Trueness and Minimal Detectable Change of Smartphone Inclinometer Measurements of Shoulder Range of Motion
Bibliographic record
Abstract
BACKGROUND: Digital inclinometer applications using data from embedded sensors on smartphone/multi-purpose pocket computers or "smart digital inclinometers" (SDIs) are now used to clinically assess range of motion (ROM). OBJECTIVES: The objectives of this study were to assess, compared with a biomechanical gold standard (GS), the trueness and minimal detectable change (MDC) of shoulder range of motion (SROM) measurements obtained from an SDI. METHODS: Twenty-five (n = 25) asymptomatic healthy participants performed three trials of shoulder flexion (SF), shoulder abduction (SA), and shoulder external rotation (SER) at full-range and mid-range. MAIN OUTCOME MEASURES: SROM was measured concurrently from sensor data (pitch, yaw, roll angles) from an iPod Touch installed on the posterior aspect of the humerus and 3D orientation of the upper arm obtained from an optical motion tracking system GS. RESULTS: The mean level of bias between SDI and the GS across all SROM measurements was 3.4°, with a 95% confidence interval varying between -8.9° and 15.8°. The mean and standard deviation absolute difference of SDI measurements with the GS were 5.8° ± 3.7° for SF, 8.7° ± 5.2° for SA, and 1.7° ± 1.4° for SER. The trueness of these values varied according to the movement. MDC was 1.9° for SF, 2° for SA, and 0.3° for SER. CONCLUSIONS: SROM measures in SER with an SDI seem to be accurate and robust for clinical use. However, SROM measures in other planes of motion should be interpreted with caution depending on the evaluation objective, the plane of motion assessed, and the range of ROM measured.
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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 | 0.028 |
| 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 | 0.001 | 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".