Enhancing COVID Rehabilitation with Technology (ECORT): protocol for an open-label, single-site randomized controlled trial evaluating the effectiveness of electronic case management for individuals with persistent COVID-19 symptoms
Bibliographic record
Abstract
BACKGROUND: As of May 2022, Ontario has seen more than 1.3 million cases of COVID-19. While the majority of individuals will recover from infection within 4 weeks, a significant subset experience persistent and often debilitating symptoms, known as "post-COVID syndrome" or "Long COVID." Those with Long COVID experience a wide array of symptoms, with variable severity, including fatigue, cognitive impairment, and shortness of breath. Further, the prevalence and duration of Long COVID is not clear, nor is there evidence on the best course of rehabilitation for individuals to return to their desired level of function. Previous work with chronic conditions has suggested that the addition of electronic case management (ECM) may help to improve outcomes. These platforms provide enhanced connection with care providers, detailed symptom tracking and goal setting, and access to relevant resources. In this study, our primary aim is to determine if the addition of ECM with health coaching improves Long COVID outcomes at 3 months compared to health coaching alone. METHODS: The trial is an open-label, single-site, randomized controlled trial of ECM with health coaching (ECM+) compared to health coaching alone (HC). Both groups will continue to receive usual care. Participants will be randomized equally to receive health coaching (± ECM) for a period of 8 weeks and a 12-week follow-up. Our primary outcome is the WHO Disability Assessment Scale (WHODAS), 36-item self-report total score. Participants will also complete measures of cognition, fatigue, breathlessness, and mental health. Participants and care providers will be asked to complete a brief qualitative interview at the end of the study to evaluate acceptability and implementation of the intervention. DISCUSSION: There is currently little evidence about the optimal treatment of Long COVID patients or the use of digital health platforms in this population. The results of this trial could result in rapid, scalable, and personalized care for people with Long COVID which will decrease morbidity after an acute infection. Results from this study will also inform decision making in Long COVID and treatment guidelines at provincial and national levels. TRIAL REGISTRATION: ClinicalTrials.gov NCT05019963. Registered on 25 August 2021.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.033 |
| Meta-epidemiology (narrow) | 0.009 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.071 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".