Apple Watch can help predict pain in people with sickle cell disease

Sickle cell disease, a genetic disorder of red blood cells associated with serious complications, including chronic anemia, stroke, and vaso-occlusive crises (VOCs), often requires patients to be hospitalized. A new study shows that the Apple Watch can be very useful for both patients and caring doctors.

The primary reason for hospitalization is due to vaso-occlusive crises. VOCs create a very painful experience for these patients and require them to seek hospital treatment where they are treated with pain medication and saline hydration.

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A team of researchers from Duke University, Northwestern University and others investigated whether the Apple Watch could be useful in predicting pain in people with sickle cell disease, and the results of the study were published yesterday.

The goal of this new Apple Watch study was to

  1. determine the feasibility of using the Apple Watch to predict pain scores in individuals with sickle cell disease admitted to the Duke University SCD Day Hospital, referred to as the Day Hospital, and
  2. Build and evaluate machine learning algorithms to predict the pain scores of VOCs with the Apple Watch.

Recent research efforts in the treatment strategies for sickle cell disease have focused on the use of mobile health technology to develop algorithms to predict pain in people with sickle cell disease.

The researchers claim that a combination of the Apple Watch’s data collection capabilities and machine learning techniques can help us better understand the pain experience and find trends in predicting pain from VOCs.

The researchers enrolled patients with sickle cell disease, older than 18 years, and admitted to a day hospital for a VOC between July 2021 and September 2021.

These participants were given an Apple Watch Series 3 to wear during their visit.

The median age of the population was 35.5 (IQR 30-41) years. The median time each person spent wearing the Apple Watch was 2 hours and 17 minutes, and a total of 15,683 data points were collected across the population.

Data collected from the Apple Watch included heart rate, heart rate variability (calculated) and calories. Pain scores and vital signs were collected from the electronic medical record.

Various machine learning models was used on the collected data to evaluate whether pain due to VOCs could be predicted.

The researchers found that the model’s strong performance in all metrics validates the feasibility and ability to use data collected from an Apple Watch to predict pain scores during VOCs.

According to these researchers, it is a novel and feasible approach and presents an inexpensive method that may benefit clinicians and people with sickle cell disease in the treatment of VOCs.

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