Our aim in this study was to test the effect of 10 sessions of biofeedback (BFB) on physiological, psychological, and cognitive functioning of international tennis players. In this randomized controlled trial, we recruited 16 international tennis players (11 male, 5 female; Mage = 17.31, SD = 0.87 years), who were randomly assigned to either an intervention group (IG; n = 8) or a control group (CG; n = 8). Those in the IG received 10 multimodal BFB sessions over four weeks, while those in the CG received no intervention. We assessed physiological, psychological, and cognitive parameters before and after the intervention and found a positive effect for skin temperature, state anxiety, and cognitive performance in the IG versus the CG. We provide preliminary data that 10 sessions of multimodal BFB improved cognitive functions and reduced anxiety symptoms in international tennis players. Future investigators should consider increasing sample size, incorporating an active CG, and studying these effects across diverse athletic disciplines.

暂无摘要。

Sex difference stands as a crucial factor necessitating consideration in personalized thermal environment control, with the mechanisms of its emergence potentially differing across different thermal environments. However, a comparative analysis of sex differences regarding body temperature (skin and core body temperature) and thermal perception across different environments is lacking. A stable environmental experiment (comprising three conditions: 16 °C, 20 °C, and 24 °C) and a transient environmental experiment (involving a whole-body step-change from 19 °C to 35 °C and back to 19 °C) were conducted, with participation from 20 young males and 20 young females. Skin temperature and core body temperature were continuously recorded during the experiments, and three types of thermal perceptions were regularly collected. The results showed that: (1) The impact of thermal environment on females\' skin temperature surpassed that on males, in stable environment, with every 1 °C rise in ambient temperature, the mean skin temperature increased by 0.28 °C for males and 0.35 °C for females respectively; in transient environment, females\' mean skin temperature raise and fell at a faster rate. (2) Males exhibited stronger thermal regulation abilities than females, particularly evident during sudden increase in ambient temperature (from 19 °C to 35 °C), where the reduction magnitude of males\' core body temperature was notably larger. (3) Whether in stable or transient environments, significant sex differences often occurred in skin temperature and thermal sensation at distal parts, particularly at the hand. (4) Males typically fed back higher levels of thermal comfort and thermal acceptability than females, suggesting that in addition to physiological sex differences, psychological sex distinctions also play a role. In summary, personalized design for stable thermal environment can focus on sex differences in skin temperature, while transient thermal environment requires consideration of both skin temperature and core body temperature. A comprehensive consideration of physiological and psychological sex differences aids in creating personalized thermal environments with greater precision.

The transition from wakefulness to sleep occurs when the core body temperature decreases. The latter is facilitated by an increase in the cutaneous blood flow, which dissipates internal heat into the micro-environment surrounding the sleeper\'s body. The rise in cutaneous blood flow near sleep onset causes the distal (hands and feet) and proximal (abdomen) temperatures to increase by about 1 °C and 0.5 °C, respectively. Characterizing the dynamics of skin temperature changes throughout sleep phases and understanding its relationship with sleep quality requires a means to unobtrusively and longitudinally estimate the skin temperature. Leveraging the data from a temperature sensor strip (TSS) with five individual temperature sensors embedded near the surface of a smart bed\'s mattress, we have developed an algorithm to estimate the distal skin temperature with a minute-long temporal resolution. The data from 18 participants who recorded TSS and ground-truth temperature data from sleep during 14 nights at home and 2 nights in a lab were used to develop an algorithm that uses a two-stage regression model (gradient boosted tree followed by a random forest) to estimate the distal skin temperature. A five-fold cross-validation procedure was applied to train and validate the model such that the data from a participant could only be either in the training or validation set but not in both. The algorithm verification was performed with the in-lab data. The algorithm presented in this research can estimate the distal skin temperature at a minute-level resolution, with accuracy characterized by the mean limits of agreement [-0.79 to +0.79 °C] and mean coefficient of determination R2=0.87. This method may enable the unobtrusive, longitudinal and ecologically valid collection of distal skin temperature values during sleep. Therelatively small sample size motivates the need for further validation efforts.

Compromised heat loss due to limited convection and evaporation can increase thermal strain. We aimed to determine the effectiveness of ice slurry ingestion to reduce thermal strain following hyperthermia in a state of compromised heat loss. Twelve healthy males (age: 25 ± 4y) underwent hot water immersion to elevate rectal temperature (Trec) by 1.82 ± 0.08°C on four occasions. In the subsequent 60-min of seated recovery, participants ingested either 6.8 g·kg-1 of ice slurry (-0.6°C) or control drink (37°C) in ambient conditions (21 ± 1°C, 39 ± 10% relative humidity), wearing either t-shirt and shorts (2 trials: ICE and CON) or a whole-body sweat suit (2 trials: ICE-SS and CON-SS). Trec and mean skin temperature (Tsk) were recorded and a two-compartment thermometry model of heat storage was calculated. Heat storage was lower in ICE compared with CON at 20-40min (p ≤ 0.044, d ≥ 0.88) and for ICE-SS compared with CON-SS at 40-60 min (p ≤ 0.012, d ≥ 0.93). Trec was lower in ICE compared with CON from 30-60min (p ≤ 0.034, d ≥ 0.65), with a trend for a reduced Trec in ICE-SS compared with CON-SS at 40min (p = 0.079, d = 0.60). A greater Tsk was found in ICE-SS and CON-SS compared with ICE and CON (p < 0.001, d ≥ 3.37). A trend for a lower Tsk for ICE compared with CON was found at 20-40min (p ≤ 0.099, d ≥ 0.53), no differences were found for ICE-SS vs CON-SS (p ≥ 0.554, d ≤ 0.43). Ice slurry ingestion can effectively reduce heat storage when heat loss through convection and evaporation is compromised, relevant to those wearing personal protective equipment or those with compromised sweat loss. Compromised heat loss delays the reduction in heat storage, possibly related to ice slurry ingestion not lowering Tsk.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals\' functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored.
METHODS: The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection.
RESULTS: Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD.
CONCLUSIONS: This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.

Rapid development and implementation of vaccines constituted a crucial step in containing the COVID-19 pandemic. A comprehensive understanding of physiological responses to these vaccines is important to build trust in medicine.
This study aims to investigate temporal dynamics before and after COVID-19 vaccination in 4 physiological parameters as well as the duration of menstrual cycle phases.
In a prospective trial, 17,825 adults in the Netherlands wore a medical device on their wrist for up to 9 months. The device recorded their physiological signals and synchronized with a complementary smartphone app. By means of multilevel quadratic regression, we examined changes in wearable-recorded breathing rate, wrist skin temperature, heart rate, heart rate variability, and objectively assessed the duration of menstrual cycle phases in menstruating participants to assess the effects of COVID-19 vaccination.
The recorded physiological signals demonstrated short-term increases in breathing rate and heart rate after COVID-19 vaccination followed by a prompt rebound to baseline levels likely reflecting biological mechanisms accompanying the immune response to vaccination. No sex differences were evident in the measured physiological responses. In menstruating participants, we found a 0.8% decrease in the duration of the menstrual phase following vaccination.
The observed short-term changes suggest that COVID-19 vaccines are not associated with long-term biophysical issues. Taken together, our work provides valuable insights into continuous fluctuations of physiological responses to vaccination and highlights the importance of digital solutions in health care.
RR2-10.1186/s13063-021-05241-5.

The aim of the present study was to evaluate skin temperature (Tsk) asymmetries, using infrared thermography, in professional padel players before (PRE), after (POST) and 10 min after training (POST10), and their relationship with perceptual variables and training characteristics. Thermal images were taken of 10 players before, after and 10 min after a standardized technical training. After training, Tsk of the dominant side was higher than before training in the anterior forearm (30.8 ± 0.4 °C vs. 29.1 ± 1.2 °C, p < 0.01; ES = 1.9), anterior shoulder (31.6 ± 0.6 °C vs. 30.9 ± 0.6 °C, p < 0.05; ES = 1.0) posterior arm (29.5 ± 1.0 °C vs. 28.3 ± 1.2 °C, p < 0.05; ES = 1.0), and posterior forearm (30.8 ± 0.9 °C vs. 29.3 ± 1.6 °C, p < 0.05; ES = 1.1). Likewise, these differences were significant POST10 in the anterior arm, anterior forearm, anterior shoulder, posterior arm and posterior forearm. Comparing the different moments of measurement (PRE, POST and POST10), the temperature was higher POST10 in all the regions analyzed except for the shoulder, abdominals, and lower back. Also, correlations were found between fatigue variation and temperature variation between limbs (Tsk dominance), and no correlation was found except between age and posterior thigh (|r| = 0.69; p < 0.05), and between the racket mass and anterior knee (|r| = 0.81; p < 0.01). In conclusion, infrared thermography allows monitoring of skin asymmetries between limbs in professional padel players, but these asymmetries were not related to overall fatigue variation, overall pain variation, years of experience and training hours.

Sepsis continues to be challenging to diagnose due to its non-specific clinical signs and symptoms, emphasizing the importance of early detection. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with conventional diagnostic methods. The research included a total of 121 newborns, with 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Continuous monitoring of biosignals, including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST), was conducted. An algorithm was then developed in Python to identify early signs of sepsis. The model demonstrated the capability to detect sepsis 6 to 48 h in advance with an accuracy rate of 87.67 ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. These findings highlight the critical role of non-invasive biosensing methods in conjunction with conventional biomarkers and cultures, offering a strong foundation for early sepsis detection and improved neonatal care. Further research should be conducted to validate these results across different clinical settings.

Stress is a psychological condition resulting from the body\'s response to challenging situations, which can negatively impact physical and mental health if experienced over prolonged periods. Early detection of stress is crucial to prevent chronic health problems. Wearable sensors offer an effective solution for continuous and real-time stress monitoring due to their non-intrusive nature and ability to monitor vital signs, e.g., heart rate and activity. Typically, most existing research has focused on data collected in controlled environments. Yet, our study aims to propose a machine learning-based approach for detecting stress in a free-living environment using wearable sensors. We utilized the SWEET dataset, which includes data from 240 subjects collected via electrocardiography (ECG), skin temperature (ST), and skin conductance (SC). We assessed four machine learning models, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF), and XGBoost (XGB) in four different settings. This study evaluates the performance of various machine learning models for stress classification using the SWEET dataset. The analysis included two binary classification scenarios (with and without SMOTE) and two multi-class classification scenarios (with and without SMOTE). The Random Forest model demonstrated superior performance in the binary classification without SMOTE, achieving an accuracy of 98.29 % and an F1-score of 97.89 %. For binary classification with SMOTE, the K-Nearest Neighbors model performed best, with an accuracy of 95.70 % and an F1-score of 95.70 %. In the three-level classification without SMOTE, the Random Forest model again excelled, achieving an accuracy of 97.98 % and an F1-score of 97.22 %. For three-level classification with SMOTE, XGBoost showed the highest performance, with an accuracy and F1-score of 98.98 %. These results highlight the effectiveness of different models under various conditions, emphasizing the importance of model selection and preprocessing techniques in enhancing classification performance.