Cognitive performance was reduced by higher air temperature even when thermal comfort was maintained over the 24-28°C range

Home indoor air quality and cognitive function over one year for people working remotely during COVID-19

The coronavirus disease 2019 (COVID-19) pandemic triggered an increase in remote work-from-home for office workers. Given that many homes now function as offices despite not being designed to support office work, it is critical to research the impact of indoor air quality (IAQ) in homes on the cognitive performance of people working from home. In this study, we followed 206 office workers across the U.S. over one year under remote or hybrid-remote settings during 2021-2022. Participants placed two real-time, consumer-grade indoor environmental monitors in their home workstation area and bedroom. Using a custom smartphone application geofenced to their residential address, participants responded to surveys and periodic cognitive function tests, including the Stroop color-word interference test, Arithmetic two-digit addition/subtraction test, and Compound Remote Associates Task (cRAT). Exposures assessed included carbon dioxide (CO2) and thermal conditions (indoor heat index: a combination of temperature and relative humidity) averaged over 30 minutes prior to each cognitive test. In fully adjusted longitudinal mixed models (n≤121), we found that indoor thermal conditions at home were associated with cognitive function outcomes non-linearly (p<0.05), with poorer cognitive performance on the Stroop test and poorer creative problem-solving on the cRAT when conditions were either too warm or too cool. Most indoor CO2 levels were <640 ppm, but there was still a slight association between higher CO2 and poorer cognitive performance on Stroop (p=0.09). Our findings highlight the need to enhance home indoor environmental quality for optimal cognitive function during remote work, with benefits for both employees and employers.

Effect of extreme high temperature on cognitive function at different time scales: A national difference-in-differences analysis

BACKGROUND

Mounting evidence has demonstrated that high temperature was associated with adverse health outcomes, especially morbidity and mortality. Nonetheless, the impact of extreme high temperature on cognitive performance, which is the fundamental capacity for interpreting one's surroundings, decision-making, and acquiring new abilities, has not been thoroughly investigated.

METHODS

We aimed to assess associations between extreme high temperature at different time scales and poor cognitive function. We used longitudinal survey data from the three waves of data from China Family Panel Study, providing an 8-year follow-up of 53,008 participants from China. We assessed temperature and extreme high temperature exposure for each participant based on the residential area and date of cognitive test. We defined the proportion of days/hours above 32 °C as the metric of the exposure to extreme high temperature. Then we used generalized additive model and difference-in-differences approach to explore the associations between extreme high temperature and cognitive function.

RESULTS

Our results demonstrated that either acute exposure or long-term exposure to extreme high temperature was associated with cognitive decline. At hourly level, 0-1 hour acute exposure to extreme high temperature would induce -0.93 % (95 % CI: -1.46 %, -0.39 %) cognitive change. At annual level, 10 percentage point increase in the hours proportion exceeding 32 °C in the past two years induced -9.87 % (95 % CI: -13.99 %, -5.75 %) cognitive change. Furthermore, subgroup analyses indicated adaptation effect: for the same 10 percentage increase in hours proportion exceeding 32 °C, people in warmer areas had cognitive change of -6.41 % (-11.22 %, -1.61 %), compared with -15.30 % (-21.07 %, -9.53 %) for people in cool areas.

CONCLUSION

Our results demonstrated that extreme high temperature was associated with reduced cognitive function at hourly, daily and annual levels, warning that people should take better measures to protect the cognitive function in the context of climate change.

Environmental Quality bOX (EQ-OX): A Portable Device Embedding Low-Cost Sensors Tailored for Comprehensive Indoor Environmental Quality Monitoring

The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users' health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system.

The field of human building interaction for convergent research and innovation for intelligent built environments

Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

The effect of inhaled air temperature on thermal comfort, perceived air quality, acute health symptoms and physiological responses at two ambient temperatures

We explored the importance of inhaled air temperature on thermal comfort, perceived air quality, acute non-clinical health symptoms, and physiological responses. Sixteen subjects stayed in a stainless-steel chamber for 90 min. They experienced four conditions with two inhaled air temperatures of 22 and 30°C and two ambient temperatures of 22 and 30°C in a 2 × 2 design. They wore breathing masks covering their mouth and nose to control the inhaled air temperature; the air was provided from an adjacent twin stainless-steel chamber. The subjects evaluated thermal conditions and health symptoms on visual-analogue scales. Skin temperature and electrocardiography were recorded. Whole-body thermal sensation and skin temperature did not change when the temperature of inhaled air was changed. Perceived air quality was significantly improved when subjects sat in the chamber at 30°C and inhaled air with a temperature of 22°C; under these conditions lip and throat dryness were significantly reduced. The lower inhaled air temperature increased time-domain heart rate variability indicators and decreased heart rate and the LF/HF ratio, suggesting that the parasympathetic nervous system was activated and the sympathetic nervous system was suppressed.

Quantitative Investigation of Body Part Selection for Data-Driven Personal Overall Thermal Preference Prediction

Personal thermal preference information can help to create a building environment that satisfies all staff, instead of an environment that only satisfies most people, to enhance personal thermal comfort. Research has shown that thermal preference can be predicted using parameters that are based on various local body parts, but the selected body parts are often different. Using too many body parts for the measurements leads to high costs, while using too few body parts results in large errors. In this study, 19 adult subjects (8 females and 11 males) were recruited, their overall and local thermal preferences were surveyed, and the skin temperature of seven body parts were measured. A machine learning algorithm, random forest, was employed to analyse the contributions of different body parts. Three criteria (the best combination, fewest combination, and common combinations) were employed to select body parts to use to establish thermal preference models for individuals and groups. The results show that the prediction power of these combinations reached 0.91 ± 0.07 (accuracy), 0.75 ± 0.16 (Cohen’s kappa), and 0.87 ± 0.09 (AUC) when using 2–8 body parts. The common combinations are recommended for their balance of their prediction power and the number of local body parts involved. This study offers a reference for efficient and economic measurements for thermal comfort research in building environments.