Sickle cell disease, a genetic red blood cell disorder associated with severe complications including chronic anemia, stroke, and vaso-occlusive crises (VOCs) often requires patients to be hospitalized. A new study shows that the Apple Watch could be very useful for both patients and caring physicians.
The primary reason for hospitalization visits is due to vaso-occlusive crises. VOCs create a very painful experience for these patients and require them to seek hospitalization care where they are treated with pain meds and saline hydration.
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A team of researchers from Duke University, Northwestern University, and others explored if 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 aim of this new Apple Watch study was to
- determine the feasibility of using the Apple Watch to predict the pain scores in people with sickle cell disease admitted to the Duke University SCD Day Hospital, referred to as the Day Hospital, and
- 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 argue that combining the data collection abilities of Apple Watch, and machine learning techniques may help us better understand the pain experience and find trends to predict pain from VOCs.
The researchers enrolled patients with sickle cell disease, older than 18 years, and admitted to Day Hospital for a VOC between July 2021 and September 2021.
These Participants were provided with an Apple Watch Series 3, which is to be worn for the duration of their visit.
The median age of the population was 35.5 (IQR 30-41) years. The median time each individual 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 were used on the collected data to evaluate if pain due to VOCs could be predicted.
The researchers found that the strong performance of the model in all metrics validates feasibility and the ability to use data collected from an Apple Watch, to predict the pain scores during VOCs.
According to these researchers, it is a novel and feasible approach and presents a low-cost method that could benefit clinicians and individuals with sickle cell disease in the treatment of VOCs.