FITT Odyssey: A Scoping Review of Exercise Programs for Managing Rotator Cuff–Related Shoulder Pain
Bibliographic record
Abstract
OBJECTIVE: To summarize the FITT (frequency, intensity, time, type), components of exercise programs included in randomized controlled trials (RCTs) that compared 2 or more programs for managing rotator cuff–related shoulder pain (RCRSP). DESIGN: Scoping review. LITERATURE SEARCH: Electronic searches were conducted up to May 2023. STUDY SELECTION CRITERIA: RCTs comparing the effects of 2 or more types of exercise programs, differing in prescription according to the FITT principle, in people with RCRSP. DATA SYNTHESIS: We extracted data from each trial report so that we could answer items 1 to 10 and 13 to 15 from the Consensus on Exercise Reporting Template (CERT). Descriptive analysis of the exercise programs was performed by summarizing and presenting the FITT characteristics, and other relevant CERT characteristics (material, provider, delivery, tailoring). RESULTS: FITT characteristics from 46 exercise programs included in 22 trials were extracted. The exercise programs were divided into 4 categories (defined in accordance to the original authors’ description and proposed rationale): motor control (n = 8), scapula-focused (n = 7), eccentric (n = 8), and nonspecific exercise programs (n = 28). Five programs were allocated to 2 different categories. The different program types had similar parameters. Exercise programs frequency ranged from 2 to 7 times per week, dose ranged from 1 to 3 sets and 4 to 30 repetitions per sets, and exercise program duration ranged from 4 to 16 weeks. CONCLUSION: There was considerable variability in the parameters used to prescribe exercises for RCRSP. Clinicians seeking guidance on FITT parameters derived from trials should do so cautiously because there was no one-size-fits-all approach. J Orthop Sports Phys Ther 2024;54(8):513-529. Epub 4 June 2024. doi:10.2519/jospt.2024.12452
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.098 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.047 | 0.031 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 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".