Optimizing Voice Sample Quantity and Recording Settings for the Prediction of Type 2 Diabetes Mellitus: Retrospective Study.

BACKGROUND

The use of acoustic biomarkers derived from speech signals is a promising non-invasive technique for diagnosing type 2 diabetes mellitus (T2DM). Despite its potential, there remains a critical gap in knowledge regarding the optimal number of voice recordings and recording schedule necessary to achieve effective diagnostic accuracy.

OBJECTIVE

This study aimed to determine the optimal number of voice samples and the ideal recording schedule (frequency and timing), required to maintain the T2DM diagnostic efficacy while reducing patient burden.

METHODS

We analyzed voice recordings from 78 adults (22 women), including 39 individuals diagnosed with T2DM. Participants had a mean (SD) age of 45.26 (10.63) years and mean (SD) BMI of 28.07 (4.59) kg/m². In total, 5035 voice recordings were collected, with a mean (SD) of 4.91 (1.45) recordings per day; higher adherence was observed among women (5.13 [1.38] vs 4.82 [1.46] in men). We evaluated the diagnostic accuracy of a previously developed voice-based model under different recording conditions. Segmented linear regression analysis was used to assess model accuracy across varying numbers of voice recordings, and the Kendall tau correlation was used to measure the relationship between recording settings and accuracy. A significance threshold of P<.05 was applied.

RESULTS

Our results showed that including up to 6 voice recordings notably improved the model accuracy for T2DM compared to using only one recording, with accuracy increasing from 59.61 to 65.02 for men and from 65.55 to 69.43 for women. Additionally, the day on which voice recordings were collected did not significantly affect model accuracy (P>.05). However, adhering to recording within a single day demonstrated higher accuracy, with accuracy of 73.95% for women and 85.48% for men when all recordings were from the first and second days.

CONCLUSIONS

This study underscores the optimal voice recording settings to reduce patient burden while maintaining diagnostic efficacy.