Age related changes in skin sensitivity assessed with smartphone vibration testing
Bibliographic record
Abstract
The capacity to perceive tactile input at the fingertips, referred to as tactile sensitivity, is known to diminish with age due to regressive changes to mechanoreceptor density and morphology. Sensitivity is measured as perceptual responses to stimuli of varying intensity. Contrary to traditional sensitivity monitoring instruments, smartphones are uniquely suited for remote assessment and have shown to deliver highly calibrated stimuli along a broad spectrum of intensity, which may improve test reliability. The aim of this study was to evaluate a vibration-emitting smartphone application, the Vibratus App, as a mode of estimating tactile sensory thresholds in the aging adult. The peripheral nerve function of 40 neurologically healthy volunteers (ages 18–71) was measured using monofilaments, a 128-Hz tuning fork, the Vibratus App, and nerve conduction studies (NCS). Between group differences were analyzed to determine each measurement’s sensitivity to age. Spearman correlation coefficients depicted the associative strength between hand-held measurements and sensory nerve action potential (SNAP) amplitude. Inter-rater reliability of traditional instruments and the software-operated smartphone were assessed by intraclass correlation coefficient (ICC 2, k ). Measurements taken with Vibratus App were significantly different between age groups (p < 0.001). The inter-rater reliability of monofilament, smartphone vibration, and tuning fork testing was moderate to good (ICC 2,k = 0.65, 0.69, and 0.79, respectively). The findings of this study support further investigation of smartphones as sensitivity monitoring devices for at home monitoring of skin sensitivity.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".