Reliability of a Smartphone App to Objectively Monitor Performance Outcomes in Degenerative Cervical Myelopathy: Observational Study
Bibliographic record
Abstract
BACKGROUND: Developing new clinical measures for degenerative cervical myelopathy (DCM) is an AO Spine RECODE-DCM Research, an international and multi-stakeholder partnership, priority. Difficulties in detecting DCM and its changes cause diagnostic and treatment delays in clinical settings and heightened costs in clinical trials due to elevated recruitment targets. Digital outcome measures can tackle these challenges due to their ability to measure disease remotely, repeatedly, and more economically. OBJECTIVE: The aim of this study is to assess the reliability of the MoveMed battery of performance outcome measures. METHODS: A prospective observational study in decentralized secondary care was performed in England, United Kingdom. The primary outcome was to determine the test-retest reliability of the MoveMed performance outcomes using the intraclass correlation (ICC) of agreement . The secondary outcome was to determine the measurement error of the MoveMed performance outcomes using both the SE of the mean (SEM) of agreement and the smallest detectable change (SDC) of agreement . Criteria from the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) manual were used to determine adequate reliability (ie, ICC of agreement ≥0.7) and risk of bias. Disease stability was controlled using 2 minimum clinically important difference (MCID) thresholds obtained from the literature on the patient-derived modified Japanese Orthopaedic Association (p-mJOA) score, namely, MCID ≤1 point and MCID ≤2 points. RESULTS: In total, 7 adults aged 59.5 (SD 12.4) years who live with DCM and possess an approved smartphone participated in the study. All tests demonstrated moderate to excellent test-retest coefficients and low measurement errors. In the MCID ≤1 group, ICC of agreement values were 0.84-0.94 in the fast tap test, 0.89-0.95 in the hold test, 0.95 in the typing test, and 0.98 in the stand and walk test. SEM of agreement values were ±1 tap, ±1%-3% stability score points, ±0.06 keys per second, and ±10 steps per minute, respectively. SDC of agreement values were ±3 taps, ±4%-7% stability score points, ±0.2 keys per second, and ±27 steps per minute, respectively. In the MCID ≤2 group, ICC of agreement values were 0.61-0.91, 0.75-0.77, 0.98, and 0.62, respectively; SEM of agreement values were ±1 tap, ±2%-4% stability score points, ±0.06 keys per second, and ±10 steps per minute, respectively; and SDC of agreement values were ±3-7 taps, ±7%-10% stability score points, ±0.2 keys per second, and ±27 steps per minute, respectively. Furthermore, the fast tap, hold, and typing tests obtained sufficient ratings (ICC of agreement ≥0.7) in both MCID ≤1 and MCID ≤2 groups. No risk of bias factors from the COSMIN Risk of Bias checklist were recorded. CONCLUSIONS: The criteria from COSMIN provide "very good" quality evidence of the reliability of the MoveMed tests in an adult population living with DCM.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".