The Efficacy of Artificial Intelligence-Based Self-Management Health Programs on Workers with Neck and Shoulder Pain/Stiffness and Lower Back Pain: A Systematic Review
Bibliographic record
Abstract
Background. Work-related musculoskeletal disorders (WRMDs) are common health problems in the working population and have increased the number of lost work days per year. However, this increase in the number of WRMDs has yet to meet with the limited availability of healthcare resources that promote self-management for WRMDs. This study aims to highlight the efficacy of Artificial Intelligence-based, self-management health programs as support for workers suffering from WRMDs. Methods. Databases, such as PubMed and Google Scholar, were systematically analyzed and extracted on June 26, 2023. Keywords including “artificial intelligence” AND “musculoskeletal pain” were used to gain relevant studies. Studies in English from the last five years, mainly randomized controlled trials, were included in this review. Studies that were systematic reviews, meta-analyses, animal studies, and case reports were excluded. The Newcastle Ottawa Scale (NOS) was then used to assess the studies' quality. Results. Four RCTs and one CT are used in this review, including 1550 participants. All of the studies used were of good quality based on the NOS. All studies show that AI-based health programs improve work productivity as a whole in patients with co-occurring WRMDs, and are said to be user-friendly, potentially outperforming online search engines for symptom assessment, saving time, increasing helpfulness, and boosting worker productivity. Conclusion. AI-based Health Programs are new, effective ways of solving WRMDs. However, further studies and trials are needed to identify elements of the program that lead to this successful outcome for further development, followed by the implementation of digitally supported self-management interventions in specialist care.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".