Virtual rehabilitation for individuals with Long COVID: a randomized controlled trial
Bibliographic record
Abstract
ABSTRACT Background Our primary objective was to investigate whether an 8-week virtual rehabilitation program for individuals with long COVID improves functional mobility compared to usual care. Methods Subjects were randomly assigned to receive either i) virtual rehabilitation plus usual outpatient care or ii) usual outpatient care. The intervention group underwent an 8-week virtual rehabilitation program which consisted of personalised and symptom-titrated functional aerobic and resistance exercises as well as long COVID educational sessions. The primary outcome was the Activity Measure for Post-Acute Care (AM-PAC) mobility score. Secondary outcomes included the Baseline and Transition Dyspnea Index (BDI/TDI), the Fatigue Visual Analog Scale, 12-item short-form, EuroQol 5 Dimension 5 Level (EQ-5D-5L), DePaul Symptom Questionnaire – PEM, physical function tests, questionnaires on mental health, acceptability and adverse events. Findings 132 individuals with long COVID (mean age 48 ± 11.8; 75% female) were enrolled. The adherence rate was 96%; however, 25 participants (39%) in the intervention group were unable to progress their exercises through the FITT (frequency, intensity, time, and type) principle due to symptoms. No between group differences were found for change in AM-PAC mobility (95% CI –0.91 to 2.13). The proportion of participants achieving the minimal detectable change in the AM-PAC mobility at the end of the intervention period was higher in the intervention group (35.8% (SE 6.0%) vs. 17.0% (SE 4.7%)) (95% CI 3.9 to 33.8). Compared with controls, scores on the EQ-5D-5L pain/discomfort (95% CI –0.70 to –0.03), EQ-5D-5L VAS (95% CI 1.05 to 14.43), VAS fatigue (95% CI –1.78 to –0.02), as well as for the TDI functional (95% CI 0.07 to 0.72), effort (95% CI 0.10 to 1.12) and total scores (95% CI 0.10 to 2.37) were greater in the intervention group. There were no between-group differences in other outcomes and no serious adverse events. Interpretation An 8-week virtual rehabilitation program did not improve self-reported mobility for most patients with long COVID, however we did find improvements in health status and symptom persistence. Progression of exercise training is challenging in this population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".