Smart Bracelet May Predict Mood Changes in Bipolar Disorder

Liam Davenport

October 12, 2023

Electrodermal activity (EDA) measured via a smart bracelet/wristband may help predict and track changes in mood and more rapidly assess treatment response in patients with bipolar disorder (BD), early research suggests.

In a small observational pilot study, researchers found the E4 wristband (Empatica, Inc) was able to detect fluctuations in mood.

The results highlight the potential of EDA to serve as an objective BD biomarker, the investigators, led by Diego Hidalgo-Mazzei, MD, PhD, Bipolar and Depressive Disorders Unit, University of Barcelona, Barcelona, Spain, note.

The findings were presented at the 36th European College of Neuropsychopharmacology (ECNP) Congress.

A Need for Objective Markers

The evaluation of BD currently consists of clinical interviews, questionnaires, and scales, which largely rely on physician assessment, highlighting the need for objective biomarkers.

Previous studies show that EDA, which tracks changes in the skin due to sweat gland activity in response to psychological stimuli, is reduced in unipolar depression.

The researchers hypothesized that EDA could be a biomarker of mood changes in patients with BD. They recruited 38 patients experiencing manic (n = 12) or depressive (n = 9) episodes or who were euthymic (n = 17) and compared their responses to those of 19 healthy control persons.

Study participants were asked to wear the wristband continuously for approximately 48 hours to measure EDA, motion-based activity, blood volume pulse, and skin temperature.

The 48-hour monitoringsession was determined by the battery life of the device, Hidalgo-Mazzei told Medscape Medical News.

The acute phasepatients in the study had three sessions at different time points ― one during the acute state, another when the clinician determined there was a response to treatment, and again at remission. Euthymic patients and healthy control pesons only had a single monitoring session.

Hidalgo-Mazzei said the study's protocol is unique because it involves unusually long sessions with the device. In this setup, each sensor collects a sample every second, resulting in highly detailed and granular data.

"At the end, it is a trade-off, as handling such an enormous amount of data for each session requires equally large preprocessing, computing power, and analysis."

Hidalgo-Mazzei characterized compliance with the device as "outstanding" for the majority of study participants.

Results showed that mean EDA was notably and significantly lower in BD patients during depressive episodes in comparison with those in other groups. Patients with depression also had significantly less frequent EDA peaks per minute (P = .001 for both).

There were also significant differences in EDA measures between baseline and after treatment in the acute BD groups.

Patients with depression had significant increases in mean EDA (P = .033), EDA peaks per minute (P = .002), and the mean amplitude of EDA peaks (P = .001) from baseline, while manic patients experienced a decrease in the mean amplitude of EDA peaks (P = .001).

It is important for the patient and doctor to know how and when mood fluctuations take place, said Hidalgo-Mazzei, because treatment for manic and depressive states differ.

"Until now, these mood swings have mostly been diagnosed subjectively, through interview with doctors or by questionnaires, and this had led to real difficulties.

"Arriving at the correct drug is difficult, with only around 30% to 40% of treated individuals having the expected response. We hope that the additional information these systems can provide will give us greater certainty in treating patients."

However, Hidalgo-Mazzei said that is still a long way off, noting that this is an exploratory, observational study.

"We need to look at a larger sample and use machine learning to analyze all the biomarkers collected by the wearers to confirm the findings," he said.

A True Biomarker?

Commenting on the research for Medscape Medical News, Joseph F. Goldberg, MD, clinical professor of psychiatry at Icahn School of Medicine at Mount Sinai, New York, said the study is an "interesting use of this technology to differentiate physiological correlates of mood states."

However, he told Medscape Medical News the findings are limited and preliminary because the sample sizes were small and the measures weren't repeated.

In addition, medications or other factors that may influence electrophysiologic activity, such as anxiety or panic, were not considered, and Goldberg noted the researchers did not compare the results with those in patients with other diagnoses.

"So, I don't think one could call this a biomarker in the sense of having diagnostic specificity," he said, making the comparison with body temperature, which "goes up in an infection; but fever alone doesn't tell us much about the nature or cause of a presumed infection. More studies are needed before generalizable conclusion can be drawn."

Also commenting on the research, Paolo Ossola, MD, PhD, assistant professor of psychiatry, Department of Medicine and Surgery, University of Parma, Parma, Italy, described the study as exploratory but preliminary.

He said the researchers have "laid the foundation for a new approach to diagnosing and treating bipolar disorders.

"The shift from the subjective to the biological level could also promote understanding of the underlying mechanistic dynamics of mood swings."

The study was funded by the Instituto de Salud Carlos III and a Baszucki Brain Research Fund grant from the Milken Foundation.The authors have disclosed no relevant financial relationships.

36th European College of Neuropsychopharmacology (ECNP) Congress: Abstract P.2027. Presented October 7, 2023.

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