Physical activity supported by mobile technology program (PAT-Back) for older adults with back pain at primary care: a feasibility study protocol
Bibliographic record
Abstract
Aim: Low back pain (LBP) is disabling in older adults. Although physical activity interventions positively affect LBP, older adults are underrepresented in the literature. We aim to investigate the feasibility of conducting a study to evaluate a primary care program of exercise therapy and pain education, supported by mobile technology, for older adults with chronic LBP (compared to best practice advice). Methods: In this parallel, two-arm randomized pilot trial, we will recruit adults aged 60 years and older with chronic LBP. The experimental group (Physical Activity supported by low-cost mobile technology for Back pain-PAT-Back) will consist of an 8-week group exercise program based on pain education, exercises, graded activities, and in-home physical activity. Text messages will be sent to promote adherence to home exercises. The control group will receive an evidence-based educational booklet given during one individual consultation. Outcomes will include recruitment rate, adherence and retention rates, level of understanding of the intervention content, perception of the utility of mobile technology, compliance with the accelerometer in a sub-sample of patients, and adverse events. Discussion: The results of this study will form the basis for a large randomized controlled trial. This innovative approach to managing LBP in the primary care setting for older adults, if proven to be effective, can bring an important advance in the knowledge of chronic LBP management to this population.
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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 | 0.022 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".