Consistency of the Signature of Phonotraumatic Vocal Hyperfunction Across Different Ambulatory Voice Measures.

PURPOSE: Although different factors and voice measures have been associated with phonotraumatic vocal hyperfunction (PVH), it is unclear what percentage of individuals with PVH exhibit such differences during their daily lives. This study used a machine learning approach to quantify the consistency with which PVH manifests according to ambulatory voice measures. Analyses included acoustic parameters of phonation as well as temporal aspects of phonation and rest, with the goal of determining optimally consistent signatures of PVH. METHOD: Ambulatory neck-surface acceleration signals were recorded over 1 week from 116 female participants diagnosed with PVH and age-, sex-, and occupation-matched vocally healthy controls. The consistency of the manifestation of PVH was defined as the percentage of participants in each group that exhibited an atypical signature based on a target voice measure. Evaluation of each machine learning model used nested 10-fold cross-validation to improve the generalizability of findings. In Experiment 1, we trained separate logistic regression models based on the distributional characteristics of 14 voice measures and durations of voicing and resting segments. In Experiments 2 and 3, features of voicing and resting duration augmented the existing distributional characteristics to examine whether more consistent signatures would result. RESULTS: Experiment 1 showed that the difference in the magnitude of the first two harmonics (H1-H2) exhibited the most consistent signature (69.4% of participants with PVH and 20.4% of controls had an atypical H1-H2 signature), followed by spectral tilt over eight harmonics (73.6% participants with PVH and 32.1% of controls had an atypical spectral tilt signature) and estimated sound pressure level (SPL; 66.9% participants with PVH and 27.6% of controls had an atypical SPL signature). Additionally, 77.6% of participants with PVH had atypical resting duration, with 68.9% exhibiting atypical voicing duration. Experiments 2 and 3 showed that augmenting the best-performing voice measures with univariate features of voicing or resting durations yielded only incremental improvement in the classifier's performance. CONCLUSIONS: Females with PVH were more likely to use more abrupt vocal fold closure (lower H1-H2), phonate louder (higher SPL), and take shorter vocal rests. They were also less likely to use higher fundamental frequency during their daily activities. The difference in the voicing duration signature between participants with PVH and controls had a large effect size, providing strong empirical evidence regarding the role of voice use in the development of PVH.

Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Estimating Sample Size and Reducing Overfitting.

PURPOSE: Many studies using machine learning (ML) in speech, language, and hearing sciences rely upon cross-validations with single data splitting. This study's first purpose is to provide quantitative evidence that would incentivize researchers to instead use the more robust data splitting method of nested k-fold cross-validation. The second purpose is to present methods and MATLAB code to perform power analysis for ML-based analysis during the design of a study. METHOD: First, the significant impact of different cross-validations on ML outcomes was demonstrated using real-world clinical data. Then, Monte Carlo simulations were used to quantify the interactions among the employed cross-validation method, the discriminative power of features, the dimensionality of the feature space, the dimensionality of the model, and the sample size. Four different cross-validation methods (single holdout, 10-fold, train-validation-test, and nested 10-fold) were compared based on the statistical power and confidence of the resulting ML models. Distributions of the null and alternative hypotheses were used to determine the minimum required sample size for obtaining a statistically significant outcome (5% significance) with 80% power. Statistical confidence of the model was defined as the probability of correct features being selected for inclusion in the final model. RESULTS: ML models generated based on the single holdout method had very low statistical power and confidence, leading to overestimation of classification accuracy. Conversely, the nested 10-fold cross-validation method resulted in the highest statistical confidence and power while also providing an unbiased estimate of accuracy. The required sample size using the single holdout method could be 50% higher than what would be needed if nested k-fold cross-validation were used. Statistical confidence in the model based on nested k-fold cross-validation was as much as four times higher than the confidence obtained with the single holdout-based model. A computational model, MATLAB code, and lookup tables are provided to assist researchers with estimating the minimum sample size needed during study design. CONCLUSION: The adoption of nested k-fold cross-validation is critical for unbiased and robust ML studies in the speech, language, and hearing sciences. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25237045.

Rehabilitation Treatment Specification System for Voice Therapy: Application to Everyday Clinical Care.

PURPOSE: Rehabilitation intervention descriptions often do not explicitly identify active ingredients or how those ingredients lead to changes in patient functioning. The Rehabilitation Treatment Specification System (RTSS) provides guidance to identify the critical aspects of any rehabilitation therapy and supported the development of standardly named ingredients and targets in voice therapy (Rehabilitation Treatment Specification System for Voice Therapy [RTSS-Voice]). This study sought to test the content validity of the RTSS-Voice and determine if the RTSS-Voice can be used to identify commonalities and differences in treatment (criterion validity) across clinicians in everyday clinical practice. METHOD: Five speech-language pathologists from different institutions videotaped one therapy session for 59 patients diagnosed with a voice or upper airway disorder. Specifications were created for each video, and iterative rounds of revisions were completed with the treating clinician and two RTSS experts until consensus was reached on each specification. RESULTS: All 59 sessions were specified without the addition of any targets or ingredients. There were two frequent targets: (a) increased volition and (b) decreased strained voice quality. There were three frequent ingredients: (a) information regarding the patient's capability and motivation to perform a therapeutic behavior, (b) knowledge of results feedback, and (c) opportunities to practice voicing with improved resonance and mean airflow. Across sessions treating vocal hyperfunction, there was large variability across clinicians regarding the types and number of treatment components introduced, types of feedback provided, and vocal practice within spontaneous speech and negative practice. CONCLUSIONS: The RTSS and the RTSS-Voice demonstrated strong content validity, as they comprehensively characterized 59 therapy sessions. They also demonstrated strong criterion validity, as commonalities and differences were identified in everyday voice therapy for vocal hyperfunction across multiple clinicians. Future work to translate RTSS principles and RTSS-Voice terms into clinical documentation can help to understand how clinician and patient variability impacts outcomes and bridge the research-practice gap. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24796875.

Evaluation of a tiered opioid prescription algorithm in an ERAS pathway: exploring opportunities for further refinement.

BACKGROUND: Opioid over-prescription is wasteful and contributes to the opioid crisis. We implemented a personalized tiered discharge opioid protocol and education on opioid disposal to minimize over-prescription. OBJECTIVE: To evaluate the intervention by investigating opioid use post-discharge for women undergoing abdomino-pelvic surgery, and patient adherence to opioid disposal education. METHODS: We analyzed post-discharge opioid consumption among 558 patients. Eligible patients included those who underwent elective gynecologic surgery, were not taking scheduled opioids pre-operatively, and received discharge opioids according to a tiered prescribing algorithm. A survey assessing discharge opioid consumption and disposal safety knowledge was distributed on post-discharge day 21. Over-prescription was defined as >20% of the original prescription left over. Descriptive statistics were used for analysis. RESULTS: The survey response rate was 61% and 59% in the minimally invasive surgery and open surgery cohorts, respectively. Overall, 42.8% of patients reported using no opioids after hospital discharge, 45.2% in the minimally invasive surgery and 38.6% in the open surgery cohort. Furthermore, 74.9% of respondents were over-prescribed, with median age being statistically significant for this group (p=0.004). Finally, 46.4% of respondents expressed no knowledge regarding safe disposal practices, with no statistically significant difference between groups (p>0.99). CONCLUSION: Despite implementation of the tiered discharge opioid algorithm aimed to personalize opioid prescriptions to estimated need, we still over-prescribed opioids. Additionally, despite targeted education, nearly half of all patients who completed the survey did not know how to dispose of their opioid tablets. Additional efforts are needed to further refine the algorithm to reduce over-prescription of opioids and improve disposal education.

Quantifying the Occupational Voice Use of Teachers.

Purpose: The teaching profession is a high-voice use occupation at elevated risk for developing voice disorders. Continued research on teachers' vocal demands is necessary to advocate for and establish vocal health programs. This study quantified ambulatory vocal dose measures for teachers during both on- and off-work periods, comparing their occupational voice use to that in other studies that have reported percent phonation ranging from 17% to 30%. Method: Participants included 26 full-time, female school teachers between 23 and 55 years of age across multiple grades and subjects, including individuals with and without a voice disorder. Ambulatory voice data were collected from weeklong voice monitoring that recorded phonatory activity through anterior neck-surface vibration. Three vocal dose measures-time, cycle, and distance doses-were computed for each participant for three time periods: on-work weekdays, off-work weekdays, and off-work weekend days. Results: The teachers' average percent phonation was 16.2% on-work weekdays, 8.4% off-work weekdays, and 8.0% off-work weekend days. No statistically significant differences for vocal dose measures were found between off-work weekdays and weekend days. Overall, all vocal dose measures were approximately 2 times higher during work relative to off-work time periods. Conclusions: This study provides values for vocal dose measures for school teachers using ambulatory voice-monitoring technology. The vocal demands of this particular teacher sample and voice activity detection algorithm are potential factors contributing to percent phonation values on the lower end of the range reported in the literature. Future work is needed to continue to understand occupational voice use and its associated risks related to voice health, with the ultimate goal of preventing and managing voice disorders in individuals engaged in high-risk occupations.