Preventing Medication Mismanagement in People Living with Dementia Through Automated Medication Dispensing with Facial Recognition and Video Observation

NCT ID: NCT06691256

Last Updated: 2025-01-30

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Clinical Phase: PHASE2
• Total Enrollment: 70 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-09-24
• Study Completion Date: 2026-05-31

Brief Summary

Globally, >47M individuals live with dementia, with new incidence of 7.7M annually. Medication mismanagement is one of the most common and concerning risk factors in people with dementia (PwD), as it leads to undertreatment, emergency room visits, hospital admissions/readmissions, and serious adverse events. 3M older U.S. adults are admitted to nursing homes due to drug-related adherence problems with costs >$14B/year. Furthermore, 30% of hospital admissions of older adults are drug related with 11% attributed to medication non-adherence and 17% to adverse drug reactions. While Alzheimer's disease (AD) & type 2 diabetes mellitus (DM) individually have considerable morbidity & mortality, they often occur together, worsening adverse outcomes, quality of life, & care costs. This is especially true as the AD/DM combination creates a complex balancing act of med management & symptom monitoring in older populations. While the goal is to keep older adults with dementia at home as long as possible, these challenges lead to untold personal & family suffering, as well as billions in potentially avoidable healthcare costs annually. The HiDO-ALZ platform will solve these challenges by automating medication administration for PwD to eliminate mismanagement, decrease caregiver burden, reduce healthcare utilization, and facilitate PwD to age in place. HiDO is being developed as an automated, AI driven medication dispensing and direct observation platform to optimize adherence. The device integrates medication dispensing, dose administration time, medication synchronization, & pair of front-facing cameras to validate the right meds, right route, right time, right dosage to the right patient (5R's). Cameras record every dose using facial recognition & provide real -time medication consumption recordings. Through cloud connectivity, providers & caregivers have access to video observation logs, dose administration time, adherence trends, & longitudinal adherence via web dashboard. Patients & caregivers can easily setup complex medication protocols in minutes using a smartphone app. The device then alerts patients and dispenses up to 7 different types of meds simultaneously, with up to 90 days of medication. Connected data sources including remote blood pressure and weight measurements, as well as electronic health record lab results and videoconferencing integrate in a single dashboard. The project will build on successful Phase I, in which the medication dispensing unit was updated with modifications for dementia, passed all bench testing, and was successfully validated in pilot usability with dementia subjects. Phase II will expand the foundation with four Aims: 1) Enhance device with remote sensors for diabetes management, expanded data integration, and video conferencing, 2) Test enhanced platform for usability in dementia subjects, 3) Transition the design to formal manufacturing process to ensure system meets performance standards and regulatory requirements & produce pre-production devices for testing, & 4) Conduct in-home clinical trial to demonstrate adherence and efficacy.

Conditions

Alzheimer Disease; Dementia; Medication Adherence; Type 2 Diabetes Mellitus (T2DM)

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SUPPORTIVE_CARE
• Blinding Strategy: NONE

Study Groups

Field Efficacy Study - Experimental Group

Participants in the experimental group will receive the HiDO-ALZ platform for 12 months, which provides automated medication dispensing, real-time facial recognition to ensure correct medication delivery, and biometric monitoring for blood pressure and weight. This arms plan to recruit up to 25 participants aged 65 or older, along with their caregivers, to assess the usability and human factors of the HiDO-ALZ platform.

Group Type: EXPERIMENTAL

HiDO-ALZ

Intervention Type: DEVICE

HiDO-ALZ platform, an AI-driven medication dispensing and observation system designed to improve adherence in individuals with dementia and diabetes.

Field Efficacy Study - Control Group

Participants in the control group will receive standard care without the HiDO-ALZ device. They will continue with their usual medication routines and any standard biometric monitoring methods as directed by their healthcare providers. This arms plan to recruit up to 25 participants aged 65 or older, along with their caregivers, to assess the standard of care and not using the HiDO-ALZ device.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

HiDO-ALZ

HiDO-ALZ platform, an AI-driven medication dispensing and observation system designed to improve adherence in individuals with dementia and diabetes.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• Participants aged 65 or older.
• Diagnosed with both dementia and diabetes, with documented difficulty in maintaining at least one health marker (such as glucose, HbA1c, cholesterol, blood pressure, or weight) within target levels over the past 18-24 months.
• Mild cognitive impairment or early-stage dementia, as verified by a Montreal Cognitive Assessment (MoCA) score between 18 and 24.

Exclusion Criteria

• Cognitive deficits or serious mental health or medical conditions that would compromise safety or the reliability of feedback.
• Contraindications to taking low-dose Vitamin C (100 mg).

• Minimum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Rush University Medical Center

OTHER

Sponsor Role: collaborator

HiDO Technologies

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Rush University Medical Center

Chicago, Illinois, United States

Countries

United States

Other Identifiers

R44AG077737

Identifier Type: NIH

Identifier Source: org_study_id

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