Reliability and validity of using smartphone sensor and photography to measure hand and upper extremity joint range of motion: A systematic review
Bibliographic record
Abstract
BACKGROUND: Accurate Range of Motion measurement is vital for clinical decision-making. Traditional goniometers are reliable and valid tools but face challenges. Smartphones, with advanced technologies, are emerging as promising tools, necessitating validation for clinical integration. PURPOSE: The study aim is to appraise and synthesize the available evidence on the reliability and validity of smartphone sensors and photography in assessing the ROM of hand and upper extremity joints. STUDY DESIGN: Systematic review. METHODS: We searched the studies in which "smartphone sensor" or "smartphone photography" was employed as the method of upper limb ROM measurement from January 2001 to January 2023 to find relevant studies. We compared the studied methods to conventional goniometer as the gold standard and validated ROM measurement techniques. Two independent reviewers (SM and ES) assessed the methodological quality of reliability and validity of both category of studies using the Quality Appraisal Tool for studies of diagnostic Reliability (QAREL) and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tools, respectively. Qualitative synthesis was the preferred method of summarizing and presenting the results. RESULTS: A total of 31 studies were included in this study. The sample size across studies ranged from 10 to 171, and the mean age was 41 years old. Eleven out of 12 studies included in the photography category stated the good to excellent reliability or validity with respect to the goniometric measurements. Eight studies in the smartphone sensor category reported excellent reliability or validity (47%), seven studies stated good level of reliability or validity (41%), and two studies reported average or moderate level of reliability (12%). The quality assessment using the QAREL assessment tool was high in 11 studies (35%), moderate in 8 studies (26%) or low in 12 studies (39%). CONCLUSIONS: This review provides clinicians and researchers with evidence to support using smartphone photography and sensor applications as valid and reliable methods for ROM measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".