Linear effects of glucose levels on voice fundamental frequency in type 2 diabetes and individuals with normoglycemia
Bibliographic record
Abstract
Glucose levels in the body have been hypothesized to affect voice characteristics. One of the primary justifications for voice changes are due to Hooke's law, in which a variation in the tension, mass, or length of the vocal folds, mediated by the body's glucose levels, results in an alteration in their vibrational frequency. To explore this hypothesis, 505 participants were fitted with a continuous glucose monitor (CGM) and instructed to record their voice using a custom mobile application up to six times daily for 2 weeks. Glucose values from CGM were paired to voice recordings to create a sampled dataset that closely resembled the glucose profile of the comprehensive CGM dataset. Glucose levels and fundamental frequency (F0) had a significant positive association within an individual, and a 1 mg/dL increase in CGM recorded glucose corresponded to a 0.02 Hz increase in F0 (CI 0.01-0.03 Hz, P < 0.001). This effect was also observed when the participants were split into non-diabetic, prediabetic, and Type 2 Diabetic classifications (P = 0.03, P = 0.01, & P = 0.01 respectively). Vocal F0 increased with blood glucose levels, but future predictive models of glucose levels based on voice may need to be personalized due to high intraclass correlation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".