The effect of strengthening and relaxation exercises on musculoskeletal pain, anxiety, and sleep quality in COVID-19 survivors
Bibliographic record
Abstract
BACKGROUND AND AIM: Musculoskeletal pain, anxiety, and sleep problems may persist in people after Coronavirus Disease 2019 (COVID-19).The aim of this study was to examine the effects of strengthening and relaxation exercises on musculoskeletal pain, anxiety, and sleep quality in COVID-19 survivors. METHODS:The study was conducted at Gaziosmanpaşa Training and Research Hospital and included outpatients aged between 18 and 65 who were diagnosed with COVID-19 in the last 3 months.Subjects were randomly assigned to either experimental or control groups.The experimental group participated in a home-based strengthening and relaxation exercises program 3 times a week for 8 weeks, while control group participants did not receive any exercise program.McGill Pain Scale Short Form (SF-MPQ), Short-Form 36 (SF-36), Beck Anxiety Scale, and Pittsburgh Sleep Quality Index (PSQI) were conducted on all patients before and after the study. RESULTS:A total of 117 COVID-19 survivors were screened for eligibility, and 76 eligible subjects were randomized into groups.Baseline characteristics and assessment results were similar between the groups (p>0.05).After the study, a significant difference was found in the experimental group in terms of all outcome results (p<0.05).In the control group, there was a statistically significant difference in all assessments except McGill-Current score, SF-36-Physical Role Difficulty, SF-36-Social Functioning, and SF-36-Pain sub-dimension scores (p<0.05).The improvement was significantly higher in the experimental group than in the control group except for the SF-36-Emotional Role Difficulty sub-dimension (p<0.05). CONCLUSIONS:In conclusion, strengthening and relaxation exercises had a significantly positive effect on post-COVID-19 musculoskeletal pain, anxiety, and sleep quality.
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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 | 0.000 | 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) | 0.002 | 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".