Sleep data captured with a wearable device could help clinicians identify patients with chronic obstructive pulmonary disease (COPD) who may need additional support, according to researchers at Mayo Clinic.

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 programmes,” said Stephanie Zawada, Mayo Clinic research associate and first author of the study. “By better understanding a patient’s day-to-day life, we can make more personalised and potentially more effective care plan recommendation.”

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 programme.

The team analysed already collected sleep measures, as part of a large study aimed to test a home-based programme of pulmonary rehabilitation led by Roberto Benzo and the Mindful Breathing Laboratory (www.mayo.edu/research/labs/mindful-breathing/overview). Investigators generated a composite sleep health score before the home-based pulmonary rehabilitation began. At the end of the 12-week programme, analysis showed that including the health score improved prediction of patient engagement over the study period.

This information can help clinicians better tailor rehabilitation programmes and identify patients who may benefit from additional support. It also may inform the design of future remote-care programmes.

“Adding wearable data provides a more comprehensive view of a patient’s daily pattern,” said Emma Fortune Ngufor, senior author of the study and a Mayo Clinic (www.mayo.edu) researcher. She noted that sleep data are among 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.

To read the study, go to www.mcpdigitalhealth.org/article/S2949-7612(26)00011-8/fulltext.