Effectiveness and cost-utility of a multifaceted eHealth strategy to improve back pain beliefs of patients with non-specific low back pain: a cluster randomised trial
Bibliographic record
Abstract
OBJECTIVES: To assess the effectiveness and cost-utility of a multifaceted eHealth strategy compared to usual care in improving patients' back pain beliefs, and in decreasing disability and absenteeism. DESIGN: Stepped-wedge cluster randomised trial with parallel economic evaluation. SETTING: Dutch primary healthcare. PARTICIPANTS: Patients diagnosed with non-specific low back pain by their general practitioner or physiotherapist. Patients with serious comorbidities or confirmed pregnancy were excluded. 779 patients were randomised into intervention group (n=331, 59% female; 60.4% completed study) or control group (n=448, 57% female; 77.5% completed study). INTERVENTIONS: The intervention consisted of a multifaceted eHealth strategy that included a (mobile) website, digital monthly newsletters, and social media platforms. The website provided information about back pain, practical advice (eg, on self-management), working and returning to work with back pain, exercise tips, and short video messages from healthcare providers and patients providing information and tips. The control consisted of a digital patient information letter. Patients and outcome assessors were blinded to group allocation. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was back pain beliefs. Secondary outcome measures were disability and absenteeism, and for the preplanned economic evaluation quality of life and societal costs were measured. RESULTS: There were no between-group differences in back pain beliefs, disability, or absenteeism. Mean intervention costs were €70- and the societal cost difference was €535-in favour of the intervention group, but no significant cost savings were found. The incremental cost-effectiveness ratio indicated that the intervention dominated usual care and the probability of cost-effectiveness was 0.85 on a willingness-to-pay of €10.000/quality adjusted life year (QALY). CONCLUSIONS: A multifaceted eHealth strategy was not effective in improving patients' back pain beliefs or in decreasing disability and absenteeism, but showed promising cost-utility results based on QALYs. TRIAL REGISTRATION NUMBER: NTR4329.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".