Assessing Cognitive Decline at Home

NCT06704971 | Active Not Recruiting

• 1 other identifier: 68794
• Study Type: observational
• Target: 25
• Locations: 1 country
• Sites: 1

Brief Summary

Neuropsychiatric symptoms (NPS) refer to a range of mental and emotional issues that can be observed through how patients move, perform daily tasks, and express feelings on their faces. In this study, the investigators want to find ways to accurately and unobtrusively track these symptoms in people's homes over time. Our goals are to note when these symptoms happen, predict potential problems, and gather clear data to help doctors make accurate diagnoses. To do this, the investigators will first collect information from participants who have in-home sensors. the investigators will then use special computer programs that can recognize everyday activities and identify features that connect to scores from the Mild Behavioral Impairment Checklist (MBI-C). These scores will be compared to a questionnaire (NPIQ) filled out by caregivers or family members, along with any relevant information from doctors about the patients' symptoms. The investigators aim to see how these features can help differentiate between types of NPS, such as mood changes and agitation. Finally, the investigators will create a dashboard for doctors that summarizes the patterns of these symptoms in patients, making it easier to monitor and manage their mental health.

Conditions

Mild Cognitive Impairment; Mild Dementia; Dementia; Memory Impairment; NPS; Dementia Alzheimers

Outcome Measures

Primary Outcomes (2)

• develop a personalized and clinically interpretable NPS assessment system

  Assessing NPS relies primarily on clinician observation \& interviews with caregivers, usually within brief \& infrequent clinical visits. Precise assessment and monitoring methods would incorporate objective, patient- \& caregiver-friendly, real-time tools and provide reliable \& frequently captured data. We aim to create a system that can monitoring individual and subsyndromes of NPS; and detect early changes in NPS by automatically quantifying \& detecting relevant behavioral abnormalities based on both intra- \& inter-individual comparisons. Our approach is a video sensor, information processing, \& alert system that utilizes in-home cameras \& ambient sensors with video features that can recognize \& track behaviors securely, pre-defines domains involved in a behavior; personalizes the results by assessing the individual's behavioral norms and the home environment; and analyzes, learns, and provides explainable items from different modalities of data: time, location, video, and audio.

  2 year

• NPS Clinical Dashboard

  Overall, the investigators envision the clinical dashboard as a transformative tool that delivers clear and comprehensive data on each individual. Therefore enhancing the quality of diagnosis of NPS for individuals, leading to better patient interventions and more effective long term management strategies.

  1 year

Secondary Outcomes (1)

• generate Data and support for a large-scale study or clinical trial within the context of an R01.

  2 years

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Older Adults - 65+

You may qualify if:

• Age 65+ years old
• Any age and ethnicity.
• With or without, or at risk of mild to moderate dementia and healthy controls.
• Availability of a study partner (informant) who has contact with the patient at least once a week (this can also be the surrogate/LAR but does not necessarily have to be)

You may not qualify if:

• Those with severe mobility impairments
• participants in non-home settings (skilled nursing facilities, etc.).

Sponsors & Collaborators

• Stanford University: lead
• Johns Hopkins University: collaborator
• University of Rochester: collaborator

Study Sites (1)

Clinical Excellence Research Center

Palo Alto, California, 94304, United States

Location

Related Publications (1)

• Gould CE, Davis CH, Schuz N, Lin FV, Samus QM, Terada T, Daniel M, Tee S, Adeli E. Designing AI-Enabled Video Monitoring Clinician Dashboard for Neuropsychiatric Symptoms: A Survey of User Needs. Am J Geriatr Psychiatry Open Sci Educ Pract. 2026 Mar;9:46-51. doi: 10.1016/j.osep.2026.01.001. Epub 2026 Feb 8.

  PMID: 41987876 RESULT

MeSH Terms

Conditions

Cognitive Dysfunction; Alzheimer Disease; Dementia; Memory Disorders

Condition Hierarchy (Ancestors)

Cognition Disorders; Neurocognitive Disorders; Mental Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Tauopathies; Neurodegenerative Diseases; Neurobehavioral Manifestations; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Ehsan Adeli, PhD

  Stanford University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Asst. Professor

Study Record Dates

• First Submitted: November 21, 2024
• First Posted: November 26, 2024
• Study Start: October 1, 2024
• Primary Completion: October 30, 2025
• Study Completion (Estimated): December 30, 2026
• Last Updated: May 18, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)