Managing flexible care with a context aware system for ageing-in-place

Robben, Saskia; Bosch, Lilian; Wiggers, Pascal; Decancq, Jasmien; Kanis, Marije

doi:10.4108/icst.pervasivehealth.2015.259229

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Caregivers form a difficult-to-reach user group for study participation, as they are already heavily taxed. In concurrence with other studies [6,8], challenges came up in recruiting participants for the more extensive field study, resulting in a small sample size. Participants were easier to recruit in the initial questionnaire study [8] than for the more time-consuming study with the toolkit.…”

Section: Discussionsupporting

confidence: 64%

“…Technology has considerable potential to support home care provision. A large body of research exists in human-computer interaction (HCI) to mainly support patients [4-7] but also caregivers [6,8-13] in the context of in-home care. Thus far, however, in developing technologies for caregivers, research has generally emphasized functional support for caregiving (how can a caregiver provide better, more effective care?…”

Section: Introductionmentioning

confidence: 99%

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How Is the Caregiver Doing? Capturing Caregivers’ Experiences With a Reflective Toolkit

Bosch¹,

Kanis²,

Dunn³

et al. 2019

JMIR Ment Health

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BackgroundThis paper describes the Co-Care-KIT, a reflective toolkit designed to provide insights into the diverse experiences of home-based informal caregivers during the delivery of care to a relative or loved one.ObjectiveThe aim of this study was to evaluate the toolkit, including a custom-designed journal, tools for photography-based experience sampling, and heart rate tracking, which enables caregivers to collect and reflect on their positive and negative daily experiences in situ.MethodsA 2-week field study with informal caregivers (N=7) was conducted to evaluate the Co-Care-KIT and to capture their daily personal emotional experiences. The collected data samples were analyzed and used for collaborative dialogue between the researcher and caregiver.ResultsThe results suggest that the toolkit (1) increased caregivers’ awareness of their own well-being through in situ reflection on their experiences; (2) empowered caregivers to share their identities and experiences as a caregiver within their social networks; (3) enabled the capturing of particularly positive experiences; and (4) provided caregivers reassurance with regards to their own mental health.ConclusionBy enabling capturing and collaborative reflection, the kit helped to gain a new understanding of caregivers’ day-to-day needs and emotional experiences.

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Section: Discussionsupporting

confidence: 64%

Section: Introductionmentioning

confidence: 99%

How Is the Caregiver Doing? Capturing Caregivers’ Experiences With a Reflective Toolkit

Bosch¹,

Kanis²,

Dunn³

et al. 2019

JMIR Ment Health

Self Cite

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“…It is estimated that by the year 2050, 25% of the population will be aged 65 or older [1]. Older adults prefer to remain in their own home as they age [2], but certain health conditions can cause this to be potentially hazardous. ambient assisted living (AAL) technology can allow older adults to safely stay in their homes as they age, and indoor positioning is one area of AAL which can help during emergency situations [3].…”

Section: Introductionmentioning

confidence: 99%

“…(1) We introduce the subject and our approach to the issue. (2) We review the current literature on the common approaches for indoor tracking and localization. (3) We move on to the methods of how we developed our system.…”

Section: Introductionmentioning

confidence: 99%

Simplified Indoor Localization Using Bluetooth Beacons and Received Signal Strength Fingerprinting with Smartwatch

Bouse,

King,

Chu

2024

Sensors

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Variations in Global Positioning Systems (GPSs) have been used for tracking users’ locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user’s room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently.

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Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

et al. 2021

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Background Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. Objective This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. Methods Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. Results The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. Conclusions The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

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Managing flexible care with a context aware system for ageing-in-place

Cited by 3 publications

References 17 publications

How Is the Caregiver Doing? Capturing Caregivers’ Experiences With a Reflective Toolkit

How Is the Caregiver Doing? Capturing Caregivers’ Experiences With a Reflective Toolkit

Simplified Indoor Localization Using Bluetooth Beacons and Received Signal Strength Fingerprinting with Smartwatch

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

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