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Digital home monitoring for capturing daily fluctuation of symptoms; A longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study)

Digital home monitoring for capturing daily fluctuation of symptoms; A longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study)
Digital home monitoring for capturing daily fluctuation of symptoms; A longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study)

Introduction A substantial proportion of COVID-19 survivors continue to have symptoms more than 3 months after infection, especially of those who required medical intervention. Lasting symptoms are wide-ranging, and presentation varies between individuals and fluctuates within an individual. Improved understanding of undulation in symptoms and triggers may improve efficacy of healthcare providers and enable individuals to better self-manage their Long Covid. We present a protocol where we aim to develop and examine the feasibility and usability of digital home monitoring for capturing daily fluctuation of symptoms in individuals with Long Covid and provide data to facilitate a personalised approach to the classification and management of Long Covid symptoms. Methods and analysis This study is a longitudinal prospective cohort study of adults with Long Covid accessing 10 National Health Service (NHS) rehabilitation services in the UK. We aim to recruit 400 people from participating NHS sites. At referral to study, 6 weeks and 12 weeks, participants will complete demographic data (referral to study) and clinical outcome measures, including ecological momentary assessment (EMA) using personal mobile devices. EMA items are adapted from the COVID-19 Yorkshire Rehabilitation Scale items and include self-reported activities, symptoms and psychological factors. Passive activity data will be collected through wrist-worn sensors. We will use latent class growth models to identify trajectories of experience, potential phenotypes defined by co-occurrence of symptoms and inter-relationships between stressors, symptoms and participation in daily activities. We anticipate that n=300 participants provide 80% power to detect a 20% improvement in fatigue over 12 weeks in one class of patients relative to another. Ethics and dissemination The study was approved by the Yorkshire & The Humber - Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Findings will be disseminated in peer-reviewed publications and presented at conferences. Trial registration number ISRCTN15022307.

COVID-19, Fatigue, PAIN MANAGEMENT, Rehabilitation medicine
2044-6055
e071428
Mansoubi, Maedeh
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Dawes, Joanna
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Bhatia, Aishwarya
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Vashisht, Himanshu
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Collett, Johnny
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Greenwood, Darren
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Ezekiel, Leisle
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O Connor, Daryl
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Leveridge, Phaedra
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Ward, Tomas
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Rayner, Clare
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Read, Flo
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Sivan, Manoj
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Tucker-Bell, Ian
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Delaney, Brendan
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Muhlhausen, Willie
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Dawes, Helen
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Mansoubi, Maedeh
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Dawes, Joanna
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Bhatia, Aishwarya
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Vashisht, Himanshu
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Collett, Johnny
8614f84c-eea0-4c4d-9662-8ddcc6856337
Greenwood, Darren
ac1ae402-b3a7-446e-9565-52055b895296
Ezekiel, Leisle
aee53f24-cd28-400e-85c1-95c8a9b21f2a
O Connor, Daryl
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Leveridge, Phaedra
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Ward, Tomas
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Rayner, Clare
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Read, Flo
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Sivan, Manoj
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Tucker-Bell, Ian
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Delaney, Brendan
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Muhlhausen, Willie
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Dawes, Helen
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Mansoubi, Maedeh, Dawes, Joanna, Bhatia, Aishwarya, Vashisht, Himanshu, Collett, Johnny, Greenwood, Darren, Ezekiel, Leisle, O Connor, Daryl, Leveridge, Phaedra, Ward, Tomas, Rayner, Clare, Read, Flo, Sivan, Manoj, Tucker-Bell, Ian, Delaney, Brendan, Muhlhausen, Willie and Dawes, Helen (2023) Digital home monitoring for capturing daily fluctuation of symptoms; A longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study). BMJ Open, 13 (8), e071428, [e071428]. (doi:10.1136/bmjopen-2022-071428).

Record type: Article

Abstract

Introduction A substantial proportion of COVID-19 survivors continue to have symptoms more than 3 months after infection, especially of those who required medical intervention. Lasting symptoms are wide-ranging, and presentation varies between individuals and fluctuates within an individual. Improved understanding of undulation in symptoms and triggers may improve efficacy of healthcare providers and enable individuals to better self-manage their Long Covid. We present a protocol where we aim to develop and examine the feasibility and usability of digital home monitoring for capturing daily fluctuation of symptoms in individuals with Long Covid and provide data to facilitate a personalised approach to the classification and management of Long Covid symptoms. Methods and analysis This study is a longitudinal prospective cohort study of adults with Long Covid accessing 10 National Health Service (NHS) rehabilitation services in the UK. We aim to recruit 400 people from participating NHS sites. At referral to study, 6 weeks and 12 weeks, participants will complete demographic data (referral to study) and clinical outcome measures, including ecological momentary assessment (EMA) using personal mobile devices. EMA items are adapted from the COVID-19 Yorkshire Rehabilitation Scale items and include self-reported activities, symptoms and psychological factors. Passive activity data will be collected through wrist-worn sensors. We will use latent class growth models to identify trajectories of experience, potential phenotypes defined by co-occurrence of symptoms and inter-relationships between stressors, symptoms and participation in daily activities. We anticipate that n=300 participants provide 80% power to detect a 20% improvement in fatigue over 12 weeks in one class of patients relative to another. Ethics and dissemination The study was approved by the Yorkshire & The Humber - Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Findings will be disseminated in peer-reviewed publications and presented at conferences. Trial registration number ISRCTN15022307.

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Accepted/In Press date: 28 June 2023
e-pub ahead of print date: 8 August 2023
Published date: 8 August 2023
Additional Information: Funding Information: This study was supported by the National Institute for Health and Care Research. HD is funded and supported, and MM’s work is supported by the National Institute for Health and Care Research Exeter Biomedical Research Centre. JC is funded by the NIHR Oxford Health Biomedical Research Centre. Funding Information: This project has been funded by NIHR, grant reference number: COV-LT2-0016. Publisher Copyright: © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.
Keywords: COVID-19, Fatigue, PAIN MANAGEMENT, Rehabilitation medicine

Identifiers

Local EPrints ID: 481082
URI: http://eprints.soton.ac.uk/id/eprint/481082
ISSN: 2044-6055
PURE UUID: db3c0742-a20c-4613-a946-34cdca60268c
ORCID for Leisle Ezekiel: ORCID iD orcid.org/0000-0002-5904-3019

Catalogue record

Date deposited: 15 Aug 2023 16:45
Last modified: 17 Mar 2024 04:11

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Contributors

Author: Maedeh Mansoubi
Author: Joanna Dawes
Author: Aishwarya Bhatia
Author: Himanshu Vashisht
Author: Johnny Collett
Author: Darren Greenwood
Author: Leisle Ezekiel ORCID iD
Author: Daryl O Connor
Author: Phaedra Leveridge
Author: Tomas Ward
Author: Clare Rayner
Author: Flo Read
Author: Manoj Sivan
Author: Ian Tucker-Bell
Author: Brendan Delaney
Author: Willie Muhlhausen
Author: Helen Dawes

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