Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153764
Title: Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study
Authors: Nur Amirah Abdul Rashid
Martanto, Wijaya
Yang, Zixu
Wang, Xuancong
Heaukulani, Creighton
Vouk, Nikola
Buddhika, Thisum
Wei, Yuan
Verma, Swapna
Tang, Charmaine
Morris, Robert J. T.
Lee, Jimmy
Keywords: Science::Medicine
Issue Date: 2021
Source: Nur Amirah Abdul Rashid, Martanto, W., Yang, Z., Wang, X., Heaukulani, C., Vouk, N., Buddhika, T., Wei, Y., Verma, S., Tang, C., Morris, R. J. T. & Lee, J. (2021). Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study. BMJ Open, 11(10), e046552-. https://dx.doi.org/10.1136/bmjopen-2020-046552
Project: NMRC/CG/M002/2017_IMH 
NMRC/CSAINV17nov005 
Journal: BMJ Open 
Abstract: Introduction The course of schizophrenia illness is characterised by recurrent relapses which are associated with adverse clinical outcomes such as treatmentresistance, functional and cognitive decline. Early identification is essential and relapse prevention remains a primary treatment goal for long-term management of schizophrenia. With the ubiquity of devices such as smartphones, objective digital biomarkers can be harnessed and may offer alternative means for symptom monitoring and relapse prediction. The acceptability of digital sensors (smartphone and wrist-wearable device) and the association between the captured digital data with clinical and health outcomes in individuals with schizophrenia will be examined. Methods and analysis In this study, we aim to recruit 100 individuals with schizophrenia spectrum disorders who are recently discharged from the Institute of Mental Health (IMH), Singapore. Participants are followed up for 6 months, where digital, clinical, cognitive and functioning data are collected while health utilisation data are obtained at the 6month and 1 year timepoint from study enrolment. Associations between digital, clinical and health outcomes data will be examined. A data-driven machine learning approach will be used to develop prediction algorithms to detect clinically significant outcomes. Study findings will inform the design, data collection procedures and protocol of future interventional randomised controlled trial, testing the effectiveness of digital phenotyping in clinical management of individuals with schizophrenia spectrum disorders.
URI: https://hdl.handle.net/10356/153764
ISSN: 2044-6055
DOI: 10.1136/bmjopen-2020-046552
Rights: © 2021 The Author(s) (or their employer(s)). Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

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