Mobile apps for dementia awareness, support, and prevention - review and evaluation

The effectiveness of virtual passport, an app-based intervention, for dementia care

Background and objectives

This study aimed to confirm the effectiveness of the virtual passport for dementia care.

Research design and methods

The virtual passport is an application (app) tool connecting healthcare professionals, dementia care sites, and people living with dementia (PLWD) and their family caregivers. This tool assists case managers in hospitals by providing individualized care plans and health education to PLWD and their caregivers. The dementia quality indicator achievement rates, care needs investigation and fulfillment, severity of behavioral and psychological symptoms of dementia (BPSD), and changes in caregiver burden and depression are measured at the initial interview and 6 and 12 months after the intervention.

Results

We enrolled 57 and 54 patients and their caregivers in the virtual passport and routine care groups, respectively. Compared to the control group, six quality indicators in the passport group showed significantly higher achievement at 6 months after using the virtual passport. Case managers addressed more care needs at 6 months (1.37 vs 0, p < 0.001) and 12 months (1.32 vs 0, p < 0.001). Improvement in severity of neuropsychiatric symptoms (neuropsychiatric inventory (NPI) irritability/lability difference: -0.58 vs 0.22, p = 0.044; NPI agitation/aggression difference =-0.78 vs 0.00, p = 0.042) were also observed. No obvious influence was found in caregiver burden and depression after using the virtual passport.

Discussion and implications

The virtual passport is an effective information technology tool in improving the quality of dementia care, assisting case management in identifying more care needs, and reducing the severity of BPSD.

Mobile Health Apps, Family Caregivers, and Care Planning: Scoping Review

BACKGROUND

People living with multiple chronic conditions (MCCs) face substantial challenges in planning and coordinating increasingly complex care. Family caregivers provide important assistance for people with MCCs but lack sufficient support. Caregiver apps have the potential to help by enhancing care coordination and planning among the health care team, including patients, caregivers, and clinicians.

OBJECTIVE

We aim to conduct a scoping review to assess the evidence on the development and use of caregiver apps that support care planning and coordination, as well as to identify key factors (ie, needs, barriers, and facilitators) related to their use and desired caregiver app functionalities.

METHODS

Papers intersecting 2 major domains, mobile health (mHealth) apps and caregivers, that were in English and published from 2015 to 2021 were included in the initial search from 6 databases and gray literature and ancestry searches. As per JBI (Joanna Briggs Institute) Scoping Review guidelines and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews), 2 authors independently screened full texts with disagreements resolved by a third author. Working in pairs, the authors extracted data using a pilot-tested JBI extraction table and compared results for consensus.

RESULTS

We identified 34 papers representing 25 individual studies, including 18 (53%) pilot and feasibility studies, 13 (38%) qualitative studies, and 2 experimental or quasi-experimental studies. None of the identified studies assessed an intervention of a caregiver app for care planning and coordination for people with MCCs. We identified important caregiver needs in terms of information, support, and care coordination related to both caregiving and self-care. We compiled desired functionalities and features enabling apps to meet the care planning and care coordination needs of caregivers, in particular, the integration of caregiver roles into the electronic health record.

CONCLUSIONS

Caregiver needs identified through this study can inform developers and researchers in the design and implementation of mHealth apps that integrate with the electronic health record to link caregivers, patients, and clinicians to support coordinated care for people with MCCs. In addition, this study highlights the need for more rigorous research on the use of mHealth apps to support caregivers in care planning and coordination.

The Olera.care Digital Caregiving Assistance Platform for Dementia Caregivers: Preliminary Evaluation Study

BACKGROUND

The increasing prevalence of Alzheimer disease and Alzheimer disease-related dementia in the United States has amplified the health care burden and caregiving challenges, especially for caregivers of people living with dementia. A web-based care planning tool, Olera.care, was developed to aid caregivers in managing common challenges associated with dementia care.

OBJECTIVE

This study aims to preliminarily evaluate the quality and usability of the Olera.care platform and assess the preferences of using the technology and interests in learning about different older adult care services among caregivers.

METHODS

For interview 1, we aim to understand caregiving needs and let the participants start engaging with the platform. After they engage with the platform, we schedule the second interview and let the participants complete the Mobile Application Rating Scale. The survey also included sociodemographic characteristics, caregiving experiences, communication preferences in technology adoption, and older adult care service use and interests. Descriptive statistics were used to describe the quality and usability of the platform and characteristics of the participants. We conducted 2-sample 2-tailed t tests to examine the differences in the Mobile Application Rating Scale evaluation scores by caregiver characteristics.

RESULTS

Overall, 30 adult caregivers in Texas completed the evaluation. The majority were aged ≥50 years (25/30, 83%), women (23/30, 77%), White (25/30, 83%), and financially stable (20/30, 67%). The Olera.care platform evaluation showed high satisfaction, with an overall mean rating of 4.57 (SD 0.57) of 5, and scored well in engagement (mean 4.10, SD 0.61), functionality (mean 4.46, SD 0.44), aesthetics (mean 4.58, SD 0.53), and information quality (mean 4.76, SD 0.44) consistently across all participants. A statistically significant difference (P=.02) was observed in functionality evaluation scores by duration of caregiving, with caregivers dedicating more hours to care rating it higher than those providing less care (mean 4.6, SD 0.4 vs mean 4.2, SD 0.5). In addition, caregivers with less caregiving experience reported significantly higher evaluation scores for aesthetics (P=.04) and information quality (P=.03) compared to those with longer years of caregiving. All participants expressed a willingness to recommend the app to others, and 90% (27/30) rated the app overall positively. Most of the participants (21/30, 70%) favored anonymous interactions before receiving personalized feedback and preferred computer browsers over mobile apps. Medical home health services were the most used, with a diverse range of services being used. Caregiver support groups, medical providers, memory care, meal services, and adult day care were among the most desired services for future exploration.

CONCLUSIONS

The Olera.care web-based platform is a practical, engaging, easy-to-use, visually appealing, and informative tool for dementia caregivers. Future development and research are essential to enhance the platform and comprehensively evaluate it among a broader population.

Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care

This perspective outlines the Artificial Intelligence and Technology Collaboratories (AITC) at Johns Hopkins University, University of Pennsylvania, and University of Massachusetts, highlighting their roles in developing AI-based technologies for older adult care, particularly targeting Alzheimer's disease (AD). These National Institute on Aging (NIA) centers foster collaboration among clinicians, gerontologists, ethicists, business professionals, and engineers to create AI solutions. Key activities include identifying technology needs, stakeholder engagement, training, mentoring, data integration, and navigating ethical challenges. The objective is to apply these innovations effectively in real-world scenarios, including in rural settings. In addition, the AITC focuses on developing best practices for AI application in the care of older adults, facilitating pilot studies, and addressing ethical concerns related to technology development for older adults with cognitive impairment, with the ultimate aim of improving the lives of older adults and their caregivers. HIGHLIGHTS: Addressing the complex needs of older adults with Alzheimer's disease (AD) requires a comprehensive approach, integrating medical and social support. Current gaps in training, techniques, tools, and expertise hinder uniform access across communities and health care settings. Artificial intelligence (AI) and digital technologies hold promise in transforming care for this demographic. Yet, transitioning these innovations from concept to marketable products presents significant challenges, often stalling promising advancements in the developmental phase. The Artificial Intelligence and Technology Collaboratories (AITC) program, funded by the National Institute on Aging (NIA), presents a viable model. These Collaboratories foster the development and implementation of AI methods and technologies through projects aimed at improving care for older Americans, particularly those with AD, and promote the sharing of best practices in AI and technology integration. Why Does This Matter? The National Institute on Aging (NIA) Artificial Intelligence and Technology Collaboratories (AITC) program's mission is to accelerate the adoption of artificial intelligence (AI) and new technologies for the betterment of older adults, especially those with dementia. By bridging scientific and technological expertise, fostering clinical and industry partnerships, and enhancing the sharing of best practices, this program can significantly improve the health and quality of life for older adults with Alzheimer's disease (AD).

A Mobile Application to Assist Alzheimer's Caregivers During COVID-19 Pandemic: Development and Evaluation

Introduction

Access to healthcare services for patients with Alzheimer's disease (AD) was limited during the COVID-19 pandemic. A mobile application (app) can help overcome this limitation for patients and caregivers. Our study aims to develop and evaluate an app to help caregivers of patients with AD during COVID-19.

Methods

The study was performed in three steps. First, a questionnaire of features required for the app design was prepared based on the interviews with caregivers of AD patients and neurologists. Then, questionnaire was provided to neurologists, medical informatics, and health information management specialists to identify the final features. Second, the app was designed using the information obtained from the previous phase. Third, the quality of the app and the level of user satisfaction were evaluated using the mobile app rating scale (MARS) and the questionnaire for user interface satisfaction (QUIS), respectively.

Results

The number of 41 data elements in four groups (patient's profile, COVID-19 management and control, AD management and control, and program functions) were identified for designing the app. The quality evaluation of the app based on MARS and user satisfaction evaluation based on QUIS showed the app was good.

Conclusion

This is the first study that focused on developing and evaluating a mobile app for assisting Alzheimer's caregivers during the COVID-19 pandemic. As the app was designed based on users' needs and covered both information about AD and COVID-19, it can help caregivers perform their tasks more efficiently.

An update on mobile applications collecting data among subjects with or at risk of Alzheimer's disease

Smart mobile phone use is increasing worldwide, as is the ability of mobile devices to monitor daily routines, behaviors, and even cognitive changes. There is a growing opportunity for users to share the data collected with their medical providers which may serve as an accessible cognitive impairment screening tool. Data logged or tracked in an app and analyzed with machine learning (ML) could identify subtle cognitive changes and lead to more timely diagnoses on an individual and population level. This review comments on existing evidence of mobile device applications designed to passively and/or actively collect data on cognition relevant for early detection and diagnosis of Alzheimer's disease (AD). The PubMed database was searched to identify existing literature on apps related to dementia and cognitive health data collection. The initial search deadline was December 1, 2022. Additional literature published in 2023 was accounted for with a follow-up search prior to publication. Criteria for inclusion was limited to articles in English which referenced data collection via mobile app from adults 50+ concerned, at risk of, or diagnosed with AD dementia. We identified relevant literature (n = 25) which fit our criteria. Many publications were excluded because they focused on apps which fail to collect data and simply provide users with cognitive health information. We found that although data collecting cognition-related apps have existed for years, the use of these apps as screening tools remains underdeveloped; however, it may serve as proof of concept and feasibility as there is much supporting evidence on their predictive utility. Concerns about the validity of mobile apps for cognitive screening and privacy issues remain prevalent. Mobile applications and use of ML is widely considered a financially and socially viable method of compiling symptomatic data but currently this large potential dataset, screening tool, and research resource is still largely untapped.

[Mobile everyday-life digital technologies for the prevention of Alzheimer's dementia: cognitive health and cognitive safety]

It is generally accepted that the treatment of Alzheimer's disease should be flanked by preventive measures for risk reduction in order to maintain cognitive functions for as long as possible; however, the research and development of treatment concepts are both faced with challenges. The preventive risk reduction necessitates a high level of coordination of neurology and psychiatry with other disciplines. Also, patients must develop a high level of health competence and summon up self-motivation and adherence. This concept article deals with the question of how mobile everyday-life digital technologies can help to address these challenges. The core prerequisite is the interdisciplinary coordinated structuring of prevention with the focus on cognitive health and cognitive safety. Cognitive health relates to a reduction of risk factors associated with lifestyle. Cognitive safety concerns the avoidance of iatrogenic side effects on cognitive functions. Digital technologies that are relevant in this context are mobile apps based on smartphones or tablets for everyday-life and high-frequency recording of cognitive functions, apps that can coach the implementation of lifestyle changes as companion technologies, apps that can assist in the reduction of iatrogenic risks and those that can improve the health competence of patients and relatives. The state of development of such medical products is at different stages of progress. Therefore, this concept article does not provide a review of existing products but rather deals with the fundamental interplay of potential solutions in the prevention of Alzheimer dementia in the areas of cognitive health and cognitive safety.

Applications of artificial intelligence in dementia research

More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings.