The Impact of Indoor Environmental Quality on Symptoms in Patients With Chronic Obstructive Pulmonary Disease (COPD-AIR)

The Impact of Indoor Environmental Quality on Symptoms in Patients With Chronic Obstructive Pulmonary Disease: A Prospective Pilot Study

This prospective, monocenter pilot study will investigate the relationship between indoor environmental quality (including air quality, lighting, noise, and thermal conditions), wearable-derived health insights, and patient-reported symptoms in patients recovering at home after hospitalization for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Participants will undergo continuous monitoring of indoor environmental conditions and health parameters using a Garmin Venu 4 smartwatch, together with validated questionnaires assessing COPD symptoms, sleep quality, and daytime sleepiness during one month. The study will assess the feasibility and potential added value of high-resolution home monitoring and explore whether environmental and wearable-derived measures are associated with symptom burden and early signs of clinical deterioration following hospital discharge.

The investigators hypothesize that continuous in-home monitoring of indoor environmental quality is feasible and provides clinically relevant insights into symptom burden in recently hospitalized COPD patients. The primary objective is to determine the correlation between indoor environmental quality (IEQ) variables and symptom burden in COPD patients following hospitalization for AECOPD.

Study Overview

• Status: Not yet recruiting
• Conditions: Acute Exacerbation of COPD; COPD (Chronic Obstructive Pulmonary Disease)
• Intervention / Treatment: Device: Wearable mobile device; Device: Indoor environmental quality sensor; Other: CAT Questionnaire; Other: PSQI Questionnaire; Other: KSS Questionnaire; Other: SQS Questionnaire; Other: PROMs Questionnaire; Other: eHLQ Questionnaire; Other: Housing Questionnaire

Detailed Description

Patients hospitalized for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) remain highly vulnerable after discharge, with persistent symptom burden and an increased risk of early recurrent exacerbations. During this post-hospitalization period, patients spend most of their time indoors, making indoor environmental quality (IEQ, including air quality, lighting, noise and thermal aspects) a potentially important and modifiable determinant of recovery. For example, exposure to particulate matter (PM) has been associated with airway inflammation, symptom worsening, and increased exacerbation risk in COPD. However, evidence on continuously measured IEQ at home and its relationship with symptoms after hospitalization for acute exacerbation of COPD remains limited.

Recent advances in wearable technology allow continuous monitoring of health insights in real-world settings. Prior studies have demonstrated the feasibility of wearable devices in patients with COPD and suggest that changes in activity, heart rate, and sleep may precede clinical deterioration or exacerbations. Integrating continuous IEQ monitoring with wearable-derived health insights (i.e., from a Garmin Venu 4 watch) and validated patient-reported outcomes, such as the widely used COPD Assessment Test (CAT), Pittsburgh Sleep Quality Index (PSQI), Karolinska Sleepiness Scale (KSS), Sleep Quality Scale (SQS), may improve understanding of symptom dynamics, uncover risk factors, and identify early indicators of worsening disease. This prospective monocenter pilot study aims to explore these relationships and to assess the feasibility and added value of high-resolution IEQ monitoring in the home setting of recently hospitalized COPD patients.

• Study Type: Interventional
• Enrollment (Estimated): 15
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Ruben Knevels, MsC; Phone Number: +3289804029; Email: ruben.knevels@zol.be
• Study Contact Backup: Name: Femke Wouters, PhD; Email: femke.wouters@zol.be

Study Locations

• Belgium
  • Genk, Belgium, 3600
    • Ziekenhuis Oost-Limburg
    • Contact: Ruben Knevels, MsC; Phone Number: +3289804029; Email: ruben.knevels@zol.be

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Provide signed and dated informed consent
• Adults older than 18 years of age
• Previously diagnosed with COPD according to GOLD guidelines
• Hospitalization due to AECOPD
• Spend >50% of time indoor at home
• Speak and understand the Dutch language

Exclusion Criteria:

• Previous diagnosis of asthma
• (History of) chronic macrolide use (> 3 months)
• Living in institution or residential care center
• Estimated life expectancy of less than 6 months
• Not in the possession of a smartphone

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Supportive Care
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: Patients admitted with an acute COPD exacerbation; Wearable mobile device (Garmin Venu) & Indoor environmental quality sensor

Device: Wearable mobile device; Patients admitted due to an acute COPD exacerbation will be asked to wear the wearable mobile device for a period of one month after hospital discharge. An app linked to the device will be installed on their phone, and used to send health insights to the investigators.; Other Names: Garmin Venu; Device: Indoor environmental quality sensor; When the participants are discharged home, an indoor environmental quality sensor will be installed in the patient's home. One sensor will be installed in the bedroom, one in the living room or kitchen (wherever the patient spends the most time). Data from these sensors will be sent to the hub (placed central between the sensors) and thereafter, to the investigators. The sensors measure particulate matter concentrations, CO2 concentrations, VOC concentrations, temperature, relative humidity, ambient light, and ambient noise.; Other: CAT Questionnaire; A COPD Assessment Test questionnaire will be taken during the hospitalization (start of the study), and weekly after discharge.; Other: PSQI Questionnaire; A Pittsburgh Sleep Quality Index questionnaire will be taken 4 weeks after hospital discharge (end of the study).; Other: KSS Questionnaire; A Karolinska Sleepiness Scale questionnaire will be taken daily after discharge.; Other: SQS Questionnaire; A sleep quality scale questionnaire will be taken daily after discharge.; Other: PROMs Questionnaire; A PROMs questionnaire will be taken daily after discharge.; Other: eHLQ Questionnaire; An eHealth literacy questionnaire will be taken during the hospitalization (start of the study), and 4 weeks after hospital discharge (end of the study).; Other: Housing Questionnaire; A housing questionnaire will be taken during the hospitalization (start of the study).

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Weekly score of COPD symptom burden assessed using the COPD Assessment Test (CAT)	COPD symptom burden will be assessed using the COPD Assessment Test (CAT). The CAT is an 8-item questionnaire with a total score ranging from 0 to 40 points, with higher scores indicating greater COPD symptom burden and a greater impact of COPD on daily life.	Baseline and weekly during the 1-month follow-up
Weekly mean PM0.5 concentration	Indoor PM0.5 concentration (µg/m³) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week will be aggregated to calculate a weekly mean PM0.5 concentration.	Weekly during the 1-month follow-up
Weekly mean PM2.5 concentration	Indoor PM2.5 concentration (µg/m³) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week will be aggregated to calculate a weekly mean PM2.5 concentration.	Weekly during the 1-month follow-up
Weekly mean PM4 concentration	Indoor PM4 concentration (µg/m³) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week will be aggregated to calculate a weekly mean PM4 concentration.	Weekly during the 1-month follow-up
Weekly mean PM10 concentration	Indoor PM10 concentration (µg/m³) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week will be aggregated to calculate a weekly mean PM10 concentration.	Weekly during the 1-month follow-up
Weekly mean CO2 concentration	Indoor CO2 concentration (ppm) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week will be aggregated to calculate a weekly mean CO2 concentration.	Weekly during the 1-month follow-up
Weekly mean VOC concentration	Indoor volatile organic compound (VOC) concentration (ppb) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week will be aggregated to calculate a weekly mean VOC concentration.	Weekly during the 1-month follow-up
Weekly mean temperature	Indoor temperature concentration (°C) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week will be aggregated to calculate a weekly mean temperature.	Weekly during the 1-month follow-up
Weekly mean relative humidity	Indoor relative humidity (%) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week will be aggregated to calculate a weekly mean relative humidity.	Weekly during the 1-month follow-up
Weekly mean ambient light - day	Indoor ambient light (lux) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week during day-time hours will be aggregated to calculate a weekly mean ambient light - day.	Weekly during the 1-month follow-up
Weekly mean ambient light - night	Indoor ambient light (lux) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week during night-time hours will be aggregated to calculate a weekly mean ambient light - night.	Weekly during the 1-month follow-up
Weekly mean ambient noise - night	Indoor ambient noise (dB) will be measured at 15-minute intervals in the participant's home during the 1-month follow-up. The measurements collected during each week during night-time hours will be aggregated to calculate a weekly mean ambient noise - night.	Weekly during the 1-month follow-up
Weekly overall proportion of time with at least one IEQ threshold violated	Overall proportion of monitoring time per week during which at least one predefined indoor environmental quality (IEQ) threshold is violated. IEQ variables will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, the IEQ variables will be compared with their respective predefined thresholds. The proportion of monitoring time points at which at least one IEQ threshold is violated will be calculated for each week.	Weekly during the 1-month follow-up

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Feasibility - Measurement completeness	Proportion of expected IEQ measurements successfully recorded.	During follow-up (1 month)
Feasibility - Valid monitoring time	Duration of valid monitoring time per participant.	During follow-up (1 month)
Daily perceived change in health status	Patient-reported change in overall health status compared with the previous day, assessed using a single question: "How is your health today compared with yesterday?" Participants will select one of three response options: better, about the same, or worse.	Daily during follow-up (1 month)
Feasibility - Frequency of data gaps	Frequency of gaps in monitoring data.	During follow-up (1 month)
Feasibility - Duration of data gaps	Duration of gaps in monitoring data.	During follow-up (1 month)
Wearable-derived health insights - Heart rate	Heart rate (BPM) measured using the wearable.	Continuous monitoring during follow-up (1 month)
Wearable-derived health insights - Respiratory rate	Respiratory rate (BPM) measured using the wearable.	Continuous monitoring during follow-up (1 month)
Wearable-derived health insights - Oxygen saturation	Oxygen saturation (%) measured using the wearable.	Continuous monitoring during follow-up (1 month)
Wearable-derived health insights - Skin temperature	Nightly deviation in skin temperature (°C) compared with the previous night, measured using the wearable.	Continuous monitoring during follow-up (1 month)
Sleep quality - PSQI	Pittsburgh Sleep Quality Index (PSQI) assessing subjective sleep quality over the preceding month. The PSQI total score ranges from 0 to 21 points, with higher scores indicating poorer sleep quality.	End of study (one month)
Sleepiness - KSS	Karolinska Sleepiness Scale (KSS) assessing subjective sleepiness at a specific time point. The KSS score ranges from 1 to 9 points, with higher scores indicating greater subjective sleepiness.	Daily during the 1-month follow-up
Sleep quality - SQS	Single-item Sleep Quality Scale (SQS) assessing perceived sleep quality of the previous night. The SQS score ranges from 0 to 10 points, with higher scores indicating better perceived sleep quality.	Daily during the 1-month follow-up
Wearable-derived sleep metrics - Sleep duration	Nightly sleep duration (hours) measured using the wearable.	Nightly during the 1-month follow-up
Wearable-derived sleep metrics - Sleep score	Nightly sleep score measured using the wearable. Score from 0-100, with a higher score indicating better quality of sleep.	Nightly during the 1-month follow-up
Wearable-derived sleep metrics - Time in sleep cycle	Nightly time spent in REM, deep, and light sleep (hours).	Nightly during the 1-month follow-up
Wearable-derived physical activity - Daily step count	Daily step count (number) measured by the wearable.	Daily during the 1-month follow-up
Wearable-derived physical activity - Activity minutes	Daily activity minutes (minutes) measured by the wearable.	Daily during the 1-month follow-up
Concentration of outside air quality	Concentration of outside air quality measure (PM, expressed in µg/m³) based on the participant's residential area.	During follow-up (1 month)
COPD exacerbations	Occurrence of a COPD exacerbation during the follow-up period.	During follow-up (1 month)
eHealth literacy	eHealth Literacy Questionnaire (eHLQ) domain scores describing participants' strengths and needs related to eHealth literacy. The eHLQ consists of 35 items across 7 domains. Each domain is scored from 1 to 4 points, with higher scores indicating a greater level of the characteristic assessed by the respective domain.	Baseline and end of study (one month)
Change in eHealth literacy	Change in eHealth literacy from baseline to the end of the 1-month follow-up, assessed using the eHealth Literacy Questionnaire (eHLQ). The eHLQ consists of 35 items across 7 domains. Each domain is scored from 1 to 4 points. Higher scores indicate a greater level of the characteristic assessed by the respective domain. Change in each domain score from baseline to 1 month will be calculated.	Baseline and end of study (one month)
Housing characteristics	Sociodemographic characteristics, housing characteristics, and indoor environmental factors assessed using the Housing Conditions Questionnaire.	Baseline
Weekly overall proportion of time with PM0.5 threshold exceeded	Overall proportion of monitoring time per week during which PM0.5 threshold is exceeded. PM0.5 will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, PM0.5 will be compared with its respective predefined threshold. The proportion of monitoring time points at which PM0.5 is exceeded will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with PM1 threshold exceeded	Overall proportion of monitoring time per week during which PM1 threshold is exceeded. PM1 will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, PM1 will be compared with its respective predefined threshold. The proportion of monitoring time points at which PM1 is exceeded will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with PM2.5 threshold exceeded	Overall proportion of monitoring time per week during which PM2.5 threshold is exceeded. PM2.5 will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, PM2.5 will be compared with its respective predefined threshold. The proportion of monitoring time points at which PM2.5 is exceeded will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with PM4 threshold exceeded	Overall proportion of monitoring time per week during which PM4 threshold is exceeded. PM4 will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, PM4 will be compared with its respective predefined threshold. The proportion of monitoring time points at which PM4 is exceeded will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with PM10 threshold exceeded	Overall proportion of monitoring time per week during which PM10 threshold is exceeded. PM10 will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, PM10 will be compared with its respective predefined threshold. The proportion of monitoring time points at which PM10 is exceeded will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with CO2 threshold exceeded	Overall proportion of monitoring time per week during which the CO2 threshold is exceeded. CO2 will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, CO2 will be compared with its respective predefined threshold. The proportion of monitoring time points at which CO2 is exceeded will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with VOC threshold exceeded	Overall proportion of monitoring time per week during which the VOC threshold is exceeded. VOC will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, VOC will be compared with its respective predefined threshold. The proportion of monitoring time points at which VOC is exceeded will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with temperature threshold violated	Overall proportion of monitoring time per week during which the temperature threshold is violated. The temperature will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, the temperature will be compared with its respective predefined threshold. The proportion of monitoring time points at which the temperature is violated will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with the relative humidity threshold violated	Overall proportion of monitoring time per week during which the relative humidity threshold is violated. The relative humidity will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, the relative humidity will be compared with its respective predefined threshold. The proportion of monitoring time points at which the relative humidity is violated will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with ambient light (day) threshold violated	Overall proportion of monitoring time per week during which the ambient light threshold is violated during the day-time hours. The ambient light will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, ambient light will be compared with its respective predefined threshold. The proportion of monitoring time points at which ambient light is violated during the day-time hours will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with ambient light (night) threshold violated	Overall proportion of monitoring time per week during which the ambient light threshold is violated during the night-time hours. The ambient light will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, ambient light will be compared with its respective predefined threshold. The proportion of monitoring time points at which ambient light is violated during the night-time hours will be calculated for each week.	Weekly during the 1-month follow-up
Weekly overall proportion of time with the ambient noise (night) threshold exceeded	Overall proportion of monitoring time per week during which the ambient noise threshold is exceeded during night-time hours. The ambient noise will be monitored at predefined measurement intervals during the 1-month follow-up. For each measurement time point, the ambient noise will be compared with its respective predefined threshold. The proportion of monitoring time points at which ambient noise is exceeded during night-time hours will be calculated for each week.	Weekly during the 1-month follow-up

Collaborators and Investigators

• Sponsor: Ziekenhuis Oost-Limburg
• Collaborators: KU Leuven; Hasselt University; University of Liege; Maastricht University
• Investigators: Principal Investigator: David Ruttens, MD, PhD, Ziekenhuis Oost-Limburg

Publications and helpful links

General Publications

• O'Connor L, Wang B, Ye Z, Behar S, Tarrant S, Stamegna P, Pretz C, Dunkel L, Savage B, Scornavacca T, Shirshac J, Wilkie T, Toomey S, Mullen M, Fisher K, Tigas E, Wong S, McManus DD, Alper E, Martinez F, Walkey A, Lindenauer PK, Dickson E, Broach JP, Kheterpal V, Soni A. Evaluation of an integrated digital and mobile intervention to improve outcomes for patients with moderate to severe COPD. NPJ Digit Med. 2025 Jul 17;8(1):451. doi: 10.1038/s41746-025-01871-0.
• Shah AJ, Althobiani MA, Saigal A, Ogbonnaya CE, Hurst JR, Mandal S. Wearable technology interventions in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. NPJ Digit Med. 2023 Nov 27;6(1):222. doi: 10.1038/s41746-023-00962-0.
• Hansel NN, McCormack MC, Belli AJ, Matsui EC, Peng RD, Aloe C, Paulin L, Williams DL, Diette GB, Breysse PN. In-home air pollution is linked to respiratory morbidity in former smokers with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2013 May 15;187(10):1085-90. doi: 10.1164/rccm.201211-1987OC.

Study record dates

Study Major Dates

• Study Start (Estimated): October 1, 2026
• Primary Completion (Estimated): October 1, 2027
• Study Completion (Estimated): October 1, 2027

Study Registration Dates

• First Submitted: August 17, 2026
• First Submitted That Met QC Criteria: August 27, 2026
• First Posted (Actual): September 2, 2026

Study Record Updates

• Last Update Posted (Actual): September 2, 2026
• Last Update Submitted That Met QC Criteria: August 27, 2026
• Last Verified: August 1, 2026

More Information

Terms related to this study

• Keywords: COPD; AECOPD; Remote monitoring; Mobile health; Indoor air quality; Air quality; Environmental quality
• Additional Relevant MeSH Terms: Pathologic Processes; Chronic Disease; Disease Attributes; Respiratory Tract Diseases; Lung Diseases; Lung Diseases, Obstructive; Pathological Conditions, Signs and Symptoms; Pulmonary Disease, Chronic Obstructive
• Other Study ID Numbers: Z-2026041; IMR6-00019 (Other Grant/Funding Number: Interreg Meuse-Rhine); UH-2026049 (Other Identifier: CME Hasselt University)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No