Temporal relationships between latent symptoms in psychosis: a longitudinal experience sampling methodology study
| Autor: | G. Gillett, D. Joyce, C. Karr, M. de Vos, D.-J. Dijk, N. Jacobson, J. MacCabe, N. Meyer |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | European Psychiatry, Vol 66, Pp S112-S113 (2023) |
| Druh dokumentu: | article |
| ISSN: | 0924-9338 1778-3585 |
| DOI: | 10.1192/j.eurpsy.2023.308 |
| Popis: | Introduction A variety of dimensions of psychopathology are observed in psychosis. However, the validation of clinical assessment scales, and their latent variable structure, is often derived from cross-sectional rather than longitudinal data, limiting our understanding of how variables interact and reinforce one another. Objectives Using experience sampling methodology (ESM) and analytic approaches optimised for longitudinal data, we assess potential latent variables of commonly-reported symptoms in psychosis, and explore the temporal relationship between them. Methods N=36 participants with a diagnosis of schizophrenia or schizoaffective disorder provided data for up to one year, as part of the Sleepsight study. Using a smartphone app, participants self-reported clinical symptoms once daily for a mean duration of 323 days (SD: 88), with a response rate of 69%. Symptoms were rated using seven-point Likert scale items. Items included symptoms traditionally implicated in psychosis (feeling “cheerful”, “anxious”, “relaxed”, “irritable”, “sad”, “in control”, “stressed”, “suspicious”, “trouble concentrating”, “preoccupied by thoughts”, “others dislike me”, “confused”, “others influence my thoughts” and “unusual sights and sounds”). We used a sparse PCA (SPCA) model to identify latent variables in the longitudinal data. SPCA has previously been applied to longitudinal ESM data, and was developed to achieve a compromise between the explained variance and the interpretability of the principal components. We then used a multistage exploratory and confirmatory differential time-varying effect model (DTVEM) to explore the temporal relationship between the latent variables. DTVEM generates a standardised β coefficient reflecting the strength of relationship between variables across multiple time lags. Only significant lags (p |
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