Friday July 19, 2019

Mindfulness works as good as antidepressants to prevent relapses of depression: Study

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Mindulness

By NewsGram Staff Writer

A new study has unveiled that practicing mindfulness works just as good as gulping down antidepressants to prevent relapses of depression.

Having its root in Buddhism, mindfulness includes paying attention on ‘the purpose.’ It is an emotionally non-reactive state, which helps people to refrain from tagging a situation good or bad.

A latest study published in ‘The Lancet’ revealed that practicing mindfulness is as effective as taking antidepressants to treat depression. The study also suggested that patients taking part in the mindfulness-based group therapy sessions were less likely to slide back into depression.

The sessions aimed at teaching mediation tricks along with ‘mindfulness principles’ with the goal of bolstering the patients to react differently to patterns of negative thinking that could give a way to depression to creep in slowly.

The study, conducted by researchers at Oxford University, followed 424 people from the South West of England for two years. Half of the people received mindfulness therapy whereas the other half continued taking antidepressants.

Surprisingly, the rate of depression relapse in the people who continuously practiced mindfulness was 44% compared to 47% among those who carried on with their medication.

“Recurrent depression is characterized by people who have very negative thoughts about themselves, other people and the world, and those negative thoughts can quickly go into a downward spiral of depressive relapse,” said Willem Kuyken, professor of clinical psychology at the University of Oxford and lead author of the study.

“Whilst this study doesn’t show that mindfulness-based cognitive therapy works any better than maintenance antidepressant medication in reducing the rate of relapse in depression, we believe these results suggest a new choice for the millions of people with recurrent depression on repeat prescriptions,” he added.

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Novel AI Tool to Detect Depression Via Sound of Your Voice

Such a tool could prove useful to support work with care providers or to help individuals reflect on their own moods over time

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Depression is a major issue affecting millions of people, especially the teenagers. Pixabay

India — the sixth most depressed country in the world — has an estimated 56 million people suffering from depression and 38 million from anxiety disorders, according to a recent report by the World Health Organisation (WHO).

To help identify depression early, scientists have now enhanced a technology that uses Artificial Intelligence (AI) to sift through sound of your voice to gauge whether you are depressed or not.

Computing science researchers from University of Alberta in Canada have improved technology for identifying depression through vocal cues.

The study, conducted by Mashrura Tasnim and Professor Eleni Stroulia, builds on past research that suggests that the timbre of our voice contains information about our mood.

Using standard benchmark data sets, Tasnim and Stroulia developed a methodology that combines several Machine Learning (ML) algorithms to recognize depression more accurately using acoustic cues.

A realistic scenario is to have people use an app that will collect voice samples as they speak naturally.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

“The app, running on the user’s phone, will recognize and track indicators of mood, such as depression, over time. Much like you have a step counter on your phone, you could have a depression indicator based on your voice as you use the phone,” said Stroulia.

Depression is ranked by WHO as the single largest contributor to global disability. It is also the major contributor to suicide deaths.

The ultimate goal, said researchers, is to develop meaningful applications from this technology.

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Such a tool could prove useful to support work with care providers or to help individuals reflect on their own moods over time.

“This work, developing more accurate detection in standard benchmark data sets, is the first step,” added Stroulia while presenting the paper at the Canadian Conference on Artificial Intelligence recently. (IANS)