Home-care robots - Attitudes and perceptions among older people, carers and care professionals in Ireland: A questionnaire study

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI's role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI's role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers' effectiveness and well-being.

Privacy Barriers in Health Monitoring: Scoping Review

BACKGROUND

Health monitoring technologies help patients and older adults live better and stay longer in their own homes. However, there are many factors influencing their adoption of these technologies. Privacy is one of them.

OBJECTIVE

The aim of this study was to provide an overview of the privacy barriers in health monitoring from current research, analyze the factors that influence patients to adopt assisted living technologies, provide a social psychological explanation, and propose suggestions for mitigating these barriers in future research.

METHODS

A scoping review was conducted, and web-based literature databases were searched for published studies to explore the available research on privacy barriers in a health monitoring environment.

RESULTS

In total, 65 articles met the inclusion criteria and were selected and analyzed. Contradictory findings and results were found in some of the included articles. We analyzed the contradictory findings and provided possible explanations for current barriers, such as demographic differences, information asymmetry, researchers' conceptual confusion, inducible experiment design and its psychological impacts on participants, researchers' confirmation bias, and a lack of distinction among different user roles. We found that few exploratory studies have been conducted so far to collect privacy-related legal norms in a health monitoring environment. Four research questions related to privacy barriers were raised, and an attempt was made to provide answers.

CONCLUSIONS

This review highlights the problems of some research, summarizes patients' privacy concerns and legal concerns from the studies conducted, and lists the factors that should be considered when gathering and analyzing people's privacy attitudes.

Modeling and mobile home monitoring of behavioral and psychological symptoms of dementia (BPSD)

With the increasing global aging population, dementia care has rapidly become a major social problem. Current diagnosis of Behavior and Psychological Symptoms of Dementia (BPSD) relies on clinical interviews, and behavioral rating scales based on a period of behavior observation, but these methods are not suitable for identification of occurrence of BPSD in the daily living, which is necessary for providing appropriate interventions for dementia, though, has been studied by few research groups in the literature. To address these issues, in this study developed a BPSD monitoring system consisting of a Psycho-Cognitive (PsyCo) BPSD model, a Behavior-Physio-Environment (BePhyEn) BPSD model, and an implementation platform. The PsyCo BPSD model provides BPSD assessment support to caregivers and care providers, while the BePhyEn BPSD model provides instantaneous alerts for BPSD enabled by a 24-hour home monitoring platform for early intervention, and thereby alleviation of burden to patients and caregivers. Data for acquiring the models were generated through extensive literature review and regularity determined. A mobile robot was utilized as the implementation platform for improving sensitivity of sensors for home monitoring, and elderly individual following algorithms were investigated. Experiments in a virtual home environment showed that, a virtual BPSD elderly individual can be followed safely by the robot, and BPSD occurrence could be identified accurately, demonstrating the possibility of modeling and identification of BPSD in home environment.

Technology Acceptance in Socially Assistive Robots: Scoping Review of Models, Measurement, and Influencing Factors

Objectives

We summarized technology acceptance and the influencing factors of elderly people toward socially assistive robots (SARs).

Methods

A scoping review whereby a literature search was conducted in Embase, Cochrane, Scopus, PubMed, and Web of Science databases (2006-2021) to retrieve studies. No restrictions on study methodology were imposed.

Results

Out of the 1187 retrieved papers, 35 studies were finally included in the study. The articles covered various aspects, including general attitudes towards using SARs, technology acceptance theory models, and factors associated with technology acceptance. Twelve studies reported a positive attitude towards SARs. Three explicit theoretical frameworks were reported. Studies involving the elderly reported three themes that influence attitudes towards SARs: individual characteristics, concerns/problems regarding robots, and social factors.

Conclusions

This review elucidates on the suitability of theory-based framework as applied to acceptance of SARs. We found that research on technology acceptance with regard to SARs is still in the developmental stages, and further studies of assessment tools for SARs are required. It is also essential to consider the factors that influence the acceptance of SARs by older people to ensure that they meet the end goal requirements of the user.

Assistive Technology in Multiple Sclerosis Patients—Two Points of View

Objective: The goal of our study was determining the current needs and acceptance of patients with multiple sclerosis (MS) in the field of assistive technologies using materials from the “RAMCIP” project (Robotic Assistant for Mild Cognitive Impairment Patient at Home). Methods: There were two target groups: a population with MS, and medical personnel experienced in treating MS patients. This study was based on a two-step design method (workshops and surveys). Using the Likert scale, we identified the prioritization of users’ needs. Additionally, demographic and disease-specific data and their correlations with each other and with the level of priority of functionality were analyzed. Moreover, the acceptance aspect of the assistant robot and the respondents’ readiness to use it were determined. Results: We gathered 307 completed surveys (176 from MS patients, 131 from medical personnel). Functional capabilities from the safety category were a high priority in most cases. The medium priority functions concerned daily activities that required physical assistance and home management. The differences in prioritization between the two groups were also found. Variables such as age, level of disability, cognitive impairment, depression, and fatigue were associated with the priority level of the functionalities. Conclusion: In summary, our findings might contribute to a better adaptation of robotic assistants to the needs and expectations of the MS population.