Investigating Cumulative Exposures among 3- to 4-Year-Old Children Using Wearable Ultrafine Particle Sensors and Language Environment Devices: A Pilot and Feasibility Study

Abstract

Interdisciplinary approaches are needed to measure the additive or multiplicative impacts of chemical and non-chemical stressors on child development outcomes. The lack of interdisciplinary approaches to environmental health and child development has led to a gap in the development of effective intervention strategies. It is hypothesized that a broader systems approach can support more effective interventions over time. To achieve these goals, detailed study protocols are needed. Researchers in child development typically focus on psychosocial stressors. Less attention is paid to chemical and non-chemical stressors and how the interaction of these stressors may impact child development. This feasibility study aims to bridge the gap between child development and environmental epidemiology research by trialing novel methods of gathering ultrafine particle data with a wearable air sensor, while simultaneously gathering language and noise data with the Language Environment Analysis (LENA) system. Additionally, psychosocial data (e.g., parenting quality, caregiver depression, and household chaos) was gathered from parent reports. Child participants (age 3–4 years) completed cognitive tasks to assess self-regulation and receptive language skills, and provided a biospecimen analyzed for inflammatory biomarkers. Data collection was completed at two time points, roughly corresponding to fall and spring. Twenty-six participants were recruited for baseline data, and 11 participants completed a follow-up session. Preliminary results indicate that it is feasible to gather personal Particulate Matter (PM2.5), language, and noise data, cognitive assessments, and biospecimens from our sample of 3-4-year-old children. While there are obstacles to overcome when working with this age group, future studies can benefit from adapting lessons learned regarding recruitment strategies, study design, and protocol implementation.

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

“…In the previously presented study [ 76 ], the authors used wearables to monitor noise and assess personal exposure of UFP (ultrafine particles) in the pediatric population, allowing researchers to examine the independent and/or combined effects of these health-related environmental exposures. The same research objective is pursued in the previously presented work [ 79 ], in which, in addition to the thermal and acoustic aspect, wearables worn by 3-4-year-old children are used to collect PM 2.5 and PM 10 data at school and home, demonstrating that it is possible to collect personal Particulate Matter with wearables when considering this population. In the previously presented work [ 90 ], the EnviroSensor 2.0 wearable was used in addition to thermal and location data in a laboratory test to evaluate the potential of this device to collect particulate matter, ozone, and CO concentration data.…”

“…Otoacoustic emissions, and particularly distortion products, were able to discriminate the exposed workers from the controls, providing also a rough estimate of the slope of the dose-response relation between otoacoustic levels and styrene exposure [ 69 ] Sound pressure level (Eco-Mini) Ozone, Sulfur Dioxide, Volatile Organic Compounds, humidity, temperature, and ambient light color balance, 3-axis accelerometer and GPS location (Eco-Mini) Indoor/Outdoor The presented wearable environmental monitoring, called “Eco-Mini”, overcomes practical challenges (calibration, reproducibility, form factor, and battery life) and can be used in clinical studies. [ 79 ] Noise [db] (LENA-Language Environment Analysis System) Thermal: air humidity, temperature. Air quality: PM 2.5 an PM 10 .…”

“…reported via the smartphone App CrowdAir Outdoor The data collection operation and data analysis results demonstrate the feasibility of the adopted methodology and the developed platform to identify the correlations among air quality indicators, participants’ subjective feelings of air quality, physical activity status measured by wearable sensors, and reported symptoms. [ 79 ] PM 2.5 an PM 10 (PLANTOWER PM5003). Thermal: air humidity, temperature (PLANTOWER PM5003).…”

“…In [69], the calibrated portable device Eco-Mini, already mentioned in the section of Visual EF, also allowed the measurement of Sound Pressure Levels. In [79], the authors demonstrated the feasibility of collecting personal Particulate Matter (PM2.5), language, noise data, cognitive assessments, and biospecimens from a sample of 3-4-yearold children using wearable ultrafine particle sensors and LENA-Language Environment Analysis System. Two different approaches were then collected to provide an example of real-time information on urban acoustic pollution [80] and overall personal pollution [81], where the PONG device can monitor ambient sound levels as well as VOCs and NO 2 , UVA and UVB, air temperature, relative humidity and air pressure data.…”

The Survey of the Health of Wisconsin (SHOW) Program: An infrastructure for Advancing Population Health Sciences

“…A neighborhood perceptions questionnaire captures community assets and perceived neighborhood stressors. A personal exposure history 9 – 11 , includes information on residential history, household characteristics including the age of the home, pet ownership, use of indoor/outdoor pesticides, and smoking policies and water source (private well vs. municipal) 12 including use of water filtration. Health behaviors include physical activity, diet, sleep, smoking, and drug and alcohol use.…”