Design and Implementation of a Smart Home in a Box to Monitor the Wellbeing of Residents With Dementia in Care Homes

Bridging gaps in the design and implementation of socially assistive technologies for dementia care: the role of occupational therapy

Dementia is a global health challenge, and people living with dementia (PLWD) are especially susceptible to reduced engagement in meaningful occupations, including social participation. In the past few decades, socially assistive technologies continue to be developed amidst a rapidly evolving technological landscape to support the social health of PLWD and their caregivers. Examples include social robots, virtual reality, smart home technology, and various digital technologies, such as mobile applications for tablets and smartphones. Despite an increasing body of research and interest in this field, several gaps relating to the design and implementation process of socially assistive technologies continue to undermine their relevance for PLWD in daily life. In this paper, some of these gaps are highlighted and the role of occupational therapy in the design and implementation of socially assistive technology is presented. In the design process, occupational therapists are uniquely skilled to advise and advocate for the tailoring and personalisation of technology to address the occupational needs of PLWD. In the implementation of socially assistive technologies, occupational therapists are skilled to educate, train, and conduct ongoing evaluations with PLWD and their caregivers, to incorporate socially assistive technologies into their routine and daily lives. We recommend that occupational therapists should continue to be acquainted with such technologies through continuous professional development and educational curricula. Moreover, we highlight the necessary collaboration between occupational therapists, technology developers, and researchers to enhance the process of designing and implementing socially assistive technology, so that their relevance for PLWD and their caregivers can be maximised.Implications for rehabilitationDevelopers and designers of socially assistive technology should consider the disease trajectory of different types of dementia, as well as the different needs, abilities, preferences, occupations and routines of people living with dementia (PLWD) and/or their caregivers.Collaborations between technology developers, researchers, and occupational therapists should take place iteratively throughout the process of designing and implementing socially assistive technology to maximise their relevance and applicability for people living with dementia and their caregivers.To continue enhancing the current role of occupational therapy in socially assistive technology provision, occupational therapists should keep up to date with socially assistive technology that are being developed to support the social health of PLWD.

Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study

Introduction

Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring.

Methods

A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated.

Results

The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups.

Discussion

This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers.

Maintaining High-Touch in High-Tech Digital Health Monitoring and Multi-Omics Prognostication: Ethical, Equity, and Societal Considerations in Precision Health for Palliative Care

Advances in digital health, systems biology, environmental monitoring, and artificial intelligence (AI) continue to revolutionize health care, ushering a precision health future. More than disease treatment and prevention, precision health aims at maintaining good health throughout the lifespan. However, how can precision health impact care for people with a terminal or life-limiting condition? We examine here the ethical, equity, and societal/relational implications of two precision health modalities, (1) integrated systems biology/multi-omics analysis for disease prognostication and (2) digital health technologies for health status monitoring and communication. We focus on three main ethical and societal considerations: benefits and risks associated with integration of these modalities into the palliative care system; inclusion of underrepresented and marginalized groups in technology development and deployment; and the impact of high-tech modalities on palliative care's highly personalized and "high-touch" practice. We conclude with 10 recommendations for ensuring that precision health technologies, such as multi-omics prognostication and digital health monitoring, for palliative care are developed, tested, and implemented ethically, inclusively, and equitably.

Informal Caregiving and Alzheimer's Disease: The Psychological Effect

Background and Objectives: People with Alzheimer's disease and dementia in general benefit from home-based care as demonstrated via their better quality of life, increased lifespan, and delayed disease progression. Since currently nearly half of the dementia care is being provided by informal and unpaid caregiving, the health, wellbeing and quality of life of informal dementia caregivers is extremely important. Materials and Methods: We used a systematic review process with searches based upon the six elements from the "Quality of Life Scale for Informal Carers of Older Adults" with additional items on traditional and non-traditional caregiving ideologies, as well as caregivers' experiences. Results: We identified 19 studies with primary data. Informal caregivers of older adults with Alzheimer's Disease experience significant emotional strain, documented through increased levels of anxiety and depression, as well as increased caregiver burden and poorer quality of life, primarily due to caregiving ideologies, financial strain and a lack of support. Conclusions: Our findings suggest that caregiving should be a normative component of adult education to better prepare individuals with the mental and physical skills required for undertaking informal caregiving. They should also help inform policy makers to develop novel programs and services to both assist and reduce informal caregivers' strain, whilst considering their different social and cultural contexts.

Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients

The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person’s intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)>90%).

Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review

Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD.