Using a Robot to Address the Well-Being, Social Isolation, and Loneliness of Care Home Residents via Video Calls: Qualitative Feasibility Study

Background

About 40,000 people are living in Norwegian care homes, where a majority are living with a dementia diagnosis. Social isolation and loneliness are common issues affecting care home residents' quality of life. Due to visitation restrictions during the pandemic, residents and family members started using digital solutions to keep in contact. There is no framework or guidelines to inform the uptake and use of technologies in the care home context, and this often results in non-adoption and a lack of use after the introduction phase. Hence, there is a great need for research on the feasibility of a robot that can facilitate video communication between residents and family members.

Objective

This study aimed to (1) introduce video communication through a robot to address social isolation and loneliness in a care home during a period of 6 weeks and (2) identify elements central to the feasibility concerning testing and evaluating the use of the robot.

Methods

Three focus group interviews were undertaken: 1 with family members (n=4) and 2 with care staff (n=2 each). The informants were purposely selected to ensure that they had the proper amount of experience with the robot to have the ability to inform this study's objectives. The focus group interviews were tape-recorded and transcribed verbatim, then subsequently analyzed using systematic text condensation.

Results

The data analysis of focus group interviews and individual interviews resulted in three categories: (1) organizing the facilitation of video calls, (2) using a robot in dementia care, and (3) user experience with the robot.

Conclusions

Video communication in care homes is a feasible alternative to face-to-face interactions, but it depends on organizational factors such as information flow, resources, and scheduling. In dementia care, the user-friendly robot supports person-centered care through tailored social interaction. Both family members and staff express enthusiasm for video calls as an option and see its potential for future use.

The Co-Piloting Model for Using Artificial Intelligence Systems in Medicine: Implementing the Constrained-Disorder-Principle-Based Second-Generation System

The development of artificial intelligence (AI) and machine learning (ML)-based systems in medicine is growing, and these systems are being used for disease diagnosis, drug development, and treatment personalization. Some of these systems are designed to perform activities that demand human cognitive function. However, use of these systems in routine care by patients and caregivers lags behind expectations. This paper reviews several challenges that healthcare systems face and the obstacles of integrating digital systems into routine care. This paper focuses on integrating digital systems with human physicians. It describes second-generation AI systems designed to move closer to biology and reduce complexity, augmenting but not replacing physicians to improve patient outcomes. The constrained disorder principle (CDP) defines complex biological systems by their degree of regulated variability. This paper describes the CDP-based second-generation AI platform, which is the basis for the Digital Pill that is humanizing AI by moving closer to human biology via using the inherent variability of biological systems for improving outcomes. This system augments physicians, assisting them in decision-making to improve patients' responses and adherence but not replacing healthcare providers. It restores the efficacy of chronic drugs and improves adherence while generating data-driven therapeutic regimens. While AI can substitute for many medical activities, it is unlikely to replace human physicians. Human doctors will continue serving patients with capabilities augmented by AI. The described co-piloting model better reflects biological pathways and provides assistance to physicians for better care.

Adaptive FPGA-Based Accelerators for Human-Robot Interaction in Indoor Environments

This study addresses the challenges of human-robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot's intention to serve based on the human's location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human-robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation.

A Social Exergame Intervention to Promote Physical Activity, Social Support, and Well-Being in Family Caregivers

BACKGROUND AND OBJECTIVES

Family caregivers often experience a high level of stress, social isolation, a sedentary lifestyle, and poor mental and physical health. An exergame intervention was developed to promote physical activity and well-being in family caregivers and to test social support as a mechanism for behavior change.

RESEARCH DESIGN AND METHODS

The current study was a randomized pilot trial (N = 76) to compare the effectiveness of Go&Grow (social vs nonsocial exergame) to promote well-being through increased social support and physical activity for family caregivers over a 6-week intervention.

RESULTS

The treatment group increased significantly more than the control group in well-being (management of distress) and social support (satisfaction with contact quality). Social support served as a mechanism (mediator and moderator): The treatment group increased more than the control group in satisfaction with social contact quality, which led to more positive affect and less loneliness. Moreover, those in the treatment group who increased more in overall social support and knowing others' experiences increased their steps more than those with less support, whereas the change in steps for the control group was not related to a support level. Those in the treatment group who used more social features of the app had a greater increase in steps compared with those who used it less.

DISCUSSION AND IMPLICATIONS

Social support in technology interventions is a promising direction to promote caregivers' well-being and physical activity. Social support served as a mechanism of behavior change that can inform more engaging, sustainable, portable, and scalable interventions in the future for sedentary and socially isolated family caregivers.Clinical Trial Registration Number: NCT05032872.

LETHE: A Digital Intervention for Cognitive Decline

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform development.Clinical Relevance - Patient Management Systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

James A, Dupuis K, Cohen D. Developing a music-based digital therapeutic to help manage the neuropsychiatric symptoms of dementia

The greying of the world is leading to a rapid acceleration in both the healthcare costs and caregiver burden that are associated with dementia. There is an urgent need to develop new, easily scalable modalities of support. This perspective paper presents the theoretical background, rationale, and development plans for a music-based digital therapeutic to manage the neuropsychiatric symptoms of dementia, particularly agitation and anxiety. We begin by presenting the findings of a survey we conducted with key opinion leaders. The findings highlight the value of a music-based digital therapeutic for treating neuropsychiatric symptoms, particularly agitation and anxiety. We then consider the neural substrates of these neuropsychiatric symptoms before going on to evaluate randomized control trials on the efficacy of music-based interventions in their treatment. Finally, we present our development plans for the adaptation of an existing music-based digital therapeutic that was previously shown to be efficacious in the treatment of adult anxiety symptoms.

A human factors and ergonomics approach to conceptualizing care work among caregivers of people with dementia

Society relies upon informal (family, friend) caregivers to provide much of the care to the estimated 43.8 million individuals living with Alzheimer's disease and related dementias globally. Caregivers rarely receive sufficient training, resources, or support to meet the demands associated with dementia care, which is often associated with increased risk of suboptimal outcomes. Human factors and ergonomics (HFE) can address the call for new approaches to better understand caregiving and support caregiver performance through systematic attention to and design of systems that support the work of caregivers- their care work. Thus, our objective was to perform a work system analysis of care work. We conducted a qualitative study using a Critical Incident Technique interviewing approach and Grounded Dimensional Analysis analytic procedures. Our findings introduce a new conceptual framework for understanding the care work system of dementia caregivers and suggest that care work is influenced by interactions among distinct caregiver goals, the task demands of the care needs of the person with dementia, daily life needs of the caregiver and family, and contextual factors that shape caregivers' perceptions surrounding care. The initial work system model produced by this study provides a foundation from which future work can further elucidate the care work system, determine how the care work system intersects and coordinates with other work systems such as the patient work system, and design systems that address caregivers' individual caregiving context.

Factors Influencing Barriers and Facilitators to In-home Video Telehealth for Dementia Management

OBJECTIVES

Quality dementia care, which recognizes caregivers as vital care partners, is a scarce resource. Innovative solutions like video telehealth may increase the reach of extant clinicians; however, little is known about perceived barriers and facilitators to in-home video telehealth for dementia management from the perspectives of caregivers.

METHODS

Twenty-four caregivers of community-dwelling Veterans with dementia participated in semi-structured interviews. Questions gathered perceived facilitators and barriers to in-home video telehealth for dementia management through experience with related technology. Transcripts were analyzed using directed content analysis which was guided by factors previously identified as influencing older adults' adoption of technology.

RESULTS

Caregiver experience with related technology was mostly facilitative to video telehealth, which was thought best suited for follow-up care. Increased access and decreased patient-caregiver stress were potential benefits. Barriers included perceived limitations of video and the belief that persons with dementia would have limited ability to manage technological aspects and to engage in video telehealth on their own.

CONCLUSIONS

This study improves our understanding of the factors that caregivers perceive as barriers and facilitators to in-home video telehealth for dementia management.

CLINICAL IMPLICATIONS

Strategies to optimize video telehealth include capitalizing on caregivers' social network and providing targeted training.

Remote activity monitoring for family caregivers of persons living with dementia: a mixed methods, randomized controlled evaluation

BACKGROUND

The goal of the present study was to determine whether a remote activity monitoring (RAM) system benefited caregivers who aided relatives with Alzheimer's disease or related dementias (ADRD) living at home. We hypothesized that over 18 months, families randomly assigned to receive RAM technology in the home of the person with ADRD would experience statistically significant (p < .05): 1) improvements in caregiver self-efficacy and sense of competence when managing their relative's dementia; and 2) reductions in caregiver distress (e.g., burden, role captivity, and depression).

METHODS

An embedded mixed methods design was utilized, where 179 dementia caregivers were randomly assigned to receive RAM or not. Caregivers were surveyed bi-annually over an 18-month period to collect quantitative and qualitative data on RAM's effects. Semi-structured interviews with 30 caregivers were completed following the 18-month data collection period to explore more in-depth how and why RAM was perceived as helpful or not.

RESULTS

Growth curve models showed no direct or moderation effect of RAM on dementia caregiver outcomes. The qualitative data revealed a complex utilization process of RAM influenced by the care environment/context as well as the temporal progression of ADRD and the caregiving trajectory.

CONCLUSIONS

The findings suggest the need for developing more effective mechanisms to match appropriate technologies with the heterogeneous needs and care contexts of people living with ADRD and their caregivers. A triadic approach that incorporates professional care management alongside passive monitoring systems such as RAM may also enhance potential benefits.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03665909 , retrospectively registered on 11 Sept 2018.

The Use of Mobile Applications as Communication Aids for People with Dementia: Opportunities and Limitations

BACKGROUND

Communication difficulties are one of the primary symptoms associated with dementia, and mobile applications have shown promise as tools for facilitating communication in patients with dementia (PwD). The literature regarding mobile health (mHealth) applications, especially communications-based mHealth applications, is limited.

OBJECTIVE

This review aims to compile the existing literature on communications-based mobile applications regarding dementia and assess their opportunities and limitations. A PICO framework was applied with a Population consisting of PwD, Interventions consisting of communication technology, focusing primarily on mobile applications, Comparisons between patient well-being with and without technological intervention, and Outcomes that vary but can include usability of technology, quality of communication, and user acceptance.

METHODS

Searches of PubMed, IEEE XPLORE, and ACM Digital Library databases were conducted to establish a comprehensive understanding of the current literature on dementia care as related to 1) mobile applications, 2) communication technology, and 3) communications-based mobile applications. Applying certain inclusion and exclusion criteria, yielded a set of articles (n = 11).

RESULTS

The literature suggests that mobile applications as tools for facilitating communication in PwD are promising. Mobile applications are not only feasible socially, logistically, and financially, but also produce meaningful communication improvements in PwD and their caregivers. However, the number of satisfactory communications-based mobile applications in the mHealth marketplace and their usability is still insufficient.

CONCLUSION

Despite favorable outcomes, more research involving PwD using these applications are imperative to shed further light on their communication needs and on the role of mHealth.

Simultaneous imputation and classification using Multigraph Geometric Matrix Completion (MGMC): Application to neurodegenerative disease classification

Large-scale population-based studies in medicine are a key resource towards better diagnosis, monitoring, and treatment of diseases. They also serve as enablers of clinical decision support systems, in particular computer-aided diagnosis (CADx) using machine learning (ML). Numerous ML approaches for CADx have been proposed in literature. However, these approaches assume feature-complete data, which is often not the case in clinical data. To account for missing data, incomplete data samples are either removed or imputed, which could lead to data bias and may negatively affect classification performance. As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multi-graph Geometric Matrix Completion (MGMC). MGMC uses multiple recurrent graph convolutional networks, where each graph represents an independent population model based on a key clinical meta-feature like age, sex, or cognitive function. Graph signal aggregation from local patient neighborhoods, combined with multi-graph signal fusion via self-attention, has a regularizing effect on both matrix reconstruction and classification performance. Our proposed approach is able to impute class relevant features as well as perform accurate and robust classification on two publicly available medical datasets. We empirically show the superiority of our proposed approach in terms of classification and imputation performance when compared with state-of-the-art approaches. MGMC enables disease prediction in multimodal and incomplete medical datasets. These findings could serve as baseline for future CADx approaches which utilize incomplete datasets.

Virtual Reality as a Technological-Aided Solution to Support Communication in Persons With Neurodegenerative Diseases and Acquired Brain Injury During COVID-19 Pandemic

The COVID-19 poses an ongoing threat to lives around the world and challenges the existing public health and medical service delivery. The lockdown or quarantine measures adopted to prevent the spread of COVID-19 has caused the interruption in ongoing care and access to medical care including to patients with existing neurological conditions. Besides the passivity, isolation, and withdrawal, patients with neurodegenerative diseases experience difficulties in communication due to a limited access to leisure opportunities and interaction with friends and relatives. The communication difficulties may exacerbate the burden on the caregivers. Therefore, assistive-technologies may be a useful strategy in mitigating challenges associated with remote communication. The current paper presents an overview of the use of assistive technologies using virtual reality and virtual body ownership in providing communication opportunities to isolated patients, during COVID-19, with neurological diseases and moderate-to-severe communication difficulties. We postulate that the assistive technologies-based intervention may improve social interactions in patients with neurodegenerative diseases and acquired brain injury-thereby reducing isolation and improving their quality of life and mental well-being.

The Perspectives of Professional Caregivers on Implementing Audio-Based Technology in Residential Dementia Care

Music and familiar everyday sounds can be meaningful for people with dementia by providing benefits such as evoking memories and emotions or prompting social interactions with caregivers or relatives. Motivated by this potential, researchers and designers are investigating how to leverage these beneficial effects of sound in care environments through audio-based technology. However, there is a gap in the knowledge of how audio-based technology can be successfully implemented within everyday care practice. In this paper, we present the outcome of three participatory workshops with 18 professional caregivers to explore how audio-based technology can add value to existing care processes and activities in residential dementia care. During the participatory workshops, professional caregivers (1) mapped existing care activities; (2) linked findings in research with practice, and (3) designed scenarios for the Vita sound cushion. Care professionals indicate how audio-based technology can support existing care practice by influencing the mood of residents and by supporting social interaction during moments of care, daytime activities, or situational sessions. This study bridges research findings with insights from practice, contributing to a shared understanding of opportunities for embedding audio-based technology in dementia care. These opportunities motivate future research to implement and evaluate audio-based technology in residential dementia care.

Teleoperation of Collaborative Robot for Remote Dementia Care in Home Environments

As a senile chronic, progressive and currently incurable disease, dementia has an enormous impact on society and life quality of the elderly. The development of teleoperation technology has changed the traditional way of care delivery and brought a variety of novel applications for dementia care. In this paper, a telerobotic system is presented which gives the caregivers the capability of assisting dementia elderly remotely. The proposed system is composed of a dual-arm collaborative robot (YuMi) and a wearable motion capture device. The communication architecture is achieved by the robot operation system (ROS). The position-orientation data of the operator's hand are obtained and used to control the YuMi robot. Besides, a path-constrained mapping method is designed for motion trajectory tracking between the robot and the operator in the progress of teleoperation. Meanwhile, corresponding experiments are conducted to verify the performance of the trajectory tracking using the path-constrained mapping method. Results show that the position tracking deviation between the trajectory of the operator and the robot measured by dynamic time warping distance is 1.05 mm at the sampling frequency of 7.5 Hz. Moreover, the practicability of the proposed system was verified by teleoperating the YuMi robot to pick up a medicine bottle and further demonstrated by assisting an elderly woman in picking up a cup remotely. The proposed telerobotic system has potential utility for improving the life quality of dementia elderly and the care effect of their caregivers.

Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

The rise of artificial intelligence in healthcare applications

Big data and machine learning are having an impact on most aspects of modern life, from entertainment, commerce, and healthcare. Netflix knows which films and series people prefer to watch, Amazon knows which items people like to buy when and where, and Google knows which symptoms and conditions people are searching for. All this data can be used for very detailed personal profiling, which may be of great value for behavioral understanding and targeting but also has potential for predicting healthcare trends. There is great optimism that the application of artificial intelligence (AI) can provide substantial improvements in all areas of healthcare from diagnostics to treatment. It is generally believed that AI tools will facilitate and enhance human work and not replace the work of physicians and other healthcare staff as such. AI is ready to support healthcare personnel with a variety of tasks from administrative workflow to clinical documentation and patient outreach as well as specialized support such as in image analysis, medical device automation, and patient monitoring. In this chapter, some of the major applications of AI in healthcare will be discussed covering both the applications that are directly associated with healthcare and those in the healthcare value chain such as drug development and ambient assisted living.

In-Home Video Telehealth for Dementia Management: Implications for Rehabilitation

PURPOSE OF REVIEW

The progressive nature of dementia requires ongoing care delivered by multidisciplinary teams, including rehabilitation professionals, that is individualized to patient and caregiver needs at various points on the disease trajectory. Video telehealth is a rapidly expanding model of care with the potential to expand dementia best practices by increasing the reach of dementia providers to flexible locations, including patients' homes. We review recent evidence for in-home video telehealth for patients with dementia and their caregivers with emphasis on implications for rehabilitation professionals.

RECENT FINDINGS

Eleven studies were identified that involved video visits into the home targeting patients with dementia and/or their family caregivers. The majority describe protocolized interventions targeting caregivers in a group format over a finite, pre-determined period. For most, the discipline of the interventionist was unclear, though two studies included rehabilitation interventions. While descriptions of utilized technology were often lacking, many reported that devices were issued to participants when needed, and that technical support was provided by study teams. Positive caregiver outcomes were noted but evidence for patient-level outcomes and cost data are mostly lacking.

SUMMARY

More research is needed to demonstrate implementation of dementia best care practices through in-home video telehealth. Though interventions delivered using in-home video telehealth appear to be effective at addressing caregivers' psychosocial concerns, the impact on patients and the implications for rehabilitation remain unclear. Larger, more systematic inquiries comparing in-home video telehealth to traditional visit formats are needed to better define best practices.