Hatha yoga is more effective in improving kinesiophobia and subjective well-being when combined with self-compassion meditation in people with CLBP: a randomized control trial
Bibliographic record
Abstract
Objective: The present study aims to examine whether combining hatha yoga practice with self-compassion meditation could influence kinesiophobia, emotions, perceived stress, and perceived disability among individuals with chronic low back pain when compared with hatha yoga alone. Methods: The randomized controlled study included 70 participants with chronic low back pain (CLBP) who were randomly assigned to a Hatha yoga group (HY; n = 35) or a Hatha yoga group plus self-compassion meditation (HYSCM; n = 35). Participants followed a protocol for 8 weeks, and the assessments were carried out before and after the intervention, with a follow-up evaluation conducted after one month. The participants completed the PANAS, PSS, TSK, and RMQ questionnaires. A Generalized Estimating Equation was used to explore the effect of interventions. Results: Both HY and HYSCM groups improved kinesiophobia, perceived disability, and stress at the follow-up. However, the HYSCM group shown a more significant reduction in kinesiophobia compared to the HY group. Moreover, significant improvement in positive affect and a reduction in negative affect over time in the HYSCM group were observed. Conclusion: Hatha yoga practice when combined with self-compassion meditation led to extra benefits by exhibiting a prolonged effect, especially on kinesiophobia positive and negative effects.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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) | 0.008 | 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".