MHealth System for Chronic Health Conditions

Overseen by Andi Saptono, Ph.D.

Age: Any Age

Sex: Any

Travel: May Be Covered

Time Reimbursement: Varies

Trial Phase: Academic

Recruiting

Sponsor: University of Pittsburgh

Disqualifiers: Severe intellectual disability, psychiatric illness, others

No Placebo Group

Approved in 1 Jurisdiction

Trial Summary

What is the purpose of this trial?

This study will assess the benefits of using mobile health system designed for individuals with chronic and complex health conditions (such as those with Spinal Cord Injury,Cerebral Palsy, Spina Bifida, and Traumatic Brain Injury) to improve their wellness and self-management skills compared to those who receive standard of care only.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications, so it's best to ask the trial coordinators for more details.

What data supports the effectiveness of the treatment MHealth, Mobile Health, mHealth, Telehealth, Digital Health for chronic health conditions?

Research shows that mobile health (mHealth) technologies can improve self-management of chronic conditions by enhancing patient-provider communication and providing real-time interventions. Studies indicate that digital health tools can lead to better clinical outcomes, especially when they incorporate patient input and tailored messaging. ^{1 2 3 4 5}

Is mHealth generally safe for humans?

mHealth apps are generally considered safe, but privacy and data security are major concerns. Some apps have been shown to improve medication safety for older adults, but more high-quality studies are needed to confirm their safety and effectiveness. ^{6 7 8 9 10}

How is the MHealth treatment for chronic health conditions different from other treatments?

MHealth treatment is unique because it uses mobile devices to help patients manage their chronic conditions in real-time, allowing for immediate decision-making without waiting for a clinician. It integrates personal health data and preferences to provide personalized care, which is not typically possible with traditional treatments. ^{3 8 11 12 13}

Research Team

Bambang Parmanto, Ph.D.

Principal Investigator

University of Pittsburgh

Brad E. Dicianno, M.D.

Principal Investigator

University of Pittsburgh

Andi Saptono, Ph.D.

Principal Investigator

University of Pittsburgh

Eligibility Criteria

This trial is for individuals aged 12 or older with chronic conditions like Spinal Cord Injury, Cerebral Palsy, Spina Bifida, or Traumatic Brain Injury. They must be able to use a smartphone and live outside of care facilities. Those with severe intellectual disabilities or persistent psychiatric illnesses cannot participate.

Inclusion Criteria

I have a chronic condition like Spina Bifida or Cerebral Palsy.

Live in a community setting (not within a residential facility that provides care to them)

Pass all the functional screening tests which include basic usage of a smart phone, and impairment severity assessments

Exclusion Criteria

Diagnosis of severe intellectual disability or severe and persistent psychiatric illness

Failing all the screening tests due to severe impairments that cannot be accommodated by the mHealth system (for patient participants)

Trial Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline Assessment

Initial assessment of psychosocial and medical outcomes using various questionnaires

1 week

1 visit (in-person)

Treatment

Participants in the mHealth group use the mobile health system alongside standard care, while the control group continues with standard care only

12 months

Quarterly assessments (every 3 months)

Follow-up

Participants are monitored for changes in psychosocial outcomes and medical conditions

4 weeks

Treatment Details

Interventions

• MHealth (Behavioural Intervention)

Trial Overview The study tests a mobile health (mHealth) system designed to help people with complex health conditions manage their wellness better compared to standard care. It aims to improve self-management skills through the use of technology.

Participant Groups

Group I: MHealth Group Experimental Treatment 1 Intervention

Participants randomized into this group will use the mHealth system throughout the study in conjunction with their standard of care

Group II: Control Active Control 1 Intervention

Participants randomized into this group will not use the mHealth system throughout the study, but will continue with their standard of care.

Who Is Running the Clinical Trial?

University of Pittsburgh

Lead Sponsor

Trials

1,820

Recruited

16,360,000+

Department of Health and Human Services

Collaborator

Trials

240

Recruited

944,000+

Findings from Research

A systematic review of 32 studies on mobile health (mHealth) interventions for chronic illness self-care revealed that those with positive clinical outcomes often utilized more digital input, suggesting that technology features play a crucial role in effectiveness.

Most studies lacked detailed descriptions of the software used, and only two involved the target audience in the development process, highlighting the need for better system descriptions and user involvement in future mHealth research.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An integrative review of chronic illness mHealth self-care interventions: Mapping technology features to patient outcomes. Sockolow, PS., Buck, HG., Shadmi, E.[2021]

A new Chronic Disease mHealth App Intervention Design Framework was developed to guide the creation of mobile health applications, integrating clinical and behavioral evidence to improve chronic disease management.

This framework supports the systematic development and evaluation of mHealth solutions, making it easier to create effective apps tailored to different diseases and populations, ultimately enhancing patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evidence-Based mHealth Chronic Disease Mobile App Intervention Design: Development of a Framework. Wilhide Iii, CC., Peeples, MM., Anthony Kouyaté, RC.[2022]

A study involving 129 patients using a mobile health app for hypertension management developed a model that explains factors influencing continuous use of mobile health services, highlighting the importance of perceived usefulness, user satisfaction, and perceived health status.

The model showed that service quality, information quality, and system quality positively influenced perceived usefulness, which in turn significantly affected user satisfaction and the intention to continue using the app for self-management of chronic conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Measuring Success of Patients' Continuous Use of Mobile Health Services for Self-management of Chronic Conditions: Model Development and Validation. Song, T., Deng, N., Cui, T., et al.[2021]

Mobile health (mHealth) applications can enhance communication and collaboration between patients and providers, potentially improving the management of chronic diseases.

The review critically evaluates three theoretical frameworks that can guide the research and practice of mHealth in facilitating better patient-provider interactions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Utilization of Mobile Applications in Collaborative Patient-Provider Monitoring of Chronic Health Conditions: An Examination of Three Theoretical Frameworks to Guide Practice. Donevant, SB., Messias, DKH., Estrada, RD.[2020]

Improving self-management skills is crucial for individuals with chronic conditions like diabetes, and having high self-efficacy plays a significant role in this process.

The digital health intervention 'Better Choices, Better Health' effectively enhances self-efficacy and supports self-management in adults with chronic conditions, demonstrating a scalable and affordable approach to improving health outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Digital Therapeutics: Leading the Way to Improved Outcomes for People With Diabetes. Kaufman, N.[2020]

Mobile health (mHealth) apps are categorized by their functions, such as behavior change and self-management, but there is limited clinical guidance on their effective use in patient care, highlighting a gap in evidence-based recommendations.

Data security and patient privacy are major concerns when using mHealth apps, and there is a need for better involvement of patients in the development of guidelines to ensure that these apps meet their needs and improve clinical outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

What is the clinical value of mHealth for patients? Rowland, SP., Fitzgerald, JE., Holme, T., et al.[2023]

Out of 45 chronic disease management apps evaluated in China, 73% had a privacy policy, but the overall compliance with the Personal Information Security Specification was low, averaging only 40.4 out of 100.

The weakest area of compliance was in information storage and protection, with an average score of just 29.4%, highlighting significant privacy risks for users' health data.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Privacy Policy Compliance of Chronic Disease Management Apps in China: Scale Development and Content Evaluation. Ni, Z., Wang, Y., Qian, Y.[2021]

mHealth systems can automatically collect and analyze glucose data, providing real-time decision support for both patients and healthcare professionals, which can lead to timely and personalized treatment decisions.

While mHealth shows promise for diabetes management, rigorous testing and regulatory approval are necessary to ensure safety, effectiveness, and to address privacy, clinical, and economic concerns for widespread adoption.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The current status of mHealth for diabetes: will it be the next big thing? Klonoff, DC.[2022]

A survey of 480 patients and 31 general practitioners (GPs) revealed that while 56% of patients and 42% of GPs were willing to use mobile health (mHealth) applications for reporting adverse drug reactions (ADRs), willingness decreased with age for both groups.

Patients valued rapid feedback on consultation urgency, while GPs preferred information on medication administration difficulties; however, barriers included older patients' preference for personal contact with their GP and GPs' concerns about time and resource demands.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Developing a Mobile Health Application to Communicate Adverse Drug Reactions - Preconditions, Assessment of Possible Functionalities and Barriers for Patients and Their General Practitioners. Wakob, I., Schmid, GL., Nöhring, I., et al.[2022]

The Brain Buddy mobile health technology was found to have good usability, with a mean score of 78.8 on the System Usability Scale, indicating that all 23 older adult participants could complete tasks effectively.

After using Brain Buddy, 100% of participants felt more informed about their anticholinergic medications, and 82% reported discussing their medication risks with their physician, demonstrating the tool's potential to enhance medication safety and patient engagement.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Usability and feasibility of consumer-facing technology to reduce unsafe medication use by older adults. Holden, RJ., Campbell, NL., Abebe, E., et al.[2020]

Biomarkers play a crucial role in diagnosing and managing chronic diseases, which are significant health and economic challenges globally.

The integration of mobile health (mHealth) technologies with biomarkers is set to revolutionize chronic disease management, providing caregivers with more efficient and interactive tools for patient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Laboratory medicine and mobile health technologies at crossroads: Perspectives for the management of chronic diseases. Gruson, D., Ko, G.[2018]

The development of a remote monitoring system for individuals with chronic diseases involves multiple stages, focusing on assessing health status and predicting disease exacerbations.

Effective monitoring and control of chronic diseases can be achieved through systematic approaches that utilize technology to track ongoing health conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Remote System for Monitoring the State of Health of People with Chronic Diseases and Predicting Periods of Exacerbation. Yuldashev, ZM.[2023]

The development of a community-based self-management system for diabetes incorporates SMS, web-based, and mobile functionalities, aiming to improve diabetes management and reduce health complications.

This system addresses the significant public health burden caused by poor diabetes self-management, highlighting the growing importance of telehealth solutions in global health systems.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Design of a large scale community-based self-management system for diabetes mellitus. Chan, KC., Wong, L., Chan, DB.[2017]

References

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

An integrative review of chronic illness mHealth self-care interventions: Mapping technology features to patient outcomes. [2021]

2. Canada pubmed.ncbi.nlm.nih.gov

Evidence-Based mHealth Chronic Disease Mobile App Intervention Design: Development of a Framework. [2022]

3. Canada pubmed.ncbi.nlm.nih.gov

Measuring Success of Patients' Continuous Use of Mobile Health Services for Self-management of Chronic Conditions: Model Development and Validation. [2021]

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

Utilization of Mobile Applications in Collaborative Patient-Provider Monitoring of Chronic Health Conditions: An Examination of Three Theoretical Frameworks to Guide Practice. [2020]

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

Digital Therapeutics: Leading the Way to Improved Outcomes for People With Diabetes. [2020]

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

What is the clinical value of mHealth for patients? [2023]

7. Canada pubmed.ncbi.nlm.nih.gov

Privacy Policy Compliance of Chronic Disease Management Apps in China: Scale Development and Content Evaluation. [2021]

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

The current status of mHealth for diabetes: will it be the next big thing? [2022]

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

Developing a Mobile Health Application to Communicate Adverse Drug Reactions - Preconditions, Assessment of Possible Functionalities and Barriers for Patients and Their General Practitioners. [2022]

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

Usability and feasibility of consumer-facing technology to reduce unsafe medication use by older adults. [2020]

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

Laboratory medicine and mobile health technologies at crossroads: Perspectives for the management of chronic diseases. [2018]

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

A Remote System for Monitoring the State of Health of People with Chronic Diseases and Predicting Periods of Exacerbation. [2023]

13. Netherlands pubmed.ncbi.nlm.nih.gov

Design of a large scale community-based self-management system for diabetes mellitus. [2017]