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Infectious diseases
Protocol
LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK
  1. Manoj Sivan1,
  2. Trisha Greenhalgh2,
  3. Julie Lorraine Darbyshire2,
  4. Ghazala Mir3,
  5. Rory J O'Connor1,
  6. Helen Dawes4,
  7. Darren Greenwood5,
  8. Daryl O’Connor6,
  9. Mike Horton1,
  10. Stavros Petrou2,
  11. Simon de Lusignan2,7,
  12. Vasa Curcin8,
  13. Erik Mayer9,
  14. Alexander Casson10,
  15. Ruairidh Milne11,
  16. Clare Rayner12,
  17. Nikki Smith13,
  18. Amy Parkin14,
  19. Nick Preston1,
  20. Brendan Delaney15
  21. on behalf of the LOCOMOTION consortium
    1. 1 Academic Department of Rehabilitation Medicine, University of Leeds, Leeds, UK
    2. 2 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
    3. 3 Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
    4. 4 College of Medicine and Health, University of Exeter, Exeter, UK
    5. 5 Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
    6. 6 School of Psychology, University of Leeds, Leeds, UK
    7. 7 Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
    8. 8 Department of Primary Care and Public Health Sciences, King's College London, London, UK
    9. 9 Department of Biosurgery and Surgical Technology, Imperial College London, London, UK
    10. 10 Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK
    11. 11 Public Health, Wessex Institute, University of Southampton, Southampton, UK
    12. 12 Long Covid Support, Leeds, UK
    13. 13 Long Covid Support, London, UK
    14. 14 Department of Occupational Therapy, Leeds Teaching Hospitals NHS Trust, Leeds, UK
    15. 15 Department of Surgery and Cancer, Imperial College London, London, UK
    1. Correspondence to Dr Manoj Sivan; m.sivan{at}leeds.ac.uk

    Abstract

    Introduction Long COVID, a new condition whose origins and natural history are not yet fully established, currently affects 1.5 million people in the UK. Most do not have access to specialist long COVID services. We seek to optimise long COVID care both within and outside specialist clinics, including improving access, reducing inequalities, helping self-management and providing guidance and decision support for primary care. We aim to establish a ‘gold standard’ of care by systematically analysing current practices, iteratively improving pathways and systems of care.

    Methods and analysis This mixed-methods, multisite study is informed by the principles of applied health services research, quality improvement, co-design, outcome measurement and learning health systems. It was developed in close partnership with patients (whose stated priorities are prompt clinical assessment; evidence-based advice and treatment and help with returning to work and other roles) and with front-line clinicians. Workstreams and tasks to optimise assessment, treatment and monitoring are based in three contrasting settings: workstream 1 (qualitative research, up to 100 participants), specialist management in 10 long COVID clinics across the UK, via a quality improvement collaborative, experience-based co-design and targeted efforts to reduce inequalities of access, return to work and peer support; workstream 2 (quantitative research, up to 5000 participants), patient self-management at home, technology-supported monitoring and validation of condition-specific outcome measures and workstream 3 (quantitative research, up to 5000 participants), generalist management in primary care, harnessing electronic record data to study population phenotypes and develop evidence-based decision support, referral pathways and analysis of costs. Study governance includes an active patient advisory group.

    Ethics and dissemination LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber—Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Participants will provide informed consent. Dissemination plans include academic and lay publications, and partnerships with national and regional policymakers.

    Trial registration number NCT05057260, ISRCTN15022307.

    • COVID-19
    • rehabilitation medicine
    • health services administration & management
    • health economics
    • qualitative research
    https://creativecommons.org/licenses/by/4.0/

    This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

    http://dx.doi.org/10.1136/bmjopen-2022-063505

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      Strengths and limitations of this study

      • Mixed-methods, real-world study co-designed with patients and frontline staff.

      • Largest cohort of non-hospitalised patients with long COVID followed up longitudinally.

      • Aims to identify condition phenotypes and traits, individual-level and group-level symptom to activity relationships, stratify and evaluate treatments, understand barriers to health equality and return to work and validate condition-specific patient-reported outcome measures.

      • Not designed to test specific hypotheses about efficacy of interventions or quantify the comparative effects of treatments.

      Introduction

      What is long COVID?

      We use the patient-made term ‘long COVID’1 to embrace the official categories of ‘ongoing symptomatic COVID-19’ (symptoms between 4 and 12 weeks) and ‘post-COVID-19 syndrome’ (symptoms beyond 12 weeks) in any patient, irrespective of whether they were hospitalised or had a positive or negative SARS‐CoV‐2 test.2 Long COVID’s varied symptoms include fatigue, breathlessness, palpitations, dizziness, pain, neurocognitive dysfunction (‘brain fog’), sleep problems, exercise intolerance, functional disability in daily activities and reduced quality of life.3–9

      Long COVID may be more common in people who had more severe acute illness or those with pre-existing conditions.10 11 Dysregulated immune response, immunothrombosis, endothelial dysfunction, multiple organ damage and dysautonomia all appear to play a role in its aetiology.10 12 13 Structural inequalities are important in the development and course of COVID and may play a role in long COVID.5 14–20

      People with lived experience of long COVID have a strong track record of contributing to the knowledge base on their condition. Long COVID was initially characterised by patients who came together in online communities after healthcare professionals (who thought of COVID as a short-lived acute illness) had disbelieved or dismissed their stories.1 21 Many of those affected were health professionals themselves.22 Published patient-led research on long COVID includes large-scale symptom surveys,18 23 personal testimony and autoethnography,24 25 co-design of services22 26 and a manifesto for assessment and treatment.27

      Long COVID management and services

      Long COVID symptoms are characterised by symptoms and functional impairment that are multidimensional, episodic and unpredictable in nature.28 The cornerstone of management is prompt multidisciplinary assessment and treatment with an emphasis on excluding serious complications, managing specific symptom clusters and supporting whole-person rehabilitation.22 26 29 30 But it is unclear which patients need to be referred to specialist clinics and which tests and treatments are needed for whom.11 31 Recording of long COVID in primary care systems appears to be low.32

      As of March 2022, 1.5 million people in the UK reported symptoms of long COVID.33 In 2021, NHS England invested £24 million to set up over 80 multidisciplinary long COVID clinics in England.34 Waiting lists for these specialist clinics are long and there are no equivalent services in Scotland, Wales or Northern Ireland. Specialist long COVID clinics vary in medical staffing levels, referral pathways, investigations and treatments. Demand exceeds supply, and the current demographics of clinic populations raise questions about inequalities of access in minority ethnic and other disadvantaged groups.

      While many patients with long COVID could probably benefit from self-management resources (eg, ‘Your COVID recovery’ website),35 these are not currently well signposted or universally accessible, nor is there adequate guidance on how to support self-monitoring patients or when they may need escalation of care.

      A new service specification expects primary care services to take on substantial elements of long COVID management.36 General practitioners—who claim chronic underfunding, understaffing and task-shifting from secondary care—feel unable to cope with a new condition affecting large numbers of patients, especially in the absence of clear guidance and referral pathways.19 37

      Given the numbers of people affected, the high levels of unmet need and burden of long-term disability, the disproportionate effect on disadvantaged people, the implications for the economy of long-term sickness absence and the limited capacity in primary care, long COVID is an impending ‘system crisis’.38 There is an urgent need for applied research to optimise services and care pathways in a way that takes account of resource constraints and recognises the potential of patients themselves and generalist services to contribute to care, given appropriate support and resources.

      Methods and analysis

      Research aim

      To optimise all aspects of long COVID care in the UK, including access to services, care pathways and practices and equity.

      Strategic objectives

      Working in partnership with those who have lived experience of long COVID:

      1. Establish a multisite learning network of long COVID clinics and services across the UK to capture and disseminate evidence-based practice.

      2. Iteratively improve practices and protocols in long COVID clinics using a quality improvement collaborative and experience-based co-design to define best practice that ensures equitable access and care pathways.

      3. Study self-management and symptom fluctuation in patients at home using a validated patient-reported outcome measure.

      4. Use general practice electronic records to study population phenotypes and thereby develop evidence-based clinical templates, decision support, referral pathways and prioritisation criteria.

      5. Evaluate the cost-effectiveness of different pathways.

      Research questions

      Our research questions are shown in box 1.

      Box 1

      Research questions

      1. For specialist long COVID clinics:

        • How can we use the quality improvement cycle to optimise the multidisciplinary assessment and care of people with long COVID?

        • How can we draw on patients’ lived experience of services and the experience of frontline staff to inform this quality improvement?

        • How can we improve access and reduce inequalities for disadvantaged and underserved groups?

        • How can we best integrate clinical rehabilitation with vocational rehabilitation in the workplace?

        • How can we optimise peer support for patients with long COVID?

      2. For patients at home:

        • How can we monitor fluctuations and triggers in long COVID and use these data to aid self-management?

        • How can we use patient-reported outcome measures to estimate condition severity, functional impact and quality of life in long COVID?

      3. For integrated long COVID services across primary, secondary and community care:

        • What referral and triage criteria are appropriate for referring patients to specialist services?

        • What clinic investigations are appropriate for patients with specific symptoms or symptom clusters (eg, chest pain, resting or exertional hypoxaemia, dysautonomia, symptoms suggestive of mast cell disorder, cognitive difficulties)?

        • What is the effectiveness of specific interventions for patients with these and other relevant symptoms and symptom clusters?

        • What are the appropriate skill mix and staffing levels for long COVID services of different types?

        • What is the cost-effectiveness and healthcare utilisation of different care models?

      Study design

      This mixed-method, multisite study is co-designed with patients with long COVID (whose stated priorities are prompt clinical assessment; evidence-based advice and treatment and help with returning to work and other roles1 22) and frontline clinicians.

      The study has three workstreams, each designed to optimise assessment, treatment and monitoring in a different setting (figure 1). Workstream 1 addresses specialist management in 10 long COVID clinics. Workstream 2 addresses patient monitoring at home using a validated outcome measure (box 1). Workstream 3 addresses generalist management in primary care and pathways (including cost-effectiveness) across the primary/secondary care interface. A cross-cutting theme of patient and public involvement informs and supports all workstreams.

      Figure 1
      Figure 1

      LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS (LOCOMOTION) project workstreams (WS) and tasks. LC, long COVID; NHS, National Health Service.

      Workstream 1: long COVID specialist clinics

      Multisite quality improvement collaborative

      Quality improvement collaboratives are networks of healthcare organisations that engage collectively in a cycle of data gathering, goal setting, action and evaluation, meeting regularly to compare findings and share resources.39 Although this model has had mixed success,40 if core principles are followed (eg, good facilitation, clear goal setting, ensuring representatives from each organisation have clear channels for feeding their learning into a coordinated and strategic change effort),39 41 results can be dramatic.41 42

      We will establish a quality improvement collaborative across 10 geographically and organisationally diverse long COVID services, with participation from site principal investigators, embedded clinician-researchers and patient partners. The collaborative will be chaired by an experienced clinician-researcher (TG) and will meet approximately monthly in a 2-hour video conference. Participants will share experience-based knowledge and research evidence, deliberate on best practice and plan and evaluate practice change. The quality improvement cycle—prioritise a topic, set goals, identify data sources, implement change, collect data on performance and outcomes, then repeat—will be followed for both clinical (eg, investigations, treatments) and more operational (eg, referral criteria, service model, workforce) aspects of long COVID management.

      Experience-based co-design

      Experience-based (co-)design is an established improvement approach intended to ensure that health services are designed, redesigned and improved around the needs and experiences of patients and frontline staff.43 Key features include a grounding in the perceptions and reactions of individual patients and staff, a focus on ‘emotional touch-points’ (points in the patient pathway that generate strong emotions such as frustration, anger, fear or hopelessness). Experience-based co-design has a solid theoretical grounding in phenomenology (which, in this context, approximates to lived experience).44 This approach has been extensively applied in health service research and quality improvement.45 46

      As a first step in the experience-based co-design method, a maximum-variety sample of approximately 15 adult patients and staff from each site will be interviewed about their experience of services using semi-structured or narrative interviews. Patient interviews will focus on emotional touch-points as these are likely to identify aspects of the service that need improvement.47 Staff interviews will capture ideas for service improvement. Interviews will be analysed thematically and fed back into local quality improvement.

      Addressing inequalities

      A qualitative substudy will seek to understand and address the multiple intersecting inequalities in long COVID service utilisation.12 Working with and through participating NHS organisations, as well as community and advocacy organisations and selected social media outlets, we will recruit a maximum-variety sample of 30 people with long COVID representing key characteristics of underserved groups who have not (yet) been seen in a long COVID clinic. Sampling criteria include: poverty, homelessness, non-white ethnic groups (including traveller communities) and those with disabilities; within all these groups, we will seek to include a gender balance and recruit diverse age groups. In addition, we will interview a diverse sample of 15 key informants (individuals with relevant expertise on long COVID and health inequalities) via clinical, academic, policy and advocacy organisations. Interviews will explore symptom recognition (both self-reported and by clinicians), health-seeking behaviour, care pathways, motivations and disincentives to accessing healthcare support and attitudes towards long COVID and stigma (eg, relating to psychological symptoms). We will also explore emotional touch-points, patient support networks and trajectories of care for those not receiving specialist long COVID healthcare.

      All interviews will be transcribed, entered into a qualitative software package and thematically analysed before being synthesised and fed into the work of the quality improvement collaborative (see section ‘Multisite quality improvement collaborative’), co-design (see section ‘Experience-based co-design’), rehabilitation (see section ‘Vocational rehabilitation’), home management (workstream 2) and trajectories of care and pathway redesign (workstream 3).

      Vocational rehabilitation

      People living with long COVID can find it difficult to return to work, and those who have been able to return to work are experiencing work instability (defined as a mismatch between an individual’s abilities and the demands of their job).48 We will explore the needs of 20 working-age people recruited from long COVID clinics and selected for diversity in age, gender, ethnicity, occupation and career stage to inform the development of appropriate return-to-work programmes (vocational rehabilitation pathway). To understand return-to-work policies and procedures, we will interview a purposive sample of occupational health, human resource and managerial professionals from a representative range of organisations, recruited via social media and researchers’ local contacts. Based on these interviews and our experience of designing vocational rehabilitation programmes for other acute-onset, long-term conditions, we will develop an individually tailorable, co-ordinated programme of support, education and advice. This advice will be relevant for people with long COVID, their family and others involved in the person’s vocational role such as employers and disability employment advisors. We will test the proposed programme for acceptability and feasibility with a further sample of 20 diverse participants, as well as clinicians and therapists.

      Peer support

      Long COVID was first characterised in online peer support groups and several participating sites have local peer support groups, but it is not known how best to structure and support such groups. We will undertake a hermeneutic literature review of peer support models in comparable conditions (eg, chronic pain) and conduct interviews with stakeholders involved in delivering or supporting peer support for long COVID. We will share findings with workstream ‘Multisite quality improvement collaborative’. Through discussion, we will identify which features of successful peer support are relevant to long COVID and consider how to improve existing models (if present) or establish new peer support services. Using the quality improvement cycle, we will collect data to evaluate and improve as the peer support groups evolve.

      Workstream 2: home monitoring and self-management by patients with long COVID

      Monitoring fluctuations, symptoms and associated triggers

      Long COVID is characterised by fluctuating and difficult to manage symptoms with high variability between individuals. Its relapsing and remitting presentation may be exacerbated by triggers, although patients report difficulty with pacing strategies, and little is known about what causes fluctuations or the nature of triggers. We propose disordered relationships in symptoms and activities are underpinned by possible pathologies across multiple body systems including the central nervous system and interoceptive pathways.

      This substudy uses an intensive longitudinal design. Some of the key COVID-19 Yorkshire Rehabilitation Scale questions (box 2) have been embedded into a bespoke digital platform which monitors general health, symptom fluctuations and potential triggers, and is linked to wearable sensors. We hypothesise that physical, cognitive and emotional triggers will predict symptoms experienced at subsequent timepoints the same day or day after. The relationship between triggers and symptoms will vary within and between individuals and bespoke understanding is required for effective self-management.

      Box 2

      The COVID-19 Yorkshire Rehabilitation Scale: a validated patient-reported outcome measure for long COVID

      We have previously developed the COVID-19 Yorkshire Rehabilitation Scale, the first patient-reported outcome measure for long COVID. This instrument is recommended in the National Institute for Health and Care Excellence (NICE) rapid guideline for long COVID and NHS England service guidance and adapted in the WHO self-management booklet.2 66–68 The scale has been digitised by a private digital health company (ELAROS). The patient completes the questionnaire on a smartphone application and the clinicians access the results on a web portal and both use the system to monitor progress, fluctuations and response to ongoing treatments for long COVID.

      The original COVID-19 Yorkshire Rehabilitation Scale is a 23-item patient-reported outcome measure which grades the severity of key symptoms, functional limitations, overall health and additional symptoms on an 11-point Likert scale and also captures pre-COVID-19 scores for comparison.50 Questions 1–10 form the symptom severity subscale (score 0–100), 11–15 the functional disability subscale (0–50), 16 is the overall health score (0–10) and 17–23 the additional symptoms subscale (0–60). The psychometric analysis in a sample of 187 patients with long COVID showed good data quality, satisfactory scaling and targeting and good reliability both overall (Cronbach’s alpha 0.891) and for individual subscales.69

      Initial testing of a previous version of the COVID-19 Yorkshire Rehabilitation Scale in 370 community patients from a single long COVID clinic (Leeds) appears to reveal three clinical severity phenotypes (mild, moderate and severe) for both individual symptom clusters and functional disability (figure 2).12 Such condition severity phenotypes with remitting relapsing nature of the condition suggest common mechanisms driving the array of symptoms but this is yet to be fully established.

      Preliminary Rasch analysis of the original version of COVID-19 Yorkshire Rehabilitation Scale revealed dysfunctional scale response categories. The instrument was modified by replacing the 11-point Likert scale with a simpler 4-point scale. This version will be made available on the digital platform for the LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study so it can be used to gather individual data for clinical assessment and also present these data in pseudonymised form for aggregated analysis (eg, to derive psychometric properties of the scale).

      A full version of the modified COVID-19 Yorkshire Rehabilitation Scale is available in online supplemental appendix file 1.

      Supplemental material

      Figure 2
      Figure 2

      Aggregate scores from a clinic population on the COVID-19 Yorkshire Rehabilitation Scale patient-reported outcome measure. (A) Mean symptom severity score of 370 patients, plotted as three subgroups (severe >6, moderate 3–5.9 and mild <3). Since radar plots do not intersect, these preliminary data suggest a single syndrome rather than several different syndromes of long COVID. (B) Mean functional ability scores on same sample. Figure reproduced with permission from Sivan et al.12 ADL, Activities of Daily Living; PTSD, post-traumatic stress disorder.

      We will recruit a diverse sample of 400 patients who are awaiting their initial long COVID appointment. We will conduct a time-series study (referral, 6 weeks, 12 weeks) comprising brief symptom surveys six times daily for a 7-day period, along with sensor and accelerometer data plus self-reported activities and emotional triggers. This daily ecological momentary assessment includes continuous data collection of the following using activity sensors (Axivity): physical activity levels (step count, intensity of physical activity, timings and duration of physical activity), sleep (timing and duration). Participants will also answer a series of questions throughout the day about their sleep, daily activities, symptoms and symptom impact, postexertional malaise, stress and anxiety.

      We will also recruit 50 participants from one site (Oxford) who will additionally be monitored to explore the potential for using heart rate and heart rate variability in biofeedback therapy. Physiological phenotyping will include the COVID-19 Yorkshire Rehabilitation Scale plus additional measures of heart rate and heart rate variability (using a wrist or chest-worn sensor—Fitbit Sense and Polar H10).

      Part-way through the main workstream 2 study, a small qualitative evaluation will be undertaken using semi-structured interviews with 20 patient and 10 clinician participants. For patients this will include reflections on their COVID-19 experience, a think-aloud exercise informed by theories of technology usability (dimensions of efficiency, effectiveness and satisfaction) and suggestions for improving the app or the study. Data will be analysed thematically using insights from sociotechnical theories.

      Findings from ‘Monitoring fluctuations, symptoms and associated triggers’ section will feed into the quality improvement collaborative (workstream ‘Multisite quality improvement collaborative’) and the pathway redesign (workstream ‘Pathway development’).

      Use the COVID-19 Yorkshire Rehabilitation Scale to monitor symptoms in clinic cohorts

      Preliminary validation data from the COVID-19 Yorkshire Rehabilitation Scale, a condition-specific outcome measure for long COVID,49 suggest that patients scoring low on one symptom tend to score low on other symptoms too (see figure 2)12—consistent with severity phenotypes seen in the posthospitalisation COVID-19 study,10 and suggesting that long COVID may be driven by common mechanisms affecting multiple body systems. Workstream ‘Use the COVID-19 Yorkshire Rehabilitation Scale to monitor symptoms in clinic cohorts’ will test this hypothesis to determine the extent to which the COVID-19 Yorkshire Rehabilitation Scale can be used for triage, targeting interventions and capturing the response to treatments.

      Longitudinal COVID-19 Yorkshire Rehabilitation Scale data on a large sample of clinic patients with long COVID will be collected quarterly from participants using the ELAROS digital app and web portal50 or equivalent. The COVID-19 Yorkshire Rehabilitation Scale has been modified based on increasing knowledge of the condition and preliminary psychometric analysis.51 Further psychometric evaluation of the modified COVID-19 Yorkshire Rehabilitation Scale in this new sample will use a Rasch measurement model to explore the scale item’s model fit, local dependency, response category functioning and differential item functioning. If necessary, we will refine the scale through an iterative process of psychometric testing and modifications. Each Rasch scale assessment will use data from 500 to 600 patients who have completed the COVID-19 Yorkshire Rehabilitation Scale as part of their standard assessment procedures across our 10 clinics. We anticipate iterations to the scale during the project depending on findings of the Rasch scale assessment. When a stable item set is decided, the total available sample will be used to provide final scale calibrations.

      The digital platform will also capture other aspects of long COVID using symptom-specific patient-reported outcome measures and quality of life measures. Data from patient-completed COVID Yorkshire Rehabilitation Scale and EuroQoL EQ-5D questionnaires will be collected from all sites uniformly and each site can capture other measures in the digital platform if they wish. The platform will also have the ability to include WHO’s core set of outcome measures that is currently being developed.52 This work will enable testing of a core set of measures that can capture this new condition comprehensively and compare with the WHO’s International Classification of Functioning Disability and Health framework.53

      Workstream 3: developing and evaluating long COVID integrated care pathways

      Pathway development

      We will use both retrospective and prospective cohorts to answer research questions 3a–3d in box 1. For patients newly referred to long COVID clinics, we will collect prospective data (to inform national standards for routine data collection moving forward) and link to clinical information captured in routine care. In a second cohort of patients suspected of having long COVID but not referred to long COVID clinics, routine data capture from healthcare provision will be used to understand pathways and demographic factors to increase appropriate referrals moving forward. An integrated dataset will be constructed from primary care, community care and specialist long COVID clinics to develop and evaluate effective and cost-effective service models which incorporate the quality principles and best practice guidance developed in workstream ‘Multisite quality improvement collaborative’.

      We will use advanced analytical techniques (such as latent class analysis and clustering) pertinent to providing insight into long COVID phenotypes based on pseudonymised patient-level variables, including patient outcomes and comorbidities. These patient groupings will then be analysed in the context of healthcare settings, using network analysis to understand the effect of patient trajectories, enabling validation or modification of existing guidelines, leading to implementation of new clinical decision support tools in electronic record systems.

      We will use approved mechanisms for data linkage via NHS Digital and work within established trusted research environments. Data linkage for practice data and patient clinic data for Imperial will take place in North West London Whole Systems Integrated Care, Salford’s Integrated Record for Salford and via the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub for all other sites.54 Brief details of these datasets are shown in box 3 and further detail given in online supplemental appendix file. All research analysis will be undertaken on de-identified data.

      Supplemental material

      Box 3

      Datasets that will contribute towards modelling long COVID phenotypes and cost-effectiveness of treatments

      Data resource 1: iCARE/North West London Whole Systems Integrated Care is a Heath Data Research UK Alliance Trusted Research Environment and Salford—unique health management platforms covering a population of 2.6 million in NW London and the Salford Integrated Record (includes the Imperial College Healthcare NHS Trust (ICHT) and Salford long COVID clinics).

      Data resource 2: The Oxford-Royal College of General Practitioners Research and Surveillance Centre70 is one of Europe’s oldest sentinel networks and recruited to be nationally representative. It now consists of a network of in excess of 1800 practices’ data (n>18 million, 32% of the English national population). Data from this Research and Surveillance Centre, as well as other data from across LOCOMOTION are being made available via ORCHID (http://orchid.phc.ox.ac.uk), a Health Data Research UK listed trusted research environment and meets NHS Digital’s Data Security and Protection requirements. Researchers across LOCOMOTION with an approved analysis requirement can apply to access data remotely within ORCHID. Data go through a privacy protecting statistical disclosure control process prior to leaving the ORCHID trusted research environment. ORCHID is supporting national COVID surveillance of the UK Health Security Agency and four national core studies.54 ORCHID is a pseudonymised dataset using an NHS Digital-approved method allowing data to be linked to national test results, immunisation, emergency care, hospital and death datasets at an individual patient level.

      Data resource 3: National General Practice Data for Research and Planning via the National Core Studies Portal.

      LOCOMOTION, LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS.

      Pathway cost-effectiveness analysis and model testing

      This workstream aims to assess the cost-effectiveness of different care pathways and develop and test efficient service models. Service models vary substantially across England.55 Comparing these using standardised health economic outcome measures will enable an understanding of the cost-effectiveness aspects of service delivery.

      Following established frameworks for model conceptualisation, we will incorporate collaborative stakeholder and public involvement,56 57 and address known methodological challenges for public health economic modelling.58 We will evaluate the cost-effectiveness of alternative care pathways from a societal perspective, incorporating health and social care costs incurred by the public sector as well as productivity losses and out-of-pocket expenditures. The primary analysis will assess the cost-effectiveness of alternative strategies in terms of incremental cost per quality-adjusted life year gained. We will use cost-effectiveness thresholds of £20 000–30 000 and £60 000 per quality-adjusted life year gained.59 60 Secondary analysis will evaluate cost-consequences of alternative care pathways and disaggregate these in terms of disparate outcomes such as the proportions of individuals who return to pre-COVID levels of productivity and those who continue to experience excessive out-of-pocket expenditures.61

      We will validate models using real-world data from existing platforms (data resources 1–3, box 3).62 Probabilistic sensitivity analysis will assess the impact of uncertainties of all incorporated parameters.63 We will use cost-effectiveness acceptability curves to show the probability of cost-effectiveness of each evaluated strategy at alternative cost-effectiveness thresholds held by decision-makers. Scenario analyses will assess the impacts of different model structural assumptions.

      Equality, diversity and inclusion

      Meeting the needs of all patients with long COVID is at the heart of the LOCOMOTION study. Specifically, workstream 1.3 is dedicated to exploring the experiences of underserved population groups. This workstream will identify key facilitators and barriers to delivering good quality healthcare to these communities and findings will feed into co-design of long COVID clinics, understanding the features of long COVID and validating patient-reported outcome measures. In addition, workstreams exploring vocational rehabilitation, home management, peer support and care pathway redesign will specifically gather and analyse data relating to populations most at risk of long COVID.

      To further understand the disproportionate impact of long COVID across the population, we created a seven-member overarching patient advisory group inclusive of diverse cultural, ethnic and socioeconomic groups. This group contributes to the governance of LOCOMOTION and provides overall management of patient and public involvement, which is embedded throughout the study. Members of the group link into each workstream to ensure relevant and meaningful involvement activity within each one. The advisory group has worked with all workstreams to maximise inclusivity and reduce exclusion by design. Revisions to plans include widening accessibility to the COVID-19 Yorkshire Rehabilitation Scale and EuroQol EQ-5D patient-reported outcome measures and supporting alternative formats for interviews where digital/remote attendance may be difficult.

      To support recruitment across all workstreams, we will offer translation services on an individual basis required to facilitate qualitative interviews and the consent process. This will include translation of patient information sheets, consent forms and outcome measures and use of bilingual researchers or interpreters as necessary. For participants not currently receiving specialist support for long COVID, we will also try to offer incentive payments in recognition of the barriers to engaging with research experienced by underserved groups.64

      Patient and public involvement

      For the outputs of LOCOMOTION to meet patient need, the study must be responsive to the patient voice that lies at the centre of its design, development and delivery. Members of the patient advisory group attended proposal research planning meetings and met separately to analyse and develop the research aims, objectives and questions, ensuring these align with the key research priorities of those with long COVID. All advisory group members have lived experience of long COVID. Some also have experience of design and evaluation of research bids and policy task forces. They have contacts with wider patient community groups. The advisory group meets quarterly to review progress, ensure the research continues to answer relevant issues and that findings can inform long COVID care. In addition, two patients from each of the 10 long COVID NHS clinics will form the patient advisory network, which will liaise with the advisory group, providing local intelligence about long COVID experiences and clinic access. The advisory group and advisory network are supported by an experienced Patient and Public Involvement/Engagement Manager. The patient-level and service-level measures have been determined by patients, healthcare professionals and researchers using consensus methods (tables 1 and 2).

      View this table:
      Table 1

      Patient-level measures to be used in LOCOMOTION sites

      View this table:
      Table 2

      Service-level measures to be used in LOCOMOTION sites

      Statistical analysis

      The rationale for sample size for the tasks involving quantitative data (task 2.1, task 2.2, tasks 3.1 and 3.2) is uploaded as a separate document (see online supplemental file 2).

      Ethics and dissemination

      Ethics approval was obtained from Bradford and Leeds Research Ethics Committee on behalf of Health Research Authority and Health and Care Research Wales on 6 January 2022 (reference: 21/YH/0276). Participants will provide informed consent for workstreams 1 and 2. Retrospective data required for workstream 3 general practice and hospital episode statistics linkage cannot be provided in anonymised form. These data will be collected under the Control of Patient Information notice initially and subsequently under authorisation from the Health Research Authority Confidentiality Advisory Group.

      Dissemination will include both academic publications and lay summaries in various formats. Relevant long COVID phenotypes will be published in the Health Data Research UK National Phenotype Library, together with their computable definition, and assigned a Digital Object Identifier. Policy impact will be aided by our strong existing links to NHS England, and by the fact that several of the co-investigators are on the UK Long COVID National Task Force. MS, who co-leads LOCOMOTION, is advisor for WHO (Europe) on COVID-19 rehabilitation and is also involved in the WHO working party to develop a core set of outcome measures for long COVID.

      We have links with other long COVID projects based in the UK and beyond to enable co-learning and maximise impact. In particular, our links with the Symptoms, Trajectory, Inequalities and Management: Understanding Long-COVID to Address and Transform Existing Integrate Care Pathways (STIMULATE ICP) study (https://www.stimulate-icp.org/) will enable sharing and evaluating clinic data for exploring mechanisms and developing treatment algorithms. Links with the therapies for long COVID platform65 will facilitate development of condition-specific measures for long COVID and compare the psychometric properties of these new measures.

      Ethics statements

      Patient consent for publication

      References

      1. ↵
        2. Perego E ,
        3. Callard F ,
        4. Stras L , et al
        . Why the Patient-Made Term ‘Long Covid’ is needed. Wellcome Open Res 2020;5:224.doi:10.12688/wellcomeopenres.16307.1
        OpenUrl
      2. ↵
        1. National Institute for Health and Care Excellence (NICE) Scottish Intercollegiate Guidelines Network (SIGN) and Royal College of General Practitioners (RCGP)
        . COVID-19 rapid guideline: managing the long- term effects of COVID-19. London: NICE, 2022.
      3. ↵
        1. World Health Organisation
        . A clinical case definition of post COVID-19 condition by a Delphi consensus. Geneva: WHO, 2021.
      4. ↵
        2. Sivan M ,
        3. Rayner C ,
        4. Delaney B
        . Fresh evidence of the scale and scope of long covid. 52. British Medical Journal Publishing Group, 2021: n853.doi:10.1136/bmj.n853
        OpenUrl
      5. ↵
        2. Jiang DH ,
        3. Roy DJ ,
        4. Gu BJ , et al
        . Postacute Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2 Infection: A State-of-the-Art Review. JACC Basic Transl Sci 2021;6:796–811.doi:10.1016/j.jacbts.2021.07.002 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34541421
        OpenUrlPubMed
      6. ↵
        2. Domingo FR ,
        3. Waddell LA ,
        4. Cheung AM
        . Prevalence of long-term effects in individuals diagnosed with COVID-19: a living systematic review. medRxiv 2021.
      7. ↵
        2. Ayoubkhani D ,
        3. Khunti K ,
        4. Nafilyan V , et al
        . Post-covid syndrome in individuals admitted to hospital with covid-19: retrospective cohort study. BMJ 2021;372:n693.doi:10.1136/bmj.n693 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33789877
        OpenUrlAbstract/FREE Full Text
      8. ↵
        2. Dennis A ,
        3. Wamil M ,
        4. Alberts J , et al
        . Multiorgan impairment in low-risk individuals with post-COVID-19 syndrome: a prospective, community-based study. BMJ Open 2021;11:e048391.doi:10.1136/bmjopen-2020-048391 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33785495
        OpenUrlAbstract/FREE Full Text
      9. ↵
        2. Lambert N ,
        3. Corps S ,
        4. El-Azab SA
        . COVID-19 Survivors’ Reports of the Timing, Duration, and Health Impacts of Post-Acute Sequelae of SARS-CoV-2 (PASC) Infection 2021.
      10. ↵
        2. Evans RA ,
        3. McAuley H ,
        4. Harrison EM , et al
        . Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study. Lancet Respir Med 2021;9:1275–87.doi:10.1016/S2213-2600(21)00383-0 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34627560
        OpenUrlCrossRefPubMed
      11. ↵
        2. Sudre CH ,
        3. Murray B ,
        4. Varsavsky T , et al
        . Attributes and predictors of long COVID. Nat Med 2021;27:626–31.doi:10.1038/s41591-021-01292-y pmid:http://www.ncbi.nlm.nih.gov/pubmed/33692530
        OpenUrlCrossRefPubMed
      12. ↵
        2. Sivan M ,
        3. Parkin A ,
        4. Makower S , et al
        . Post-COVID syndrome symptoms, functional disability, and clinical severity phenotypes in hospitalized and nonhospitalized individuals: a cross-sectional evaluation from a community COVID rehabilitation service. J Med Virol 2022;94:1419–27.doi:10.1002/jmv.27456 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34783052
        OpenUrlPubMed
      13. ↵
        2. McGonagle D ,
        3. Kearney MF ,
        4. O'Regan A , et al
        . Therapeutic implications of ongoing alveolar viral replication in COVID-19. Lancet Rheumatol 2022;4:e135–44.doi:10.1016/S2665-9913(21)00322-2 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34873587
        OpenUrlPubMed
      14. ↵
        2. Berger Z ,
        3. Altiery DE Jesus V ,
        4. Assoumou SA , et al
        . Long COVID and health inequities: the role of primary care. Milbank Q 2021;99:519–41.doi:10.1111/1468-0009.12505 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33783907
        OpenUrlCrossRefPubMed
      15. ↵
        2. Fowler-Davis S ,
        3. Young R ,
        4. Maden-Wilkinson T , et al
        . Assessing the acceptability of a co-produced long COVID intervention in an underserved community in the UK. Int J Environ Res Public Health 2021;18:13191.doi:10.3390/ijerph182413191 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34948798
        OpenUrlPubMed
      16. ↵
        2. Phiri P ,
        3. Delanerolle G ,
        4. Al-Sudani A , et al
        . COVID-19 and black, Asian, and minority ethnic communities: a complex relationship without just cause. JMIR Public Health Surveill 2021;7:e22581.doi:10.2196/22581 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33481752
        OpenUrlPubMed
      17. ↵
        1. House of Commons Health and Social Care and Science and Technology Committees
        . Coronavirus: lessons learned to date. sixth report of the health and social care Committee and third report of the science and technology Committee of session 2021-22. London: Her Majesty’s Government, 2021.
      18. ↵
        2. Ziauddeen N ,
        3. Gurdasani D ,
        4. O'Hara ME
        . Characteristics of long Covid: findings from a social media survey. medRxiv 2021.
      19. ↵
        2. Hutchinson J ,
        3. Checkland K ,
        4. Munford L , et al
        . Long COVID in general practice: an analysis of the equity of NHS England’s enhanced service specification. British Journal of General Practice 2022;72:85–6.doi:10.3399/bjgp22X718505
        OpenUrlFREE Full Text
      20. ↵
        2. Meza-Torres BDG ,
        3. Okusi C ,
        4. Mayer N
        . Differences in clinical presentation with long covid following community and hospital infection, and associations with all-cause mortality: English sentinel network database study. JMIR Preprints 2022:37668.
      21. ↵
        2. Ladds E ,
        3. Rushforth A ,
        4. Wieringa S , et al
        . Persistent symptoms after Covid-19: qualitative study of 114 "long Covid" patients and draft quality principles for services. BMC Health Serv Res 2020;20:1144.doi:10.1186/s12913-020-06001-y pmid:http://www.ncbi.nlm.nih.gov/pubmed/33342437
        OpenUrlCrossRefPubMed
      22. ↵
        2. Ladds E ,
        3. Rushforth A ,
        4. Wieringa S , et al
        . Developing services for long COVID: lessons from a study of wounded healers. Clin Med 2021;21:59–65.doi:10.7861/clinmed.2020-0962
        OpenUrlAbstract/FREE Full Text
      23. ↵
        2. Davis HE ,
        3. Assaf GS ,
        4. McCorkell L
        . Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. medRxiv 2020.
      24. ↵
        2. Verduzco-Gutierrez M ,
        3. Rydberg L ,
        4. Sullivan MN , et al
        . In this for the long Haul: ethics, COVID-19, and rehabilitation. Pm R 2021;13:325.doi:10.1002/pmrj.12554 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33476491
        OpenUrlPubMed
      25. ↵
        2. Callard F
        . Epidemic time: thinking from the Sickbed. Bull Hist Med 2020;94:727–43.doi:10.1353/bhm.2020.0093 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33775950
        OpenUrlPubMed
      26. ↵
        2. Nurek M ,
        3. Rayner C ,
        4. Freyer A , et al
        . Recommendations for the recognition, diagnosis, and management of long COVID: a Delphi study. Br J Gen Pract 2021;71:e815–25.doi:10.3399/BJGP.2021.0265 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34607799
        OpenUrlAbstract/FREE Full Text
      27. ↵
        2. Alwan NA ,
        3. Attree E ,
        4. Blair JM , et al
        . From doctors as patients: a manifesto for tackling persisting symptoms of covid-19. BMJ 2020;370:m3565.doi:10.1136/bmj.m3565 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32933949
        OpenUrlFREE Full Text
      28. ↵
        2. Brown DA ,
        3. O'Brien KK
        . Conceptualising long COVID as an episodic health condition. BMJ Glob Health 2021;6:e007004.doi:10.1136/bmjgh-2021-007004 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34551971
        OpenUrlFREE Full Text
      29. ↵
        2. Greenhalgh T ,
        3. Knight M ,
        4. A'Court C , et al
        . Management of post-acute covid-19 in primary care. BMJ 2020;370:m3026.doi:10.1136/bmj.m3026 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32784198
        OpenUrlFREE Full Text
      30. ↵
        2. Crook H ,
        3. Raza S ,
        4. Nowell J , et al
        . Long covid-mechanisms, risk factors, and management. BMJ 2021;374:n1648.doi:10.1136/bmj.n1648 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34312178
        OpenUrlAbstract/FREE Full Text
      31. ↵
        2. Khunti K ,
        3. Davies MJ ,
        4. Kosiborod MN , et al
        . Long COVID - metabolic risk factors and novel therapeutic management. Nat Rev Endocrinol 2021;17:379–80.doi:10.1038/s41574-021-00495-0 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33875855
        OpenUrlPubMed
      32. ↵
        2. Walker AJ ,
        3. MacKenna B ,
        4. Inglesby P , et al
        . Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY. British Journal of General Practice 2021;71:e806–14.doi:10.3399/BJGP.2021.0301
        OpenUrlAbstract/FREE Full Text
      33. ↵
        1. Office of National Statistics
        . Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK. London: ONS, 2022.
      34. ↵
        1. NHS England
        . NHS to offer ‘long covid’ sufferers help at specialist centres. London: NHS England, 2020. https://www.england.nhs.uk/2020/10/nhs-to-offer-long-covid-help/
      35. ↵
        1. England N
        . Supporting your recovery after COVID-19, 2021. Available: https://www.yourcovidrecovery.nhs.uk/
      36. ↵
        1. NHS England
        . Enhanced service specifiction: long covid (version 1, 21st June. London: NHS England, 2021.
      37. ↵
        2. Singh C
        . Long COVID: playing the long game. Br J Gen Pract 2021;71:507–07.doi:10.3399/bjgp21X717521 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34711561
        OpenUrlFREE Full Text
      38. ↵
        2. Roth A ,
        3. Chan PS ,
        4. Jonas W
        . Addressing the long COVID crisis: integrative health and long COVID. Glob Adv Health Med 2021;10:21649561211056597.doi:10.1177/21649561211056597 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34820152
        OpenUrlPubMed
      39. ↵
        2. Schouten LMT ,
        3. Hulscher MEJL ,
        4. van Everdingen JJE , et al
        . Evidence for the impact of quality improvement Collaboratives: systematic review. BMJ 2008;336:1491–4.doi:10.1136/bmj.39570.749884.BE pmid:http://www.ncbi.nlm.nih.gov/pubmed/18577559
        OpenUrlAbstract/FREE Full Text
      40. ↵
        2. Carter P ,
        3. Ozieranski P ,
        4. McNicol S , et al
        . How collaborative are quality improvement Collaboratives: a qualitative study in stroke care. Implement Sci 2014;9:32.doi:10.1186/1748-5908-9-32 pmid:http://www.ncbi.nlm.nih.gov/pubmed/24612637
        OpenUrlCrossRefPubMed
      41. ↵
        2. Parand A ,
        3. Benn J ,
        4. Burnett S , et al
        . Strategies for sustaining a quality improvement collaborative and its patient safety gains. Int J Qual Health Care 2012;24:380–90.doi:10.1093/intqhc/mzs030 pmid:http://www.ncbi.nlm.nih.gov/pubmed/22669328
        OpenUrlCrossRefPubMedWeb of Science
      42. ↵
        2. Hemmila MR ,
        3. Cain-Nielsen AH ,
        4. Jakubus JL , et al
        . Association of hospital participation in a regional trauma quality improvement collaborative with patient outcomes. JAMA Surg 2018;153:747–56.doi:10.1001/jamasurg.2018.0985 pmid:http://www.ncbi.nlm.nih.gov/pubmed/29800946
        OpenUrlPubMed
      43. ↵
        2. Bate P ,
        3. Robert G
        . Experience-Based design: from redesigning the system around the patient to co-designing services with the patient. Qual Saf Health Care 2006;15:307–10.doi:10.1136/qshc.2005.016527 pmid:http://www.ncbi.nlm.nih.gov/pubmed/17074863
        OpenUrlAbstract/FREE Full Text
      44. ↵
        2. Palmer VJ ,
        3. Weavell W ,
        4. Callander R , et al
        . The participatory Zeitgeist: an explanatory theoretical model of change in an era of coproduction and codesign in healthcare improvement. Med Humanit 2019;45:247–57.doi:10.1136/medhum-2017-011398 pmid:http://www.ncbi.nlm.nih.gov/pubmed/29954854
        OpenUrlAbstract/FREE Full Text
      45. ↵
        2. Raynor DK ,
        3. Ismail H ,
        4. Blenkinsopp A , et al
        . Experience-Based co-design-Adapting the method for a researcher-initiated study in a multi-site setting. Health Expect 2020;23:562–70.doi:10.1111/hex.13028 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32045087
        OpenUrlCrossRefPubMed
      46. ↵
        2. Toledo-Chávarri A ,
        3. Ramos-García V ,
        4. Koatz D , et al
        . Co-Design process of a virtual community of practice for the Empowerment of people with ischemic heart disease. Int J Integr Care 2020;20:9.doi:10.5334/ijic.5514 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33250674
        OpenUrlCrossRefPubMed
      47. ↵
      48. ↵
        2. Chamberlain MA ,
        3. Fialka Moser V ,
        4. Schüldt Ekholm K , et al
        . Vocational rehabilitation: an educational review. J Rehabil Med 2009;41:856–69.doi:10.2340/16501977-0457 pmid:http://www.ncbi.nlm.nih.gov/pubmed/19841836
        OpenUrlCrossRefPubMedWeb of Science
      49. ↵
        2. Sivan M ,
        3. Wright S ,
        4. Hughes S , et al
        . Using condition specific patient reported outcome measures for long covid. BMJ 2022;376:o257.doi:10.1136/bmj.o257 pmid:http://www.ncbi.nlm.nih.gov/pubmed/35091425
        OpenUrlFREE Full Text
      50. ↵
        2. Sivan M ,
        3. Halpin S ,
        4. Gees J , et al
        . The self-report version and digital format of the COVID-19 Yorkshire rehabilitation scale (C19-YRS) for long Covid or Post-COVID syndrome assessment and monitoring. ACNR 2021;20.doi:10.47795/QROO4168
      51. ↵
        2. Sivan M ,
        3. Preston NJ ,
        4. Parkin A
        . The modified COVID-19 Yorkshire rehabilitation scale (C19-YRSm) patient-reported outcome measure for long Covid or Post-COVID syndrome. medRxiv 2022:2022.03.24.22272892.
      52. ↵
        1. COMET (Core Outcome Measures in Effectiveness Trials) Initiative
        . Core outcome measures for post-covid condition / long covid. Sheffield: COMET, 2021.
      53. ↵
        2. Patel K ,
        3. Straudi S ,
        4. Yee Sien N , et al
        . Applying the who ICF framework to the outcome measures used in the evaluation of long-term clinical outcomes in coronavirus outbreaks. Int J Environ Res Public Health 2020;17:6476.doi:10.3390/ijerph17186476
      54. ↵
        2. de Lusignan S ,
        3. Jones N ,
        4. Dorward J , et al
        . The Oxford Royal College of general practitioners clinical informatics digital hub: protocol to develop extended COVID-19 surveillance and trial platforms. JMIR Public Health Surveill 2020;6:e19773.doi:10.2196/19773 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32484782
        OpenUrlCrossRefPubMed
      55. ↵
        1. NHS England
        . Enhanced service specification: long COVID 2021/22. London: NHS England, 2022. https://www.england.nhs.uk/wp-content/uploads/2021/06/C1313-ess-long-covid-21-22.pdf
      56. ↵
        2. Staniszewska S ,
        3. Hill EM ,
        4. Grant R , et al
        . Developing a framework for public involvement in mathematical and economic modelling: bringing new dynamism to vaccination policy recommendations. Patient 2021;14:435–45.doi:10.1007/s40271-020-00476-x pmid:http://www.ncbi.nlm.nih.gov/pubmed/33462773
        OpenUrlPubMed
      57. ↵
        2. Squires H ,
        3. Chilcott J ,
        4. Akehurst R , et al
        . A framework for developing the structure of public health economic models. Value Health 2016;19:588–601.doi:10.1016/j.jval.2016.02.011 pmid:http://www.ncbi.nlm.nih.gov/pubmed/27565276
        OpenUrlCrossRefPubMed
      58. ↵
        2. Squires H ,
        3. Chilcott J ,
        4. Akehurst R , et al
        . A systematic literature review of the key challenges for developing the structure of public health economic models. Int J Public Health 2016;61:289–98.doi:10.1007/s00038-015-0775-7 pmid:http://www.ncbi.nlm.nih.gov/pubmed/26747470
        OpenUrlCrossRefPubMed
      59. ↵
        1. National Institute for Health and Care Excellence
        . Guide to the methods of technology appraisal 2013, 2013.
      60. ↵
        2. Glover D ,
        3. Henderson J
        . Quantifying health impacts of government policies. London: Department of Health, 2010.
      61. ↵
        2. Verguet S ,
        3. Kim JJ ,
        4. Jamison DT
        . Extended cost-effectiveness analysis for health policy assessment: a tutorial. Pharmacoeconomics 2016;34:913–23.doi:10.1007/s40273-016-0414-z pmid:http://www.ncbi.nlm.nih.gov/pubmed/27374172
        OpenUrlCrossRefPubMed
      62. ↵
        2. Eddy DM ,
        3. Hollingworth W ,
        4. Caro JJ , et al
        . Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task Force-7. Med Decis Making 2012;32:733–43.doi:10.1177/0272989X12454579 pmid:http://www.ncbi.nlm.nih.gov/pubmed/22990088
        OpenUrlCrossRefPubMedWeb of Science
      63. ↵
        2. Davis S ,
        3. Stevenson M ,
        4. Tappenden P
        . NICE DSU Technical Support Document 15: Cost-Effectiveness Modelling Using Patient-Level Simulation [Internet]. London: National Institute for Health and Care Excellence (NICE), 2014.
      64. ↵
        1. National Institute for Health Research
        . Improving inclusion of under-served groups in clinical research: guidance from include project. Leeds: NIHR, 2020. https://www.nihr.ac.uk/documents/improving-inclusion-of-under-served-groups-in-clinical-research-guidance-from-include-project/25435
      65. ↵
        2. Haroon S ,
        3. Nirantharakumar K ,
        4. Hughes S
        . Therapies for long COVID in non-hospitalised individuals-from symptoms, patient-reported outcomes, and immunology to targeted therapies (the TLC study): study protocol. medRxiv 2021.
      66. ↵
        1. NHS England
        . Post-COVID syndrome (long COVID. London: NHS England, 2021.
      67. ↵
        1. NHS England
        . National guidance for post-COVID syndrome assessment clinics. London: NHS England and NHS Improvement, 2021.
      68. ↵
        1. World Health Organisation
        . Support for rehabilitation: self-management after COVID-19-related illness. Copenhagen: WHO Regional Office for Europe, 2020.
      69. ↵
        2. O'Connor RJ ,
        3. Preston N ,
        4. Parkin A , et al
        . The COVID‐19 Yorkshire rehabilitation scale (C19‐YRS): application and psychometric analysis in a post‐COVID‐19 syndrome cohort. J Med Virol 2022;94:1027–34.doi:10.1002/jmv.27415
        OpenUrl
      70. ↵
        2. de Lusignan S ,
        3. Correa A ,
        4. Smith GE , et al
        . RCGP research and surveillance centre: 50 years' surveillance of influenza, infections, and respiratory conditions. Br J Gen Pract 2017;67:440.doi:10.3399/bjgp17X692645 pmid:http://www.ncbi.nlm.nih.gov/pubmed/28963401
        OpenUrlFREE Full Text
        2. Bangor A ,
        3. Kortum PT ,
        4. Miller JT
        . An empirical evaluation of the system usability scale. Int J Hum Comput Interact 2008;24:574–94.doi:10.1080/10447310802205776
        OpenUrlCrossRefWeb of Science

      Supplementary materials

      • Supplementary Data

        This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

      Footnotes

      • MS and TG are joint first authors.

      • Twitter @trishgreenhalgh, @LeedsADRM, @Lusignan_S

      • Collaborators MS, Academic Department of Rehabilitation Medicine, University of Leeds, m.sivan@leeds.ac.uk; TG, Nuffield Department of Primary Care Health Sciences, University of Oxford, trish.greenhalgh@phc.ox.ac.uk; NDB, Northern Care Alliance, Manchester, UK. Manchester Metropolitan University, Manchester, UK, nawar.bakerly@srft.nhs.uk; MB, Faculty of Natural Sciences, Department of Mathematics, Imperial College London, UK, m.barahona@imperial.ac.uk; AC, Department of Electrical & Electronic Engineering, University of Manchester, UK, alex.casson@manchester.ac.uk; JC, Faculty of Natural Sciences, Department of Mathematics, Imperial College London, UK, j.clarke@imperial.ac.uk; VC, School of Life Course and Population Sciences, King’s College London, vasa.curcin@kcl.ac.uk; JLD, Nuffield Department of Primary Care Health Sciences, University of Oxford, julie.darbyshire@phc.ox.ac.uk; HDav, Consultant in Respiratory Medicine, University Hospital of Wales, Cardiff, UK, helen.davies30@wales.nhs.uk; HDaw, Intersect@Exeter, College of Medicine and Health, University of Exeter, h.dawes@exeter.ac.uk; BD, Institute of Global health Innovation, Imperial College London, brendan.delaney@imperial.ac.uk; CE, Consultant in Respiratory Medicine, Royal Victoria Infirmary, Newcastle upon Tyne, UK, carlos.echevarria@nhs.net; Sarah Elkin, Consultant in General and Respiratory Medicine, Imperial NHS Trust, London, UK, sarah.elkin@nhs.net; RE, Consultant Respiratory Physician, University Hospitals of Leicester NHS Trust, Leicester, UK, re66@leicester.ac.uk; ZF, Consultant in Rehabilitation Medicine, Queen Elizabeth Hospital NHS Foundation Trust, Birmingham, UK, zfalope@nhs.net; BG, iCARE, Imperial College Healthcare NHS Trust, ben.glampson1@nhs.net; DG, 1. Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds. 2. Leeds Institute for Data Analytics, University of Leeds, d.c.greenwood@leeds.ac.uk; SH, 1. Community Covid Rehabilitation Service, Leeds Community Healthcare NHS Trust. 2. Academic Department of Rehabilitation Medicine, University of Leeds, stephen.halpin@nhs.net; MH, Psychometric Laboratory for Health Sciences, University of Leeds, m.c.horton@leeds.ac.uk; JK, Nuffield Department of Primary Care Health Sciences, University of Oxford, joseph.kwon@phc.ox.ac.uk; SdeL, Nuffield Department of Primary Care Health Sciences, University of Oxford, simon.delusignan@phc.ox.ac.uk; GD, Nuffield Department of Primary Care Health Sciences, University of Oxford, gayathri.delanerolle@phc.ox.ac.uk; EM, Department of Surgery & Cancer, Imperial College London iCARE, Imperial College Healthcare NHS Trust, e.mayer@imperial.ac.uk; HM, General Practitioner, Hertfordshire Community NHS Trust, hmaster@nhs.net; Ruairidh Milne, Person with long COVID, r.milne@soton.ac.uk; GM, Leeds Institute of Health Sciences, University of Leeds, g.mir@leeds.ac.uk; JM, School of Health Sciences, University of Dundee, jymorris@dundee.ac.uk; DBO’C, School of Psychology, University of Leeds, d.b.oconnor@leeds.ac.uk; RJO’C, Academic Department of Rehabilitation Medicine, University of Leeds, r.j.o'connor@leeds.ac.uk; APa, Leeds Community Healthcare NHS Trust, amy.parkin1@nhs.net; SP, Nuffield Department of Primary Care Health Sciences, University of Oxford, stavros.petrou@phc.ox.ac.uk; APi, Consultant in Rehabilitation Medicine, Oxford University Hospitals NHS Foundation Trust, anton.pick@ouh.nhs.uk; NP, Academic Department of Rehabilitation Medicine, University of Leeds, n.preston@leeds.ac.uk; CR, Person with long COVID, clarerayner@clara.co.uk; AR, NIHR Leeds Biomedical Research Centre, a.k.rebane@leeds.ac.uk; NS, Person with long COVID, nikki.smith@forensic-science.uk.net; ET, Respiratory Physiotherapist, Oxford Health NHS Foundation Trust, emma.tucker@oxfordhealth.nhs.uk; ABEG, Institute of Global health Innovation, Imperial College London, a.espinosa-gonzalez15@imperial.ac.uk; SB, Birmingham Community Healthcare NHS Trust, sareeta.baley@nhs.net; AR, Birmingham Community Healthcare NHS Trust, annette.rolls@nhs.net; EB, Northern Care Alliance, Manchester, UK, emily.bullock@nca.nhs.uk; MB, Royal Victoria Infirmary, Newcastle upon Tyne, UK, megan.ball@nhs.net; SB, NHS Tayside, Scotland, shehnaz.bashir@nhs.scot; MM, College of Medicine and Health, University of Exeter, m.mansoubi@exeter.ac.uk; JE, School of Health Sciences, University of Dundee, j.m.elwin@dundee.ac.uk; DP, Institute of Global health Innovation, Imperial College London, denys.prociuk16@imperial.ac.uk; IQ, University Hospitals of Leicester NHS Trust, Leicester, UK, iram.n.qureshi@uhl-tr.nhs.uk; SJ, University Hospital of Wales, Cardiff, UK, samantha.jones1a0ee@wales.nhs.uk.

      • Contributors MS and BD are joint chief investigators on the LOCOMOTION study. TG, JLD, GM, RJO’C, HD, DG, DO’C, MH, SP, SdeL, VC, AC, EM, RM and CR are workstream leads for the LOCOMOTION study. NS is a person with long COVID who contributed to the grant application and study design. NP and AP wrote the protocol from the grant proposal. TG wrote an initial draft of the paper by adapting the protocol and grant proposal. All authors contributed to refining it. All authors approved the final manuscript. MS is corresponding author and guarantor.

      • Funding This research is supported by National Institute for Health Research (NIHR) long COVID grant (Ref COV-LT-0016) with University of Leeds as the lead organisation.

      • Competing interests MS is advisor to WHO for long COVID policy in Europe. TG is member of the UK long COVID national task force and member of the National Institute for Health and Care Excellence (NICE) postacute COVID guideline oversight committee and Independent SAGE member. SdeL is the Director of the Oxford-RCGP RSC (primary care surveillance network); has received grants through his University from AstraZeneca, Eli Lilly, GSK, MSD, NovoNordisk, Sanofi, Seqirus and Takeda; and has sat on advisory boards for AstraZeneca, Sanofi and Seqirus. CR Member of Society of Occupational Medicine Taskforce on long COVID; member of WHO guideline development group on rehabilitation for post-COVID condition; community representative on the Access to COVID-19 Tools Accelerator (ACT-A) committee; member of long COVID support employment group (unpaid advocacy work for workers with long COVID); and has undertaken paid work for Nestle, advising on support for employees with long COVID.

      • Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the 'Methods and analysis' section for further details.

      • Provenance and peer review Not commissioned; peer reviewed for ethical and funding approval prior to submission.

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