Reliability and criterion validity of two applications of the iPhone™ to measure cervical range of motion in healthy participants
Bibliographic record
Abstract
SUMMARY OF BACKGROUND DATA: Recent smartphones, such as the iPhone, are often equipped with an accelerometer and magnetometer, which, through software applications, can perform various inclinometric functions. Although these applications are intended for recreational use, they have the potential to measure and quantify range of motion. The purpose of this study was to estimate the intra and inter-rater reliability as well as the criterion validity of the clinometer and compass applications of the iPhone in the assessment cervical range of motion in healthy participants. METHODS: The sample consisted of 28 healthy participants. Two examiners measured cervical range of motion of each participant twice using the iPhone (for the estimation of intra and inter-reliability) and once with the CROM (for the estimation of criterion validity). Estimates of reliability and validity were then established using the intraclass correlation coefficient (ICC). RESULTS: We observed a moderate intra-rater reliability for each movement (ICC = 0.65-0.85) but a poor inter-rater reliability (ICC < 0.60). For the criterion validity, the ICCs are moderate (>0.50) to good (>0.65) for movements of flexion, extension, lateral flexions and right rotation, but poor (<0.50) for the movement left rotation. CONCLUSION: We found good intra-rater reliability and lower inter-rater reliability. When compared to the gold standard, these applications showed moderate to good validity. However, before using the iPhone as an outcome measure in clinical settings, studies should be done on patients presenting with cervical problems.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".