Effectiveness of internet-based exercises aimed at treating knee osteoarthritis (iBEAT-OA): a randomized clinical trial
Bibliographic record
Abstract
Importance Osteoarthritis is a prevalent, debilitating and costly chronic disease for which recommended first-line treatment is underused. Objective To compare the effect of digital treatment for knee osteoarthritis via app versus routine self-management in a randomized, parallel-group clinical trial. Design A 6-week randomised controlled trial (iBEAT-OA) started in winter 2018. Setting Primary care. Participants 551 participants, 45 years or older, with a diagnosis of knee osteoarthritis from an existing primary care database or from social media advertisements, were invited. Intervention The intervention (n=48) and control group (n=57) conformed to first-line knee osteoarthritis treatment. For intervention group, treatment was delivered via a smartphone application. The control group received routine self-management care. Main outcome and measures Primary outcome at 6 weeks was change from baseline in self-reported pain during the last seven days, reported on a Numerical Rating Scale (NRS, 0-10, 0 no pain, 10 worst pain), compared between the two groups. Secondary outcomes included two physical functioning scores, hamstring and quadriceps muscle strength, Sleep assessment, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Pittsburgh Sleep Quality Index (PSQI), General health questionnaire (MSK-HQ), Inflammatory markers on ultrasound (synovial fluid, synovial hypertrophy and hypervascularity) and quantitative sensory testing (QST). Results 48 participants in the intervention group (mean age 65.2, 70.8% female, 30.4 BMI) and 57 participants in the control group (age 68, 64.9% female, 31.9 BMI) completed this study with no notable demographic difference between groups. The intervention group showed a greater NRS pain score decrease at follow-up than the control group (between-group difference -1.5 [95%CI, -0.8 to -2.2; P<0.001]. Similarly, the 30-second sit to stand test (30CST) and Timed Up and Go test (TUAG) improved more in the intervention group, 3.4 (95%CI, -2.2 to -4.5) and -1.8 (95%CI, -0.5 to -3.0), as did the WOMAC subscales for pain, stiffness and physical function (-1.1 [95%CI, -0.2 to -2.0], -1.0 [95%CI, -0.5 to -1.5], and -3.4 [95%CI, -0.7 to -6.2]). The magnitude of within-group changes in pain and function outcomes in the intervention group corresponded to medium to very strong effects. There was no between-group difference seen in actigraphy sleep data, PSQI, MSK-HQ, QST and sonographic features of knee OA. Conclusions and relevance Digitally delivered evidence-based first-line OA treatment is superior to routine self-managed care as usual and can be given without harming people with osteoarthritis. Effect sizes observed in the intervention group correspond to clinically important improvements.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".