Mayo Clinic researchers found that sleep metrics from wrist monitors can identify which COPD patients may need more support during pulmonary rehab.
RT’s Three Key Takeaways:
- Predictive Sleep Data: Baseline sleep measures from wearable devices, when combined with machine learning, improved the ability to predict how consistently patients with COPD would participate in home rehabilitation programs.
- Personalized Care Delivery: Utilizing a Composite Sleep Health Score allows clinicians to identify patients at risk of dropping out and provide targeted support to improve rehabilitation outcomes.
- Enhanced Remote Monitoring: Integrating wearable data into healthcare workflows provides a more detailed view of a patient’s daily patterns than traditional clinical assessments alone, according to researchers.
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 research published in Mayo Clinic Proceedings: Digital Health.
COPD is a long-term lung disease that makes it difficult to breathe after airways become inflamed and narrowed and mucus builds up. The condition can also make sleeping more difficult, affecting a patient’s energy levels and overall health. These factors often influence participation in pulmonary rehabilitation, which includes a combination of exercise, education, and support.
Researchers at Mayo Clinic set out to understand whether a patient’s sleep quality could help predict their level of participation in remote rehabilitation activities. 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 the prediction of patient engagement over the study period.
“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,” said Stephanie Zawada, PhD, MS, a Mayo Clinic research associate, in a news release.
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 healthcare programs.
“Adding wearable data provides a more comprehensive view of a patient’s daily pattern,” said Emma Fortune Ngufor, PhD, a Mayo Clinic researcher, in a news release.
Ngufor noted that sleep data is one of several inputs that can help inform care decisions, alongside clinical assessments and patient-reported information. Researchers noted that additional investigation is needed to validate and refine the model in broader patient populations before broader clinical application.
