SBIR-STTR Award

Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRD) are age-associated neurodegenerative diseases that are reaching epidemic proportions. Progression of AD is characterized by losses in memory, orientation, independent decision-making capacity, and self-care. Gains in understanding AD pathogenesis have not yet translated into pharmacological therapies that effectively slow or halt disease progression. Evidence-based behavioral approaches are rapidly becoming recognized as methods to provide effective neurocognitive and therapeutic support for AD/ADRD patients and their caregivers1. Behavioral approaches such as lifestyle changes and risk reduction are non-pharmacological therapies that are accessible, personalizable, have no side effects, and are low in cost. To that end, we are developing mobile device software that is patient and caregiver centered, and provides behavioral-based assistance through visual mapping. The MapHabitTM system (MHS) uses pictures and keywords to assist memory- impaired patients and caregivers in organizing and successfully accomplishing their activities of daily living. This approach is innovative through its unique recruitment of the brain’s habit learning system (neostriatum) rather than the hippocampal structures damaged in AD. Preliminary work revealed that commercially available visual mapping software is too complicated for memory-impaired and technology-naïve individuals to use effectively. Commercially available software is proprietary and cannot be modified to meet their needs. In this Phase 1 SBIR application, we propose to further develop and enhance MHS by integrating three novel specific aims that involve (1) development of adaptive user interfaces which can be personalized and dynamically adjusted for cognitive status, allowing for a greater range of memory-impaired individuals to benefit from visual mapping; (2) linkage of personalized visual maps to smart devices, including wearables (e.g., Apple iWatch) and audio interfaces (e.g., Amazon Echo); (3) establishment of a predictive analytics tool that will accurately track and predict changes in functional status. We are advantaged in this SBIR Phase 1 application by having access to patients and caregivers, including underrepresented minority populations, who are currently involved in our preliminary studies assessing the impact of visual mapping on quality of life measures. All of these individuals are already well- characterized in terms of their cognitive and emotional behavior, both before and after the use of visual mapping (see letters of support from Dr. E. Vaughn, Atlanta VA Health Care System, Dr. M. Parker, Emory University Alzheimer’s Disease Research Center, and F. Boatman, RN, Speak Life Management). Those studies will contribute to the preliminary data section of our planned SBIR Phase 2 application that will: assess the effectiveness of MHS on a broad range of large clinical populations, improve the user-experience for memory-impaired individuals, and refine our methods of machine learning to predict healthcare outcomes.

Public Health Relevance Statement:
Impressive gains in our understanding of Alzheimer’s disease (AD) have not created therapies that are safe and effective. We are developing the MapHabitTM system, an application for mobile devices that is safe, non- invasive, and low in cost, which helps memory-impaired individuals and caregivers accomplish activities and improve independent function. This is done by using a technique called visual mapping to utilize areas of the brain that are not damaged by AD.

Project Terms:
Activities of Daily Living; Age; aging population; Alzheimer's Disease; Alzheimer's disease related dementia; analytical tool; Apple; Area; base; Behavior; Behavioral; Biometry; Brain; Brain region; Businesses; Bypass; Car Phone; care outcomes; Caregivers; Caring; Clinical; clinical care; Cognitive; Computer software; Conscious; cost; Data; decision-making capacity; design; Development; Devices; digital; Discipline; Disease; Disease Progression; Effectiveness; Elements; emotional behavior; Epidemic; evidence base; experience; Feedback; functional status; Geriatrics; Goals; habit learning; Habits; handheld mobile device; Health; Health Care Costs; Healthcare; Healthcare Systems; Hippocampus (Brain); Image; Impairment; improved; Individual; innovation; Learning; Letters; Life; Life Style; Link; Machine Learning; Maps; Measures; Memory; Memory impairment; Memory Loss; Methods; Modeling; Neostriatum; Neurocognitive; Neurodegenerative Disorders; Neurosciences; Nonpharmacologic Therapy; novel; novel strategies; Pathogenesis; Patients; Pharmacology; Phase; Population; Predictive Analytics; Quality of life; recruit; Research; Risk Reduction; Self Care; Self-Help Devices; side effect; Small Business Innovation Research Grant; smart watch; software development; Structure; success; System; Tablets; Techniques; Technology; Temporal Lobe; Therapeutic; Time; Touch sensation; Translating; Unconscious State; Underrepresented Minority; Universities; Visual; visual map; Voice; wearable device; Work; Wrist

Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRD) are age-associated neurodegenerative diseases that are reaching epidemic proportions. Progression of AD is characterized by losses in memory, orientation, independent decision-making capacity, and self-care. Gains in understanding AD pathogenesis have not yet translated into pharmacological therapies that effectively slow or halt disease progression. Evidence-based behavioral approaches are rapidly becoming recognized as methods to provide effective neurocognitive and therapeutic support for AD/ADRD patients and their caregivers1. Behavioral approaches such as lifestyle changes and risk reduction are non-pharmacological therapies that are accessible, personalizable, have no side effects, and are low in cost. To that end, we are developing mobile device software that is patient and caregiver centered, and provides behavioral-based assistance through visual mapping. The MapHabitTM system (MHS) uses pictures and keywords to assist memory- impaired patients and caregivers in organizing and successfully accomplishing their activities of daily living. This approach is innovative through its unique recruitment of the brain’s habit learning system (neostriatum) rather than the hippocampal structures damaged in AD. Preliminary work revealed that commercially available visual mapping software is too complicated for memory-impaired and technology-naïve individuals to use effectively. Commercially available software is proprietary and cannot be modified to meet their needs. In this Phase 1 SBIR application, we propose to further develop and enhance MHS by integrating three novel specific aims that involve (1) development of adaptive user interfaces which can be personalized and dynamically adjusted for cognitive status, allowing for a greater range of memory-impaired individuals to benefit from visual mapping; (2) linkage of personalized visual maps to smart devices, including wearables (e.g., Apple iWatch) and audio interfaces (e.g., Amazon Echo); (3) establishment of a predictive analytics tool that will accurately track and predict changes in functional status. We are advantaged in this SBIR Phase 1 application by having access to patients and caregivers, including underrepresented minority populations, who are currently involved in our preliminary studies assessing the impact of visual mapping on quality of life measures. All of these individuals are already well- characterized in terms of their cognitive and emotional behavior, both before and after the use of visual mapping (see letters of support from Dr. E. Vaughn, Atlanta VA Health Care System, Dr. M. Parker, Emory University Alzheimer’s Disease Research Center, and F. Boatman, RN, Speak Life Management). Those studies will contribute to the preliminary data section of our planned SBIR Phase 2 application that will: assess the effectiveness of MHS on a broad range of large clinical populations, improve the user-experience for memory-impaired individuals, and refine our methods of machine learning to predict healthcare outcomes.

Public Health Relevance Statement:
Impressive gains in our understanding of Alzheimer’s disease (AD) have not created therapies that are safe and effective. We are developing the MapHabitTM system, an application for mobile devices that is safe, non- invasive, and low in cost, which helps memory-impaired individuals and caregivers accomplish activities and improve independent function. This is done by using a technique called visual mapping to utilize areas of the brain that are not damaged by AD.

Project Terms:
Activities of Daily Living; Age; aging population; Alzheimer's Disease; Alzheimer's disease related dementia; analytical tool; Apple; Area; base; Behavior; Behavioral; Biometry; Brain; Brain region; Businesses; Bypass; Car Phone; care outcomes; Caregivers; Caring; Clinical; clinical care; Cognitive; Computer software; Conscious; cost; Data; decision-making capacity; design; Development; Devices; digital; Discipline; Disease; Disease Progression; Effectiveness; Elements; emotional behavior; Epidemic; evidence base; experience; Feedback; functional status; Geriatrics; Goals; habit learning; Habits; handheld mobile device; Health; Health Care Costs; Healthcare; Healthcare Systems; Hippocampus (Brain); Image; Impairment; improved; Individual; innovation; Learning; Letters; Life; Life Style; Link; Machine Learning; machine learning method; Maps; Measures; Memory; Memory impairment; Memory Loss; Methods; Modeling; Neostriatum; Neurocognitive; Neurodegenerative Disorders; Neurosciences; Nonpharmacologic Therapy; novel; novel strategies; Pathogenesis; Patients; Pharmacology; Phase; Population; Predictive Analytics; Quality of life; recruit; Research; Risk Reduction; Self Care; Self-Help Devices; side effect; Small Business Innovation Research Grant; smart watch; software development; Structure; success; System; Tablets; Techniques; Technology; Temporal Lobe; Therapeutic; Time; Touch sensation; Translating; Unconscious State; Underrepresented Minority; Universities; Visual; visual map; Voice; wearable device; Work; Wrist