Physiological Data Research Articles

Using Wearable Skin Temperature Data to Advance Tracking and Characterization of the Menstrual Cycle in a Real-World Setting.

The menstrual cycle is a loop involving the interplay of different organs and hormones, with the capacity to impact numerous physiological processes, including body temperature and heart rate, which in turn display menstrual rhythms. The advent of wearable devices that can continuously track physiological data opens the possibility of using these prolonged time series of skin temperature data to noninvasively detect the temperature variations that occur in ovulatory menstrual cycles. Here, we show that the menstrual skin temperature variation is better represented by a model of oscillation, the cosinor, than by a biphasic square wave model. We describe how applying a cosinor model to a menstrual cycle of distal skin temperature data can be used to assess whether the data oscillate or not, and in cases of oscillation, rhythm metrics for the cycle, including mesor, amplitude, and acrophase, can be obtained. We apply the method to wearable temperature data collected at a minute resolution each day from 120 female individuals over a menstrual cycle to illustrate how the method can be used to derive and present menstrual cycle characteristics, which can be used in other analyses examining indicators of female health. The cosinor method, frequently used in circadian rhythms studies, can be employed in research to facilitate the assessment of menstrual cycle effects on physiological parameters, and in clinical settings to use the characteristics of the menstrual cycles as health markers or to facilitate menstrual chronotherapy.

Surface‐Level Muscle Deformation as a Correlate for Joint Torque

AbstractWearable technology excels in estimating kinematic and physiological data, but estimating biological torques remains an open challenge. Deformation of the skin above contracting muscles—surface‐level muscle deformation—has emerged as a promising signal for joint torque estimation. However, a lack of ground‐truth measures of surface‐level muscle deformation has complicated the evaluation of wearable sensors designed to measure surface‐level muscle deformation. A non‐contact methodology is proposed for ground‐truth measurement of surface‐level muscle deformation using a 2D laser profilometer. It shows how three metrics of surface‐level muscle deformation—peak radial displacement: r = 0.94 ± 0.05, surface curvature: r = 0.78 ± 0.10, surface strain: r = 0.83 ± 0.12—correlate strongly to changes in volitional elbow torque, further exploring the impact of measurement location or joint angle on these relationships. A nonlinear, lead‐lag relationship between surface‐level muscle deformation and torque is also found. The findings suggest that surface‐level muscle deformation is a promising signal for non‐invasive, real‐time estimates of torque. By standardizing measurement, the methodology can help inform the design of future wearable sensors.

Stress Detection Based on Naïve Bayes Algorithm

Tension is a widespread concern impacting the mental and physical health of individuals, resulting in various health issues. Machine-based learning (ML) has emerged as a favored tool for identifying tension. ML strategies have displayed encouraging outcomes in recognizing trends and attributes from different physiological and behavioral data streams such as heart rate, blood pressure, and vocal signals. The main objective of utilizing ML for tension detection is to offer precise, non-invasive, and economical approaches for early tension identification and intervention. Overall, ML-based tension detection shows substantial potential for delivering an impartial, effective, and expandable strategy for tension monitoring and intervention. Additional exploration is needed to tackle the obstacles linked to data compilation, attribute extraction, and model adaptation to varied populations and scenarios. Keywords: Automated Learning, Simple-minded Bayes, Information, Gloom, Tension.

Exploring the Potential of Biostimulants to Optimize Lettuce Cultivation in Coupled and Decoupled Aquaponics Systems: Growth Performance, Functional Characteristics and Metabolomic Analysis

Zero-discharge and low-input aquaponics systems are a promising alternative to the intensive agricultural and aquacultural production systems currently used, ensuring high environmental sustainability. However, new approaches and management practices are needed to increase their productivity to reach the yields of classic production systems. In this context, the present study investigated for the first time the potential of two biostimulants to improve lettuce performance in aquaponics, whether coupled or decoupled, with hydroponics serving as a control. A comprehensive evaluation was conducted to assess the plant functional (focusing on the photosynthetic process evaluation) and growth responses at the whole-plant level. In addition, the nutritional state of the leaves was determined and metabolomic analysis was performed at the cellular level, the latter also for the first time in aquaponics research. The results demonstrated the limitations that coupled aquaponics poses in relation to lettuce growth, function and metabolism, which were already obvious from the 12th day of the experiment. Indicatively, the plants grown under coupled aquaponics exhibited a notable decrease in the leaf fresh weight, potassium content and nitrogen content, with reductions of 80%, 60%, and 30%, respectively, in comparison to the hydroponics control. However, the combined physiological and metabolomic data indicate that these plants down-regulate processes and metabolism to acclimate to low nutrient levels in lettuce leaves rather than experiencing damage. The application of biostimulants did not significantly optimize the plants’ performance, though one of them appeared to be effective in improving some aspects of the photochemical efficiency. The decoupled and hydroponics systems resulted in similarly high yields and efficiency in terms of plant function, without any marked contribution from the biostimulants. We conclude that the decoupled aquaponics system has been successful in achieving yields comparable to those of hydroponics, with lower chemical inputs. Future studies should focus on examining other biostimulants in this system to further improve its performance while maintaining its environmental benefits within a circular economy framework.

Identification and expression analysis of Jr4CLs gene family based on transcriptome and physiological data in walnut (Juglans regia)

Identification and expression analysis of Jr4CLs gene family based on transcriptome and physiological data in walnut (Juglans regia)

Assessing the Legibility of Arabic Road Signage Using Eye Gazing and Cognitive Loading Metrics

This research study aimed to evaluate the legibility of Arabic road signage using an eye-tracking approach within a virtual reality (VR) environment. The study was conducted in a controlled setting involving 20 participants who watched two videos using the HP Omnicept Reverb G2. The VR device recorded eye gazing details in addition to other physiological data of the participants, providing an overlay of heart rate, eye movement, and cognitive load, which in combination were used to determine the participants’ focus during the experiment. The data were processed through a schematic design, and the final files were saved in .txt format, which was later used for data extraction and analysis. Through the execution of this study, it became apparent that employing eye-tracking technology within a VR setting offers a promising method for assessing the legibility of road signs. The outcomes of the current research enlightened the vital role of legibility in ensuring road safety and facilitating effective communication with drivers. Clear and easily comprehensible road signs were found to be pivotal in delivering timely information, aiding navigation, and ultimately mitigating accidents or confusion on the road. As a result, this study advocates for the utilization of VR as a valuable platform for enhancing the design and functionality of road signage systems, recognizing its potential to contribute significantly to the improvement of road safety and navigation for drivers.

Physiological Studies and Transcriptomic Analysis Reveal the Mechanism of Saline-Alkali Stress Resistance of Malus sieversii f. niedzwetzkyan

Malus sieversii f. niedzwetzkyan, a wild species capable of growing on saline-alkali soil in Xinjiang, is the most promising horticultural crop for improving the saline-alkali wasteland. However, the tolerance of M. niedzwetzkyan to saline-alkali stress and the underlying molecular mechanisms remains largely unknown. Here, we conducted a hydroponic experiment in which M. niedzwetzkyana and M. domestica “Royal Gala” seedlings were subjected to 150 mM saline-alkali stress. Physiological data showed that M. niedzwetzkyana had a strong ROS scavenging ability and ion transport ability, and its saline-alkali resistance was higher than that of M. “Royal Gala”. Saline-alkali stress also promoted the synthesis of anthocyanins in M. niedzwetzkyana. Transcriptome analysis was conducted on the leaves and roots of M. niedzwetzkyana at different time points under saline-alkali stress (0 h, 6 h, and 12 h). Transcriptome analysis revealed that saline stress down-regulated most genes involved in the anthocyanin flavonoid synthesis pathway. Transcription levels of genes involved in antioxidant enzyme activity and ion transport were altered. We identified hub genes related to superoxide dismutase as well as Na+ and K+ transport using weighted gene co-expression network analysis. This study elucidated, for the first time at the molecular level, the saline-alkali tolerance of M. niedzwetzkyana, including the complex changes in pathways that regulate reactive oxygen species homeostasis, ion uptake, and anthocyanoside synthesis under saline-alkali stress conditions. This research provides an important genetic resource for identifying genes involved in responses to saline-alkali stress.

Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study

BackgroundSignal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet.MethodsaSAH patients from 2 prospective cohorts (Zurich—derivation cohort, Aachen—validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1–4 vs. 5–8) or ordinal outcome (GOSE—extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination.ResultsA total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity.ConclusionsMSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

A chronic intermittent haemodialysis pig model for functional evaluation of dialysis membranes.

Performance evaluation of new dialysis membranes is primarily performed in vitro, which can lead to differences in clinical results. Currently, data on dialysis membrane performance and safety are available only for haemodialysis patients. Herein, we aimed to establish an in vivo animal model of dialysis that could be extrapolated to humans. We created a bilateral nephrectomy pig model of renal failure, which placed a double-lumen catheter with the hub exposed dorsally. Haemodialysis was performed in the same manner as in humans, during which clinically relevant physiologic data were evaluated. Next, to evaluate the utility of this model, the biocompatibility of two kinds of membranes coated with or without vitamin E used in haemodiafiltration therapy were compared. Haemodialysis treatment was successfully performed in nephrectomized pigs under the same dialysis conditions (4 h per session, every other day, for 2 weeks). In accordance with human clinical data, regular dialysis alleviated renal failure in pigs. The vitamin E-coated membrane showed a significant reduction rate of advanced oxidation protein products during dialysis than non-coated membrane. In conclusion, this model mimics the pathophysiology and dialysis condition of patients undergoing haemodialysis. This dialysis treatment model of renal failure will be useful for evaluating the performance and safety of dialysis membranes.

A bibliometric review of perceptions of the built environment supported by multi-source data

ABSTRACT Information and digital technologies have revolutionized research methodologies for measuring people’s perceptions of built environments. Vast amounts of urban data can now be collected, providing more accurate and intuitive presentation and analysis tools. This enhances the research on the built environment from a human-centered perspective. In this paper, with the help of the CiteSpace knowledge map analysis tool, manual deep intervention was combined with clustering and stratified sampling of literature information to identify hot topics and research trends. The results demonstrate that there are two new perspectives. The first is the up-bottom urban/group perspective, specifically a holistic study of group perceptions by large-scale data, represented by big data, with technologies such as artificial intelligence. The second is the bottom-up humanistic/individual perspective, which accurately examines individual perceptions through physiological data based on ergonomics techniques, psychological, and other small-scale data. Furthermore, these two perspectives are interconnected, indicating a cross-trend in both directions. By bridging the gap between subjective perception and the real-world environment, integrating multi-source data offers a more precise and efficient foundation for thoroughly evaluating the built environment, which would help us better understand the built environment and satisfy the users’ demands in the research and practice of urban design.

Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults

Identifying stress in older adults is a crucial field of research in health and well-being. This allows us to take timely preventive measures that can help save lives. That is why a nonobtrusive way of accurate and precise stress detection is necessary. Researchers have proposed many statistical measurements to associate stress with sensor readings from digital biomarkers. With the recent progress of Artificial Intelligence in the healthcare domain, the application of machine learning is showing promising results in stress detection. Still, the viability of machine learning for digital biomarkers of stress is under-explored. In this work, we first investigate the performance of a supervised machine learning algorithm (Random Forest) with manual feature engineering for stress detection with contextual information. The concentration of salivary cortisol was used as the golden standard here. Our framework categorizes stress into No Stress, Low Stress, and High Stress by analyzing digital biomarkers gathered from wearable sensors. We also provide a thorough knowledge of stress in older adults by combining physiological data obtained from wearable sensors with contextual clues from a stress protocol. Our context-aware machine learning model, using sensor fusion, achieved a macroaverage F-1 score of 0.937 and an accuracy of 92.48% in identifying three stress levels. We further extend our work to get rid of the burden of manual feature engineering. We explore Convolutional Neural Network (CNN)-based feature encoder and cortisol biomarkers to detect stress using contextual information. We provide an in-depth look at the CNN-based feature encoder, which effectively separates useful features from physiological inputs. Both of our proposed frameworks, i.e., Random Forest with engineered features and a Fully Connected Network with CNN-based features validate that the integration of digital biomarkers of stress can provide more insight into the stress response even without any self-reporting or caregiver labels. Our method with sensor fusion shows an accuracy and F-1 score of 83.7797% and 0.7552, respectively, without context and 96.7525% accuracy and 0.9745 F-1 score with context, which also constitutes a 4% increase in accuracy and a 0.4 increase in F-1 score from RF.

Quantitative flow ratio versus fractional flow reserve for Heart Team decision-making in multivessel disease: the randomised, multicentre DECISION QFR trial.

Vessel-level physiological data derived from pressure wire measurements are one of the important determinant factors in the optimal revascularisation strategy for patients with multivessel disease (MVD). However, these may result in complications and a prolonged procedure time. The feasibility of using the quantitative flow ratio (QFR), an angiography-derived fractional flow reserve (FFR), in Heart Team discussions to determine the optimal revascularisation strategy for patients with MVD was investigated. Two Heart Teams were randomly assigned either QFR- or FFR-based data of the included patients. They then discussed the optimal revascularisation mode (percutaneous coronary intervention [PCI] or coronary artery bypass grafting [CABG]) for each patient and made treatment recommendations. The primary endpoint of the trial was the level of agreement between the treatment recommendations of both teams as assessed using Cohen's kappa. The trial included 248 patients with MVD from 10 study sites. Cohen's kappa in the recommended revascularisation modes between the QFR and FFR approaches was 0.73 [95% confidence interval {CI} : 0.62-0.83]. As for the revascularisation planning, agreements in the target vessels for PCI and CABG were substantial for both revascularisation modes (Cohen's kappa=0.72 [95% CI: 0.66-0.78] and 0.72 [95% CI: 0.66-0.78], respectively). The team assigned to the QFR approach provided consistent recommended revascularisation modes even after being made aware of the FFR data (Cohen's kappa=0.95 [95% CI:0.90-1.00]). QFR provided feasible physiological data in Heart Team discussions to determine the optimal revascularisation strategy for MVD. The QFR and FFR approaches agreed substantially in terms of treatment recommendations.

EmoVis: exploring data-enabled analogue journaling to promote self-reflection for mental wellness among college students

ABSTRACT College students are known to face many stressors, making them one of the most vulnerable groups to mental disorders. To tackle such an issue, this paper presents a design study that investigated data-enabled analogue journaling (DEAJ) to help college students improve their mental wellness through paper-based self-reflection aided by automatic self-tracking. Specifically, based on several design iterations we developed a DEAJ tool, called EmoVis, which could generate a printed visualisation based on daily physiological data and event tags to support doodling or structured writing in analogue journaling for everyday stress management. We conducted a six-day mixed methods field study with 32 college students to evaluate the effectiveness and user experience of EmoVis. Results suggested that EmoVis can significantly improve the engagement and need for self-reflection due to context-based reflection and data-enabled exploratory journaling. Furthermore, the doodling canvas was experienced as a joyful tool for mindfulness and creative DEAJ, while reflecting with a writing template was perceived as efficient. The paper concludes with a discussion of the implications of DEAJ approaches for daily stress coping in students’ college life.

Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries

BackgroundIn low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data.MethodsWe implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software.ResultsThe model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%.ConclusionsWe demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

Psychophysiological Investigation of the Effects of Virtual Reality, the New Dimension of Retail Shopping, on Generation Z

Virtual reality has begun to show itself in many areas of our lives as its accessibility becomes easier. Especially in recent years, it has been observed that investments have been made in this field in the retail sector and many brands have created virtual reality stores. The research emphasizes the need for new studies on consumer behavior in virtual reality stores, the new interface of the retail sector, in order to shed light on future investments. The attitude of Generation Z, who grew up with technology and is open to innovation, towards the virtual reality store is important because they are the consumers of today and the future. Given this situation, in the current study, the attitude of Generation Z towards the virtual reality store is examined psychophysiologically. The physiological outcomes of university students (n = 40) between the ages of 18–29 during the shopping experience in a virtual reality store were analyzed with electrodermal activity (EDA) and photoplethysmography (PPG) research techniques. Physiological data were analyzed by comparing them with the data obtained from the Socio-Demographic characteristics and the Hedonic and Utilitarian Consumption Behaviors Scale. In line with the findings in the study, the psychophysiological effects of the virtual reality store on Generation Z participants did not show significant differences in terms of demographic characteristics and hedonic and utilitarian consumption behaviors. However, the increase in the heart rate averages of the majority of the participants during the experience was significant. This is attributed to the fact that virtual reality adresses both hedonic and utilitarian consumption.

An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors

Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals’CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts.

Risk prediction strategies using intraoperative physiological data in adults undergoing surgery: a systematic review study protocol

Abstract Postoperative mortality accounts for 7.7% of all global deaths, while postoperative complications increase 1-year mortality by 60%. Risk prediction models for postoperative complications and mortality can facilitate tailored risk mitigation strategies. However, most models incorporate only preoperative patient-related factors as predictors and do not capture dynamic risks or intraoperative events. This systematic review seeks to evaluate the predictive capability of intraoperative physiology derived from routine anaesthetic monitoring and the feature extraction methods for these variables. This review will include both prospective and retrospective studies that incorporate intraoperative physiological measurements into the development, validation or updating of a statistical prediction model, to identify those at risk of major end-organ (cardiovascular, pulmonary, renal and neurological) complications and mortality up-to 90 days postoperatively. We will identify models developed in two settings: those undergoing cardiac surgery and heterogeneous adult patient cohorts undergoing non-cardiac surgery. The review will be reported according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This review will evaluate the available literature on intraoperative physiology as predictor variables, to improve feature extraction methods for risk prediction models development while understanding how to capitalize on the vast routinely collected intraoperative physiological data sets that are increasingly available. This review is registered on PROSPERO, registration number CRD42023474384.

Digital Interventions to Understand and Mitigate Stress Response: Protocol for Process and Content Evaluation of a Cohort Study.

Staffing and resource shortages, especially during the COVID-19 pandemic, have increased stress levels among health care workers. Many health care workers have reported feeling unable to maintain the quality of care expected within their profession, which, at times, may lead to moral distress and moral injury. Currently, interventions for moral distress and moral injury are limited. This study has the following aims: (1) to characterize and reduce stress and moral distress related to decision-making in morally complex situations using a virtual reality (VR) scenario and a didactic intervention; (2) to identify features contributing to mental health outcomes using wearable, physiological, and self-reported questionnaire data; and (3) to create a personal digital phenotype profile that characterizes stress and moral distress at the individual level. This will be a single cohort, pre- and posttest study of 100 nursing professionals in Ontario, Canada. Participants will undergo a VR simulation that requires them to make morally complex decisions related to patient care, which will be administered before and after an educational video on techniques to mitigate distress. During the VR session, participants will complete questionnaires measuring their distress and moral distress, and physiological data (electrocardiogram, electrodermal activity, plethysmography, and respiration) will be collected to assess their stress response. In a subsequent 12-week follow-up period, participants will complete regular assessments measuring clinical outcomes, including distress, moral distress, anxiety, depression, and loneliness. A wearable device will also be used to collect continuous data for 2 weeks before, throughout, and for 12 weeks after the VR session. A pre-post comparison will be conducted to analyze the effects of the VR intervention, and machine learning will be used to create a personal digital phenotype profile for each participant using the physiological, wearable, and self-reported data. Finally, thematic analysis of post-VR debriefing sessions and exit interviews will examine reoccurring codes and overarching themes expressed across participants' experiences. The study was funded in 2022 and received research ethics board approval in April 2023. The study is ongoing. It is expected that the VR scenario will elicit stress and moral distress. Additionally, the didactic intervention is anticipated to improve understanding of and decrease feelings of stress and moral distress. Models of digital phenotypes developed and integrated with wearables could allow for the prediction of risk and the assessment of treatment responses in individuals experiencing moral distress in real-time and naturalistic contexts. This paradigm could also be used in other populations prone to moral distress and injury, such as military and public safety personnel. ClinicalTrials.gov NCT05923398; https://clinicaltrials.gov/study/NCT05923398. DERR1-10.2196/54180.

Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: study protocol of a randomized controlled trial

BackgroundStress is a widespread phenomenon and reality of everyday life, entailing negative consequences for physical and psychological wellbeing. Previous studies have indicated that exposure to greenspaces and nature-based interventions are promising approaches to reducing stress and promoting restoration. However, an increasing percentage of the population lives in urban regions with limited opportunities to spend time in greenspaces. In addition, urban settings typically feature increased levels of noise, which represents a major environmental stressor. Although various studies have compared the effects of exposure to greenspaces versus urban built environments, evidence of the effects of noise in this context is very limited. Psychophysiological benefits of exposure to greenspaces compared to urban built environments reported in earlier studies might be less (or at least not only) due to features of the greenspaces than to additional stressors, such as road traffic noise in the urban built environment. Hence, differences in the effects attributed to greenness in previous studies may also be due to potentially detrimental noise effects in comparison settings. This paper reports the study protocol for a randomized, controlled intervention study comparing the effects of walking in forest versus urban built environments, taking road traffic noise exposure during walks in the respective settings into account.MethodsThe protocol envisages a field study employing a pretest–posttest design to compare the effects of 30-min walks in urban built environments and forests with different road traffic noise levels. Assessments will consist of self-reported measures, physiological data (salivary cortisol and skin conductance), an attention test, and noise, as well as greenness measurements. The outcomes will be restoration, stress, positive and negative affect, attention, rumination, and nature connectedness.DiscussionThe results will inform about the restorative effect of walking in general, of exposure to different types of environments, and to different noise levels in these sites. The study will provide insights into the benefits of walking and nature-based interventions, taking into account the potential detrimental effects of noise exposure. It will thus facilitate a better understanding of low-threshold interventions to prevent stress and foster wellbeing.Trial registrationISRCTN48943261; Registered 23.11.2023.

Predictive Modeling of Heart Rate Dynamics based on Physical Characteristics and Exercise Parameters: A Machine Learning Approach

To accurately forecast heart rate changes during exercise, which is essential for customized health monitoring and improving training regimens, it is necessary to comprehend both the physiological foundations and the technical capacities for data processing. This research utilizes Machine Learning (ML) methodologies to predict heart rate reactions based on physical characteristics and activity variables. Our research focuses on the health and sports aspects of our results, using a comprehensive dataset that includes a wide range of activity types and ambient circumstances across 12,000 sets. We establish a connection between the ability of models such as Linear Regression (LR) and Extreme Gradient Boosting (XGB) to predict outcomes and their practical use in exercise management and optimizing athlete performance. These models accurately forecast variations in heart rate and also provide insights into the cardiovascular demands of various physical activities. Standard metrics measure the effectiveness of these models. The Linear Regression (LR) model achieved a Mean Absolute Error (MAE) of 0.419, a Mean Squared Error (MSE) of 0.294, a Root Mean Squared Error (RMSE) of 0.543, and an R-Squared value of 0.997. On the other hand, the Extreme Gradient Boosting (XGB) Regressor model achieved a Mean Absolute Error (MAE) of 0.421, a Mean Squared Error (MSE) of 0.335, a Root Mean Squared Error (RMSE) of 0.578, and an R-Squared value of 0.996. These metrics demonstrate the usefulness of these models in real-world scenarios. Our study's findings demonstrate that the combination of physiological data and powerful machine learning models may improve an individual's comprehension of fitness levels and the requirements for adaptive training. This study not only adds to the field of computational physiology, but it also aids in the creation of adaptive, real-time therapies for improving health and performance.