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In this Collection, we highlight recent research papers, reviews and commentaries from npj Digital Medicine and npj Schizophrenia that illustrate new developments in digital technology related to mental health. The papers included look at the effect of digital technology on health and well-being, and how the development of new technology could be used to better diagnose and treat mental health conditions. This is an area of growing of clinical interest, as well as an exciting field in basic and translational research.
A computerized cognitive–emotional training program significantly reduces symptom severity in patients with Major Depressive Disorder (MDD). The Emotional Faces Memory Task (EFMT) in which patients are asked to identify and remember the emotions displayed in a series of faces, has been designed to activate brain regions involved in emotion processing and cognitive control simultaneously. A study led by Brian Iacoviello, Icahn School of Medicine at Mount Sinai, involving 51 non-medicated participants with MDD shows that after 6 weeks EFMT reduced both clinician-rated and self-reported measures of MDD symptoms. These findings suggest that digital therapies that can modulate activation patterns in brain areas affected in neuropsychiatric disorders hold great promise as a novel treatment approach.
A pilot study shows that smartphone-collected data from patients with schizophrenia could be used to infer their mental-health status. Using smartphones as scientific data gathering tools holds great promise for understanding some of the behavioral features of psychiatric disorders and could provide an early indication of worsening symptoms. However, few studies have assessed the quality of the collected data, and thus the accuracy of clinical outcome prediction. Patrick Staples at the Harvard T. H. Chan School of Public Health in Boston, MA, and colleagues examined the relationship between data quality and future symptom-related survey responses in 16 patients with schizophrenia. They found that smartphone sensor data as well as phone-use metrics related to the completion of symptom-related surveys were significantly associated with survey results, highlighting the clinical relevance of this approach.
A computer program that analyses natural speech could help predict the onset of psychosis in young people at risk. People with schizophrenia have subtle disorganization in speech, even before they first develop psychosis. In a collaboration between IBM, Columbia University Medical Center, and researchers in South America, an automated program that simulates how the human brain understands language was used to analyze interview transcripts from 34 ‘at risk’ youths. Decrease in the flow of meaning from one spoken phrase to the next, and grammatical markers of speech complexity, identified the five individuals who later developed psychosis. The computer program outperformed clinical assessments in predicting psychosis. While numbers are small in this proof-of-principle study, the authors suggest automated analysis could lay the foundation for a simple clinical test of emerging schizophrenia, which would inform early intervention.