Abstract
Background This confirmatory study aimed to examine whether we can foresee recurrence of depressive symptoms using personalized modeling of rises in restlessness. Methods Participants were formerly depressed patients (N = 41) in remission who (gradually) discontinued antidepressants. Participants completed five smartphone-based Ecological Momentary Assessments (EMA) a day, for a period of 4 months, yielding a total of 21 180 observations. Statistical Process Control by means of Exponentially Weighted Moving Average (EWMA) control charts was used to detect rises in the EMA item 'I feel restless', for each individual separately. Results An increase in restlessness was detected in 68.3% of the participants with recurring depressive symptoms, and in 26.3% of those who stayed in remission (Fisher's exact test p = 0.01, sensitivity was 68.3%, specificity was 73.7%). In the participants with a recurrence and an increase in restlessness, this increase could be detected in the prodromal phase of depression in 93.3% of the cases and at least a month before the onset of the core symptoms of depression in 66.7% of the cases. Conclusions Restlessness is a common prodromal symptom of depression. The sensitivity and specificity of the EWMA charts was at least as good as prognostic models based on cross-sectional patient characteristics. An advantage of the current idiographic method is that the EWMA charts provide real-time personalized insight in a within-person increase in early signs of depression, which is key to alert the right patient at the right time.
Original language | English |
---|---|
Pages (from-to) | 5060-5069 |
Number of pages | 10 |
Journal | Psychological Medicine |
Volume | 53 |
Issue number | 11 |
Early online date | 14 Jul 2023 |
DOIs | |
Publication status | Published - Aug 2023 |
Bibliographical note
Funding Information:This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovative program (ERC-CoG-2015; No 681466 to M. Wichers) and the Netherlands Organisation for Health Research and Development (ZonMw Off Road; project 451001029 to E. Snippe).
Publisher Copyright:
Copyright © The Author(s), 2022. Published by Cambridge University Press.
Keywords
- Anxiety
- early detection
- early warning signals
- experience sampling method
- mobile monitoring
- prediction tool
- replicated single-subject design