ROCHESTER, Minn. — Sleep data captured with a wearable device could help clinicians better tailor care by identifying patients with chronic obstructive pulmonary disease (COPD) who may need additional support to participate in pulmonary rehabilitation, according to new research published in Mayo Clinic Proceedings: Digital Health.
COPD is a long-term lung disease that makes it hard to breathe after airways become inflamed and narrowed and mucus builds up. COPD can also make sleeping more difficult, affecting a patient's energy levels and overall health. These factors can influence participation in pulmonary rehabilitation, which includes a combination of exercise, education and support.
Researchers set out to understand whether a patient's sleep quality could help predict their level of participation in remote rehabilitation activities.
"As a scientist and engineer, I wanted to explore how wearable data could improve the drop-out rates of remote pulmonary rehabilitation programs. By better understanding a patient's day-to-day life, we can make more personalized and potentially more effective care plan recommendations," says Stephanie Zawada, Ph.D., M.S., a Mayo Clinic research associate and first author of the study. Dr. Zawada is committed to finding ways to use data to personalize care through her work on the team at the Kern Center for the Science of Health Care Delivery .
In the study, researchers found that using baseline sleep data from a wrist activity monitor, combined with machine learning and traditional clinical indicators, improved the prediction of how consistently patients would participate in a 12-week home pulmonary rehabilitation program.
The team made those calculations after collecting sleep measures for one week to generate a Composite Sleep Health Score before the home-based pulmonary rehabilitation began. At the end of the 12-week program, analysis showed that including the health score improved prediction of patient engagement over the study period.
This information can help clinicians better tailor rehabilitation programs and identify patients who may benefit from additional support. It also may inform the design of future remote-care programs.
"Adding wearable data provides a more comprehensive view of a patient's daily pattern," says Emma Fortune Ngufor, Ph.D. , senior author of the study and a Mayo Clinic researcher in the Kern Center. She noted that sleep data is one of several inputs that can help inform care decisions, alongside clinical assessments and patient-reported information.
Researchers note that additional investigation is needed to validate and refine the model in broader patient populations before broader clinical application.
For a complete list of authors, disclosures and funding, review the study .
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Mayo Clinic Proceedings Digital Health
Wearable Sleep Measures May Improve Machine Learning Prediction of Home-Based Pulmonary Rehabilitation Engagement Among Patients With Chronic Obstructive Pulmonary Disease: A Proof-of-Concept Study