Depression affects 16 million Americans and 322 million people globally, according to the National Alliance on Mental Illness and the World Health Organization. According to emerging research, the COVID-19 pandemic is worsening the frequency of depression in the general population. With this trend, it is clear that more effective therapy solutions for this crucial public health concern are required.
Smartphones Can Foretell Depression And Customize Treatments
Researchers at the University of California San Diego School of Medicine used a combination of modalities, such as measuring brain function, cognition, and lifestyle factors, to generate individualized predictions of depression in a recent study, which is published in the online edition of Nature Translational Psychiatry on June 9, 2021.
Several parameters associated with an individual’s subjective symptoms, such as sleep, exercise, food, stress, cognitive function, and brain activity, were included in the machine learning and tailored approach.
The study analyzed data from mobile phone applications and watches, as well as brain activity and lifestyle characteristics, to predict depression; the findings might lead to more personalized mental health treatment programs.
Mobile phone applications may make it simpler to follow a person’s progress in treatment or the effects of medicines. Furthermore, understanding how and when mood transitions occur can assist a person in identifying significant trends in their behavior.
According to Jyoti Mishra, PhD, senior author of the study, director of NEATLabs, and assistant professor in the Department of Psychiatry at UC San Diego School of Medicine, there are several underlying reasons and causes for depression. Simply put, current healthcare standards consist mostly of asking individuals how they feel and then issuing a prescription for drugs. In major studies, these first-line therapies were revealed to be only mildly to moderately effective.
Depression is a multidimensional disorder that requires individualized treatment, whether it is counseling with a mental health professional, greater exercise, or a mix of techniques.
The one-month study collected data from 14 depressed participants using smartphone apps and wearable (like smartwatches) to measure mood and lifestyle variables like sleep, exercise, diet, and stress and paired these with cognitive evaluations and electroencephalography, which records brain activity using electrodes on the scalp.
The purpose was not to conduct any cross-individual comparisons, but rather to model the predictors of each person’s daily changes in a melancholy mood.
The researchers used a novel machine-learning pipeline to find specific predictors of depression in each individual.
For example, for one individual, exercise and daily coffee intake were major predictors of mood, whereas, for another, sleep and stress were more predictive, and for a third, brain function and cognitive reactions to incentives were the top predictors.
They should not approach mental health in a one-size-fits-all manner. Patients will benefit from having more clear and quantifiable information about how certain habits may be contributing to their sadness. Clinicians may use this data to better understand how their patients are feeling and to better combine medical and behavioral methods to improving and maintaining mental health, according to Mishra.
The study demonstrates that they may utilize easily accessible technology and tools, such as mobile phone applications, to collect information from people who have or are at risk of having depression and then utilize that information to build tailored treatment regimens.
Mishra stated that the next stage would be to see if the individualized treatment strategies informed by data and machine learning are successful.
The discoveries might have far-reaching effects beyond depression. Anyone looking to improve their well-being might benefit from insights derived from their data. How can he feel better if he doesn’t know what’s wrong?
The study was partially financed by the University of California, San Diego, as well as seed funding from the UC San Diego Mental Health Technology Center and the Sanford Institute for Empathy and Compassion.
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