Study Results
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Basic Information
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RECRUITING
NA
200 participants
INTERVENTIONAL
2025-09-18
2028-09-30
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
HEALTH_SERVICES_RESEARCH
NONE
Study Groups
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ASTHMAXcel
Participants in this arm will be provided with the adapted and refined ASTHMAXcel Voice platform.
ASTHMAXcel Voice platform
ASTHMAXcel Voice is a mobile health application with a multi-level approach to address barriers with intervention components and facilitate health behavior change through the use of push notifications and interactive educational content.
Usual Care (UC)
Participants in this arm will receive standard care.
No interventions assigned to this group
Interventions
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ASTHMAXcel Voice platform
ASTHMAXcel Voice is a mobile health application with a multi-level approach to address barriers with intervention components and facilitate health behavior change through the use of push notifications and interactive educational content.
Eligibility Criteria
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Inclusion Criteria
* 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
* Severe psychiatric or cognitive problems that would prohibit completion of protocol
18 Years
ALL
No
Sponsors
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Yeshiva University
OTHER
Agency for Healthcare Research and Quality (AHRQ)
FED
Montefiore Medical Center
OTHER
Responsible Party
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Principal Investigators
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Sunit Jariwala, MD
Role: PRINCIPAL_INVESTIGATOR
Montefiore Medical Center
Locations
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Montefiore Medical Center
The Bronx, New York, United States
Countries
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Central Contacts
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Facility Contacts
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References
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Nathan RA, Sorkness CA, Kosinski M, Schatz M, Li JT, Marcus P, Murray JJ, Pendergraft TB. Development of the asthma control test: a survey for assessing asthma control. J Allergy Clin Immunol. 2004 Jan;113(1):59-65. doi: 10.1016/j.jaci.2003.09.008.
Larsen DL, Attkisson CC, Hargreaves WA, Nguyen TD. Assessment of client/patient satisfaction: development of a general scale. Eval Program Plann. 1979;2(3):197-207. doi: 10.1016/0149-7189(79)90094-6. No abstract available.
Kriston L, Scholl I, Holzel L, Simon D, Loh A, Harter M. The 9-item Shared Decision Making Questionnaire (SDM-Q-9). Development and psychometric properties in a primary care sample. Patient Educ Couns. 2010 Jul;80(1):94-9. doi: 10.1016/j.pec.2009.09.034. Epub 2009 Oct 30.
Juniper EF, Guyatt GH, Cox FM, Ferrie PJ, King DR. Development and validation of the Mini Asthma Quality of Life Questionnaire. Eur Respir J. 1999 Jul;14(1):32-8. doi: 10.1034/j.1399-3003.1999.14a08.x.
Chan AHY, Horne R, Hankins M, Chisari C. The Medication Adherence Report Scale: A measurement tool for eliciting patients' reports of nonadherence. Br J Clin Pharmacol. 2020 Jul;86(7):1281-1288. doi: 10.1111/bcp.14193. Epub 2020 May 18.
Ritter PL, Lorig K. The English and Spanish Self-Efficacy to Manage Chronic Disease Scale measures were validated using multiple studies. J Clin Epidemiol. 2014 Nov;67(11):1265-73. doi: 10.1016/j.jclinepi.2014.06.009. Epub 2014 Aug 3.
Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999 Sep;89(9):1322-7. doi: 10.2105/ajph.89.9.1322.
Leventhal H, Brissette, I., Leventhal, E. A. The common sense model of self-regulation of health and illness. In: Cameron LD, Leventhal, H., ed. The self-regulation of health and illness behavior. London, UK: Taylor and Francis Books; 2003:42-65.
Sofianou A, Martynenko M, Wolf MS, Wisnivesky JP, Krauskopf K, Wilson EA, Goel MS, Leventhal H, Halm EA, Federman AD. Asthma beliefs are associated with medication adherence in older asthmatics. J Gen Intern Med. 2013 Jan;28(1):67-73. doi: 10.1007/s11606-012-2160-z. Epub 2012 Aug 10.
Arcoleo KJ, McGovern C, Kaur K, Halterman JS, Mammen J, Crean H, Rastogi D, Feldman JM. Longitudinal Patterns of Mexican and Puerto Rican Children's Asthma Controller Medication Adherence and Acute Healthcare Use. Ann Am Thorac Soc. 2019 Jun;16(6):715-723. doi: 10.1513/AnnalsATS.201807-462OC.
U B. Toward an experimental ecology of human development. American Psychologist. 1977;32(7):513 531.
Kolff CA, Scott VP, Stockwell MS. The use of technology to promote vaccination: A social ecological model based framework. Hum Vaccin Immunother. 2018 Jul 3;14(7):1636-1646. doi: 10.1080/21645515.2018.1477458. Epub 2018 Jul 3.
Venkatesh V MM, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS quarterly. 2003;1:425-478.
U.S. Department of Health and Human Services NIoH. Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma (EPR-3). 2007 Jul.
Schatz M, Kosinski M, Yarlas AS, Hanlon J, Watson ME, Jhingran P. The minimally important difference of the Asthma Control Test. J Allergy Clin Immunol. 2009 Oct;124(4):719-23.e1. doi: 10.1016/j.jaci.2009.06.053. Epub 2009 Sep 19.
Hsia BC, Wu S, Mowrey WB, Jariwala SP. Evaluating the ASTHMAXcel Mobile Application Regarding Asthma Knowledge and Clinical Outcomes. Respir Care. 2020 Aug;65(8):1112-1119. doi: 10.4187/respcare.07550. Epub 2020 Jun 2.
Hanson JL, Balmer DF, Giardino AP. Qualitative research methods for medical educators. Acad Pediatr. 2011 Sep-Oct;11(5):375-86. doi: 10.1016/j.acap.2011.05.001. Epub 2011 Jul 23.
Hsia B, Mowrey W, Keskin T, Wu S, Aita R, Kwak L, Ferastraoarou D, Rosenstreich D, Jariwala SP. Developing and pilot testing ASTHMAXcel, a mobile app for adults with asthma. J Asthma. 2021 Jun;58(6):834-847. doi: 10.1080/02770903.2020.1728770. Epub 2020 Feb 19.
Figueroa JF, Frakt AB, Jha AK. Addressing Social Determinants of Health: Time for a Polysocial Risk Score. JAMA. 2020 Apr 28;323(16):1553-1554. doi: 10.1001/jama.2020.2436. No abstract available.
Other Identifiers
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2025-16587
Identifier Type: -
Identifier Source: org_study_id
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