In the trial, patients were given wristbands and psychological testing. (Credit: Steve Buissinne/Pixabay)

A team of researchers used data from wearable devices to predict outcomes of treatment for depression on individuals who took part in a randomized clinical trial. They developed a novel machine learning model that analyzes data from two sets of patients — those randomly selected to receive treatment and those who did not receive treatment — instead of developing a separate model for each group.

This unified multitask model is a step toward personalized medicine, in which physicians design a treatment plan specific to each patient’s needs and predict outcome based on an individual’s data.

In the trial, patients were given Fitbit wristbands and psychological testing. About two-thirds of the patients received behavioral therapy, and the remaining patients did not. Patients in both groups were statistically similar at baseline, which gave the researchers a level playing field from which to discern whether treatment would lead to improved outcomes based on individual data.

Clinical trials of behavioral therapies often involved relatively small cohorts due to the cost and duration of such interventions. The small number of patients created a challenge for a machine learning model, which typically performs better with more data. However, by combining the data of the two groups, the model could learn from a larger dataset, which captured the differences in those who had undergone treatment and those who had not. They found that their multitask model predicted depression outcomes better than a model looking at each of the groups separately.

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Topics:
Medical Smartwatches, Wristbands, and Eyeglasses Wearables

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