Relapse identification in severe mental illness using smartphone app

Severe psychiatric disorders are among the most important medical causes of disability and reduced life expectancy. These disorders, including for example schizophrenia, bipolar disorder or major depression, usually have an episodic course, characterised by periods with few or no symptoms, followed by relapses. In the case of schizophrenia, for example, about 80% of people treated for a first episode relapse within five years. Relapses appear to cause neurobiological changes, which increase the likelihood of future relapses.

Currently available monitoring methods are very expensive, rely on retrospective assessments that often do not reflect actual functioning and disease progression, and require an investment by patients that they are often not able to make. The intervention with the best empirical evidence includes an increase in short-term medication, as well as continuous maintenance medication without dose reductions that would prevent relapses, thus increasing the risk of side effects such as weight gain or diabetes for example. For all these reasons, a treatment strategy that uses continuous data collection has the potential to contribute to safer and more acceptable treatment strategies for people with severe mental illness.

In this project, the aim of our researchers is to launch a study to develop an application that supports the relapse prevention approach by identifying early warning signs on a day-to-day basis and to improve the management of these illnesses in daily clinical practice.

Discreet monitoring helps patients and carers to be aware of the increased risk of relapse and enables the activation of a rapid and appropriate management strategy. In this project, our scientists are actively collaborating with the Fribourg mental health network.

Acknowledgments

This second study is supported by a generous foundation that wishes to remain anonymous. We sincerely thank this foundation for its invaluable support.