ASTHMAXcel Voice Study

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.

• Change in Asthma Control [Time frame: Change from Baseline to 2 months after randomization]
• Change in User Acceptance of ASTHMAXcel Voice Application [Time frame: Baseline, 2 months, and 6 months after randomization]
• Change in User Satisfaction of Interaction with the ASTHMAXcel Voice application [Time frame: Baseline, 2 months, and 6 months after randomization]
• ASTHMAXcel Voice application Usage [Time frame: 2 months and 6 months after randomization]
• Change in Overall User Satisfaction [Time frame: Baseline, 2 months, and 6 months after randomization]
• Change in Shared Decision Making [Time frame: Baseline to 2 months and 6 months after randomization]
• Change in Asthma-related Quality of Life [Time frame: Baseline to 2 months and 6 months after randomization]
• Asthma Healthcare Utilization - Emergency Department (ED) Visits [Time frame: 2 months and 6 months after randomization]
• Asthma Healthcare Utilization - Hospitalizations [Time frame: 2 months and 6 months after randomization]
• Change in Self-efficacy for managing chronic diseases [Time frame: Baseline to 2 months and 6 months after randomization]
• Change in Self-reported Medication Adherence [Time frame: Baseline to 2 months and 6 months after randomization]
• SDoH Screening [Time frame: 2 months and 6 months after randomization]