THE EFFECT OF STRENGTHENING AND RELAXATION EXERCISES ON PAIN, ANXIETY AND SLEEP QUALITY IN PEOPLE WITH COVID-19
Bibliographic record
Abstract
Musculoskeletal pain, anxiety and sleep problems may continue after COVID-19. In our study, it was aimed to examine the effects of strengthening and relaxation exercises on musculoskeletal pain, anxiety and sleep quality in people with ongoing COVID-19 symptoms. The study, which was carried out at XXX Hospital between 18 January 2021 and 31 March 2021, included 76 patients aged between 18-65 who were diagnosed with COVID-19 in last 3 months. While strengthening and relaxation exercises were applied to 38 of the patients included in the study (experimental group), no exercise was applied to 38 of them (control group). Personal information form, McGill pain scale short form (SFMPQ), short form 36 (SF-36), Pittsburgh sleep quality scale (PUKI) were administered to all patients before and after the study. There was no significant difference between the experimental and control groups for the demographic characteristics and baseline results (p>0.05). After the study, a significant difference was found in the experimental group in terms of SF-36, Mcgill, Beck Anxiety Scale and PUKI (p<0.05). In the control group after the study, statistically significant difference was found in all evaluations except, SF-369s Physical Role Difficulty, Social Functioning and Pain sub-dimension scores (p<0.05). The changes before and after the study for Mcgill, Beck Anxiety scale, PUKI, SF-36 (except emotional) scores were significantly higher in the experimental group than the control group (p<0.05). In conclusion, it was determined that strengthening and relaxation exercises had a significantly positive effect on post-COVID-19 musculoskeletal pain, anxiety and sleep quality in our study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".