Model Using Wearable Tech, Smartphone App Able to Predict AECOPD

A prediction model for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) incorporating data recorded using wearable devices, environmental sensors, and a smartphone app proved successful during a recent prospective study.

The study results, published in JMIR mHealth and uHealth, build on previous AECOPD prediction models, which primarily focused on questionnaire data, by including environmental and physiological data in addition to clinical questionnaires.

“Overall, our study constitutes a novel solution making use of various data sources for superior AECOPD prediction performance,” wrote the investigators.

AECOPD often results in decreased quality of life, accelerated lung function deterioration, and increased mortality risks in patients with COPD. Among clinicians, exacerbation prevention tactics are usually guided by a history of 2 or more exacerbations and 1 severe exacerbation annually.

However, this approach is flawed because significant heterogeneity in risk can present, even among patients who have frequent exacerbations. Additionally, no current prediction models for AECOPD include both lifestyle data and medical questionnaires, the authors note.

They enrolled 67 patients 20 years or older who did not have a pacemaker and who were not pregnant. The patients were recruited from the National Taiwan University Hospital between March 2019 and February 2020.

The mean (SD) age of the participants was 66.62 (11.38) years and most were men. Never-smokers accounted for 18% of the cohort. The rest were either current or former smokers.

Upon enrollment and during each month of the study, patient symptom data were generated using the Medical Research Council dyspnea scale and the COPD assessment test, which are used to gauge functional impairment due to dyspnea and the impact of COPD on health status, respectively.

Physiological and lifestyle data, including walking steps, climbing stairs, distance, calorie conception, heart rate, and sleep status were recorded using a wearable device, such as a FitBit tracker, and lifestyle data were recorded by a self-management smartphone app developed by the investigators. Environmental data, such as temperature, humidity, and fine particulate matter, were collected using an at-home air quality-sensing device.

The data were then visualized by combining trend charts that physicians could use as a reference to better understand a patient’s status. If the system predicts that the AECOPD probability exceeds 0.7, a red icon is displayed to alert case managers to intervene. Other color markers were used to indicate different risk levels.

The investigators designed 6 models for predicting AECOPD in 7 days and trained them using various combinations of data features.

Factors that were significantly different between participants with and without AECOPD were average heart rate, fine particulate matter levels, step counts, and calorie consumption, suggesting that physiological and environmental factors are useful for predicting AECOPD.

The AECOPD prediction model that used a deep neural network to aid in comparing the performance between machine-learning and deep-learning approaches achieved the best prediction results, demonstrating 6 metrics higher than 90%. This further confirmed that physiological and environmental attributes were more effective at AECOPD prediction than conventional clinical questionnaires.

When the prediction system used only lifestyle or environmental data, the system could still predict that an AECOPD event would occur within the next 7 days. Daily prediction reports are made available to physicians for monitoring purposes and to help them make informed treatment decisions.

Environmental data were limited because the air quality–sensing device could only generate results from the patient’s bedroom. Going forward, the investigators said that they plan to use a GPS function to track user movements to get a more accurate reading.

Additionally, patients with AECOPD reported a higher level of physical activity than patients without AECOPD, which contradicts the traditional view held by many medical professionals. The investigators said that more data are needed to shed light on the observed paradox.

“Our results indicate that lifestyle and environmental data facilitate the precise management of users’ health conditions, and can even produce early warnings of AECOPD,” wrote the investigators.

Reference

Wu C-T, LI G-H, Huang C-T, et al. Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: Development and cohort study. JMIR Mhealth Uhealth. May 2021;9(5):e22591. doi:10.2196/22591