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How Researchers Are Using Health Tech to Monitor Depression

Woman wearing a health technology watch.

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Key Takeaways

  • Some researchers are using personalized health technology to predict and monitor a patient's mental health changes.
  • Using technology can help healthcare providers tailor treatment to the individual, and assist in monitoring patients between visits.
  • Experts stress the importance of keeping costs low for health wearables and promoting their ethical use.

Over 260 million people worldwide are estimated to have depressive disorders. But the condition varies wildly from one patient to the next. People are often left to trial and error when it comes to treatment.

Now, researchers are exploring how health technology, like personalized machine learning and vocal tracking apps, could change the way we monitor and treat these mental health conditions.

Current clinical strategies are siloed and work for about 30% of patients, according to Jyoti Mishra, PhD, assistant professor of psychiatry at the University of California San Diego. That’s why, for over a decade, researchers have explored the potential of personalized medicine for depression. This involves pinpointing subtypes of depression in an effort to figure out what treatments might work for different people.

Personalized health tech, such as apps or everyday wearable tech like a watch, may make it easier to offer this type of care.

“Personalized medicine is becoming a mainstay in health care, especially cancer therapeutics. We need to make similar forays in mental healthcare,” Mishra tells Verywell. “Using data, we can not only empower the user but also their care provider to make quantified informed objective decisions about mental health.”

Mishra explains that currently mental health care is driven by the question “how do you feel?” Although this is a subjective question, it contains quantifiable drivers that researchers can now unravel and target directly through the help of technology. 

How Health Technology Can Help

Mishra’s team of researchers at UCSD developed a way to understand people’s levels of depression based on data collected from a mix of apps and wearables.

For example, over a one-month period, her team collected information from 14 patients through everything from surveys in an app, to brain monitoring in a clinic, and vitals collected through a smart-watch. They then used machine learning to generate predictions about their health.

“That we could generate unique personalized wellbeing predictions for each person with good accuracy was what was exciting and surprising to us,” Mishra says. “Importantly, we can now unravel these models and intervene on the top predictors for each person, in a precise quantified manner.”

Mishra explains that by personalizing medicine through technology like this, clinicians can take away the burden of comparing one person against others. Instead, they can sample pieces of different data from each individual to personalize their treatment.

Tracking Facial and Vocal Changes

According to another team of researchers, phone applications can also track a person’s facial and vocal changes caused by depression.

Carol Espy-Wilson, professor of electrical and computer engineering at the Institute for Systems Research at the University of Maryland, is developing a system that maps acoustic signals, the timing and spatial movement of speech gestures, to better predict a patient’s mental health.

Speech coordination changes when a person becomes depressed. “There's something when people have depression called psychomotor slowing: you talk more slowly, you can't think as fast, you can't move as fast,” Espy-Wilson says.

The researchers used data from three different studies about how people move their mouths and tongues when they talk, and the coordinates of their facial movements (experts call these vocal track variables). They looked at how this changed for patients throughout their therapy and remission for depression.

The researchers noted that they were able to classify people who are depressed and when they're not depressed just from these pieces of information about 85 to 90%, according to Espy-Wilson. In short, speech can say a lot about a person’s mental health.

Beyond just classifying whether someone is experiencing depression or not, Espy-Wilson says they "also want to be able to measure the degree of the depression" using a person's speech.

The goal, for example, would be to monitor patients between their visits to their therapists and help prioritize who needs immediate access to healthcare.

“Because of the prevalence of AI now, and how it has really revolutionized a lot of signal processing, we're going to end up with a lot more data and a very, very good predictive power,” Espy-Wilson says.

What This Means For You

If you or someone you know is struggling with depression and isn't sure where to get help, call SAMHSA’s National Helpline, 1-800-662-HELP (4357). It's confidential, free, and runs 24-hour-a-day, 365-day-a-year. It's available in English and Spanish. If you call this helpline, they can give you referrals to local treatment centers, support groups, and other organizations.

Incorporating Health Tech Will Take Some Time

It’s still going to take a couple of years for these projects to actually take off. 

But, most importantly, it’s crucial that these investments aren’t solely seen as an economic opportunity for those who look to commercialize wellbeing, Mishra says.

Tech can be a fantastic vehicle for delivering sustainable change, but everybody needs to continue to keep it affordable, back up their information by science, and promote ethical delivery practices, Mishra explains.

“The ultimate goal is to deliver sustainable wellbeing, and I think it will be very exciting to see tech and AI working hand-in-hand with individuals seeking wellbeing and mental health practitioners to make this possible,” Mishra says. “Neuro-technologies, cognitive technologies, wearables, apps can all be thoughtfully leveraged to enhance mental health and wellbeing. It is an exciting time when scientists, clinicians, engineers, and entrepreneurs are all collaborating to realize these goals.”

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  1. World Health Organization. Depression. Updated January 30, 2020.

  2. Shah, R.V., Grennan, G., Zafar-Khan, M. et al. Personalized machine learning of depressed mood using wearablesTransl Psychiatry 11, 338 (2021). https://doi.org/10.1038/s41398-021-01445-0

  3. Acoustical Society of America. Potential Vocal Tracking App Could Detect Depression Changes. June 3rd, 2021.