Comparative Efficacy of Clinic-Based and Telerehabilitation Application of McKenzie Therapy in Chronic Low-Back Pain
Bibliographic record
Abstract
Studies on validation of telerehabilitation as an effective platform to help manage as well as reduce burden of care for Low-Back Pain (LBP) are sparse. This study compared the effects of Telerehabilitation-Based McKenzie Therapy (TBMT) and Clinic-Based McKenzie Therapy (CBMT) among patients with LBP. Forty-seven consenting patients with chronic LBP who demonstrated ‘directional preference’ for McKenzie Extension Protocol (MEP) completed this quasi experimental study. The participants were assigned into either the CBMT or TBMT group using block permuted randomization. Participants in the CBMT and TBMT groups received MEP involving a specific sequence of lumbosacral repeated movements in extension aimed to centralize, decrease, or abolish symptoms, thrice weekly for eight weeks. TBMT is a comparable version of CBMT performed in the home with the assistance of a mobile phone app. Outcomes were assessed at the 4th and 8th weeks of the study in terms of Pain Intensity (PI), Back Extensors Muscles’ Endurance (BEME), Activity Limitation (AL), Participation Restriction (PR), and General Health Status (GHS). Data were analyzed using descriptive and inferential statistics. Alpha level was set at p< 0.05.Within-group comparison across baseline, 4th and 8th weeks indicate that both CBMT and TBMT had significant effects on PI (p=0.001), BEME (p=0.001), AL (p=0.001), PR (p=0.001) and GHS (p=0.001) respectively. However, there were no significant differences (p>0.05) in the treatment effects between TBMT and CBMT, except for ‘vitality’ (p=0.011) scale in the GHS where TBMT led to significantly higher mean score. Mobile-app platform of the McKenzie extension protocol has comparable clinical outcomes with the traditional clinic-based McKenzie Therapy, and thus is an effective supplementary platform for care of patients with low-back pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".