eHealth Disclosure for Genetic Disorder Results

(ROR Trial)

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Pennsylvania

Disqualifiers: Deceased, Prior genetic result, others

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

Compensation: Varies

Number of Visits: 3

2500 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the eHealth Education Intervention for genetic disorder results?

Research shows that eHealth tools, like web portals and chatbots, can help patients become more engaged and informed about their health, which can lead to better health outcomes. These tools allow patients to access information anytime and support self-management, making them feel more empowered in their healthcare decisions. ^{1 2 3 4 5}

Is the eHealth Disclosure for Genetic Disorder Results safe for humans?

The use of digital tools like chatbots and web portals for sharing genetic test results is generally considered safe for humans. These tools are designed to help people understand and share genetic information securely, although there may be concerns about privacy and data protection. ^{6 7 8 9 10}

How does the eHealth Disclosure treatment for genetic disorder results differ from other treatments?

The eHealth Disclosure treatment is unique because it uses interactive online tools and chatbots to provide genetic counseling and information, which can be more accessible and scalable compared to traditional in-person counseling. This approach helps address the shortage of genetic counselors and offers patients the flexibility to access information at their convenience. ^{6 8 11 12 13}

What is the purpose of this trial?

The goal of this hybrid type 1 effectiveness-implementation study is to evaluate and compare different ways of delivering genetic research results to participants. The main questions the study aims to answer are: * Is sharing actionable genetic research results with participants through a multimedia patient-informed eHealth intervention (e.g. patient portal) no worse than sharing results by telephone or videoconference with a genetic counselor? * Will research participants access an eHealth educational intervention or chatbot education to learn about research results being offered and the option to decline learning their individual research results and how frequently participants choose to decline actionable research results? * Who benefits less and more from digital intervention with return of actionable research results and what barriers exist to using these tools for return of research results outside this study? Participants in the biobank will be offered digital tools to learn about research results being offered and the option to decline receiving these results. Those who don't decline and have an actionable result will be randomly assigned to receive their results with a genetic counselor or through an eHealth portal. Participants will complete surveys before and after receipt of results to understand patient experiences with these methods of education and return of results to determine if digital tools can be used to help ensure more patients get access to research results which could impact their health.

Research Team

Angela Bradbury, MD

Principal Investigator

University of Pennsylvania

Eligibility Criteria

This trial is for English-speaking adults over 18 who are part of the Penn Medicine Biobank and have an actionable genetic mutation or are control participants. They must be willing to be re-contacted. Those already aware of their genetic results through clinical testing cannot join.

Inclusion Criteria

English speaking

I am open to future contact or wasn't asked about my preference.

Potential subjects will include select individuals who participated in Penn Medicine Biobank, Institutional Review Board (IRB) protocol number 813913

Exclusion Criteria

This criterion is not applicable to a living patient's perspective.

Evidence in the clinical record that the subject has already received the same actionable result through clinical genetic testing

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants receive genetic research results through either a genetic counselor or an eHealth portal

2-7 days

1 visit (virtual or in-person)

Follow-up

Participants are monitored for behavioral and psychological responses to the genetic information

6 months

Multiple assessments at baseline, 2-7 days, and 6 months

Treatment Details

Interventions

• eHealth Education Intervention (Web or Chatbot)
• Private Web-Portal eHealth Disclosure of Genetic Results

Trial Overview The study compares two methods of delivering genetic research results: via a multimedia eHealth intervention (like a patient portal) or directly from a genetic counselor by phone/video. It will assess participant engagement with digital tools and willingness to learn about their genetics.

Participant Groups

Group I: ROR Arm 2 Experimental Treatment 2 Interventions

eHealth disclosure of results by private web-portal (with option to speak with a GC).

Group II: ROR Arm 1 Experimental Treatment 1 Intervention

Disclosure of results with a Genetic Counselor.

Who Is Running the Clinical Trial?

University of Pennsylvania

Lead Sponsor

Trials

2,118

Recruited

45,270,000+

Abramson Cancer Center at Penn Medicine

Lead Sponsor

Trials

425

Recruited

464,000+

Thomas Jefferson University

Collaborator

Trials

475

Recruited

189,000+

Fox Chase Cancer Center

Collaborator

Trials

236

Recruited

39,300+

Columbia University

Collaborator

Trials

1,529

Recruited

2,832,000+

Findings from Research

The eHealth intervention was delivered with high fidelity, with 93% of participants receiving initial training and 98.45% of expected follow-ups conducted, indicating strong program adherence.

Despite high engagement from 40.4% of users over 12 months, increased usage did not lead to significant improvements in clinical outcomes or lifestyle changes, suggesting that simply using the intervention more frequently does not guarantee better health results.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An Internet-Based Intervention for Cardiovascular Disease Management Integrated With Primary Care Electronic Health Records: Mixed Methods Evaluation of Implementation Fidelity and User Engagement. Coorey, G., Peiris, D., Scaria, A., et al.[2021]

Computer-based patient education can provide personalized instruction that adapts to individual learning paces, which is crucial as healthcare becomes more patient-centered and hospital stays shorten.

This method not only helps in teaching patients about their health and treatment options but also tracks their understanding, which is important for informed consent and potential reimbursement, highlighting the need for more research in this area.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-based patient education revisited. Gillispie, MA., Ellis, LB.[2019]

Participants in focus groups found chatbots to be an engaging and acceptable method for consenting to genomic research and managing follow-up actions after receiving genetic results, indicating potential for broader use in healthcare.

Despite limited prior experience with chatbots, most participants expressed willingness to use them for sharing genetic information with relatives, suggesting that these technology-based tools could effectively facilitate communication in genetic counseling.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient assessment of chatbots for the scalable delivery of genetic counseling. Schmidlen, T., Schwartz, M., DiLoreto, K., et al.[2020]

In-person genetic counseling (GC) may not always be the most effective or accessible option for patients, especially given the increasing demand for genetic testing and limited availability of genetic counselors.

A review of 21 studies on interactive e-learning and decision support tools found that these e-tools can enhance knowledge and comfort in decision-making for patients, suggesting they could effectively supplement traditional GC methods.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Interactive e-counselling for genetics pre-test decisions: where are we now? Birch, PH.[2022]

References

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

A national action plan to support consumer engagement via e-health. [2022]

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

Exploring eHealth Ethics and Multi-Morbidity: Protocol for an Interview and Focus Group Study of Patient and Health Care Provider Views and Experiences of Using Digital Media for Health Purposes. [2022]

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

An Internet-Based Intervention for Cardiovascular Disease Management Integrated With Primary Care Electronic Health Records: Mixed Methods Evaluation of Implementation Fidelity and User Engagement. [2021]

4. France pubmed.ncbi.nlm.nih.gov

[Integrating patient education in your oncology practice]. [2017]

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

Computer-based patient education revisited. [2019]

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

Who should provide genetic education prior to gene testing? Computers and other methods for improving patient understanding. [2009]

7. Germany pubmed.ncbi.nlm.nih.gov

Digital interventions for genomics and genetics education, empowerment, and service engagement: A systematic review. [2023]

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

Patient assessment of chatbots for the scalable delivery of genetic counseling. [2020]

9. England pubmed.ncbi.nlm.nih.gov

Open AI meets open notes: surveillance capitalism, patient privacy and online record access. [2023]

10. England pubmed.ncbi.nlm.nih.gov

ShareDNA: a smartphone app to facilitate family communication of genetic results. [2023]

11. Denmark pubmed.ncbi.nlm.nih.gov

Interactive e-counselling for genetics pre-test decisions: where are we now? [2022]

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

Perceived barriers to Internet-based health communication on human genetics. [2022]

13. Switzerland pubmed.ncbi.nlm.nih.gov

Impact of delivery models on understanding genomic risk for type 2 diabetes. [2021]