# RTSS-Voice: Towards a unified system to classify treatments for muscle tension dysphonia

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $493,814

## Abstract

Project Summary
Systematically improving upon current voice therapy outcomes is problematic as the specific clinician actions
(i.e., ingredients) responsible for improved patient functioning (i.e., targets) are unknown. For example,
Comparative Effectiveness Research (CER) can show that therapy A works better than therapy B on global
outcome X. But why did therapy A provide better outcomes? Why did some patients in therapy B significantly
improve with the “worse” program; and some patients in therapy A remain unchanged with the “better”
program? A theory-based system is needed to scientifically identify a program’s ingredients associated with
improved outcomes across patients. And standard labels are needed to make the identified active ingredients
generalizable across therapy programs. Therefore, this project will use a theory-driven framework for
describing the ingredients/targets of rehabilitation treatments—called the Rehabilitation Treatment
Specification System (RTSS)—and standard voice-specific terminology/definitions—called the RTSS-Voice—
to standardly describe (Aim 1) and compare (Aim 2) variations in treatment across 9 well-known and diverse
voice therapies. Also, we will create/test an implementation toolkit to facilitate RTSS-Voice adoption in clinical
care across 5 Voice Centers (Aim 3). It is hypothesized that the RTSS and RTSS-Voice will characterize all
therapies without needing revisions (Aim 1) and identify ingredients/targets that are unique to one therapy
and/or common across multiple therapies (Aim2). And since the RTSS-Voice will help clinicians think about
their treatment more specifically and in relation to 9 evidence-based therapies, we hypothesize adoption will be
associated with improved outcomes at all 5 Voice Centers. The resulting list of mutually exclusive
ingredient/targets across therapy programs will obviously improve the state of CER by enabling the
identification/comparison of active ingredients across therapy programs; instead of the current practice of
studying/comparing entire programs. Clinical adoption will result in large datasets with standard ingredients
linked to outcomes, which will facilitate innovative hypotheses and interpretable datamining/machine learning
to realistically improve voice therapy effectiveness. This work is likely to generalize to other Centers due to the
involvement of >20 influential clinicians and the implementation toolkit.

## Key facts

- **NIH application ID:** 10584146
- **Project number:** 1R01DC020247-01A1
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Jarrad Van Stan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $493,814
- **Award type:** 1
- **Project period:** 2022-09-16 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10584146

## Citation

> US National Institutes of Health, RePORTER application 10584146, RTSS-Voice: Towards a unified system to classify treatments for muscle tension dysphonia (1R01DC020247-01A1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10584146. Licensed CC0.

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