Alzheimer Disease and Related Dementias (ADRD)NCT07120347
Remote Sensing for ADRD-Specific Activities Identification in Older Adults
A study testing Remote Ambient Sensor System in people with Alzheimer Disease and Related Dementias (ADRD) and Mild Cognitive Impairment (MCI), run by University of Missouri-Columbia at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 years and olderHealthy volunteers can take part
- Conditions the study is for
- Alzheimer Disease and Related Dementias (ADRD), Mild Cognitive Impairment (MCI)
- What is being tested?
- Remote Ambient Sensor System
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 16 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Aug 2024to 31 Jul 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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)
Who cannot join
- Clinical Dementia Rating (CDR) global score > 1 (moderate or severe dementia)
- Cognitive or functional impairments that would preclude meaningful participation in daily activities
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
University of Missouri
Columbia, Missouri, 65211
Knoo Lee, PhD, 5738840421, knoolee@missouri.edu
About this study
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.
Read the detailed description
Dementia, which impacts memory and cognitive abilities, constitutes a global concern that intensifies with the aging population. In the United States, 6.7 million individuals aged 65 and older live with Alzheimer's Disease and Related Dementias (ADRD), and projected annual healthcare costs are expected to reach $1 trillion by 2050. This underscores the urgent need for enhanced understanding and innovative support. Individuals with ADRD face unique challenges, including behavioral changes and cognitive decline, necessitating tailored strategies for their well-being. Aligning with the National Institute on Aging's research goals, the investigators' study explores a promising avenue: the use of smart-home sensors to monitor and assist ADRD patients while they reside in their homes. These sensors provide real-time insights into health, activity, and environmental factors. However, adoption of these technologies among people with ADRD is low, and the practical application of artificial intelligence (AI) in this context remains limited. This underscores the need for further research to make these devices more accessible to this population.
The investigators aim to utilize a fully modular smart-home sensor system, combined with AI-based data-analysis methods, to monitor and analyze activities specific to individuals with ADRD. Remote sensor installations have been deployed across Missouri to facilitate the seamless delivery of sensor data to the investigators' interdisciplinary team, known as the Age-friendly Smart, Sustainable, and Equitable Technologies for Access intervention research team. The investigators' approach involves applying AI with causal inference to gain a nuanced understanding of the daily activities and behavioral patterns of those with ADRD. The investigators hypothesize that incorporating modeled causal features into the AI process will 1) enable identification of ADRD-specific daily activities, and 2) enhance the AI's ability to recognize these activities.
The investigators' aims include:
Conducting focus groups with individuals with and without ADRD and their caregivers to identify daily activities measurable with in-home sensors; Collecting in-home sensor data from older adults with and without ADRD; and Developing an AI system using machine-learning (ML) models for ADRD-specific daily activity recognition. Aim 3 will encompass three key elements: identification of causal features associated with ADRD-specific daily activities, development and refinement of ML models for recognizing these activities informed by the causal features, and creation of personalized ML models for individuals diagnosed with ADRD.
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.
- Being tested
- Remote Ambient Sensor System
- Medical subject headings
- Alzheimer Disease, Cognitive Dysfunction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Remote Ambient Sensor SystemOther
Remote sensors (motion, door contact) deployed in participants' home connected through raspberry pi and mobile hotspot
| Study group | Role | What they receive |
|---|---|---|
| Pariticpants w/ ADRD | Experimental | Not described Receives: Other: Remote Ambient Sensor System |
| Participants w/o ADRD | Active comparator | Not described Receives: Other: Remote Ambient Sensor System |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: Weeks 1-4 after enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Aug 2025
- Study start1 Aug 2024
- Primary completion31 Jul 2026 (estimated)
- Study completion31 Jul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Alzheimer Disease and Related Dementias (ADRD)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Model description
- While all participants is applied with sensor system, they are divided into two groups by condition (participants w/ and w/o ADRD)
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 16 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Missouri-Columbia
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Knoo Lee, University of Missouri-Columbia
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07120347
- Sponsor's protocol ID
- 2101666
- Other IDs
- 24AARGD-NTF-1242722
- Status last verified
- May 2025
- First submitted
- 22 May 2025
- Last updated
- 13 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Remote Sensing for ADRD-Specific Activities Identification in Older Adults
- ClinicalTrials.gov ID
- NCT07120347
- Acronym / sponsor ID
- 2101666
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 16 participants (estimated)
- Lead sponsor
- University of Missouri-ColumbiaUniversity, hospital or other
- Study start
- 1 Aug 2024 (actual)
- Primary completion
- 31 Jul 2026 (estimated)
- Study completion
- 31 Jul 2027 (estimated)
- First posted
- 13 Aug 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Dementia, which impacts memory and cognitive abilities, constitutes a global concern that intensifies with the aging population. In the United States, 6.7 million individuals aged 65 and older live with Alzheimer's Disease and Related Dementias (ADRD), and projected annual healthcare costs are expected to reach $1 trillion by 2050. This underscores the urgent need for enhanced understanding and innovative support. Individuals with ADRD face unique challenges, including behavioral changes and cognitive decline, necessitating tailored strategies for their well-being. Aligning with the National Institute on Aging's research goals, the investigators' study explores a promising avenue: the use of smart-home sensors to monitor and assist ADRD patients while they reside in their homes. These sensors provide real-time insights into health, activity, and environmental factors. However, adoption of these technologies among people with ADRD is low, and the practical application of artificial intelligence (AI) in this context remains limited. This underscores the need for further research to make these devices more accessible to this population.
The investigators aim to utilize a fully modular smart-home sensor system, combined with AI-based data-analysis methods, to monitor and analyze activities specific to individuals with ADRD. Remote sensor installations have been deployed across Missouri to facilitate the seamless delivery of sensor data to the investigators' interdisciplinary team, known as the Age-friendly Smart, Sustainable, and Equitable Technologies for Access intervention research team. The investigators' approach involves applying AI with causal inference to gain a nuanced understanding of the daily activities and behavioral patterns of those with ADRD. The investigators hypothesize that incorporating modeled causal features into the AI process will 1) enable identification of ADRD-specific daily activities, and 2) enhance the AI's ability to recognize these activities.
The investigators' aims include:
Conducting focus groups with individuals with and without ADRD and their caregivers to identify daily activities measurable with in-home sensors; Collecting in-home sensor data from older adults with and without ADRD; and Developing an AI system using machine-learning (ML) models for ADRD-specific daily activity recognition. Aim 3 will encompass three key elements: identification of causal features associated with ADRD-specific daily activities, development and refinement of ML models for recognizing these activities informed by the causal features, and creation of personalized ML models for individuals diagnosed with ADRD.
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.
Study design
- Primary purpose
- Supportive care
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- While all participants is applied with sensor system, they are divided into two groups by condition (participants w/ and w/o ADRD)
| Arm | Type | Receives |
|---|---|---|
| Pariticpants w/ ADRD | Experimental | Other: Remote Ambient Sensor System |
| Participants w/o ADRD | Active comparator | Other: Remote Ambient Sensor System |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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)
Key exclusion criteria (2 listed)
- Clinical Dementia Rating (CDR) global score > 1 (moderate or severe dementia)
- Cognitive or functional impairments that would preclude meaningful participation in daily activities
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Remote Ambient Sensor SystemRemote sensors (motion, door contact) deployed in participants' home connected through raspberry pi and mobile hotspot | Other | Pariticpants w/ ADRD, Participants w/o ADRD |
Primary outcome
Classification accuracy of ambient sensor-based daily activity models
Time frame: Weeks 1-4 after enrollment
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.
F1 score of ambient sensor-based daily activity models
Time frame: Weeks 1-4 after enrollment
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.
Area under the ROC curve of ambient sensor-based daily activity models
Time frame: Weeks 1-4 after enrollment
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Aug 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Remote Ambient Sensor System in people with Alzheimer Disease and Related Dementias (ADRD) and Mild Cognitive Impairment (MCI), run by University of Missouri-Columbia at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 13 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Knoo Lee, PhD (5738840421, knoolee@missouri.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Classification accuracy of ambient sensor-based daily activity models (time frame: Weeks 1-4 after enrollment). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Aug 2025.