Author
Listed:
- Sri Mahavir Agarwal
- Joel Dissanayake
- Ofer Agid
- Christopher Bowie
- Noah Brierley
- Araba Chintoh
- Vincenzo De Luca
- Andreea Diaconescu
- Philip Gerretsen
- Ariel Graff-Guerrero
- Colin Hawco
- Yarissa Herman
- Sean Hill
- Kathryn Hum
- Muhammad Omair Husain
- James L Kennedy
- Michael Kiang
- Sean Kidd
- Nicole Kozloff
- Marta Maslej
- Daniel J Mueller
- Farooq Naeem
- Nicholas Neufeld
- Gary Remington
- Martin Rotenberg
- Peter Selby
- Ishraq Siddiqui
- Kate Szacun-Shimizu
- Arun K Tiwari
- Shanthos Thirunavukkarasu
- Wei Wang
- Joanna Yu
- Clement C Zai
- Robert Zipursky
- Margaret Hahn
- George Foussias
Abstract
Schizophrenia spectrum disorders (SSDs) are associated with significant functional impairments, disability, and low rates of personal recovery, along with tremendous economic costs linked primarily to lost productivity and premature mortality. Efforts to delineate the contributors to disability in SSDs have highlighted prominent roles for a diverse range of symptoms, physical health conditions, substance use disorders, neurobiological changes, and social factors. These findings have provided valuable advances in knowledge and helped define broad patterns of illness and outcomes across SSDs. Unsurprisingly, there have also been conflicting findings for many of these determinants that reflect the heterogeneous population of individuals with SSDs and the challenges of conceptualizing and treating SSDs as a unitary categorical construct. Presently it is not possible to identify the functional course on an individual level that would enable a personalized approach to treatment to alter the individual’s functional trajectory and mitigate the ensuing disability they would otherwise experience. To address this ongoing challenge, this study aims to conduct a longitudinal multimodal investigation of a large cohort of individuals with SSDs in order to establish discrete trajectories of personal recovery, disability, and community functioning, as well as the antecedents and predictors of these trajectories. This investigation will also provide the foundation for the co-design and testing of personalized interventions that alter these functional trajectories and improve outcomes for people with SSDs.
Suggested Citation
Sri Mahavir Agarwal & Joel Dissanayake & Ofer Agid & Christopher Bowie & Noah Brierley & Araba Chintoh & Vincenzo De Luca & Andreea Diaconescu & Philip Gerretsen & Ariel Graff-Guerrero & Colin Hawco &, 2023.
"Characterization and prediction of individual functional outcome trajectories in schizophrenia spectrum disorders (PREDICTS study): Study protocol,"
PLOS ONE, Public Library of Science, vol. 18(9), pages 1-25, September.
Handle:
RePEc:plo:pone00:0288354
DOI: 10.1371/journal.pone.0288354
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0288354. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.