Reliability and validity of digital health metrics for assessing arm and hand impairments in an ataxic disorder
Bibliographic record
Abstract
OBJECTIVES: Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is the second most frequent recessive ataxia and commonly features reduced upper limb coordination. Sensitive outcome measures of upper limb coordination are essential to track disease progression and the effect of interventions. However, available clinical assessments are insufficient to capture behavioral variability and detailed aspects of motor control. While digital health metrics extracted from technology-aided assessments promise more fine-grained outcome measures, these have not been validated in ARSACS. Thus, the aim was to document the metrological properties of metrics from a technology-aided assessment of arm and hand function in ARSACS. METHODS: We relied on the Virtual Peg Insertion Test (VPIT) and used a previously established core set of 10 digital health metrics describing upper limb movement and grip force patterns during a pick-and-place task. We evaluated reliability, measurement error, and learning effects in 23 participants with ARSACS performing three repeated assessment sessions. In addition, we documented concurrent validity in 57 participants with ARSACS performing one session. RESULTS: Eight metrics had excellent test-retest reliability (intraclass correlation coefficient 0.89 ± 0.08), five low measurement error (smallest real difference % 25.4 ± 5.7), and none strong learning effects (systematic change η -0.11 ± 2.5). Significant correlations (ρ 0.39 ± 0.13) with clinical scales describing gross and fine dexterity and lower limb coordination were observed. INTERPRETATION: This establishes eight digital health metrics as valid and robust endpoints for cross-sectional studies and five metrics as potentially sensitive endpoints for longitudinal studies in ARSACS, thereby promising novel insights into upper limb sensorimotor control.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".