Associations of bedroom air temperature and CO2 concentration with subjective perceptions and sleep quality during transition seasons

Associations between residential daytime indoor temperature and self-reported sleep disturbances in UK adults: A cross-sectional study

BACKGROUND

In the past few decades, research on the association between indoor temperature and sleep has primarily used laboratory rather than field data collected in epidemiological cohorts.

METHODS

Secondary data on 2493 individuals aged 43 years was obtained from the National Survey of Health and Development (NSHD). Logistic regression models were used to investigate the associations between temperatures (indoor at home, spot measurement when the nurses visited during the day; and outdoor, monthly average) and self-reported sleep disturbances, adjusting for socio-demographics, health variables, housing variables, and temperature-related variables.

RESULTS

Associations were found between daytime indoor temperature with difficulty initiating (OR: 0.95, 95%CI: 0.91-0.98) and maintaining sleep (OR: 0.96, 95%CI: 0.93-0.99). Compared with neutral indoor temperatures (17-28 °C), low indoor temperature (≤17 °C) was associated with difficulty initiating sleep (OR: 1.79, 95%CI: 1.21-2.65). Stratified analysis results across tertiles showed that associations with difficulty initiating (OR: 0.87, 95%CI: 0.77-0.99) and maintaining sleep (OR: 0.88, 95%CI: 0.79-0.98) were observed respectively in the lowest (≤20 °C) and highest tertile (≥23 °C) of indoor temperature. There was no association between outdoor temperature and self-reported sleep disturbances in this study.

CONCLUSION

In this first UK-based epidemiology study investigating temperature and sleep, self-reported sleep disturbances were associated with residential daytime indoor temperatures. Low indoor temperature had significantly higher odds ratio for difficulty initiating sleep compared with the neutral indoor temperature. A warmer indoor environment might be more suitable for sleep maintenance than sleep initiation. Indoor temperature in this study was a superior indicator of sleep disturbances than outdoor temperature. Although these findings are based on a UK sample, they may be relevant to other high-income settings with similar housing stock and climatic conditions.

A systematic review of ambient heat and sleep in a warming climate

Climate change is elevating nighttime and daytime temperatures worldwide, affecting a broad continuum of behavioral and health outcomes. Disturbed sleep is a plausible pathway linking rising ambient temperatures with several observed adverse human responses shown to increase during hot weather. This systematic review aims to provide a comprehensive overview of the literature investigating the relationship between ambient temperature and valid sleep outcomes measured in real-world settings, globally. We show that higher outdoor or indoor temperatures are generally associated with degraded sleep quality and quantity worldwide. The negative effect of heat persists across sleep measures, and is stronger during the hottest months and days, in vulnerable populations, and the warmest regions. Although we identify opportunities to strengthen the state of the science, limited evidence of fast sleep adaptation to heat suggests rising temperatures induced by climate change and urbanization pose a planetary threat to human sleep, and therefore health, performance, and wellbeing.

A single-blind field intervention study of whether increased bedroom ventilation improves sleep quality

A four-week-long field intervention experiment was conducted in twenty-nine bedrooms with extract ventilation systems and air inlet vents. During the first week no interventions took place. In the three weeks that followed, each participant slept for one week under a low, medium, and high ventilation rate condition in a balanced order. These conditions were established by covertly altering the fan speed of the exhaust ventilation system without changing other settings. Participants were not informed when or even whether the changes to bedroom ventilation would be executed. The bedroom environmental quality was monitored continuously and sleep quality was monitored using wrist-worn trackers. Tests of cognitive performance were conducted in the evening and morning. In twelve bedrooms where clear differences between the three ventilation conditions occurred, as indicated by the measured CO₂ concentrations, participants had significantly less deep sleep, more light sleep and more awakenings at lower ventilation rate conditions. In twenty-three bedrooms where a clear difference in ventilation rate between the high and low ventilation conditions was observed, as confirmed by the measured CO₂ concentrations, the duration of deep sleep was significantly shorter in the low ventilation rate condition. No differences in cognitive performance between conditions were observed. At lower ventilation rate conditions, the concentrations of CO₂ increased, as did the relative humidity, while bedroom temperatures remained unchanged. The present results, which were obtained in actual bedrooms, confirm the findings in previous studies of a positive effect of increased ventilation on sleep quality. Further studies with larger populations and better control of bedroom conditions, particularly ventilation, are required.

Feasibility of High-Frequency Monitoring of the Home Environment and Health in Older Adults: Proof of Concept

Technology provides new opportunities to understand and optimize the relationship between the home indoor environmental quality and health outcomes in older adults. We aimed to establish proof-of-concept and feasibility of remote, real-time, high-frequency, and simultaneous monitoring of select environmental variables and outcomes related to health and wellbeing in older adults. Thirty-four participants (27 were female) with an average age (SD) of 81 years (±7.1) were recruited from community and supportive housing environments. Environmental sensors were installed in each home and participants were asked to use a wearable device on their finger and answer smartphone-based questionnaires on a daily basis. Further, a subgroup of participants were asked to complete tablet-based cognitive tests on a daily basis. Average compliance with the wearable (time worn properly / total time with device) was 81%. Participants responded to 69% of daily smartphone surveys and completed 80% of the prescribed cognitive tests. These results suggest that it is feasible to study the impact of the home thermal environment and air quality on biological rhythms, cognition, and other outcomes in older adults. However, the success of non-passive data collection elements may be contingent upon baseline cognition.

Discussion on regression analysis with small determination coefficient in human-environment researches

As the main indicator for assessing the explanatory strength of regression model, there is no denying that a bigger value of determination coefficient (R-squared, R² ) is the consistent pursuit of researchers in human-environment field, but whether to abandon or apply the model with a small value of R² is an ongoing argument. This paper summarizes three characteristics of human-environment researches (large number of various variables, large mathematical sample size, and polynomial regression model). Based on the mathematical mechanism of regression analysis, theoretical analysis and case study are combined to point out the misconceptions that are easy to step into and the corresponding suggested methods from three perspectives: selection of determination coefficients, consideration of independent variables, and application of regression models. An extraordinary important point is, if the regression model passes the significance test, even with a small coefficient of determination, it can still quantitatively explain the impact extent of independent variables on dependent variables, but cannot comprehensively and accurately predict the specific value of dependent variable based on existing independent variables; moreover, the larger the sample size, the closer the interpretation of dependent variables in local model to ideal model. It is expected that these cases and lessons could help researchers to better apply regression analysis in human-environment researches, and that the small value of R² would not be an excessive restriction affecting the development of scientific research in this field.

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.

Objective sleep assessments for healthy people in environmental research: A literature review

To date, although many studies had focused on the impact of environmental factors on sleep, how to choose the proper assessment method for objective sleep quality was often ignored, especially for healthy subjects in bedroom environment. In order to provide methodological guidance for future research, this paper reviewed the assessments of objective sleep quality applied in environmental researches, compared them from the perspective of accuracy and interference, and statistically analyzed the impact of experimental type and subjects' information on method selection. The review results showed that, in contrast to polysomnography (PSG), the accuracy of actigraphy (ACT), respiratory monitoring-oxygen saturation monitoring (RM-OSM), and electrocardiograph (ECG) could reach up to 97%, 80.38%, and 79.95%, respectively. In terms of sleep staging, PSG and ECG performed the best, ACT the second, and RM-OSM the worst; as compared to single methods, mix methods were more accurate and better at sleep staging. PSG interfered with sleep a great deal, while ECG and ACT could be non-contact, and thus, the least interference with sleep was present. The type of experiment significantly influenced the choice of assessment method (p < 0.001), 85.3% of researchers chose PSG in laboratory study while 82.5% ACT in field study; moreover, PSG was often used in a relatively small number of young subjects, while ACT had a wide applicable population. In general, researchers need to pay more attention at selection of assessments in future studies, and this review can be used as a reliable reference for experimental design.

Effects of carbon dioxide and green space on sleep quality of the elderly in rural areas of Anhui Province, China

Sleep quality leads to many healthy problems, which has caused global concern. Most studies have focused on the sleep quality in some large cities, ignoring the elderly in rural areas who may have more serious sleep problems. Therefore, this study aimed to understand the sleep status of the elderly in rural areas of Anhui Province and the influence of environmental factors on it. The data of carbon dioxide (CO₂) concentrations for this study were obtained from the Dalhousie University atmospheric composition analysis group. The data of normalized differential vegetation index (NDVI) in 2014 was produced and processed by remote sensing inversion on the basis of medium resolution satellite images. The 2686 individuals were selected from rural areas of Anhui Province by multi-stage stratified cluster sampling. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. The independent, interactive, and mediating effects of CO₂ and green space on sleep quality were evaluated by generalized linear model, interaction model, and mediating effect model, respectively. In this study, we found that the prevalence of sleep disturbance in the elderly was 58.40%. In the univariate model, we found that the risk of sleep disturbance increased by 1.6% for each 1 μg/m³ increase in concentrations of CO₂, while decreased by 1.5% for every 0.1 increase in NDVI. In the interaction model, the negative correlation between green space and sleep quality decreased with concentrations of CO₂ increased. In addition, green space was a mediating factor between CO₂ and sleep quality in the mediating effect model.