AI-Assisted Remote Asthma Management for Childhood Asthma

Overseen By Lynnea Myers, PhD, PhD, RN

Age: < 18

Sex: Any

Trial Phase: Academic

Sponsor: Mayo Clinic

Must be taking: Inhaled corticosteroids

Disqualifiers: Cystic fibrosis, Pregnancy, Others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

Duration: 6 months

150 Participants Needed

Trial Summary

What is the purpose of this trial?

The main purpose of this study is to look at whether clinicians and their patients with asthma can satisfactorily perform remote asthma management at home (not visiting clinic) by using an artificial intelligence tool called Asthma-Guidance and Prediction System combined with a home monitoring device called AsthmaTuner.

Will I have to stop taking my current medications?

The trial protocol does not specify whether you need to stop taking your current medications. However, it mentions that participants should have active asthma and may be on asthma control or rescue medication.

What data supports the effectiveness of the treatment AsthmaTuner for childhood asthma?

Research shows that children are willing to use digital twin systems (DTS) for asthma management, especially if they are more effective than current care. Additionally, machine learning can predict asthma control and treatment success, suggesting that AI-assisted systems like AsthmaTuner could enhance asthma management by providing real-time support and personalized feedback. ^{1 2 3 4 5}

Is AI-assisted remote asthma management safe for children?

Research on AI-assisted tools for asthma management, like mobile health apps and inhaler-based devices, suggests they are generally safe and can help improve asthma care. However, there are concerns about privacy and the need for these tools to be effective and accessible for everyone. ^{6 7 8 9 10}

How is the AsthmaTuner treatment different from other asthma treatments?

The AsthmaTuner treatment is unique because it uses AI (artificial intelligence) to provide real-time, personalized asthma management from home, which can adapt care based on continuous data collection, unlike traditional treatments that may not offer such immediate and tailored support. ^{1 2 3 11 12}

Research Team

Young Juhn, MD, MPH

Principal Investigator

Mayo Clinic

Eligibility Criteria

This trial is for kids aged 6-17 with asthma and their caregivers. Kids must have had an asthma check-up in the last 3 months, be able to follow up every 3-6 months, and not be on certain other treatments or studies. Caregivers need to help out and both must read/write English.

Inclusion Criteria

I am a child with asthma and have a caregiver willing to join me in my treatment and follow-ups.

My last asthma check-up was over 3 months ago.

I am between 7-17 years old and can give consent, or my caregiver can.

Exclusion Criteria

My child has symptoms that may not be just asthma, like trouble breathing during exercise or other specific lung issues.

I am a child and not pregnant.

I am a child who does not meet the specified eligibility criteria.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants use the Asthma-Guidance and Prediction System (A-GPS) with AsthmaTuner for remote asthma management

6 months

Remote management with potential on-site visits

Follow-up

Participants are monitored for safety and effectiveness after treatment

4 weeks

Treatment Details

Interventions

• Asthma-Guidance and Prediction System
• AsthmaTuner

Trial Overview The study tests if managing asthma at home using a mobile device (AsthmaTuner) and AI system (Asthma-Guidance) is as good as usual care that happens in clinics. It's about making treatment easier by staying at home.

Participant Groups

Group I: Pediatric Asthma Intervention Group Experimental Treatment 2 Interventions

Along with clinical standard of care for asthma, subjects will have Asthma-Guidance and Prediction System (A-GPS) with AsthmaTuner (AT) integrated into care.

Group II: Clinician Intervention Group Experimental Treatment 2 Interventions

Clinicians will integrate Asthma-Guidance and Prediction System (A-GPS) with AsthmaTuner (AT) into clinical standard of care for asthma.

Group III: Asthma Care Coordinator Intervention Group Experimental Treatment 2 Interventions

Asthma Care Coordinators will integrate Asthma-Guidance and Prediction System (A-GPS) with AsthmaTuner (AT) into clinical standard of care for asthma.

Group IV: Pediatric Asthma Control Group Active Control 1 Intervention

Subjects will receive clinical standard of care for asthma.

Group V: Clinician Control Group Active Control 1 Intervention

Clinicians will provide clinical standard of care for pediatric asthma patients.

Group VI: Asthma Care Coordinator Control Group Active Control 1 Intervention

Asthma Care Coordinators will provide clinical standard of care for pediatric asthma patients.

Who Is Running the Clinical Trial?

Mayo Clinic

Lead Sponsor

Trials

3,427

Recruited

3,221,000+

Findings from Research

The modified asthma control measure (ACM) effectively identifies uncontrolled asthma with a high sensitivity of 0.99 and a specificity of 0.65 in a study of 498 subjects.

This patient-reported outcome measure (PROM) can differentiate between well-controlled and uncontrolled asthma and is suitable for use in digital remote monitoring without requiring a license.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Toward an asthma patient-reported outcome measure for use in digital remote monitoring. Rudin, RS., Qureshi, N., Foer, D., et al.[2022]

The study introduces a cloud-based wearable IoT aldehyde sensor system designed to monitor environmental factors that can trigger asthma, potentially improving asthma management.

This innovative sensor system allows for real-time data collection and analysis, which can help researchers and patients better understand and manage asthma triggers in their daily environments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Wearable IoT Aldehyde Sensor for Pediatric Asthma Research and Management. Li, B., Dong, Q., Downen, RS., et al.[2020]

The study involved 1306 quality control tests of an electronic monitoring device for asthma inhalers, showing a high reliability rate of 84% for pre-issue tests and 87% for return tests, although some devices had issues with under-recording.

Children using the device with reminder features reported significantly higher acceptability scores compared to those without reminders, with over 90% finding the device easy to use, indicating strong potential for improving asthma management in this age group.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Electronic adherence monitoring device performance and patient acceptability: a randomized control trial. Chan, AHY., Stewart, AW., Harrison, J., et al.[2018]

References

1. England pubmed.ncbi.nlm.nih.gov

Toward an asthma patient-reported outcome measure for use in digital remote monitoring. [2022]

2. Switzerland pubmed.ncbi.nlm.nih.gov

A Wearable IoT Aldehyde Sensor for Pediatric Asthma Research and Management. [2020]

3. Germany pubmed.ncbi.nlm.nih.gov

Children's views on artificial intelligence and digital twins for the daily management of their asthma: a mixed-method study. [2023]

4. England pubmed.ncbi.nlm.nih.gov

Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol. [2022]

5. Switzerland pubmed.ncbi.nlm.nih.gov

Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma. [2021]

6. England pubmed.ncbi.nlm.nih.gov

Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations. [2023]

7. England pubmed.ncbi.nlm.nih.gov

Electronic adherence monitoring device performance and patient acceptability: a randomized control trial. [2018]

8. Ireland pubmed.ncbi.nlm.nih.gov

Computer-assisted instructions for patients with bronchial asthma. [2019]

9. New Zealand pubmed.ncbi.nlm.nih.gov

Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England. [2023]

10. United States pubmed.ncbi.nlm.nih.gov

Mobile Health and Inhaler-Based Monitoring Devices for Asthma Management. [2020]

11. England pubmed.ncbi.nlm.nih.gov

A feasibility study of remote monitoring of asthmatic patients. [2017]

12. Netherlands pubmed.ncbi.nlm.nih.gov

Telematic system for monitoring of asthma severity in patients' homes. [2008]