Care.Coach Avatars for Improvement of Outcomes in Hospitalized Elders, Including Mitigation of Falls and Delirium: a Multi-Site Clinical Study

(AvatarHELP Trial)

Recruiting at 1 trial location

Overseen By Victor Wang, MS

Age: 18+

Sex: Any

Trial Phase: Phase 2

Sponsor: Victor Wang

Disqualifiers: Aggression, Severe hearing and vision impairment, No comprehension of Spanish or English

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

Prior Safety Data This treatment has passed at least one previous human trial

Compensation: Varies

Number of Visits: 3

Duration: Average of 4 days

301 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 safety data exists for the care.coach Avatar (HELP-Protocolized) treatment?

The research articles discuss systems for tracking and reporting adverse events in clinical trials, which help ensure participant safety by identifying and addressing potential issues. However, they do not provide specific safety data for the care.coach Avatar (HELP-Protocolized) treatment. ^{1 2 3 4 5}

How is the care.coach Avatar treatment different from other treatments for this condition?

The care.coach Avatar treatment is unique because it uses an interactive, computer-generated character to provide health counseling and behavior change interventions. This approach simulates human interaction through verbal and nonverbal communication, making it different from traditional face-to-face or medication-based treatments. ^{6 7 8 9 10}

What is the purpose of this trial?

This trial tests a digital tool called the care.coach avatar, which uses animated characters to help elderly hospital patients stay safe and healthy. The avatars remind patients to take their medicine and call for help if needed, while also helping doctors and nurses monitor their health. The study aims to see if this tool can reduce falls and delirium in elderly patients.

Eligibility Criteria

Inclusion Criteria

fall/delirium risk (may be based purely on age at the election of each site, e.g. 65+)

Timeline

Screening

Participants are screened for eligibility to participate in the trial

1 week

1 visit (in-person)

Treatment

Participants receive care.coach avatar intervention or usual care during their inpatient stay

Average of 4 days

Daily monitoring (in-person)

Follow-up

Participants are monitored for changes in delirium severity and cognitive function

4 weeks

2 visits (in-person)

Treatment Details

Interventions

• care.coach Avatar (HELP-Protocolized)

Participant Groups

Group I: care.coach Avatar Experimental Treatment 1 Intervention

Group II: Control Active Control 1 Intervention

Who Is Running the Clinical Trial?

Victor Wang

Lead Sponsor

Trials

1

Recruited

300+

National Institute of Nursing Research (NINR)

Collaborator

Trials

623

Recruited

10,400,000+

Pace University

Collaborator

Trials

8

Recruited

2,700+

Findings from Research

The web-based adverse event tracking system (eAETS) has effectively supported 175 clinical protocols over four years, capturing 2,440 adverse event reports, including minor symptoms that could indicate serious issues.

Out of the reported AEs, 1,053 did not align with the initial risk profiles, leading to corrective recommendations in 13% of the protocols, highlighting the system's role in enhancing subject safety by identifying unanticipated patterns.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The impact of minor adverse event tracking on subject safety: a web-based system. Shenvi, NV., Gebhart, SS.[2009]

The study highlights the inefficiency of manual management of serious adverse events (AEs) and reactions (ARs) in clinical trials, emphasizing the need for improved reporting systems.

A new data model has been developed to facilitate automatic code generation for a software tool designed to assist in the management of AE/AR data, potentially enhancing the efficiency and effectiveness of regulatory compliance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Modeling an IT Support for Handling Serious Adverse Events in Clinical Trials. Fonck, S., Deserno, T.[2018]

The University of Connecticut developed a customized online system for tracking and reviewing adverse events in clinical research, which allows for efficient classification and timely reporting of serious incidents.

In the previous academic year, the system reviewed 371 serious adverse events from over 900 studies, demonstrating its effectiveness in ensuring participant safety and providing recommendations for study modifications or suspensions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Systematic identification and classification of adverse events in human research. Silverman, DI., Cirullo, L., DeMartinis, NA., et al.[2006]

An automated vaccine adverse event surveillance system was implemented, which significantly improved the reporting rate of potential vaccine-related adverse events, with 686 alerts generated from 33,420 vaccinations over five months.

Clinicians found the system easy to use, leading to a reporting rate of 0.69 reports per 1,000 vaccine doses, nearly six times higher than the typical rate, suggesting that real-time prompts can enhance the detection and reporting of vaccine adverse events.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using electronic medical records to enhance detection and reporting of vaccine adverse events. Hinrichsen, VL., Kruskal, B., O'Brien, MA., et al.[2018]

A study involving 134 patients found that 90% rated the animated video on patient safety as good or very good, while 100% of those receiving oral information felt the same, indicating high satisfaction with both methods.

Despite the positive feedback for the video, 74% of patients who received both formats preferred the oral presentation, suggesting that while videos can enhance understanding, they should not replace traditional verbal communication for optimal patient engagement.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Communicating Patient Safety Information Through Video and Oral Formats-A Comparison. Bånnsgård, M., Nouri, A., Finizia, C., et al.[2023]

References

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

The impact of minor adverse event tracking on subject safety: a web-based system. [2009]

2. Netherlands pubmed.ncbi.nlm.nih.gov

Modeling an IT Support for Handling Serious Adverse Events in Clinical Trials. [2018]

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

Systematic identification and classification of adverse events in human research. [2006]

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

Using electronic medical records to enhance detection and reporting of vaccine adverse events. [2018]

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

Communicating Patient Safety Information Through Video and Oral Formats-A Comparison. [2023]

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

AmeriHealth brings anti-drug message to New Jersey schools. [2023]

7. Ireland pubmed.ncbi.nlm.nih.gov

Automated interventions for multiple health behaviors using conversational agents. [2022]

8. Netherlands pubmed.ncbi.nlm.nih.gov

Features for culturally appropriate avatars for behavior-change promotion in at-risk populations. [2018]

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

Movie magic in the clinic: computer-generated characters for automated health counseling. [2017]

10. Ireland pubmed.ncbi.nlm.nih.gov

Engaging women with an embodied conversational agent to deliver mindfulness and lifestyle recommendations: A feasibility randomized control trial. [2019]