Blending active and passive digital technology methods to improve symptom monitoring in early psychosis. Issue 5 (28th February 2019)

• Record Type: Journal Article
• Authors: Cella, Matteo; He, Zhimin; Killikelly, Clare; Okruszek, Łukasz; Lewis, Shon; Wykes, Til
• Is Part Of: Early intervention in psychiatry. Volume 13:Issue 5(2019)
• Page Start: 1271
• Page End: 1275
• Publication Date: 2019-02-28
• Subjects: autonomic -- eHealth -- mHealth -- psychosis -- schizophrenia -- wearable; Mental health -- Periodicals; Psychiatry -- Periodicals; Psychiatry -- Research -- Periodicals; Mental illness -- Prevention -- Research -- Periodicals; Mental illness -- Treatment -- Research -- Periodicals; 616.89
• DOI: 10.1111/eip.12796
• Languages: English
• ISSNs: 1751-7885

Abstract

Abstract : Aims: Psychotic symptoms fluctuate over time and effective and regular monitoring may contribute to relapse prevention and improve long‐term outcomes. In this proof‐of‐concept study we test the feasibility, acceptability and potential usefulness of a novel digital method assessing the association between physiological signals and psychotic symptom distress. Methods: Fifteen participants with first episode psychosis were asked to use a self‐assessment mobile phone application for psychotic symptom monitoring for 10 days while using a wrist worn device continuously recording heart rate variability (HRV) and electrodermal activity (EDA). We compared physiological activity when participants reported experiencing distressing and non‐distressing psychotic symptoms. Results: Participants completed on average 76% of the mobile phone symptom assessments. When reporting distressing hallucinations and delusions participants had significantly higher EDA levels and non‐significant lower HRV values compared to when these symptoms were non‐distressing. Conclusions: This study provides further evidence linking psychotic symptom's distress, as experienced in everyday life, and autonomic deregulation. This proof‐of‐concept study may lead to further longer‐term efforts to identify relapse biosignatures using automated methods based on passive monitoring. This method may allow for earlier interventions, contribute to improve relapse prevention and reduce symptoms interfering with recovery.