The long-term effectiveness of a blended intervention for patients with non-specific low back pain: a six months follow-up pilot study.
Bibliographic record
Abstract
Background: Low back pain (LBP) is a prevalent health care problem. National and international guidelines recommend physiotherapy for patients with LBP which can consist of information, advice and exercises. However, compliance is a challenge in exercise therapy. Blended care, which is an integration of face-to-face care with web-based care, has advantages like the ability to remind patients to exercise and being physically active. The aim of this study was to evaluate the preliminary long-term effectiveness of a blended exercise therapy for patients with LBP. The hypothesis was that blended care is effective in the long term in patients with LBP on pain, disability, physical activity and pain related fear. \nMethod: The current study is a continuation of a pilot study named e-Exercise LBP. This pilot study was focused on the feasibility and preliminary short-term effectiveness (3 months) of a blended intervention. A total of 41 patients between 18 and 65 year with non-specific LBP that participated in the original pilot study, were contacted after 6 months to assess their pain, disability, physical activity and pain related fear for this current study. For measuring these factors a questionnaire was used that contained the Quebec Back Pain Disability Score (QBPDS), Visual Analogue Scale (VAS), Short QUestionnaire to ASsess Health enancing (SQUASH) and Fear Avoidance Beliefs Questionnaire (FABQ). For analyzing the preliminary effectiveness over time (baseline, three months-, and six months follow-up), longitudinal- data analyses were performed by using the non-parametric Friedman test.\nResults: The Friedman test showed an overall significant difference in decrease of disability and pain over time (P= <0.0005, P= <0.0005). The post hoc test showed a significant difference in disability and pain between baseline and three months’ follow-up (P= <0.0005, P= <0.0005) and between baseline and six months’ follow-up (P=0.010, P= 0.001). \nConclusion: The blended intervention e-Exercise LBP seems a promising intervention for improving disability and pain in patients with LBP. The accents on technology and personalized care fit well in the current health. However, blended care is new for physiotherapists and needs further development with specific attention for implementation in the future.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".