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Title: A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation
Background The world’s aging population is increasing, with an expected increase in the prevalence of Alzheimer disease and related dementias (ADRD). Proper nutrition and good eating behavior show promise for preventing and slowing the progression of ADRD and consequently improving patients with ADRD’s health status and quality of life. Most ADRD care is provided by informal caregivers, so assisting caregivers to manage patients with ADRD’s diet is important. Objective This study aims to design, develop, and test an artificial intelligence–powered voice assistant to help informal caregivers manage the daily diet of patients with ADRD and learn food and nutrition-related knowledge. Methods The voice assistant is being implemented in several steps: construction of a comprehensive knowledge base with ontologies that define ADRD diet care and user profiles, and is extended with external knowledge graphs; management of conversation between users and the voice assistant; personalized ADRD diet services provided through a semantics-based knowledge graph search and reasoning engine; and system evaluation in use cases with additional qualitative evaluations. Results A prototype voice assistant was evaluated in the lab using various use cases. Preliminary qualitative test results demonstrate reasonable rates of dialogue success and recommendation correctness. Conclusions The voice assistant provides a natural, interactive interface for users, and it does not require the user to have a technical background, which may facilitate senior caregivers’ use in their daily care tasks. This study suggests the feasibility of using the intelligent voice assistant to help caregivers manage patients with ADRD’s diet. more »« less
Hendawi, Rasha; Li, Juan; Alian, Shadi
(, International Journal of E-Health and Medical Communications)
null
(Ed.)
The aging population worldwide is expected to increase the prevalence of Alzheimer's disease. As there is no medical curative treatment for this disease to date, alternative treatments have been applied to improve the patient's brain and general health. One of these efforts includes providing Alzheimer's patients with proper food and nutrition. In this paper, the authors propose a knowledge-powered personalized virtual coach to provide diet and nutrition assistance to patients of Alzheimer's and/or their informal caregivers. The virtual coach is built on top of an ontology-enhanced knowledge base containing knowledge about patients, Alzheimer's disease, food, and nutrition. Semantics-based searching and reasoning are performed on the knowledge base to get personalized context-aware recommendation and education about healthy eating for Alzheimer's patients. The proposed system has been implemented as a mobile application. Evaluation based on use cases has demonstrated the usefulness of this tool.
Bosco, Cristina; Otenen, Ege; Osorio_Torres, John; Nguyen, Vivian; Chheda, Darshil; Peng, Xinran; Jessup, Nenette M; Himes, Anna K; Cureton, Bianca; Lu, Yvonne; et al
(, JMIR Aging)
BackgroundMany members of Black American communities, faced with the high prevalence of Alzheimer disease and related dementias (ADRD) within their demographic, find themselves taking on the role of informal caregivers. Despite being the primary individuals responsible for the care of individuals with ADRD, these caregivers often lack sufficient knowledge about ADRD-related health literacy and feel ill-prepared for their caregiving responsibilities. Generative AI has become a new promising technological innovation in the health care domain, particularly for improving health literacy; however, some generative AI developments might lead to increased bias and potential harm toward Black American communities. Therefore, rigorous development of generative AI tools to support the Black American community is needed. ObjectiveThe goal of this study is to test Lola, a multimodal mobile app, which, by relying on generative AI, facilitates access to ADRD-related health information by enabling speech and text as inputs and providing auditory, textual, and visual outputs. MethodsTo test our mobile app, we used the cognitive walk-through methodology, and we recruited 15 informal ADRD caregivers who were older than 50 years and part of the Black American community living within the region. We asked them to perform 3 tasks on the mobile app (ie, searching for an article on brain health, searching for local events, and finally, searching for opportunities to participate in scientific research in their area), then we recorded their opinions and impressions. The main aspects to be evaluated were the mobile app’s usability, accessibility, cultural relevance, and adoption. ResultsOur findings highlight the users’ need for a system that enables interaction with different modalities, the need for a system that can provide personalized and culturally and contextually relevant information, and the role of community and physical spaces in increasing the use of Lola. ConclusionsOur study shows that, when designing for Black American older adults, a multimodal interaction with the generative AI system can allow individuals to choose their own interaction way and style based upon their interaction preferences and external constraints. This flexibility of interaction modes can guarantee an inclusive and engaging generative AI experience.
Amiri, Maryam; Li, Juan; Roy, Souradip
(, International Journal of E-Health and Medical Communications)
As healthy diets and nutrition are crucial for people with Alzheimer's disease (AD), caregivers of patients with AD need to provide a balanced diet with the correct nutrients to boost the health and well-being of patients. However, this is challenging as they are likely to suffer from aging-related problems (such as teeth or gum problems) that make eating more uncomfortable; the planners, who are usually patients' family members, generally face high pressure, a busy schedule, and little experience. To help unprofessional caregivers of AD plan meals with the right nutrition and flavors, in this paper, the authors propose a meal planning mechanism that uses a multiple criteria decision-making approach to integrate various factors that affect a caregiver's choice of meals for AD patients. Ontology-based knowledge has been used to model personal preferences and characteristics and customize general diet recommendations. Case studies have demonstrated the feasibility and usability of the proposed approach.
Li, Juan; Hendawi, Rasha; Pandey, Vikram; Alenezi, Rafa; Wang, Xin; Xie, Bo; Tao, Cui
(, 2020 IEEE International Conference on E-health Networking, Application & Services (HealthCom))
null
(Ed.)
Alzheimer's disease (AD) poses serious challenges for both patients and their family caregivers. In this paper we present the design, development, and evaluation of an ontology model, ADCareOnto, to assist family caregivers providing personalized care for persons living with AD. ADCareOnto includes top-level categories, concepts, and relations about informal care for persons with AD. To enable personalization in care, ADCareOnto also includes a comprehensive user profile modeling that includes various characteristics of both AD patients and caregivers. AD care thus can be tailored based on the user's unique concerns, preferences, and needs. We verified and validated the design of ADCareOnto and evaluated it using a real use case. The results support the quality of its content and techniques.
Bosco, Cristina; Shojaei, Fereshtehossadat; Theisz, Alec A; Nguyen, Vivian; Song, Haoru; Han, Ruixiang; Osorio_Torres, John A; Chheda, Darshil; Lin, Jenny; Peng, Xinran; et al
(, ACM Transactions on Computing for Healthcare)
Low levels of health literacy concerning Alzheimer's Disease and related dementias (ADRD) impact African American/Black communities access to appropriate ADRD care. Additionally, a legacy of mistrust in medical research due to systemic racism, has resulted in insufficient participation in ADRD clinical trials among African American/Black adults. This study explores the potential of generative AI to improve ADRD literacy and encourage participation in clinical trials among African American/Black older adults. We designed a mobile health intervention featuring AI-driven conversational agents - a chatbot and a voice assistant - specifically developed for this population. We tested the quality of the intervention using heuristics methodology adapted to the target population along with inputs from African American/ Black medical professionals and UX designers. Key findings highlight the unique needs of the African American/Black communities for culturally relevant content that is accessible to users with varying language levels and tailored to users’ geographical location. Concerning the interaction, high levels of personalization and control over the interaction can promote the use of the tool, by minimizing complexity and maximizing accessibility. These findings show the novel contribution offered by our study in the domain of designing health technology with generative AI, particularly LLMS, for African American/Black communities.
Li, Juan, Maharjan, Bikesh, Xie, Bo, and Tao, Cui. A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation. Retrieved from https://par.nsf.gov/biblio/10253480. Journal of Medical Internet Research 22.9 Web. doi:10.2196/19897.
Li, Juan, Maharjan, Bikesh, Xie, Bo, & Tao, Cui. A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation. Journal of Medical Internet Research, 22 (9). Retrieved from https://par.nsf.gov/biblio/10253480. https://doi.org/10.2196/19897
Li, Juan, Maharjan, Bikesh, Xie, Bo, and Tao, Cui.
"A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation". Journal of Medical Internet Research 22 (9). Country unknown/Code not available. https://doi.org/10.2196/19897.https://par.nsf.gov/biblio/10253480.
@article{osti_10253480,
place = {Country unknown/Code not available},
title = {A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation},
url = {https://par.nsf.gov/biblio/10253480},
DOI = {10.2196/19897},
abstractNote = {Background The world’s aging population is increasing, with an expected increase in the prevalence of Alzheimer disease and related dementias (ADRD). Proper nutrition and good eating behavior show promise for preventing and slowing the progression of ADRD and consequently improving patients with ADRD’s health status and quality of life. Most ADRD care is provided by informal caregivers, so assisting caregivers to manage patients with ADRD’s diet is important. Objective This study aims to design, develop, and test an artificial intelligence–powered voice assistant to help informal caregivers manage the daily diet of patients with ADRD and learn food and nutrition-related knowledge. Methods The voice assistant is being implemented in several steps: construction of a comprehensive knowledge base with ontologies that define ADRD diet care and user profiles, and is extended with external knowledge graphs; management of conversation between users and the voice assistant; personalized ADRD diet services provided through a semantics-based knowledge graph search and reasoning engine; and system evaluation in use cases with additional qualitative evaluations. Results A prototype voice assistant was evaluated in the lab using various use cases. Preliminary qualitative test results demonstrate reasonable rates of dialogue success and recommendation correctness. Conclusions The voice assistant provides a natural, interactive interface for users, and it does not require the user to have a technical background, which may facilitate senior caregivers’ use in their daily care tasks. This study suggests the feasibility of using the intelligent voice assistant to help caregivers manage patients with ADRD’s diet.},
journal = {Journal of Medical Internet Research},
volume = {22},
number = {9},
author = {Li, Juan and Maharjan, Bikesh and Xie, Bo and Tao, Cui},
editor = {null}
}
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