Wearable devices can predict depression treatment outcomes: Research | Health

Managing his mental health has been a higher priority in recent years, with an increased emphasis on self-care. Every year, more than 300 million people around the world suffer from depression. Recognizing this, there’s a lot of interest in using popular wearables to monitor a person’s mental health by tracking markers like activity level, sleep, and heart rate. (Also read: Exercising for 30 minutes can reduce symptoms of depression and boost effects of therapy: study )


A team of researchers from Washington University in St. Louis and the University of Illinois in Chicago used data from wearable devices to predict depression treatment outcomes in people who took part in a trial randomized clinic. They developed a new machine learning model that analyzes data from two groups 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 multitasking model is a step toward personalized medicine, in which doctors design a treatment plan specific to each patient’s needs and predict outcomes based on an individual’s data.

The research results have been published in the ACM Proceedings on Interactive, Model, Wearable and Pervasive Technologies and will be presented at the UbiComp 2022 conference in September. Chenyang Lu, a Fullgraf professor at the McKelvey School of Engineering, led a team that included Ruixuan Dai, who worked in Lu’s lab as a doctoral student and is now a software engineer at Google; Thomas Kannampallil, associate professor of anesthesiology and associate director of research information at the School of Medicine and associate professor of computer science and engineering at McKelvey Engineering; and Jun Ma, MD, PhD, professor of medicine at the University of Illinois at Chicago (UIC); and colleagues to develop the model using data from a randomized clinical trial conducted by UIC with approximately 100 adults with depression and obesity.


“Integrated behavioral therapy can be expensive and time-consuming,” Lu said. “If we can make personalized predictions for individuals about how likely a patient will respond to a particular treatment, then patients can’t continue treatment that if the model predicts that their condition will likely improve with treatment but less likely without treatment, personalized predictions of treatment response will facilitate more targeted and cost-effective therapy.”

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


Clinical trials of behavioral therapies often involved relatively small cohorts due to the cost and duration of these interventions. The small number of patients created a challenge for a machine learning model, which generally works better with more data. However, by combining data from both groups, the model could learn from a larger data set, which captured the differences between those who had undergone treatment and those who had not. They found that their multitasking model predicted depression outcomes better than a model examining each of the groups separately.

“We pioneered a multitasking framework, which combines the intervention group and the control group in a randomized controlled trial to jointly train a unified model to predict an individual’s personalized outcomes with and without treatment,” said Dai, who earned a doctorate in computer science. science in 2022. “The model integrated clinical features and wearable data into a multi-layered architecture. This approach avoids dividing study cohorts into small groups for machine learning models and enables dynamic knowledge transfer between groups to optimize prediction performance for both with and without intervention.”


“The implications of this data-driven approach extend beyond randomized clinical trials to implementation in the delivery of clinical care, where the ability to make personalized predictions of patient outcomes based on treatment received, and to do so early and throughout treatment, could significantly inform shared decision-making by the patient and treating physician to tailor the treatment plan for that patient,” Ma said.

The machine learning approach provides a promising tool for building personalized predictive models based on data collected from randomized controlled trials. Going forward, the team plans to leverage the machine learning approach in a new randomized controlled trial of telehealth behavioral interventions using Fitbit wristbands and weight scales in patients in an intervention study. of weight loss.


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