Passive and low-energy strategies to improve sleep thermal comfort and energy resilience during heat waves and cold snaps.

Sleep is a pillar of human health and wellbeing. In high- and middle-income countries, there is a great reliance on heating, ventilation, and air conditioning systems (HVAC) to control the interior thermal environment in the bedroom. However, these systems are expensive to buy, maintain, and operate while being energy and environmentally intensive-problems that may increase due to climate change. Easily-accessible passive and low-energy strategies, such as fans and electrical heated blankets, address these challenges but their comparative effectiveness for providing comfort in sleep environments has not been studied. We used a thermal manikin to experimentally show that many passive and low-energy strategies are highly effective in supplementing or replacing HVAC systems during sleep. Using passive strategies in combination with low-energy strategies that elevate air movement like ceiling or pedestal fans enhances the cooling effect by three times compared to using fans alone. We extrapolated our experimental findings to estimate heating and cooling effects in two historical case studies: the 2015 Pakistan heat wave and the 2021 Texas power crisis. Passive and low-energy strategies reduced sleep-time heat or cold exposure by 69-91%. The low-energy strategies we tested require one to two orders of magnitude less energy than HVAC systems, and the passive strategies require no energy input. These strategies can also help reduce peak load surges and total energy demand in extreme temperature events. This reduces the need for utility load shedding, which can put individuals at risk of hazardous heat or cold exposure. Our results may serve as a starting point for evidence-based public health guidelines on how individuals can sleep better during heat waves and cold snaps without relying on HVAC.

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration.

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

Thermal sensation prediction model for high-speed train occupants based on skin temperatures and skin wettedness.

Passenger thermal comfort in high-speed train (HST) carriages presents unique challenges due to factors such as extensive operational areas, longer travel durations, larger spaces, and higher passenger capacities. This study aims to propose a new prediction model to better understand and address thermal comfort in HST carriages. The proposed prediction model incorporates skin wettedness, vertical skin temperature difference (ΔTd), and skin temperature as parameters to predict the thermal sensation vote (TSV) of HST passengers. The experiments were conducted with 65 subjects, evenly distributed throughout the HST compartment. Thermal environmental conditions and physiological signals were measured to capture the subjects' thermal responses. The study also investigated regional and overall thermal sensations experienced by the subjects. Results revealed significant regional differences in skin temperature between upper and lower body parts. By analyzing data from 45 subjects, We analyzed the effect of 25 variables on TSV by partial least squares (PLS), from which we singled out 3 key factors. And the optimal multiple regression equation was derived to predict the TSV of HST occupants. Validation with an additional 20 subjects demonstrated a strong linear correlation (0.965) between the actual TSV and the predicted values, confirming the feasibility and accuracy of the developed prediction model. By integrating skin wettedness and ΔTd with skin temperature, the model provides a comprehensive approach to predicting thermal comfort in HST environments. This research contributes to advancing thermal comfort analysis in HST and offers valuable insights for optimizing HST system design and operation to meet passengers' comfort requirements.

Case Study: Impacts of Air-Conditioner Air Supply Strategy on Thermal Environment and Energy Consumption in Offices Using BES-CFD Co-Simulation.

Due to energy constraints and people's increasing requirements for indoor thermal comfort, improving energy efficiency while ensuring thermal comfort has become the focus of research in the design and operation of HVAC systems. This study took office rooms with few people occupying them in Wuhan as the research object. The EnergyPlus-Fluent co-simulation method was used to study the impact of 12 forms of air distribution on the thermal environment and air-conditioner energy consumption. The results indicate that 3 m/s supply air velocity and 45° supply air angle are more suitable for the case model in this study. The EnergyPlus-Fluent co-simulation method used in this paper provides a reference for the study of indoor environments in offices with few people occupying them.

The analysis and optimization of thermal sensation of train drivers under occupational thermal exposure.

Introduction: Prolonged exposure of train drivers to thermal discomfort can lead to occupational safety and health (OSH) risks, causing physical and mental injuries. Traditional method of treating human skin as a wall surface fail to observe accurate skin temperature changes or obtain human thermal comfort that adapts to the thermal environment. Methods: This study employs the Stolwijk human thermal regulation model to investigate and optimize the thermal comfort of train drivers. To minimize the time-consuming design optimization, a pointer optimization algorithm based on radial basis function (RBF) approximation was utilized to optimize the train cab ventilation system design and enhance drivers' thermal comfort. The train driver thermal comfort model was developed using Star-CCM+ and 60 operating conditions were sampled using an Optimal Latin Hypercube Design (Opt LHD). Results and Discussion: We analyzed the effects of air supply temperature, air supply volume, air supply angle, solar radiation intensity and solar altitude angle on the local thermal sensation vote (LTSV) and overall thermal sensation vote (OTSV) of the train driver. Finally, the study obtained the optimal air supply parameters for the Heating Ventilation and Air Conditioning (HVAC) in the train cabin under extreme summer conditions, effectively improving the thermal comfort of the driver.

Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction.

Finding the optimal balance between end-user's comfort, lifestyle preferences and the cost of the heating, ventilation and air conditioning (HVAC) system, which requires intelligent decision making and control. This paper proposes a heating control method for HVAC based on dynamic programming. The method first selects the most suitable modeling approach for the controlled building among three machine learning modeling techniques by means of statistical performance metrics, after which the control of the HVAC system is described as a constrained optimization problem, and the action of the controller is given by solving the optimization problem through dynamic programming. In this paper, the variable 'thermal energy storage in building' is introduced to solve the problem that dynamic programming is difficult to obtain the historical state of the building due to the requirement of no aftereffect, while the room temperature and the remaining start hours of the Primary Air Unit are selected to describe the system state through theoretical analysis and trial and error. The results of the TRNSYS/Python co-simulation show that the proposed method can maintain better indoor thermal environment with less energy consumption compared to carefully reviewed expert rules. Compared with expert rule set 'baseline-20 °C', which keeps the room temperature at the minimum comfort level, the proposed control algorithm can save energy and reduce emissions by 35.1% with acceptable comfort violation.

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation.

In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO2) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants' numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality.

A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation.

Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger's predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans' physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R2 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc.

Environmental conditions driven method for automobile cabin pre-conditioning with multi-satisfaction objectives.

The optimal initial pre-conditioning parameter is essential to properly adjust the temperature within the cabin in an effective and accurate way, especially while passengers' thermal comfort and energy-saving properties are both considered. Under the various environmental thermal loads, the pre-conditioning solutions resulting from those pre-fixed cooling parameters are unfeasible for achieving accurately passengers' comfort temperature. In addition, it is also difficult in such a narrow car space to identify a lot of local attributes due to the different material properties and sizes of a variety of structural parts that have various thermal responses to environmental conditions. This paper presents a data-driven decision model to numerically identify the degrees of the cabin thermal characteristic to determine satisfactory pre-conditioning parameter schemes. Initially, based on the thermal data within a vehicle recorded through the whole year at a selected hot climate region of the Middle East, the study levels multiple climate scenes corresponding to change in the cabin air temperature. Then three classification algorithms (Support Vector Machines, Decision Tree, and K-nearest neighbor model) are used to comparatively identify climate levels according to the input conditions. Based on the identified climate level, an appropriate parameters scheme for this level is applied. A comprehensive evaluation index (CEI) is proposed to characterize the passengers' satisfaction in numerical computation, on considering multi-satisfaction objectives including Predicted Mean Vote (PMV), local temperature, air quality, and energy efficiency; and it formulates the pre-conditioning parameter scheme for each climate scene with CEI. Several scene cases are carried out to verify the effectiveness of the proposed models. The result shows that the pre-conditioning schemes of the model can effectively satisfy passengers in multi-satisfaction objectives.

Assessment of thermal comfort in a hot and humid indoor built environment of a kitchen at a university canteen.

BACKGROUND: The hot and humid environment inside the kitchen is a cumulative sign of health impact that deteriorates the well-being and productivity of cooking workers, which could be a barrier to thermal comfort. As the cooking task progresses throughout the day, uncomfortable thermal conditions inside a kitchen work environment may diminish the work quality of the kitchen workers. OBJECTIVE: The objectives of the study were to evaluate the measured environmental factors of thermal comfort during various cooking periods [morning, day, evening, night] and examine the occupant's perception votes followed by further investigating the worker's thermal comfort conditions using PMV, PPD, SET, WBGT, and TSI indices. METHODS: The study was carried out inside the kitchen of the university canteen at IIT Guwahati, India. The objective and subjective measurements were accomplished during the summer season, while CBE thermal comfort software was employed for calculating the thermal comfort indices like PMV, PPD, and SET. RESULTS: The results of this study revealed that during entire cooking time, the recorded environmental factors of thermal comfort were found outside the recommended limits as per ASHRAE-55 standard, which indicates very hot prevalent conditions. Also, cook's perception vote (TSV, TCV) for the existing environment did not follow the central three categories of votes (+1, 0, -1), even the cooking workers were also not satisfied with the prevailing environmental conditions, as 88% subjects responded dissatisfaction with the thermal environment. While, estimated values of thermal comfort indices (PMV, PPD, and SET) designated morning time cooking period slightly comfortable than the other cooking periods, but still not accordance with the ASHRAE-2017 standard. The WBGT index designated day cooking period as hazardous, with rest of cooking periods under severe risk level. In contrast, the TSI index indicated entire cooking periods under "slightly warm" thermal sensation. CONCLUSION: The assessment of this study showed that the existing kitchen environment of the university canteen is not conducive for workers. Improper ventilation design may cause the overheating inside the kitchen, which may increase the dissatisfaction rate of the employed workers and also affects the energy savings in the kitchen environment, which helps maintain thermal comfort. Further studies are required to improve the thermal comfort of the kitchen occupants by providing proper design interventions based on heating and cooling air ventilation systems.

Laminar flow ventilation system to prevent airborne infection during exercise in the COVID-19 crisis: A single-center observational study.

Particulate generation occurs during exercise-induced exhalation, and research on this topic is scarce. Moreover, infection-control measures are inadequately implemented to avoid particulate generation. A laminar airflow ventilation system (LFVS) was developed to remove respiratory droplets released during treadmill exercise. This study aimed to investigate the relationship between the number of aerosols during training on a treadmill and exercise intensity and to elucidate the effect of the LFVS on aerosol removal during anaerobic exercise. In this single-center observational study, the exercise tests were performed on a treadmill at Running Science Lab in Japan on 20 healthy subjects (age: 29±12 years, men: 80%). The subjects had a broad spectrum of aerobic capacities and fitness levels, including athletes, and had no comorbidities. All of them received no medication. The exercise intensity was increased by 1-km/h increments until the heart rate reached 85% of the expected maximum rate and then maintained for 10 min. The first 10 subjects were analyzed to examine whether exercise increased the concentration of airborne particulates in the exhaled air. For the remaining 10 subjects, the LFVS was activated during constant-load exercise to compare the number of respiratory droplets before and after LFVS use. During exercise, a steady amount of particulates before the lactate threshold (LT) was followed by a significant and gradual increase in respiratory droplets after the LT, particularly during anaerobic exercise. Furthermore, respiratory droplets ≥0.3 µm significantly decreased after using LFVS (2120800±759700 vs. 560 ± 170, p<0.001). The amount of respiratory droplets significantly increased after LT. The LFVS enabled a significant decrease in respiratory droplets during anaerobic exercise in healthy subjects. This study's findings will aid in exercising safely during this pandemic.

Mitigation of Aerosols Generated During Exercise Testing With a Portable High-Efficiency Particulate Air Filter With Fume Hood.

BACKGROUND: The role of portable high-efficiency particulate air (HEPA) filters for supplemental aerosol mitigation during exercise testing is unknown and might be relevant during COVID-19 pandemic. RESEARCH QUESTION: What is the effect of portable HEPA filtering on aerosol concentration during exercise testing and its efficiency in reducing room clearance time in a clinical exercise testing laboratory? STUDY DESIGN AND METHODS: Subjects were six healthy volunteers aged 20 to 56 years. In the first experiment, exercise was performed in a small tent with controlled airflow with the use of a stationary cycle, portable HEPA filter with fume hood, and particle counter to document aerosol concentration. Subjects performed a four-stage maximal exercise test that lasted 12 min plus 5 min of pretest quiet breathing and 3 min of active recovery. First, they exercised without mitigation then with portable HEPA filter running. In a separate experiment, room aerosol clearance time was measured in a clinical exercise testing laboratory by filling it with artificially generated aerosols and measuring time to 99.9% aerosol clearance with heating, ventilation, and air conditioning (HVAC) only or HVAC plus portable HEPA filter running. RESULTS: In the exercise experiment, particle concentrations reached 1,722 ± 1,484/L vs 96 ± 124/L (P < .04) for all particles (>0.3 µm), 1,339 ± 1,281/L vs 76 ± 104/L (P < .05) for smaller particles (0.3 to 1.0 µm), and 333 ± 209/L vs 17 ± 19/L (P < .01) for larger particles (1.0 to 5.0 µm) at the end of the protocol in a comparison of mitigation vs portable HEPA filter. Use of a portable HEPA filter in a clinical exercise laboratory clearance experiment reduced aerosol clearance time 47% vs HVAC alone. INTERPRETATION: The portable HEPA filter reduced the concentration of aerosols generated during exercise testing by 96% ± 2% for all particle sizes and reduced aerosol room clearance time in clinical exercise testing laboratories. Portable HEPA filters therefore might be useful in clinical exercise testing laboratories to reduce the risk of COVID-19 transmission.

Airflow Characteristics in Aeromedical Aircraft: Considerations During COVID-19.

OBJECTIVE: The aeromedical transport of coronavirus patients presents risks to clinicians and aircrew. Patient positioning and physical barriers may provide additional protection during flight. This paper describes airflow testing undertaken on fixed wing and rotary wing aeromedical aircraft. METHODS: Airflow testing was undertaken on a stationary Hawker Beechcraft B200C and Leonardo Augusta Westland 139. Airflow was simulated using a Trainer 101 (MSS Professional A/S, Odense Sø, Syddanmark, Denmark) Smoke machine. Different cabin configurations were used along with variations in heating, cooling, and ventilation systems. RESULTS: For the Hawker Beechcraft B200C, smoke generated within the forward section of the cabin was observed to fill the cabin to a fluid boundary located in-line with the forward edge of the cargo door. With the curtain closed, smoke was only observed to enter the cockpit in very small quantities. For the Leonardo AW139, smoke generated within the cabin was observed to expand to fill the cabin evenly before dissipating. With the curtain closed, smoke was observed to enter the cockpit only in small quantities CONCLUSION: The use of physical barriers in fixed wing and rotary wing aeromedical aircraft provides some protection to aircrew. Optimal positioning of the patient is on the aft stretcher on the Beechcraft B200C and on a laterally orientated stretcher on the AW139. The results provide a baseline for further investigation into methods to protect aircrew during the coronavirus pandemic.

Experimental study on dust removal optimization of shearer external spray in air velocity.

In order to clarify the influence of air velocity on the atomization effect of shearer external spray and optimize dust suppression performance, the dust removal experiment was carried out by utilizing the self-designed external spray experiment platform. The effect of three kinds of air velocity on the atomization effect of main spray parameters was investigated to clarify the influence of air velocity on the atomization effect of shearer external spray and optimize dust suppression performance. The results showed that the influence of air velocity on droplet size and distribution width of the droplet size was slightly less than the spray pressure. The average diameter of the droplet was 54.211 µm, and the particle size distribution of the droplet was more uniform and concentrated when air velocity was 2 m/s, the pressure was 5 MPa and nozzle diameter was 1.0 mm. When the air velocity was less than 2 m/s and the spray pressure was 3-5 MPa, the atomization effect was better by using 1.0 or 1.2 mm diameter nozzle. Through on-site applications and optimization, the leeward total dust removal efficiency of the shearer can reach 79.43%, and the maximum increase range can reach 18.51 percentage points. The respiratory dust removal efficiency of the shearer can reach 87.45%, and the maximum increase range can reach 11.87 percentage points.

SARS-CoV-2: Disinfection Strategies to Prevent Transmission of Neuropathogens via Air Conditioning Systems.

Several lines of evidence suggest the role of air-conditioning systems in the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, the likelihood of novel coronavirus to take refuge inside a microbial Trojan horse, that is, Acanthamoeba, can further enhance possibility of SARS-CoV-2 transmission in the environment. Here we propose the use of various disinfection strategies that can be employed using filters with antimicrobial fabricated surfaces or using UV irradiation to achieve germicidal properties for removal of pathogenic microbes such as SARS-CoV-2 and amoebae in the ventilation systems.

Heating, ventilation and air conditioning (HVAC) in intensive care unit.

The aim of this review is to describe variation in standards and guidelines on 'heating, ventilation and air-conditioning (HVAC)' system maintenance in the intensive care units, across the world, which is required to maintain good 'indoor air quality' as an important non-pharmacological strategy in preventing hospital-acquired infections. An online search and review of standards and guidelines published by various societies including American Institute of Architects (AIA), American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), Centers for Disease Control and Prevention (CDC), Department of Health Estates and Facilities Division, Health Technical Memorandum 2025 (HTM) and Healthcare Infection Control Practices Advisory Committee (HICPAC) along with various national expert committee consensus statements, regional and hospital-based protocols available in a public domain were retrieved. Selected publications and textbooks describing HVAC structural aspects were also reviewed, and we described the basic structural details of HVAC system as well as variations in the practised standards of HVAC system in the ICU, worldwide. In summary, there is a need of universal standards for HVAC system with a specific mention on the type of ICU, which should be incorporated into existing infection control practice guidelines.

Airborne route and bad use of ventilation systems as non-negligible factors in SARS-CoV-2 transmission.

The world is facing a pandemic of unseen proportions caused by a corona virus named SARS-CoV-2 with unprecedent worldwide measures being taken to tackle its contagion. Person-to-person transmission is accepted but WHO only considers aerosol transmission when procedures or support treatments that produce aerosol are performed. Transmission mechanisms are not fully understood and there is evidence for an airborne route to be considered, as the virus remains viable in aerosols for at least 3 h and that mask usage was the best intervention to prevent infection. Heating, Ventilation and Air Conditioning Systems (HVAC) are used as a primary infection disease control measure. However, if not correctly used, they may contribute to the transmission/spreading of airborne diseases as proposed in the past for SARS. The authors believe that airborne transmission is possible and that HVAC systems when not adequately used may contribute to the transmission of the virus, as suggested by descriptions from Japan, Germany, and the Diamond Princess Cruise Ship. Previous SARS outbreaks reported at Amoy Gardens, Emergency Rooms and Hotels, also suggested an airborne transmission. Further studies are warranted to confirm our hypotheses but the assumption of such way of transmission would cause a major shift in measures recommended to prevent infection such as the disseminated use of masks and structural changes to hospital and other facilities with HVAC systems.

In situ efficiency of filters in residential central HVAC systems.

High-efficiency filtration in residential forced-air heating, ventilation, and air conditioning (HVAC) systems protects equipment and can reduce exposure to particulate matter. Laboratory tests provide a measure of the nominal efficiency, but they may not accurately reflect the in situ efficiency of the filters because of variations in system conditions and changes in filter performance over time. The primary focus of this paper is to evaluate the effective filtration efficiency, which is inclusive of any loading and system impacts, in 21 occupied residential homes through in-duct concentration measurements. We considered the role of filter media by testing both electret and non-electret media, as well as the role of loading by considering new and used filters. The results show that filters with higher nominal efficiency generally had higher effective filtration efficiency in the same home. In terms of performance change, there is no significant difference in efficiency between initial and 3-month non-electret filters, but the efficiency of electret filters generally decreased over time. However, both nominal efficiency and performance change were vastly overshadowed by the wide variety in loading and system conditions across homes, making it hard to predict filter efficiency in a given home without in situ measurements.

Comparing Forced-Air to Resistive-Polymer Warming for Perioperative Temperature Management: A Retrospective Study.

PURPOSE: Compare perioperative temperature management between forced-air warming (FAW) and resistive-polymer heating blankets (RHBs). DESIGN: A retrospective, quasi-experimental study. METHODS: Retrospective data analysis of nonspine orthopedic cases (N = 426) over a one-year period including FAW (n = 119) and RHBs (n = 307). FINDINGS: FAW was associated with a significantly higher final intraoperative temperature (P = .001, d = 0.46) than the RHB. The incidence of hypothermia was not found to be significantly different at the end (P = .102) or anytime throughout surgery (P = .270). Of all patients who started hypothermic, the FAW group had a lower incidence of hypothermia at the end of surgery (P = .023). CONCLUSIONS: FAW was associated with higher final temperatures and a greater number of normothermic patients than RHBs. However, no causal relationship between a warming device and hypothermia incidence should be assumed.