Distinguishing threshold shoulder range of motion measures collected by a breast cancer smartphone application: Assessment in healthy adults
Bibliographic record
Abstract
Background: Shoulder range of motion (ROM) limitations following breast cancer treatments are common. Remotely monitoring ROM changes after treatments through smartphone applications can expand rehabilitation options for breast cancer patients. The aim of the study was to investigate the ability of ShApp—a breast cancer smartphone application—to distinguish between different clinically useful shoulder ROM target levels, as well as the consistency of ROM measurements recorded by ShApp. Methods: Ten healthy, cancer-free, participants (mean age 32 ± 10.9 years, 4 females) with full shoulder ROM performed five shoulder movements to pre-determined target angles while holding the smartphone with ShApp open. Each movement was repeated three times bilaterally. Results: Agreement of ROM values between ShApp and the target values was assessed with interclass correlation coefficients (ICCs) and Bland–Altman analysis. Inter- and intra-rater reliability of ROM values were also assessed with ICCs, and ShApp's ability to distinguish between high, mid, and low ROM target angles with t -tests. Results showed good to excellent reliability between ShApp and target values (ICC 0.68–0.95) and mean differences were less than 10° for all movements except abduction. The reliability of ShApp measurements between participants was excellent for all movements (ICC >0.79) and within participants was excellent (ICC >0.90) for all movements except extension (ICC = 0.67). For all movements, significant differences between high, mid, and low angles were found ( P < 0.001). Conclusion: ShApp shows promise as a reliable and valid tool to remotely monitor shoulder ROM. Its ability to distinguish between clinically useful threshold angles at the shoulder highlights its clinical potential, particularly in the acute and early phases of patient recovery from breast cancer surgery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".