Remote Sensing for ADRD-Specific Activities Identification in Older Adults

NCT07120347 | Recruiting

• 2 other identifiers: 210166624AARGD-NTF-1242722
• Study Type: interventional
• Target: 16
• Locations: 1 country
• Sites: 1

Brief Summary

The investigators aim to use smart-home sensors and artificial intelligence (AI) to monitor and detect Alzheimer's Disease and Related Dementias (ADRD)-specific daily activities among older adults, with the goal of early symptom detection and personalized support. Dementia, which impacts memory and cognition, remains a global concern. In the United States, more than 6.7 million individuals aged 65 and older are living with ADRD, and projected annual healthcare costs are expected to reach $1 trillion by 2050. This underscores the need for deeper understanding and innovative support. To address the unique challenges associated with ADRD, such as cognitive decline, personalized strategies that promote independent well-being are essential. Smart-home sensors can support older adults with ADRD as they continue to live in their homes. These sensors provide real-time data on health and daily activities, offering insights into their daily lives. However, adoption of these technologies is low, and the practical application of AI remains limited. This highlights the need for further research to make these devices more accessible to this population. The investigators' aims include: Conducting focus groups with individuals with and without ADRD and their caregivers to identify daily activities that can be measured using in-home sensors; Collecting in-home sensor data from older adults with and without ADRD; and Using AI to develop a tool for recognizing daily activities. The integration of smart-home sensors with advanced data-analysis techniques holds significant potential for transforming the support and care provided to individuals with ADRD. Ultimately, the investigators' findings will contribute to improving the quality of life for affected individuals and alleviating the burden on caregivers and healthcare systems.

Conditions

Alzheimer Disease and Related Dementias (ADRD); Mild Cognitive Impairment (MCI)

Outcome Measures

Primary Outcomes (3)

• Classification accuracy of ambient sensor-based daily activity models

  Percent of daily activity labels correctly predicted by SVM, XGBoost, LSTM and Transformer models, trained and tested on ambient motion and environmental sensor data collected during weeks 1-4 from participants with and without early-stage ADRD.

  Weeks 1-4 after enrollment

• F1 score of ambient sensor-based daily activity models

  Harmonic mean of precision and recall for SVM, XGBoost, LSTM and Transformer models, evaluated on held-out portions of the week 1-4 ambient sensor data.

  Weeks 1-4 after enrollment

• Area under the ROC curve of ambient sensor-based daily activity models

  AUC of ROC curves for SVM, XGBoost, LSTM and Transformer models distinguishing among daily activity classes, based on training and testing using weeks 1-4 ambient sensor readings.

  Weeks 1-4 after enrollment

Study Arms (2)

Pariticpants w/ ADRD

EXPERIMENTAL

Other: Remote Ambient Sensor System

Participants w/o ADRD

ACTIVE COMPARATOR

Other: Remote Ambient Sensor System

Interventions

Remote Ambient Sensor System OTHER

Remote sensors (motion, door contact) deployed in participants' home connected through raspberry pi and mobile hotspot

Pariticpants w/ ADRD; Participants w/o ADRD

Eligibility Criteria

• Age: 50 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Community-dwelling, English-speaking adults aged ≥ 50 years
• Clinical diagnosis of mild cognitive impairment or mild dementia (ADRD)
• Diagnosis established by a neuropsychologist, neurologist, or geriatrician within the University of Missouri Healthcare System
• Diagnosis confirmed using the latest consensus criteria and verified through record review
• No restriction on the etiology of the cognitive disorder (e.g., Alzheimer's disease, vascular dementia, mixed dementia)

You may not qualify if:

• Clinical Dementia Rating (CDR) global score \> 1 (moderate or severe dementia)
• Cognitive or functional impairments that would preclude meaningful participation in daily activities

Sponsors & Collaborators

• University of Missouri-Columbia: lead

Study Sites (1)

University of Missouri

Columbia, Missouri, 65211, United States

RECRUITING

MeSH Terms

Conditions

Alzheimer Disease; Cognitive Dysfunction

Condition Hierarchy (Ancestors)

Dementia; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Tauopathies; Neurodegenerative Diseases; Neurocognitive Disorders; Mental Disorders; Cognition Disorders

Central Study Contacts

Knoo Lee, PhD

CONTACT

5738840421; knoolee@missouri.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Ast Professor

Model Details: While all participants is applied with sensor system, they are divided into two groups by condition (participants w/ and w/o ADRD)

Study Record Dates

• First Submitted: May 22, 2025
• First Posted: August 13, 2025
• Study Start: August 1, 2024
• Primary Completion (Estimated): July 31, 2026
• Study Completion (Estimated): July 31, 2027
• Last Updated: August 13, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)