Cognitive Screening Made Easy for PCPs - Administrative Supplement

NCT06928298 | Recruiting DMC

• 1 other identifier: AG069780-04S
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 3

Brief Summary

This project will facilitate early detection of cognitive decline in older adults through development and implementation of an automated risk assessment and cognitive screening tool for use in primary care. By providing an automated tool developed specifically to address the needs of PCPs, it will be easier to screen for cognitive impairment, increasing the number of older adults who are screened and thus identified and treated.

Conditions

Cognitive Impairment; Dementia, Mild; Cognitive Decline

Keywords

older adults; mild cognitive impairment; mild dementia; cognitive screening; primary care; dementia screening

Outcome Measures

Primary Outcomes (1)

• Cognitive Performance Score

  Combination of symbol matching and voice tasks, which generates a cognitive performance score ranging from 0 to 1 (high scores mean higher likelihood of cognitive impairment)

  baseline, 6 month, and 12 month visits

Secondary Outcomes (1)

• Usability

  baseline, 6 month, and 12 month visits

Study Arms (1)

Intervention

OTHER

Risk assessment and cognitive screening app that includes questions answered by the participant and completion of a working memory task and two speech tasks

Behavioral: RACS app

Interventions

RACS app BEHAVIORAL

risk assessment questions, symbol matching task, voice tasks

Intervention

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Aged 60 years and older;
• Ethnic/racial background consistent with NIH policy
• Male or female
• Fluent in Spanish.

You may not qualify if:

• Confounding conditions that could impact ability to participate in the study (e.g., cognitive impairment sufficient to impact ability to follow instructions on the iPad, motor impairment that would prohibit independent use of RACS, poor visual acuity)
• Prior diagnosis of dementia
• Non-Spanish speaking.

Sponsors & Collaborators

• University of Texas at Austin: lead

Study Sites (3)

Family Wellness Clinic

Austin, Texas, 78712, United States

RECRUITING

University of Texas Health Austin Primary Care Clinic

Austin, Texas, 78712, United States

RECRUITING

UT Health San Antonio's Geriatrics and Supportive Care Clinic

San Antonio, Texas, 78229, United States

RECRUITING

MeSH Terms

Conditions

Cognitive Dysfunction; Dementia; Lymphoma, Follicular

Condition Hierarchy (Ancestors)

Cognition Disorders; Neurocognitive Disorders; Mental Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Lymphoma, Non-Hodgkin; Lymphoma; Neoplasms by Histologic Type; Neoplasms; Lymphoproliferative Disorders; Lymphatic Diseases; Hemic and Lymphatic Diseases; Immunoproliferative Disorders; Immune System Diseases

Study Officials

• Robin C Hilsabeck, PhD

  University of Texas Health Sciences Center at San Antonio

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Robin C Hilsabeck, PhD

CONTACT

210-835-8748; hilsabeck@uthscsa.edu

Joshua Chang, Md, PhD

CONTACT

joshua.chang@austin.utexas.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: April 7, 2025
• First Posted: April 15, 2025
• Study Start: June 10, 2025
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): April 30, 2027
• Last Updated: February 5, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR
• Time Frame: Release of the final data and RACS app will be made available by the online publication date
• Access Criteria: The investigators will publish all of the code and scripts for statistical analysis and machine learning development on a publically available GitHub repository.

Locations

US (3)