Portable Environmental Sensor Enabling Studies of Exposome on Ocular Health

Abstract

Purpose : To introduce a custom sensor unit for collecting localized environmental conditions from indoor living environments of 4000 participants in the Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) study as part of a multimodal dataset. Traditional methods using fixed outdoor weather stations to measure air quality collect data at the city or regional level. To address this challenge, we designed and built a custom sensor unit.

Methods : Participants are being recruited from three academic centers with a planned balance of type 2 diabetes mellitus (T2DM) severity, race/ethnicity, and sex in order to build a dataset of 4000 individuals. This multimodal dataset includes electronic health records, laboratory testing, extensive retinal imaging, visual acuity, cognitive testing, fitness tracking, continuous glucose monitoring and sampling of the indoor living environment of each participant. A custom sensor unit is placed in each participant's home for 10 days to record particulate matter counts (PM 1.0, 2.5, 4, and 10), temperature, relative humidity, volatile organic compounds (VOCs), nitrogen oxides (NO and NO2), and 11 multi-spectral light intensity measurements.

Results : As of 10/30/2023, 125 participants completed study visits and took home an environmental sensor for data collection. All units were returned and data was successfully downloaded from 122/125 devices. Figure 1 shows 5 of the 19 variables recorded over a 10 day-period in the homes of 2 participants during different time spans. Temperature and humidity indicate daily patterns of highs and lows. Particulate matter and VOCs for Particant A (max PM2.5 < 10, max VOC = 470) and Participant B (max PM2.5 > 6500, max VOC = 490) provide an early indication of the range of individualized exposome measurements. Figure 2 shows details of the sensor unit.

Conclusions : The collection of environmental conditions by means of a small, highly portable and reusable sensor provides a detailed understanding of the living environments of individual study participants. Ease of use has supported the high compliance rate producing a rich exposome dataset that could be correlated with biological and behavioral data collected from AI-READI providing a unique opportunity to understand how the home environment may impact ocular health in type 2 diabetes.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

 

Representative data points from 2 participants over 10 days.

 

Portable environmental sensor unit