Individually Tailored Physiotherapy in Persons With Respiratory Symptoms Related to Post‐Acute Sequelae of COVID‐19: A Feasibility Study With Mixed Methods
Bibliographic record
Abstract
ABSTRACT Background and Aims Post‐acute sequelae of COVID‐19 (PASC) commonly present with persistent respiratory symptoms, even in individuals with normal chest imaging and pulmonary function. Given the heterogeneity within this population, a personalized approach to respiratory physiotherapy could improve outcomes. The purpose of this study was to assess the feasibility and impact of a tailored respiratory physiotherapy program on health‐related quality of life (QoL), functional impairment, and patient‐reported outcome measures (PROMs) in individuals with persistent respiratory symptoms due to PASC. Methods A single‐arm, open‐label trial was conducted with 13 adults diagnosed with PASC, recruited from Long COVID clinics in Calgary, Canada. Participants underwent an 8‐session personalized physiotherapy program, including education, breathing exercises, and strengthening. Feasibility was measured through recruitment, retention, and session completion rates. PROMs were collected at baseline and post‐intervention, and qualitative interviews explored participant perspectives. Results The program was highly feasible, with 100% retention and a 99% completion rate. Significant improvements were observed in QoL, functional status (Post COVID‐19 Function Status scale), and self‐efficacy scores. The 6‐min walk test showed clinically meaningful improvements in three out of seven participants. Qualitative interviews ( n = 8) identified three main themes: struggles with PASC, positive aspects of the program, and benefits from completing it. Participants valued the personalized approach, heart rate monitors, flexible scheduling, and a hybrid of in‐person and virtual sessions, reporting increased confidence, improved symptom management, and better mental health. Conclusion A personalized respiratory physiotherapy program is feasible and may benefit individuals with PASC. Larger trials are needed to assess long‐term efficacy and scalability. Trial Registration ClinicalTrials.gov identifier: NCT05040893
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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".