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Real-time assessment of mood changes and machine learning

Year:
2020
Duration:
15 months
Approved budget:
$30,000.00
Researchers:
Associate Professor Frederick Sundram
,
Professor Partha Roop
,
Dr Amy Chan
Health issue:
Mental health (and sleep disorders)
Proposal type:
Research Activation Grant 2
Lay summary
Depression is the most prevalent mental health disorder in New Zealand (Wells et al., 2006) and is the second leading cause of disability in the world (Ferrari et al., 2013). However, despite the availability of effective treatments for depression, less than 10% of individuals with depression in many countries receive treatment in a timely way (Reddy, 2010). There are several barriers to early diagnosis and treatment, including limited access to mental health services and a lack of objective assessment techniques for example, based on behavioural or physiological parameters. Consequently, there are often delayed presentations and poorer outcomes. Therefore, this study aims to acquire, in a pilot clinical population, physiological and behavioural data through the application of in-built sensors in smartphones and smartwatches and develop a machine-learning algorithm (MoodAI) that will help in the earlier detection/monitoring of depression. This activation work lays the foundation for future research programme work in depression.