Post-exertional malaise may persist in Long COVID despite learning STOP-REST-PACE
Bibliographic record
Abstract
Background Physical activity used in rehabilitation can trigger post-exertional malaise (PEM) in people with Long COVID. Concerns remain if the STOP-REST-PACE approach promoted by patient communities and professional organizations can be safely administered and contributes to return to usual activities.Objective (1) To observe PEM over 12 weeks of telerehabilitation based on the STOP-REST-PACE approach. (2) To describe the changes in health-related quality of life (HRQoL), respiratory symptoms, fatigue and return to work.Methods This was an observational prospective cohort of participants with Long COVID referred to a telerehabilitation service. Participants received up to 14 h of physiotherapy and occupational therapy over 12 weeks based on the STOP-REST-PACE approach. Frequency was personalized, up to two sessions weekly. An independent coordinator assessed PEM, HRQoL, respiratory symptoms, fatigue and return to work.Results Thirty-four participants were included and 30 completed the 12 weeks of telerehabilitation. Participants had an average of eight impairments. We found PEM in all participants at baseline. After 12 weeks, PEM remained present for 19 out of 30 participants. Respiratory symptoms significantly improved (COPD Assessment Test: 19.2 ± 7.3 vs 13.8 ± 7.7, p < .001). Fatigue and HRQoL did not significantly improve (p = 0.32 and p = 0.20, respectively). Only four participants were able to work full time.Conclusions PEM persisted for close to two-third of participants despite learning the STOP-REST-PACE approach through physical and occupational therapy sessions over 12 weeks. Respiratory symptoms improved, but we did not observe a difference in fatigue and HRQoL. Return to work was out of reach for most participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".