

Objective. The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake ( V ˙ O 2 ) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linear V ˙ O 2 responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR and V ˙ O 2 data of single ramp incremental running tests. Approach. A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate. Main results. As the work rate increases, τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s for V ˙ O 2 . Both increases are statistically significant (p < 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h⁻¹)⁻¹ for HR, and from 218 to 144 ml·min⁻¹ for V ˙ O 2 (p < 0.01 for decrease parameters of HR and V ˙ O 2 ). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures. Significance. We show that time-variant modeling is able to determine the time-varying dynamics HR and V ˙ O 2 responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

The rapid growth of technology in managing training processes offers both opportunities and challenges for sports practitioners. This study examines factors influencing technology adoption in four high-performance sports: golf, sprint kayak, freestyle snowboarding, and volleyball. Each discipline reflects distinct physiological and training needs. Using an interview-based approach and including 25 stakeholders (6 athletes, 14 coaches, and 5 high-performance managers), we captured qualitative and quantitative insights into the perceived benefits and challenges of technology in athlete monitoring. The thematic analysis identified key advantages of technology in athlete monitoring, including objective data support, enhanced engagement, real-time feedback, and increased confidence, while also highlighting concerns around misuse, time demands, and technological complexity. Conflicting views on motivation, comfort, and human oversight further emphasized the challenges in integrating technology effectively. To quantify these insights, we applied the Analytic Hierarchy Process (AHP), a structured prioritisation approach that is new within sports science, to rank essential technology features. Findings ranked Reliability (1^st; 18.73%) and Validity (2^nd; 18.42%) as top priorities, with Interpretability (3^rd; 9.95%), Responsiveness (4^th; 9.02%), and Complexity (5^th; 8.08%) also valued. Key training priorities included monitoring Technique (1^st; 15.94%), Performance (2^nd; 15.24%), and Benchmarking (3^rd; 11.90%). The thematic analysis highlights the importance of balanced use of technology. In a complementary approach, the AHP provides a structured method for assessing stakeholder priorities, enhancing decision-making and helping align technology development with practitioner needs. By introducing a framework that bridges communication between engineers, scientists, and practitioners, this study guides the development of user-centred sports technologies that effectively address real-world challenges in high-performance organisations.