Wearable sensors for personal temperature exposure assessments: A comparative study

Modelling personal temperature exposure using household and outdoor temperature and questionnaire data: Implications for epidemiological studies

Non-optimal temperature is a leading risk factor for global disease burden. Most epidemiological studies assessed only outdoor temperature, with important uncertainties on personal exposure misclassification. The CKB-Air study measured personal, household (kitchen and living room), and outdoor temperatures in the summer (MAY-SEP 2017) and winter (NOV 2017-JAN 2018) in 477 participants in China. After data cleaning, ∼88,000 person-hours of data were recorded across each microenvironment. Using multivariable linear regression (MLR) and random forest (RF) models, we identified key predictors and constructed personal temperature exposure prediction models. We used generalised additive mixed effect models to examine the relationships of personal and outdoor temperatures with heart rate. The 24-hour mean (SD) personal and outdoor temperatures were 29.2 (3.8) °C and 27.6 (6.4) °C in summer, and 12.0 (4.0) °C and 7.5 (4.2) °C in winter, respectively. The temperatures across microenvironments were strongly correlated (Spearman's ρ: 0.86-0.92) in summer. In winter, personal temperature was strongly related to household temperatures (ρ: 0.74-0.79) but poorly related to outdoor temperature (ρ: 0.30). RF algorithm identified household and outdoor temperatures and study date as top predictors of personal temperature exposure for both seasons, and heating-related factors were important in winter. The final MLR and RF models incorporating questionnaire and device data performed satisfactorily in predicting personal exposure in both seasons (R2summer: 0.92; R2winter: 0.68-0.70). We found consistent U-shaped associations between measured and predicted personal temperature exposures and heart rate (lowest at ∼ 14.5 °C), but a weak positive linear association with outdoor temperature. Personal and outdoor temperatures differ substantially winter, but prediction models incorporating household and outdoor temperatures and questionnaire data performed satisfactorily. Exposure misclassification from using outdoor temperature may produce inappropriate epidemiological findings.

On urban microclimate spatial-temporal dynamics: Evidence from the integration of fixed and wearable sensing and mapping techniques

Urban Heat Island (UHI) is acknowledged to generate harmful consequences on human health, and it is one of the main anthropogenic challenges to face in modern cities. Due to the urban dynamic complexity, a full microclimate decoding is required to design tailored mitigation strategies for reducing heat-related vulnerability. This study proposes a new method to assess intra-urban microclimate variability by combining for the first time two dedicated monitoring systems consisting of fixed and mobile techniques. Data from three fixed weather stations were used to analyze long-term trends, while mobile devices (a vehicle and a wearable) were used in short-term monitoring campaigns conducted in summer and winter to assess and geo-locate microclimate spatial variations. Additionally, data from mobile devices were used as input for Kriging interpolation in the urban area of Florence (Italy) as case study. Mobile monitoring sessions provided high-resolution spatial data, enabling the detection of hyperlocal variations in air temperature. The maximum air temperature amplitudes were verified with the wearable system: 3.3 °C in summer midday and 4.3 °C in winter morning. Physiological Equivalent Temperature (PET) demonstrated to be similar when comparing green areas and their adjacent built-up zone, showing up the microclimate mitigation contribution of greenery in its surrounding. Results also showed that mixing the two data acquisition and varied analysis techniques succeeded in investigating the UHI and the site-specific role of potential mitigation actions. Moreover, mobile dataset was reliable for elaborating maps by interpolating the monitored parameters. Interpolation results demonstrated the possibility of optimizing mobile monitoring campaigns by focusing on targeted streets and times of day since interpolation errors increased by 10% only with properly reduced and simplified input samples. This allowed an enhanced detection of the site-specific granularity, which is important for urban planning and policymaking, adaptation, and risk mitigation actions to overcome the UHI and anthropogenic climate change effects.

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.

Sensing the impact of extreme heat on physical activity and sleep

Introduction

This study assesses the person-specific impact of extreme heat on low-income households using wearable sensors. The focus is on the intensive and longitudinal assessment of physical activity and sleep with the rising person-specific ambient temperature.

Methods

This study recruited 30 participants in a low-income and predominantly Black community in Houston, Texas in August and September of 2022. Each participant wore on his/her wrist an accelerometer that recorded person-specific ambient temperature, sedentary behavior, physical activity intensity (low and moderate to vigorous), and sleep efficiency 24 h over 14 days. Mixed effects models were used to analyze associations among physical activity, sleep, and person-specific ambient temperature.

Results

The main findings include increased sedentary time, sleep impairment with the rise of person-level ambient temperature, and the mitigating role of AC.

Conclusions

Extreme heat negatively affects physical activity and sleep. The negative consequences are especially critical for those with limited use of AC in lower-income neighborhoods of color. Staying home with a high indoor temperature during hot days can lead to various adverse health outcomes including accelerated cognitive decline, higher cancer risk, and social isolation.

Temporal and spatial heterogeneity of indoor and outdoor temperatures and their relationship with thermal sensation from a global perspective

People spend most of their time indoors. However, indoor temperature and individual thermal exposure are generally not considered in epidemiological studies of temperature and health. Based on the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) RP-884 Database, the ASHRAE Global Thermal Comfort Database II and the Chinese Thermal Comfort Database, this study first explored the relationship between outdoor temperature, indoor temperature and thermal sensation from a global perspective. Moreover, the potential influence of spatiotemporal heterogeneity on health studies was explored. A breakpoint was found at approximately 11.5 °C in the segmented regression of indoor and outdoor temperature, and the slope of the curve was greater when outdoor temperature was above the breakpoint (n = 67,896). Based on multi-group propensity score matching (PSM) and generalizedadditivemodels (GAM), spatiotemporal heterogeneity was found in the relationship between indoor and outdoor temperatures after adjusting for building type and year. Furthermore, the relationship between indoor temperature and thermal sensation was influenced by the outdoor temperature. This study highlights the importance of considering indoor temperature or individual thermal exposure in temperature-related health studies.

Mixed methods assessment of personal heat exposure, sleep, physical activity, and heat adaptation strategies among urban residents in the Boston area, MA

The growing frequency, intensity, and duration of extreme heat events necessitates interventions to reduce heat exposures. Local opportunities for heat adaptation may be optimally identified through collection of both quantitative exposure metrics and qualitative data on perceptions of heat. In this study, we used mixed methods to characterize heat exposure among urban residents in the area of Boston, Massachusetts, US, in summer 2020. Repeated interviews of N = 24 study participants ascertained heat vulnerability and adaptation strategies. Participants also used low-cost sensors to collect temperature, location, sleep, and physical activity data. We saw significant differences across temperature metrics: median personal temperature exposures were 3.9 °C higher than median ambient weather station temperatures. Existing air conditioning (AC) units did not adequately control indoor temperatures to desired thermostat levels: even with AC use, indoor maximum temperatures increased by 0.24 °C per °C of maximum outdoor temperature. Sleep duration was not associated with indoor or outdoor temperature. On warmer days, we observed a range of changes in time-at-home, expected given our small study size. Interview results further indicated opportunities for heat adaptation interventions including AC upgrades, hydration education campaigns, and amelioration of energy costs during high heat periods. Our mixed methods design informs heat adaptation interventions tailored to the challenges faced by residents in the study area. The strength of our community-academic partnership was a large part of the success of the mixed methods approach.

A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases

Age-related diseases account for almost half of all diseases among adults worldwide, and their incidence is substantially affected by the exposome, which is the sum of all exogenous and endogenous environmental exposures and the human body’s response to these exposures throughout the entire lifespan. Herein, we perform a comprehensive review of the epidemiological literature to determine the key elements of the exposome that affect the development of age-related diseases and the roles of aging hallmarks in this process. We find that most exposure assessments in previous aging studies have used a reductionist approach, whereby the effect of only a single environmental factor or a specific class of environmental factors on the development of age-related diseases has been examined. As such, there is a lack of a holistic and unbiased understanding of the effect of multiple environmental factors on the development of age-related diseases. To address this, we propose several research strategies based on an exposomic framework that could advance our understanding—in particular, from a mechanistic perspective—of how environmental factors affect the development of age-related diseases. We discuss the statistical methods and other methods that have been used in exposome-wide association studies, with a particular focus on multiomics technologies. We also address future challenges and opportunities in the realm of multidisciplinary approaches and genome–exposome epidemiology. Furthermore, we provide perspectives on precise public health services for vulnerable populations, public communications, the integration of risk exposure information, and the bench-to-bedside translation of research on age-related diseases.

Wearables for Measuring Health Effects of Climate Change–Induced Weather Extremes: Scoping Review

Background

Although climate change is one of the biggest global health threats, individual-level and short-term data on direct exposure and health impacts are still scarce. Wearable electronic devices (wearables) present a potential solution to this research gap. Wearables have become widely accepted in various areas of health research for ecological momentary assessment, and some studies have used wearables in the field of climate change and health. However, these studies vary in study design, demographics, and outcome variables, and existing research has not been mapped.

Objective

In this review, we aimed to map existing research on wearables used to detect direct health impacts and individual exposure during climate change–induced weather extremes, such as heat waves or wildfires.

Methods

We conducted a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework and systematically searched 6 databases (PubMed [MEDLINE], IEEE Xplore, CINAHL [EBSCOhost], WoS, Scopus, Ovid [MEDLINE], and Google Scholar). The search yielded 1871 results. Abstracts and full texts were screened by 2 reviewers (MK and IM) independently using the inclusion and exclusion criteria. The inclusion criteria comprised studies published since 2010 that used off-the-shelf wearables that were neither invasive nor obtrusive to the user in the setting of climate change–related weather extremes. Data were charted using a structured form, and the study outcomes were narratively synthesized.

Results

The review included 55,284 study participants using wearables in 53 studies. Most studies were conducted in upper–middle-income and high-income countries (50/53, 94%) in urban environments (25/53, 47%) or in a climatic chamber (19/53, 36%) and assessed the health effects of heat exposure (52/53, 98%). The majority reported adverse health effects of heat exposure on sleep, physical activity, and heart rate. The remaining studies assessed occupational heat stress or compared individual- and area-level heat exposure. In total, 26% (14/53) of studies determined that all examined wearables were valid and reliable for measuring health parameters during heat exposure when compared with standard methods.

Conclusions

Wearables have been used successfully in large-scale research to measure the health implications of climate change–related weather extremes. More research is needed in low-income countries and vulnerable populations with pre-existing conditions. In addition, further research could focus on the health impacts of other climate change–related conditions and the effectiveness of adaptation measures at the individual level to such weather extremes.

Exploring a more reasonable temperature exposure calculation method based on individual exposure survey and city-scale heat exposure impact assessment

The inability to quantify the difference between ambient temperature (AT) and personal exposure temperature (PET) is a common limitation in environmental health research. The actual exposure variability is underestimated when we used measurements from fixed monitoring stations to estimate PET. The study aims to explore a more reasonable temperature exposure calculation method to relate PET to AT and links heat exposure to adverse health events. We measured hourly PET of 129 participants from July 8th to July 13th, 2021 in Xinyi City, China. The linear mixed-effects model was used to build the relationship between hourly PET and AT in rural and town. Several calculation methods that can capture the intensity, frequency and duration of daily exposure were used to calculate the daily PET and AT and establish the relationship between the two factors. A generalized linear model was used to establish the relationship between city-scale AT indicators and health endpoints from January 1st, 2013 to December 31st, 2015 in Shanghai, China. The result showed that the hourly PET was significantly related to AT, wind speed, air pressure, precipitation, outside time, and air-conditioning use. Among several daily temperature indicators, we found that AT_DHAT (Degree Hours Above Threshold (27.4 °C)) was tight with the PET_DHAT in different regions (R² > 0.99). DHAT strengthened the relationship between daily AT and health endpoint in the urban-scale heat-related health impact study, especially in respiratory diseases. The method proposed in this study can improve the accuracy of future epidemiological studies on the effects of heat exposure.

Individually experienced heat index in a coastal Southeastern US city among an occupationally exposed population

Recent studies have characterized individually experienced temperatures or individually experienced heat indices, including new exposure metrics that capture dimensions of exposure intensity, frequency, and duration. Yet, few studies have examined the personal thermal exposure in underrepresented groups, like outdoor workers, and even fewer have assessed corresponding changes in physiologic heat strain. The objective of this paper is to examine a cohort of occupationally exposed grounds and public safety workers (n = 25) to characterize their heat exposure and resulting heat strain. In addition, a secondary aim of this work is to compare individually heat index exposure (IHIE) across exposure metrics, fixed-site in situ weather stations, and raster-derived urban heat island (UHI) measurements in Charleston, SC, a humid coastal climate in the Southeastern USA. A Bland-Altman (BA) analysis was used to assess the level of agreement between the personal IHIE measurements and weather-station heat index (HI) and Urban Heat Island (UHI) measurements. Linear mixed-effect models were used to determine the association between individual risk factors and in situ weather station measurements significantly associated with IHIE measurements. Multivariable stepwise Cox proportional hazard modeling was used to identify the individual and workplace factors associated with time to heat strain in workers. We also examined the non-linear association between heat strain and exposure metrics using generalized additive models. We found significant heterogeneity in IHIE measurements across participants. We observed that time to heat strain was positively associated with a higher IHIE, older age, being male, and among Caucasian workers. Important nonlinear associations between heat strain occurrence and the intensity, frequency, and duration of personal heat metrics were observed. Lastly, our analysis found that IHIE measures were significantly similar for weather station HI, although differences were more pronounced for temperature and relative humidity measurements. Conversely, our IHIE findings were much lower than raster-derived UHI measurements. Real-time monitoring can offer important insights about unfolding temperature-health trends and emerging behaviors during thermal extreme events, which have significant potential to provide situational awareness.

Heat exposure misclassification: Do current methods of classifying diurnal range in individually experienced temperatures and heat indices accurately reflect personal exposure?

Wearable sensors have been used to collect information on individual exposure to excessive heat and humidity. To date, no consistent diurnal classification method has been established, potentially resulting in missed opportunities to understand personal diurnal patterns in heat exposure. Using individually experienced temperatures (IET) and heat indices (IEHI) collected in the southeastern United States, this work aims to determine whether current methods of classifying IETs and IEHIs accurately characterize "day," which is typically the warmest conditions, and "night," which is typically the coolest conditions. IET and IEHI data from four locations were compared with the closest hourly weather station. Different day/night classifications were compared to determine efficacy. Results indicate that diurnal IET and IEHI ranges are higher than fixed-site ranges. Maximum IETs and IEHIs are warmer and occur later in the day than ambient conditions. Minimum IETs are lower and occur earlier in the day than at weather stations, which conflicts with previous assumptions that minimum temperatures occur at night. When compared to commonly used classification methods, a method of classifying day and night based on sunrise and sunset times best captured the occurrence of maximum IETs and IEHIs. Maximum IETs and IEHIs are often identified later in the evening, while minimum IETs and IEHIs occur throughout the day. These findings support future research focusing on nighttime heat exposure, which can exacerbate heat-related health issues, and diurnal patterns of personal exposure throughout the entire day as individual patterns do not necessarily follow the diurnal pattern seen in ambient conditions.

Individually experienced heat stress among elderly residents of an urban slum and rural village in India

The elderly are one of the most vulnerable groups to heat-related illnesses and mortality. In tropical countries like India, where heat waves have increased in frequency and severity, few studies have focused on the level of stress experienced by the elderly. The study presented here included 130 elderly residents of Kolkata slums and 180 elderly residents of rural villages about 75 km south of Kolkata. It used miniature monitoring devices to continuously measure temperature, humidity, and heat index experienced during everyday activities over 24-h study periods, during hot summer months. In the Kolkata slum, construction materials and the urban heat island effect combined to create hotter indoor than outdoor conditions throughout the day, and particularly at night. As a result, elderly slum residents were 4.3 times more likely to experience dangerous heat index levels (≥ 45°C) compared to rural village elderly. In both locations, the median 24-h heat indexes of active elderly were up to 2°C higher than inactive/sedentary elderly (F = 25.479, p < 0.001). Among Kolkata slums residents, there were no significant gender differences in heat exposure during the day or night, but in the rural village, elderly women were 4 times more likely to experience dangerous heat index levels during the hottest times of the day compared to elderly men. Given the decline in thermoregulatory capacity associated with aging and the increasing severity of extreme summer heat in India, these results forecast a growing public health challenge that will require both scientific and government attention.

A randomized cross-over trial investigating differences in 24-h personal air and skin temperatures using wearable sensors between two climatologically contrasting settings

The influence of elevated air temperatures recorded in various urban microenvironments in adversely impacting biologically relevant disease end points has not yet been extensively tackled. This study is a post hoc analysis of the TEMP pilot trial, a randomized 2 × 2 cross-over trial that examined changes in metabolic and stress hormonal profiles of healthy adults in two settings (urban vs. rural) with distinctly different climatological characteristics during the Mediterranean summer. This analysis aimed to study the association between the 24-h personal air or skin temperature sensor measurements and the diary-based location type (indoors vs. outdoors) in urban (seaside) vs. rural (higher in altitude) microenvironments. Out of 41 eligible participants, a total of 37 participants were included in this post-hoc TEMP trial analysis. Wearable sensors recorded personal air temperature, skin temperature, and activity (as a surrogate marker of physical activity) in each setting, while a time-stamped personal diary recorded the types of indoor or outdoor activities. Temperature peaks during the 24-h sampling period were detected using a peak finding algorithm. Mixed effect logistic regression models were fitted for the odds of participant location (being indoors vs. outdoors) as a function of setting (urban vs. rural) and sensor-based personal temperature data (either raw temperature values or number of temperature peaks). During the study period (July-end of September), median [interquartile range, IQR] personal air temperature in the rural (higher altitude) settings was 1.5 °C lower than that in the urban settings (27.1 °C [25.4, 29.2] vs. 28.6 °C [27.1, 30.5], p < 0.001), being consistent with the Mediterranean climate. Median [IQR] personal air temperature in indoor (micro)environments was lower than those in outdoors (28.0 °C [26.4, 30.3] vs 28.5 °C [26.8, 30.7], p < 0.001). However, median [IQR] skin temperature was higher in indoor (micro)environments vs. outdoors (34.8 °C [34.0, 35.6] and 33.9 °C [32.9, 34.8], p < 0.001) and the number of both personal air and skin temperature peaks was higher indoors compared to outdoors (median [IQR] 3.0 [2.0,4.0] vs 1.0 [1.0,1.3], p < 0.007, for the skin sensors). A significant association between the number of temperature peaks and indoor location types was observed with either the personal air sensor (OR 3.1; 95% CI 1.2-8.2; p = 0.02) or the skin sensor (OR 3.7; 95% CI 1.4-9.9; p = 0.01), suggesting higher number of indoor air temperature fluctuations. Amidst the global climate crisis, more population health studies or personalized medicine approaches that utilize continuous tracking of individual-level air/skin temperatures in both indoor/outdoor locations would be warranted, if we were to better characterize the disease phenotype in response to climate change manifestations.

Novel metrics for relating personal heat exposure to social risk factors and outdoor ambient temperature

A more precise understanding of individual-level heat exposure may be helpful to advance knowledge about heat-health impacts and effective intervention strategies, especially in light of projected increases in the severity and frequency of extreme heat events. We developed and interrogated different metrics for quantifying personal heat exposure and explored their association with social risk factors. To do so, we collected simultaneous personal heat exposure data from 64 residents of metropolitan Phoenix, Arizona. From these data, we derived five exposure metrics: Mean Individually Experienced Temperature (IET), Maximum IET, Longest Exposure Period (LEP), Percentage Minutes Above Threshold (PMAT), and Degree Minutes Above Threshold (DMAT), and calculated each for Day Hours, Night Hours, and All Hours of the study period. We then calculated effect sizes for the associations between those metrics and four social risk factors: neighborhood vulnerability, income, home cooling type, and time spent outside. We also investigated exposure misclassification by constructing linear regression models of observations from a regional weather station and hourly IET for each participant. Our analysis revealed that metric choice and timeframe added depth and nuance to our understanding of differences in exposure within and between populations. We found that time spent outside and income were the two risk factors most strongly associated with personal heat exposure. We also found evidence that Mean IET is a good, but perhaps not optimal, measure for assessing group differences in exposure. Most participants' IETs were poorly correlated with regional weather station observations and the slope and correlation coefficient for linear regression models between regional weather station data and IETs varied widely among participants. We recommend continued efforts to investigate personal heat exposure, especially in combination with physiological indicators, to improve our understanding of links between ambient temperatures, social risk factors, and health outcomes.