Pain intensity, physical activity, quality of life, and disability in patients with mechanical low back pain: a cross-sectional study
Bibliographic record
Abstract
Abstract Background Poorly managed mechanical low back pain (MLBP) and its sequelae, such as severe pain, physical inactivity, and disability, negatively impact patients’ quality of life (QoL). The study aimed to determine the pain intensity (PI), physical activity (PA), QoL, and disability, the association between selected sociodemographic variables and PI, PA, QoL, and disability, and the relationship between PI, PA, QoL, and disability among Nigerians with chronic MLBP. Methods This cross-sectional study employed a consecutive sampling technique. Outcome measures included the Numeric Pain Scale, International Physical Activity Questionnaire-Short Form, WHO Quality-of-Life Brief, and Oswestry Disability Index for PI, PA, QoL, and disability, respectively. Descriptive statistics were used to summarize participants’ sociodemographic variables. Chi-square, Spearman’s correlation, and structural equation modeling (SEM) were used for inferential analyses. Results Two hundred and fifty chronic MLBP patients comprising 154 females and 96 males, completed the study. The mean PA, PI, QoL, and disability levels were 1118.03MET ± 615.30, 5.97 ± 2.69, 73.45% ± 14.21, and 21.7% ± 18.94, respectively. There was a significant correlation between PA and QoL (rho = 0.36, p = 0.001), PA and disability (rho = −0.42, p = 0.010), QoL and disability (rho = −0.21, p = 0.008), QoL and PI (rho = −6.72, p = 0.025), PI and disability (rho = 0.90, p = 0.022). Aside from age and PA (χ2 = 8.52, p = 0.045), there was no significant association between the sociodemographic variables and PI, PA, QoL, or disability. SEM showed a strong positive association between PI and disability (β = 0.80, p < 0.001). Conclusion Individuals with chronic MLBP had a low PA, moderate QoL, and significant disability. Incorporating PA, QoL, and disability assessments may enhance the evaluation and management of MLBP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".