Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study
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 lon...
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sg-ntu-dr.10356-1537642023-03-05T16:45:44Z Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study 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 Lee Kong Chian School of Medicine (LKCMedicine) Neuroscience and Mental Health Science::Medicine Schizophrenia & Psychotic Disorders Psychiatry 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. Ministry of Health (MOH) National Medical Research Council (NMRC) Published version The study is supported by the Ministry of Health Office for Healthcare Transformation (grant no.: N/A) and the Ministry of Health National Medical Research Council Centre Grant (NMRC/CG/M002/2017_IMH). JL is supported by the Ministry of Health National Medical Research Council Clinician Scientist Award (NMRC/CSAINV17nov005). 2021-12-27T03:08:21Z 2021-12-27T03:08:21Z 2021 Journal Article 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 2044-6055 https://hdl.handle.net/10356/153764 10.1136/bmjopen-2020-046552 10 11 e046552 en NMRC/CG/M002/2017_IMH NMRC/CSAINV17nov005 BMJ Open © 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/. application/pdf |
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Science::Medicine Schizophrenia & Psychotic Disorders Psychiatry 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 Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study |
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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. |
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Lee Kong Chian School of Medicine (LKCMedicine) |
author_facet |
Lee Kong Chian School of Medicine (LKCMedicine) 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 |
format |
Article |
author |
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 |
author_sort |
Nur Amirah Abdul Rashid |
title |
Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study |
title_short |
Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study |
title_full |
Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study |
title_fullStr |
Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study |
title_full_unstemmed |
Evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the HOPE-S observational study |
title_sort |
evaluating the utility of digital phenotyping to predict health outcomes in schizophrenia : protocol for the hope-s observational study |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/153764 |
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1759853625561055232 |