Clinical trial NCT06935084: ASTHMAXcel Voice Study
Official title: Conducting a Randomized Controlled Trial for A Novel Patient-Facing Mobile Platform to Collect and Implement Patient-Reported Outcomes and Voice Biomarkers in Underserved Adult Patients With Asthma
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Asthma
Keywords: Mobile Health Application
Asthma Management
Asthma Control
Lead sponsor: Montefiore Medical Center
Sponsor class: OTHER
Collaborators: name: Yeshiva University
name: Agency for Healthcare Research and Quality (AHRQ)
First posted: 2025-04-18
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Participants will be randomized in a 1:1 ratio into one of two arms: ASTHMAXcel Voice or Usual Care (UC) arms. Block randomization (block size: 10) will be used for the purpose of balancing arms within each block. The randomization scheme will be prepared by the study statistician who will not be on study site.
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: The objective of this study is to conduct a randomized controlled trial (RCT) to compare the adapted and refined ASTHMAXcel Voice platform to usual care (UC). It is hypothesized by the investigator team that ASTHMAXcel Voice will be associated with improved clinical and process outcomes, asthma quality of life (QOL), medication adherence, and self-efficacy as compared to UC.

Detailed description: Poor outcomes for minority patients with asthma have been linked to poverty and other social determinants of health (SDOH), environmental exposures, and poor self-management. In a previous Agency for Healthcare Research and Quality (AHRQ)-funded study, the researchers developed and pilot tested ASTHMAXcel PRO, a mobile app that promotes self-management of asthma (NCT03847142). The app was optimized for outpatient settings and promoted asthma self-management through the collection of patient-reported outcomes (PROs), animated videos, goal setting, personalized algorithms, and push notifications. The use of the app led to significant decreases in the need for steroids, visits for asthma to the emergency department, and hospitalizations for asthma.

In this current research, ASTHMAXcel Voice, an app developed and refined during enhancement of ASTHMAXcel PRO, will make use of voice biomarkers to detect worsening symptoms. This technology uses machine learning to assess respiratory dysfunction, including asthma, based on a 6-second voice sample. From the sample, a Respiratory Symptoms Risk Score (RSRS) is calculated that correlates with the speaker's risk of respiratory impairment. The updated platform will calculate the patient's RSRS; facilitate shared decision making, screen for SDoH, and referrals; improve the ability of patients to self-manage; and allow for remote care coordination.

This program draws upon the Common Sense Model (CSM) of Self-Regulation which describes a cognitive processing system that includes situational stimuli (asthma symptoms), objective representation of the health threat (illness representations) with its treatment decision (controller medication use), and appraisal of outcomes (asthma control) for the success/failure of those treatment decisions. The model contains a feedback loop with illness representations changing over time as patients gain experience with asthma management. Social Determinants of Health (SDoH) may also affect the representation of the health threat, treatment decisions, and appraisal of outcomes. As an example, a patient with depression, a poor social support network, insecure housing, and financial stress may view asthma as an acute disease that is uncontrollable, which in turn leads to negative beliefs about medications and low self-efficacy towards asthma management. ASTHMAXcel Voice strives to shift illness representations away from the belief that asthma only exists when there are active symptoms and change behavior towards daily controller medication use over the long term to prevent asthma symptoms. Realtime feedback based on voice samples that yield a RSRS (voice biomarker) will help the patient to accurately detect perceived threats and manage asthma exacerbations during earlier stages. ASTHMAXcel Voice is also based on the SEM that addresses causes of poor asthma control across four interconnected domains: community, medical system, interpersonal, and individual level factors. ASTHMAXcel Voice is a multilevel approach to address these barriers with intervention components that are directly applied at each level.

There is growing recognition that mobile health interventions can be applied across all these levels to facilitate health behavior change through the use of push notifications and interactive educational content. ASTHMAXcel Voice works on the individual and interpersonal levels by providing targeted asthma education and push notifications to assist with medication adherence and asthma management. Worse outcomes assessed by PROs (asthma control) and voice biomarkers may heighten the perceived threat level of asthma and prompt Just-in-Time Adaptive Interventions (JITAIs) to seek out the educational content more frequently to improve asthma control. On the organizational (medical system) level, ASTHMAXcel Voice will facilitate shared decision-making and ongoing communication between the patient, Community Health Worker (CHW) or Social Worker (SW), and Health Care Provider (HCP). For example, a monthly visual dashboard display will increase HCP awareness of deteriorating trends assessed from PROs and voice biomarkers. On the community level, the CHW or SW will provide patients with SDoH relevant community resources (e.g., pest remediation services, smoking cessation programs, support groups, food pantries) to address SDoH concerns reported in the mobile platform. Finally, to inform more effective design and implementation of ASTHMAXcel Voice, the study team will use the Unified Theory of Acceptance and use of Technology (UTAUT) health IT framework in determining a user's technology acceptance and adoption behavior.

Eligibility criteria: Inclusion Criteria:

* English speaking
* Persistent asthma (diagnosed by a healthcare provider) on a daily controller medication
* Able to provide informed consent
* Smartphone access (iOS or Android) with data plan

Exclusion Criteria:

* Pregnancy
* Severe psychiatric or cognitive problems that would prohibit completion of protocol

Primary outcomes:
measure: Change in Asthma Control
timeFrame: Change from Baseline to 6 months after randomization
Secondary outcomes:
measure: Change in Asthma Control
timeFrame: Change from Baseline to 2 months after randomization
measure: Change in User Acceptance of ASTHMAXcel Voice Application
timeFrame: Baseline, 2 months, and 6 months after randomization
measure: Change in User Satisfaction of Interaction with the ASTHMAXcel Voice application
timeFrame: Baseline, 2 months, and 6 months after randomization
measure: ASTHMAXcel Voice application Usage
timeFrame: 2 months and 6 months after randomization
measure: Change in Overall User Satisfaction
timeFrame: Baseline, 2 months, and 6 months after randomization
measure: Change in Shared Decision Making
timeFrame: Baseline to 2 months and 6 months after randomization
measure: Change in Asthma-related Quality of Life
timeFrame: Baseline to 2 months and 6 months after randomization
measure: Asthma Healthcare Utilization - Emergency Department (ED) Visits
timeFrame: 2 months and 6 months after randomization
measure: Asthma Healthcare Utilization - Hospitalizations
timeFrame: 2 months and 6 months after randomization
measure: Change in Self-efficacy for managing chronic diseases
timeFrame: Baseline to 2 months and 6 months after randomization
measure: Change in Self-reported Medication Adherence
timeFrame: Baseline to 2 months and 6 months after randomization
measure: SDoH Screening
timeFrame: 2 months and 6 months after randomization
measure: SDoH Referral
timeFrame: 2 months and 6 months after randomization
measure: Referral Completion Rate
timeFrame: 2 months and 6 months after randomization
Other outcomes:
Locations:
city: Bronx
country: United States
facility: Montefiore Medical Center
state: New York
Study officials:
affiliation: Montefiore Medical Center
name: Sunit Jariwala, MD
role: PRINCIPAL_INVESTIGATOR
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