A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia

As the demographics of many countries shift towards an ageing population it is predicted that the prevalence of diseases affecting cognitive capabilities will continually increase. One approach to enabling individuals with cognitive decline to remain in their own homes is through the use of cognitive pros-thetics such as reminding technology. However, the benefit of such technologies is intuitively predicated upon their successful adoption and subsequent use. Within this paper we present a knowledge-based feature set which may be utilized to predict technology adoption amongst Persons with Dementia (PwD). The chosen feature set is readily obtainable during a clinical visit, is based upon real data and grounded in established research. We present results demonstrating 86% accuracy in successfully predicting adopters/non-adopters amongst PwD

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Palliative care and Parkinson's disease : meeting summary and recommendations for clinical research

Introduction: Palliative care is an approach to caring for patients and families affected by serious illnesses that focuses on the relief of suffering through the management of medical symptoms, psychosocial issues, advance care planning and spiritual wellbeing. Over the past decade there has been an emerging clinical and research interest in the application of palliative care approaches to Parkinson’s disease (PD) and outpatient palliative care services are now offered by several movement disorders centers. Methods: An International Working Group Meeting on PD and Palliative Care supported by the Parkinson’s Disease Foundation was held in October 2015 to review the current state of the evidence and to make recommendations for clinical research and practice. Results: Topics included: 1) Defining palliative care for PD; 2) Lessons from palliative care for heart failure and other chronic illnesses; 3) Patient and caregiver Needs; 4) Needs assessment tools; 5) Intervention strategies; 6) Predicting prognosis and hospice referrals; 7) Choice of appropriate outcome measures; 8) Implementation, dissemination and education research; and 9) Need for research collaborations. We provide an overview of these discussions, summarize current evidence and practices, highlight gaps in our knowledge and make recommendations for future research. Conclusions: Palliative Care for PD is a rapidly growing area which holds great promise for improving outcomes for PD patients and their caregivers. While clinical research in this area can build from lessons learned in other diseases, there is a need for observational, methodological and interventional research to address the unique needs of PD patients and caregivers

Occupational therapists’ views of using a virtual reality interior design application within the pre-discharge home visit process

This article has been made available through the Brunel Open Access Publishing Fund.Background: A key role of Occupational Therapists (OTs) is to carry out pre-discharge home visits (PHV) and propose appropriate adaptations to the home environment, to enable patients to function independently after hospital-home discharge. However, research shows that more than 50% of specialist equipment installed as part of home adaptations is not used by patients. A key reason for this is that decisions about home adaptations are often made without adequate collaboration and consultation with the patient. Consequently, there is an urgent need to seek out new and innovative uses of technology to facilitate patient/practitioner collaboration, engagement and shared decision making in the PHV process. Virtual reality interior design applications (VRIDAs) primarily allow users to simulate the home environment and visualise changes prior to implementing them. Customised VRIDAs, which also model specialist occupational therapy equipment, could become a valuable tool to facilitate improved patient/practitioner collaboration if developed effectively and integrated into the PHV process. Objective: To explore the perceptions of occupational therapists with regards to using VRIDAs as an assistive tool within the PHV process. Methods: Task-oriented interactive usability sessions, utilising the think-aloud protocol and subsequent semi-structured interviews were carried out with seven Occupational Therapists who possessed significant experience across a range of clinical settings. Template analysis was carried out on the think-aloud and interview data. Analysis was both inductive and driven by theory, centring around the parameters that impact upon the acceptance, adoption and use of this technology in practice as indicated by the Technology Acceptance Model (TAM). Results: OTs’ perceptions were identified relating to three core themes: (1) perceived usefulness (PU), (2) perceived ease of use (PEoU), and (3) actual use (AU). Regarding PU, OTs believed VRIDAs had promising potential to increase understanding, enrich communications and patient involvement, and improved patient/practitioner shared understanding. However, it was unlikely that VRIDAs would be suitable for use with cognitively impaired patients. For PEoU, all OTs were able to use the software and complete the tasks successfully, however, participants noted numerous specialist equipment items that could be added to the furniture library. AU perceptions were positive regarding use of the application across a range of clinical settings including children/young adults, long-term conditions, neurology, older adults, and social services. However, some “fine tuning” may be necessary if the application is to be optimally used in practice. Conclusions: Participants perceived the use of VRIDAs in practice would enhance levels of patient/practitioner collaboration and provide a much needed mechanism via which patients are empowered to become more equal partners in decisions made about their care. Further research is needed to explore patient perceptions of VRIDAs, to make necessary customisations accordingly, and to explore deployment of the application in a collaborative patient/practitioner-based context

Addressing the Health Needs of an Aging America: New Opportunities for Evidence-Based Policy Solutions

This report systematically maps research findings to policy proposals intended to improve the health of the elderly. The study identified promising evidence-based policies, like those supporting prevention and care coordination, as well as areas where the research evidence is strong but policy activity is low, such as patient self-management and palliative care. Future work of the Stern Center will focus on these topics as well as long-term care financing, the health care workforce, and the role of family caregivers

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590This work was part of and supported by GoodBrother, COST Action 19121—Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living

HealthXAI: Collaborative and explainable AI for supporting early diagnosis of cognitive decline

Our aging society claims for innovative tools to early detect symptoms of cognitive decline. Several research efforts are being made to exploit sensorized smart-homes and artificial intelligence (AI) methods to detect a decline of the cognitive functions of the elderly in order to promptly alert practitioners. Even though those tools may provide accurate predictions, they currently provide limited support to clinicians in making a diagnosis. Indeed, most AI systems do not provide any explanation of the reason why a given prediction was computed. Other systems are based on a set of rules that are easy to interpret by a human. However, those rule-based systems can cope with a limited number of abnormal situations, and are not flexible enough to adapt to different users and contextual situations. In this paper, we tackle this challenging problem by proposing a flexible AI system to recognize early symptoms of cognitive decline in smart-homes, which is able to explain the reason of predictions at a fine-grained level. Our method relies on well known clinical indicators that consider subtle and overt behavioral anomalies, as well as spatial disorientation and wandering behaviors. In order to adapt to different individuals and situations, anomalies are recognized using a collaborative approach. We experimented our approach with a large set of real world subjects, including people with MCI and people with dementia. We also implemented a dashboard to allow clinicians to inspect anomalies together with the explanations of predictions. Results show that our system's predictions are significantly correlated to the person's actual diagnosis. Moreover, a preliminary user study with clinicians suggests that the explanation capabilities of our system are useful to improve the task performance and to increase trust. To the best of our knowledge, this is the first work that explores data-driven explainable AI for supporting the diagnosis of cognitive decline