The Effect and Safety of App-Based Interventions for Populations With Osteoarthritis: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Interventions based on apps are becoming increasingly popular for the treatment of osteoarthritis (OA), but research on the potential moderators of treatment efficacy is lacking. Objective: The aim of this study was to examine the treatment efficacy, cost-effectiveness, and safety associated with app-based interventions for populations with OA and identify the potential factors associated with better treatment outcomes. Methods: PubMed, Web of Science, Embase, and the Cochrane Library were searched from their inception to September 19, 2024, for randomized controlled trials on app-based interventions for patients with OA that report efficacy or health economic outcomes. The quality of each included study was assessed using the revised Cochrane Risk of Bias Assessment Tool (ROB 2.0). The primary outcome measure is the change in pain intensity before and after treatment. Secondary outcomes included function, quality of life, adverse events, and self-management. If I2 was >50%, a random-effects model was applied. In addition to preplanned subgroup analyses based on OA type, intervention duration, risk of bias, age, and type of app-based intervention, exploratory post hoc subgroup analyses were conducted on variables related to the population, intervention characteristics, and study design features. Results: The review includes 14 studies, comprising 12 randomized controlled trials (RCTs) and 2 health economics studies. The RCTs involved a total of 1410 participants, whose mean age ranged from 54 to 67 years. Compared with controls, app-based interventions led to a reduction in pain and improvement in physical function (standardized mean difference [SMD]=-0.36; 95% CI: -0.58 to -0.14; P<.001; I2=72% and SMD 0.39; 95% CI 0.16 to 0.62; P<.001; I2=67%; respectively), but showed no significance for quality of life and adverse events (SMD 0.23; 95% CI -0.04 to 0.50; P=.10; I2=68% and odds ratio [OR]=1.33; 95% CI 0.84 to 2.12; P=.23; I2=7%; respectively). The cost of the intervention group was lower than that of the control group. Subgroup analysis revealed a significant difference between those aged 60 years and older and those younger than 60 years (SMD -0.29; 95% CI -0.51 to -0.06 and SMD -0.84; 95% CI -1.25 to -0.43). The study also reported a high level of satisfaction and compliance rate, with all scores of the System Usability Scale exceeding 70 points, and this score is considered acceptable. Conclusions: This study showed that app-based interventions were safe and effective for patients with OA, which might provide a cost-effective option, especially in resource-limited settings. Age is a critical factor for optimizing treatment benefits, especially when considering individual needs.
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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 | 0.031 | 0.092 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.043 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".