A systematic review of studies using pedometers as an intervention for musculoskeletal diseases
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) plays an important role in the prevention and management of a number of chronic conditions. AIM: to investigate the evidence for effectiveness of pedometer-driven walking programs to promote physical activity among patients with musculoskeletal disorders (MSDs). METHOD: A comprehensive systematic review was performed using 11 electronic databases up to 20 February 2014. Keywords and MeSH terms included "musculoskeletal disorders", "walking", and "pedometer". Randomized controlled trials, published in English, that examined the effects of a pedometer-based walking intervention to increase physical activity levels and improve physical function and pain in patients with musculoskeletal disorders were included. RESULT: Of the 1996 articles retrieved, seven studies ranging in date of publication from 1998 to 2013 met the inclusion criteria, allowing data extraction on 484 participants with an age range of 40 to 82 years. Interventions lasted from 4 weeks to 12 months and the results across studies showed significant increases in step count (p < 0.05) following the intervention. Across these studies, there was a mean increase in PA of 1950 steps per day relative to baseline. Four studies reported improved scores for pain and/or physical function at the intervention completion point relative to controls. CONCLUSION: This study provides strong evidence for the effectiveness of pedometer walking interventions in increasing PA levels for patients with MSDs. Our findings suggest that a combination of interventions is likely to be the most effective strategy to maximize health benefits in the short term. Further research should include larger sample sizes, and longer intervention durations are required to support the role of pedometer walking interventions as a long term intervention for management of musculoskeletal disorders.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".