How Health Technology Helps in Assessing Depression Among People
Date: Monday 19 Jul 2021
Depression has been one of the biggest problems that people face even in the past.
This disorder is often viewed as something that requires a "simple" solution, but modernity made us more aware that it could be monitored in some ways.
through the use of health technology, experts can now present another form of solution for the disorder. Moreover, the use of machine learning and other applications are seen to be more effective than the conventional method that we know for depression.
Experts Find Health Tech as Effective 'Depression' Killer
The wonders of health technology can effectively monitor depressive disorder through the use of simple phone apps, machine learning, and other methods.
According to a report by Very Well Health, researchers are now exploring options to incorporate health technology as a treatment for depression. They have been thinking if using health apps, such as one that relies on voice tracking, might alleviate the patient's depression.
The solution in the form of artificial intelligence, wearable technology, and certain apps are seen to be potential alternatives for old depression treatments.
In an interview with the online health outlet, University of California San Diego psychiatry instructor Dr. Jyoti Mishra said that the data that they will gather can teach them more about health tech.
What the Data Says About Depression
The UCSD experts, together with Mishr, can have a clear view of an individual's mental health during depression through a series of wearables and tech apps.
They have tested that a smartwatch can amass substantial information about the patient's vitals and brain activity.
Moreover, machine learning can assist them to formulate possibilities that could take place in their mental health.
Mishra believed that the adoption of health technology was an advanced step to monitor depression. In addition, the data can provide more meaning to what patients need to achieve to lower their depression levels.
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