Mental health has come to the fore during a Covid pandemic that has placed an immeasurable strain on the world. Indeed, the World Health Organization estimates that depression alone affects 322 million people globally.
New research from the University of California San Diego School of Medicine highlights how our phones can help to identify and even predict the onset of depression. The approach utilizes machine learning and takes into account a number of factors related to each individual, including their sleep diet, brain activity, exercise, stress, and cognitive function.
“There are different underlying reasons and causes for depression,” the researchers say. “Simply put, current health care standards are mostly just asking people how they feel and then writing a prescription for medication. Those first-line treatments have been shown to be only mild to moderately effective in large trials.”
A multifaceted illness
The researchers explain that depression is inherently a multifaceted illness that needs a personalized approach to treatment, whether that’s via therapy, exercise, or some other approach.
The researchers collected data from participants with depression using a range of smartphone apps and wearable devices. This enabled them to measure a range of lifestyle and mood-related variables, such as exercise, stress, sleep, and diet, before pairing these with cognitive evaluations and electroencephalography to measure brain activity.
The ultimate aim was to model predictors of the daily fluctuations in each individual, and the researchers developed a machine learning system to identify distinct predictors of low mood for each person. For instance, one volunteer was instructed to focus on their exercise and caffeine intake, as these were strong predictors of their mood. For another, sleep and stress were bigger factors.
“We should not be approaching mental health as one size fits all. Patients will benefit by having more direct and quantified insight onto how specific behaviors may be feeding their depression. Clinicians can leverage this data to understand how their patients might be feeling and better integrate medical and behavioral approaches for improving and sustaining mental health,” the researchers say.
“Our study shows that we can use the technology and tools that are readily available, like cell phone apps, to collect information from individuals with or at risk for depression, without significant burden to them, and then harness that information to design personalized treatment plans.”
The team next hope to explore how effective these personalized treatment plans are at actually treating depression to ultimately determine whether it’s an approach that can be deployed at scale.
“Our findings could have broader implications than depression. Anyone seeking greater well-being could benefit from insights quantified from their own data,” they conclude. “If I don’t know what is wrong, how do I know how to feel better?”
