Technology and Caregiving: Emerging Interventions and Directions for Research.

An array of technology-based interventions has increasingly become available to support family caregivers, primarily focusing on health and well-being, social isolation, financial, and psychological support. More recently the emergence of new technologies such as mobile and cloud, robotics, connected sensors, virtual/augmented/mixed reality, voice, and the evermore ubiquitous tools supported by advanced data analytics, coupled with the integration of multiple technologies through platform solutions, have opened a new era of technology-enabled interventions that can empower and support family caregivers. This paper proposes a conceptual framework for identifying and addressing the challenges that may need to be overcome to effectively apply technology-enabled solutions for family caregivers. The paper identifies a number of challenges that either moderate or mediate the full use of technologies for the benefit of caregivers. The challenges include issues related to equity, inclusion, and access; ethical concerns related to privacy and security; political and regulatory factors affecting interoperability and lack of standards; inclusive/human-centric design and issues; and inherent economic and distribution channel difficulties. The paper concludes with a summary of research questions and issues that form a framework for global research priorities

Delivering elder-care environments utilizing TV-channel based mechanisms

In this paper, we present a smart environment for elderly. What makes the development of such system challenging is that the concept of smartness for elderly brings to the extreme the idea of invisibility of the technology. In our experience, elders are well-disposed to new technologies, provided that those will not require significant changes - namely, they are invisible - to their habits. Starting from this consideration, 200 caregivers responses were collected by questionnaire, so as to better understand elders' needs and habits. A system was subsequently developed allowing elders to access a number of "modern web services" as standard TV channels: at channel 43 there is the health status, at channel 45 the photos of the family, at 46 the agenda of the week, just to mention few of the available services. The content of such services is automatically generated by the smart devices in the environment and is managed by the caregivers (e.g., family members) by simple web apps. Fourteen families were asked to install the system in their house. The results of these experiments confirm that the proposed system is considered effective and user-friendly by elders

Remote Monitoring Technologies in Dementia Care: An Interpretative Phenomenological Analysis of Family Caregivers’ Experiences

The desire to maintain an independent lifestyle is one shared by an increasing number of older adults. Adult children, spouses, siblings, and other relatives, also known as family caregivers, play an integral role in helping their loved ones maintain independence. Remote monitoring technologies (RMTs) such as wearable sensors, mobile emergency devices, smartphone apps, and webcams can be used to monitor, sense, record, and communicate a person’s daily activities. However, understanding is limited of the family caregiver’s needs and perceptions of RMTs used in a home-based setting. The purpose was to explore how family caregivers perceive RMTs and their use for monitoring and supporting their care recipients who choose to live independently. We used a survey to capture some basic characteristics of family caregivers, what they know about RMTs, and to recruit interview participants. We conducted semi-structured interviews with four participants who shared the commonality of caring for a relative with dementia. We reported the survey data using descriptive statistics and we applied interpretative phenomenological analysis (IPA) to analyze and report results from the interviews. Four themes emerged including the unique relationships that exist in family care, the risk-benefit conundrum that accompanies benefits and tradeoffs of RMT use, human-technology interaction and usability, and the importance of creating tailored solutions to facilitate RMT adoption and use. Our findings provide insight into factors impacting adoption and use

Technology and dementia: the future is now

Background: Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place. Objectives: To summarise key areas of technology development in dementia and identify future directions and implications. Method: Members of the US Alzheimer’s Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia. Results: The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management. Conclusions: The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and pre- vention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia

Discovering human activities from binary data in smart homes

With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods

Ethical issues in the use of surveillance cameras to support ageing in place

Background and Objective: Surveillance technology allows family members to monitor older adults’ daily activities and their interaction with the home environment. In particular, video surveillance cameras and surveillance technology’s implementation raises critical ethical concerns due to their invasive and obtrusive nature. Thus, this paper aims to address the ethical issues regarding the use of video surveillance for older adults to age in place. Methods: A literature review is conducted using Springerlink, Sciencedirect, and PubMed Publications related to older adults’ care, ageing in place, and the use of surveillance technologies were included in this project. Results: A total of 19 publications met the inclusion criteria. Nine ethical issues emerged from the data: informed consent, privacy, conflict of interest, stigmatization and obtrusiveness, homogeneity among older adults, and imbalance relationship. These nine themes were further explored in respect to ethical principles, including autonomy, beneficence, non-maleficence, justice and fidelity) Conclusion: Although surveillance cameras can be invasive, well-grounded ethical thinking and proactive response help reduce the risk and ethical challenges associated with it. By examining the ethical issue in video surveillance, it helps to reflect and enhance the current legislation

Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Active ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, multimorbidity and neurological disorders, such as Parkinson’s disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD. Methods: ActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson’s Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques. Discussion: We hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.publishedVersio

Recommendations for ICT use in Alzheimer's Disease assessment: Monaco CTAD expert meeting

International audienceAlzheimer disease (AD) and other related dementia represent a major challenge for health care systems within the aging population. It is therefore important to develop better instruments for assessing disease severity and disease progression to optimize patient's care and support to care provide rs, and also provide better tools for clinical research. In this area, Information and Communication Technologies (ICT) are of particular interest. Such techniques enable accurate and standardized assessments of patients' performance and actions in real time and real life situations. The aim of this article is to provide basic recommendation concerning the development and the use of ICT for Alzheimer's disease and related disorders. During he ICT and Mental Health workshop (CTAD meeting held in Monaco on the 30th October 2012) an expert panel was set up to prepare the first recommendations for the use of ICT in dementia research. The expert panel included geriatrician, epidemiologist, neurologist, psychiatrist, psychologist, ICT engineers, representatives from the industry and patient association. The recommendations are divided into three sections corresponding to 1/ the clinical targets of interest for the use of ICT, 2/ the cond itions, the type of sensors and the outputs (scores) that could be used and obtained, 3/ finally the last section concerns specifically the use of ICT within clinical trials