Sleep Well: A Sleep Cycle Tracker to Optimize Morning Wake Ups

by Robert Tran

Coauthors: Thank you to Dr. Timothy Flannery for helping me develop this abstract idea. Additionally, thank you to Dr. Sharief Taraman and Dr. Anthony Chang for providing the opportunity to be a part of the MI3 internship.

Medical Devices & Digital Health

Background:
For many pediatric hospital patients, unpleasant 6am wake ups have been accepted as an unavoidable disturbance. Although sleep is immensely important for recovery and overall well being, many hospital patients experience sleep interruptions for labs and X-rays. For kids with long stays, these wake ups can quickly become stressful and draining. However, wake ups can be optimized by ensuring that they are only done at the lightest stage of sleep (Stage 2). In this period of sleep, wake ups would be least bothersome, since this is the natural wake up point in the body’s circadian rhythm. This would make patients’ hospital experiences more comfortable and allow them to sleep with minimal interruptions for optimal recovery.

Method:
The Sleep Well system will include a smart accelerometer device attached to the hospital bed and a companion app. The accelerometer will be used to track the sleep stage that the patient is in based on their movements while asleep. Data from the accelerometer will be uploaded to a cloud processing center to analyze the data. At the processing center, the movement data will be interpreted using a deep learning algorithm that correlates movements while the patient is at rest with various sleep stages. This machine learning model will also self-improve based on the data from previous nights, creating an algorithm that is optimized for each individual patient.

Hospital staff will have access to a companion app that allows them to view the sleep stage that each of their patients are in so that they can be awakened at an optimal time for any interventions needed in the morning. In addition, the companion app will show which patients are currently in the light stage of sleep so that they can perform labs and X-rays, ensuring that these are only conducted when patients will be least disturbed. For staff that are trying to enter a specific patient’s room, the app will also show which stage of sleep the patient is in. If they are in REM or deep sleep, it will show approximately when they will move to a lighter stage. Additionally, the sleep data for all patients will be recorded throughout the night so that hospital staff can track their patients’ sleep to monitor any abnormalities and ensure that they are getting enough rest to recover.

Conclusion:
Being conscious of when patients are being awakened is a simple tweak that can dramatically improve the patient experience, especially for pediatric patients. Given that movement-based sleep tracking technology is already present today, applying it to a hospital setting would be a feasible addition that would improve patient recovery and the overall hospital experience. Overall, the Sleep Well system will make it easier for patients to focus on what they are supposed to do in a hospital: heal.