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How the crowd and mobile can help fight depression

· Mobile Zone

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Recently I looked at a new Australian innovation that was aiming to help sufferers of depression by encouraging them to talk about their condition.  The service places the viewer inside an interactive music video that creates a shared experience that hopefully provokes and supports discussion.

It’s clearly a hot topic at the moment, as I’ve recently come across a couple of new services that utilize crowdsourcing and mobile innovations to lend their weight to the cause.

Peer to peer networking

Researchers from MIT and Northwestern University have recently developed a networking tool that they believe will help those with depression to build support communities.

The tool was developed out of a study, whereby the researchers tested it against the more established technique of expressive writing.  The tool performed superbly, especially in training subjects in cognitive reappraisal and in improving the mood of those with the severest symptoms.

“We really wanted to see two things,” the authors say. “Could people get clinical benefits from it? That’s hypothesis one.”

“Hypothesis two is, ‘Will people be engaged and use this regularly?'” they continue. “There’s a lot of great work in building web apps and mobile apps to provide psychotherapy without a therapist in the loop — it’s these self-guided programs. There’s almost a decade of research showing that these things can produce really profound improvements for people. The problem is that, once you release them out into the wild, people just don’t use them. The way we designed our platform was to really mimic some of the interaction paradigms that underlie very engaging social programs.”

There were strong results here too, with the average user logging in twice as often to the new tool as they did to the traditional expressive writing tool.

The tool allows users to log in and record both a trigger event and their response to it.  The social aspect of the tool, which the authors have called Panoply, then sees members of each network vote on the thought pattern input into the system by each user.  They are also free to suggest other ways of interpreting an event.

The hope is that they will both learn about cognitive reappraisal both from theory, but also by reappraising the reactions of others.  This will then make them better able to appraise their own reactions.

“We really wanted to see that people are utilizing this skill over and over again, not only in response to their own stressors but also as teachers to other people,” the authors say. “We can surmise that it’s a little easier to practice some of these psychotherapeutic skills for other people before turning them toward themselves. But we don’t have data supporting that.”

The aim now is to commercialize the tool via Koko, a New York based company set up by the lead researcher on the project.

Using your phone to monitor depression

A second interesting project is the development of a smartphone app by researchers at the University of Connecticut.

They’ve developed a new app, called LifeRhythm, which is designed to detect symptoms of depression automatically via the numerous sensors that come inbuilt to most phones.

The app will tap into things like GPS, accelerometers and the like to gage the activity levels and social interactions of the user.  This information will then be screened for the possibility of depression.

For instance, GPS data might be used to determine how far people are venturing outside their homes.  Speech may be analyzed via voice sensors, whilst accelerometers could be used to measure activity levels.  Even phone records and SMS data could be used to understand communication patterns.

The developers believe that LifeRhythm could revolutionize how depression is diagnosed, and certainly provide a better method than the interviews currently used.

The next step is to thoroughly test the system under controlled conditions.

They are certainly on the right track though, as evidenced by a similar paper emerging from a team of researchers at the University of Rochester recently.

It uses selfie videos recorded by mental health patients and analyzes the footage for signs of depression, such as heart rate, blinking rate and head movement rate.

The system also monitors what the users were posting on social media, how quickly they scrolled and various other facets of their online meanderings.

It’s currently in demo testing, but it’s another fascinating development in this rapidly moving field.

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