A nurse-driven method for developing artificial intelligence in "smart" homes for aging-in-place

Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review

BACKGROUND

Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care.

OBJECTIVE

This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings.

METHODS

The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O'Malley's methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria.

RESULTS

In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth.

CONCLUSIONS

All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.

Exploring the mediating influence of acceptance of change: A study on gerontechnology acceptance, mental well-being, and urban-rural disparities among older adults

BACKGROUND

The global aging population necessitates leveraging technology for older adults' independence and mental well-being. Gerontechnology, tailored for older users, thrives when accessible and accepted, with the pivotal role of acceptance of change shaping its adoption.

AIMS

This study investigates the mediating role of acceptance of change in the relationship between gerontechnology acceptance and mental well-being among older adults and explores disparities in urban and rural settings DESIGN & METHODS: A cross-sectional, correlational design adhering to STROBE guidelines collected data through an interview survey from 802 older adults. Instruments included the Older Adult Structured Survey, Short Version of Senior Technology Acceptance, Acceptance of Change Scale, and the World Health Organization Well-Being Index.

RESULTS

The results underscore a significant correlation between technology adoption, adaptability, and mental well-being among 60-year-olds and older. Notably, an individual's openness to change significantly influences the technology-mental well-being relationship, emphasizing its impact on overall health. Urban areas exhibit a stronger positive correlation between technology acceptance and mental well-being, whereas rural regions demonstrate a more pronounced negative correlation.

CONCLUSION

This research contributes valuable knowledge for addressing the unique challenges older adults face in diverse geographic settings, paving the way for targeted and effective initiatives.

IMPLICATIONS

Nurses should prioritize understanding the nexus between gerontechnology acceptance, change adaptability, and mental wellness, integrating technology education and culturally sensitive interventions to enhance care strategies for older adults in diverse geographic settings. This study lays the groundwork for developing person-centered geriatric nursing care plans, underscoring the importance of harnessing technology for improved mental well-being.

The Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint

Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult's home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?

Background

Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care.

Objective

This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care.

Methods

Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation.

Results

A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan.

Conclusion

This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.

Study on the measurement of coupling and coordinated development level between China's internet and elderly care services and its influencing factors

BACKGROUND

With the intensification of China's aging population, the demand for elderly care services has become increasingly prominent. At the same time, rapid development of internet technology provides more convenience and possibilities for the elderly. However, the coordinated development between the internet and elderly care services still faces challenges. This study aims to measure the level of coupling and coordinated development between the internet and elderly care services in China, and analyze the influencing factors, in order to provide reference for promoting elderly care services.

METHODS

In this paper, the entropy method and coupling coordination degree model were used to measure the coupling coordination development index of the internet and elderly care services in China from 2012 to 2021. In addition, considering that the coordinated development between the two is affected by many factors, the Tobit model was used to analyze the main factors affecting the integration of the internet and elderly care services.

RESULTS

(1) The coupling and coordination of the Internet and senior care services is in its infancy, but the coupling and coordination of the two is on the rise, and there is still a lot of room for development in the future. (2) In terms of time scale, the coupling coordination development level between the internet and elderly care services in China has gone through three stages of "disorder recession-transition coordination-coordinated development". (3) In terms of influencing factors, government management ability has a more positive impact on the development of the integration of the Internet and senior care services, financial support, scientific and technological investment and the level of innovation play a mild pulling role, while the level of informatization to a certain extent restricts the level of integration of the Internet and senior care services.

CONCLUSION

In order to promote the coordinated development of China's Internet and senior care services, it is necessary to comprehensively understand the current situation and development space of China's Internet and senior care services coupling coordination degree, accurately grasp the dynamic trend of China's Internet and senior care services coupling and coordinated development, promote the stage of leapfrogging, and fully consider the influencing factors, so as to realize the optimal allocation of policies and resources. These measures will help to promote a more coordinated and sustainable development of the internet and elderly care services in China.

Family caregiver readiness to adopt smart home technology to monitor care-Dependent older adults: A qualitative exploratory study

AIMS

The aim of this study was to explore factors that influence family caregiver readiness to adopt health smart home technology for their care-dependent older adult family member. Health smart homes are designed to remotely monitor the health and wellness of community-dwelling older adults supporting independent living for as long as possible. Accordingly, if the health smart home is deployed into the home of a care-depended older adult, it can potentially support family caregivers by facilitating workforce participation and give piece of mind to the family caregiver who may not live close to the older adult. However, wider adoption of health smart home technologies into the homes of community-older adults is low, and little is known about the factors that influence the readiness of family caregivers to adopt smart home technologies for their care-dependent older adults.

DESIGN

A qualitative Descriptive study design was utilized.

METHODS

Qualitative data were collected between 2019 and 2020 via semi-structured interviews. Thematic analysis of interviews was completed, and data were organized into themes.

RESULTS

Study findings show that caregiver readiness (N = 10) to adopt smart home technology to monitor older adult family members were influenced by five primary themes including a 'big brother effect', 'framing for acceptance', 'data privacy', 'burden' and 'cost.'

CONCLUSION

Family caregivers were open to adopting smart home technology to support the independent living of their older adult family members. However, the readiness of family caregivers was inextricably linked to the older adults' readiness for smart home adoption. The family caregiver's primary concern was on how they could frame the idea of the smart home to overcome what they viewed as hesitancy to adopt in the older adult. The findings suggest that family caregivers endeavour to balance the hesitancy in their older adult family members with the potential benefits of smart home technology.

IMPACT

Family caregivers could benefit if their care-dependent older adults adopt smart home technology. Recognizing the role of caregivers and their perspectives on using smart home technologies with their care-dependents is critical to the meaningful design, use and adoption.

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future

AIM

This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future.

DESIGN AND METHODS

A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice.

RESULTS

The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI-enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions.

Older Adults' Perceptions About Using Intelligent Toilet Seats Beyond Traditional Care: Web-Based Interview Survey

BACKGROUND

In contemporary society, age tech (age technology) represents a significant advancement in health care aimed at enhancing patient engagement, ensuring sustained independence, and promoting quality of life for older people. One innovative form of age tech is the intelligent toilet seat, which is designed to collect, analyze, and provide insights based on toileting logs and excreta data. Understanding how older people perceive and interact with such technology can offer invaluable insights to researchers, technology developers, and vendors.

OBJECTIVE

This study examined older adults' perspectives regarding the use of intelligent toilet seats. Through a qualitative methodology, this research aims to unearth the nuances of older people's opinions, shedding light on their preferences, concerns, and potential barriers to adoption.

METHODS

Data were collected using a web-based interview survey distributed on Amazon Mechanical Turk. The analyzed data set comprised 174 US-based individuals aged ≥65 years who voluntarily participated in this study. The qualitative data were carefully analyzed using NVivo (Lumivero) based on detailed content analysis, ensuring that emerging themes were coded and classified based on the conceptual similarities in the respondents' narratives.

RESULTS

The analysis revealed 5 dominant themes encompassing the opinions of aging adults. The perceived benefits and advantages of using the intelligent toilet seat were grouped into 3 primary themes: health-related benefits including the potential for early disease detection, continuous health monitoring, and seamless connection to health care insights. Technology-related advantages include the noninvasive nature of smart toilet seats and leveraging unique and innovative data collection and analysis technology. Use-related benefits include ease of use, potential for multiple users, and cost reduction owing to the reduced need for frequent clinical visits. Conversely, the concerns and perceived risks were classified into 2 significant themes: psychological concerns, which included concerns about embarrassment and aging-related stereotypes, and the potential emotional impact of constant health monitoring. Technical performance risks include concerns centered on privacy and security, device reliability, data accuracy, potential malfunctions, and the implications of false positives or negatives.

CONCLUSIONS

The decision of older adults to incorporate intelligent toilet seats into their daily lives depends on myriad factors. Although the potential health and technological benefits are evident, valid concerns that need to be addressed remain. To foster widespread adoption, it is imperative to enhance the advantages while simultaneously addressing and mitigating the identified risks. This balanced approach will pave the way for a more holistic integration of smart health care devices into the routines of the older population, ensuring that they reap the full benefits of age tech advancements.

Application of Artificial Intelligence in Geriatric Care: Bibliometric Analysis

BACKGROUND

Artificial intelligence (AI) can improve the health and well-being of older adults and has the potential to assist and improve nursing care. In recent years, research in this area has been increasing. Therefore, it is necessary to understand the status of development and main research hotspots and identify the main contributors and their relationships in the application of AI in geriatric care via bibliometric analysis.

OBJECTIVE

Using bibliometric analysis, this study aims to examine the current research hotspots and collaborative networks in the application of AI in geriatric care over the past 23 years.

METHODS

The Web of Science Core Collection database was used as a source. All publications from inception to August 2022 were downloaded. The external characteristics of the publications were summarized through HistCite and the Web of Science. Keywords and collaborative networks were analyzed using VOSviewers and Citespace.

RESULTS

We obtained a total of 230 publications. The works originated in 499 institutions in 39 countries, were published in 124 journals, and were written by 1216 authors. Publications increased sharply from 2014 to 2022, accounting for 90.87% (209/230) of all publications. The United States and the International Journal of Social Robotics had the highest number of publications on this topic. The 1216 authors were divided into 5 main clusters. Among the 230 publications, 4 clusters were modeled, including Alzheimer disease, aged care, acceptance, and the surveillance and treatment of diseases. Machine learning, deep learning, and rehabilitation had also become recent research hotspots.

CONCLUSIONS

Research on the application of AI in geriatric care has developed rapidly. The development of research and cooperation among countries/regions and institutions are limited. In the future, strengthening the cooperation and communication between different countries/regions and institutions may further drive this field's development. This study provides researchers with the information necessary to understand the current state, collaborative networks, and main research hotspots of the field. In addition, our results suggest a series of recommendations for future research.

Piloting Smart Home Sensors to Detect Overnight Respiratory and Withdrawal Symptoms in Adults Prescribed Opioids

BACKGROUND

Novel strategies are needed to curb the opioid overdose epidemic. Smart home sensors have been successfully deployed as digital biomarkers to monitor health conditions, yet they have not been used to assess symptoms important to opioid use and overdose risks.

AIM

This study piloted smart home sensors and investigated their ability to accurately detect clinically pertinent symptoms indicative of opioid withdrawal or respiratory depression in adults prescribed methadone.

METHODS

Participants (n = 4; 3 completed) were adults with opioid use disorder exhibiting moderate levels of pain intensity, withdrawal symptoms, and sleep disturbance. Participants were invited to two 8-hour nighttime sleep opportunities to be recorded in a sleep research laboratory, using observed polysomnography and ambient smart home sensors attached to lab bedroom walls. Measures of feasibility included completeness of data captured. Accuracy was determined by comparing polysomnographic data of sleep/wake and respiratory status assessments with time and event sensor data.

RESULTS

Smart home sensors captured overnight data on 48 out of 64 hours (75% completeness). Sensors detected sleep/wake patterns in alignment with observed sleep episodes captured by polysomnography 89.4% of the time. Apnea events (n = 118) were only detected with smart home sensors in two episodes where oxygen desaturations were less severe (>80%).

CONCLUSIONS

Smart home technology could serve as a less invasive substitute for biologic monitoring for adults with pain, sleep disturbances, and opioid withdrawal symptoms. Supplemental sensors should be added to detect apnea events. Such innovations could provide a step forward in assessing overnight symptoms important to populations taking opioids.

Can Smart Home Technologies Help Older Adults Manage Their Chronic Condition? A Systematic Literature Review

The management of chronic diseases requires personalized healthcare that allows older adults to manage their diseases at home. This systematic review aimed to describe the smart home technologies used in the management of chronic diseases in older people. A systematic literature review was conducted on four databases and was reported following the PRISMA statement. Nineteen articles were included. The intervention technologies were classified into three groups: smart home, characterized by environmental sensors detecting motion, contact, light, temperature, and humidity; external memory aids, characterized by a partnership between mobile apps and smart home-based activity learning; and hybrid technology, with the integration of multiple technologies, such as devices installed at patients' homes and telemedicine. The health outcomes evaluated are vital signs, medication management, ADL-IADL, mobility, falls, and quality of life. Smart homes show great potential in the management of chronic diseases by favouring the control of exacerbations and increasing patients' safety by providing support in disease management, including support for cognitively impaired older people. The use of smart homes in the community could bring numerous benefits in terms of continuity of care, allowing the constant monitoring of older people by local and hospital health services.

Nurse-in-the-loop smart home detection of health events associated with diagnosed chronic conditions: A case-event series

Background

Telehealth and home-based care options significantly expanded during the SARS-CoV2 pandemic. Sophisticated, remote monitoring technologies now exist that support at-home care. Advances in the research of smart homes for health monitoring have shown these technologies are capable of recognizing and predicting health changes in near-real time. However, few nurses are familiar enough with this technology to use smart homes for optimizing patient care or expanding their reach into the home between healthcare touch points.

Objective

The objective of this work is to explore a partnership between nurses and smart homes for automated remote monitoring and assessing of patient health. We present a series of health event cases to demonstrate how this partnership may be harnessed to effectively detect and report on clinically relevant health events that can be automatically detected by smart homes.

Participants

25 participants with multiple chronic health conditions.

Methods

Ambient sensors were installed in the homes of 25 participants with multiple chronic health conditions. Motion, light, temperature, and door usage data were continuously collected from participants' homes. Descriptions of health events and participants' associated behaviors were captured via weekly nursing telehealth visits with study participants and used to analyze sensor data representing health events. Two cases of participants with congestive heart failure exacerbations, one case of urinary tract infection, two cases of bowel inflammation flares, and four cases of participants with sleep interruption were explored.

Results

For each case, clinically relevant health events aligned with changes from baseline in behavior data patterns derived from sensors installed in the participant's home. In some cases, the detected event was precipitated by additional behavior patterns that could be used to predict the event.

Conclusions

We found evidence in this case series that continuous sensor-based monitoring of patient behavior in home settings may be used to provide automated detection of health events. Nursing insights into smart home sensor data could be used to initiate preventive strategies and provide timely intervention.

Tweetable abstract

Nurses partnered with smart homes could detect exacerbations of health conditions at home leading to early intervention.

Relational, community-based and practical: Support systems used by Canadian spousal caregivers living seasonally in the United States

Every year tens of thousands of older Canadians travel to the southern United States (US) to live there seasonally during the winter months to enjoy a warmer climate-a practice known as international retirement migration. Several factors facilitate participation in this transnational mobility, including having the financial resources needed to live abroad. For those managing chronic or acute health conditions, traveling with a caregiver (typically a spouse) is another important facilitator. In this qualitative analysis, we explore the transnational systems of support that Canadian international retirement migrant spousal caregivers draw upon to enable them to provide care while in the US. We report on the findings of ten semi-structured dyad interviews (n = 20 participants) conducted with Canadian international retirement migrants living seasonally in Yuma, Arizona. The dyads consisted of spouses, one of whom had defined care needs and the other of whom provided informal care. Through thematic analysis of these interviews, we identified three types of transnational support systems that spousal caregivers draw on: relational, community-based and practical. While aspects of these support systems have been documented in other informal care-giving studies, this analysis demonstrates their copresence in the transnational care-giving context associated with international retirement migration. Overall, this analysis highlights the benefits of close social relations enjoyed by international retirement migrants providing informal care to mitigate the lack of access to their established support networks at home.

Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature

AIM

The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications.

BACKGROUND

In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications.

EVALUATION

International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison.

KEY ISSUES

Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network.

CONCLUSION

The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care.

IMPLICATIONS FOR NURSING MANAGEMENT

Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.

Experiences and perceptions of final-year nursing students of using a chatbot in a simulated emergency situation: A qualitative study

AIM

The aim of this study is to explore the experiences and perceptions of final-year nursing students on the acceptability and feasibility of using a chatbot for clinical decision-making and patient safety.

BACKGROUND

The effective and inclusive use of new technologies such as conversational agents or chatbots could support nurses in increasing evidence-based care and decreasing low-quality services.

METHODS

A descriptive qualitative study was used through focus group interviews. The data analysis was conducted using a thematic analysis.

RESULTS

This study included 114 participants. After our data analysis, two main themes emerged: (i) experiences in the use of a chatbot service for clinical decision-making and and (ii) integrating conversational agents into the organizational safety culture.

CONCLUSIONS

The findings of our study provide preliminary support for the acceptability and feasibility of adopting SafeBot, a chatbot for clinical decision-making and patient safety. Our results revealed substantial recommendations for refining navigation, layout and content, as well as useful insights to support its acceptance in real nursing practice.

IMPLICATIONS FOR NURSING MANAGEMENT

Leaders and managers may well see artificial intelligence-based conversational agents like SafeBot as a potential solution in modern nursing practice for effective problem-solving resolution, innovative staffing and nursing care delivery models at the bedside and criteria for measuring and ensure quality and patient safety.

In-Home Monitoring Technology for Aging in Place: Scoping Review

BACKGROUND

For successful aging-in-place strategy development, in-home monitoring technology is necessary as a new home modification strategy. Monitoring an older adult's daily physical activity at home can positively impact their health and well-being by providing valuable information about functional, cognitive, and social health status. However, it is questionable how these in-home monitoring technologies have changed the traditional residential environment. A comprehensive review of existing research findings should be utilized to characterize recent relative technologies and to inform design considerations.

OBJECTIVE

The main purpose of this study was to classify recent smart home technologies that monitor older adults' health and to architecturally describe these technologies as they are used in older adults' homes.

METHODS

The scoping review method was employed to identify key characteristics of in-home monitoring technologies for older adults. In June 2021, four databases, including Web of Science, IEEE Xplore, ACM Digital Library, and Scopus, were searched for peer-reviewed articles pertaining to smart home technologies used to monitor older adults' health in their homes. We used two search strings to retrieve articles: types of technology and types of users. For the title, abstract, and full-text screening, the inclusion criteria were original and peer-reviewed research written in English, and research on monitoring, detecting, recognizing, analyzing, or tracking human physical, emotional, and social behavior. The exclusion criteria included theoretical, conceptual, or review papers; studies on wearable systems; and qualitative research.

RESULTS

This scoping review identified 30 studies published between June 2016 and 2021 providing overviews of in-home monitoring technologies, including (1) features of smart home technologies and (2) sensor locations and sensor data. First, we found six functions of in-home monitoring technology among the reviewed papers: daily activities, abnormal behaviors, cognitive impairment, falls, indoor person positioning, and sleep quality. Most of the research (n=27 articles) focused on functional monitoring and analysis, such as activities of daily living, instrumental activities of daily living, or falls among older adults; a few studies (n=3) covered social interaction monitoring. Second, this scoping review also found 16 types of sensor technologies. The most common data types encountered were passive infrared motion sensors (n=21) and contact sensors (n=19), which were used to monitor human behaviors such as bodily presence and time spent on activities. Specific locations for each sensor were also identified.

CONCLUSIONS

This wide-ranging synthesis demonstrates that in-home monitoring technologies within older adults' homes play an essential role in aging in place, in that the technology monitors older adults' daily activities and identifies various health-related issues. This research provides a key summarization of in-home monitoring technologies that can be applied in senior housing for successful aging in place. These findings will be significant when developing home modification strategies or new senior housing.

Musculoskeletal Biomarkers Response to Exercise in Older Adults

Exercise is an essential component of any good health style, being particularly important for older adults to counteract the effects of aging, including sarcopenia and osteoporosis, which can result in lower fall probability. Exercise programs for older adults are especially designed for that population. A rigorous evaluation of those programs is necessary to assure most benefit is achieved. Serum biomarkers of proteins intrinsic to musculoskeletal homeostasis could contribute objectively to the assessment of the benefits of exercise. In this work, in addition to the usual physical fitness and balance tests, ELISA assays quantified the serum levels of six proteins and one polysaccharide important for the homeostasis of muscle (troponin T and alpha-actinin), tendon/ligament (tenomodulin), cartilage (cartilage oligomeric matrix protein and hyaluronan) and bone (osteocalcin and sclerostin), before and after 8 weeks of an exercise program tailored to older adults, Stay Strong Stay Healthy, offered at a Community Center and at an Independent Senior Living facility. Statistical significance was determined by non-parametric tests (Wilcoxon Signed Ranks and Mann-Whitney U). Physical fitness and balance improved as expected along with a significant decrease in sclerostin, pointing to less inhibition of bone deposition. However, when considering each type of dwelling separately, older adults always saw a significant decrease of the isoform of troponin T associated with fast-twitch muscles, suggesting that daily levels of physical activity may also have a role in the benefit of older adults from exercise.

Health Monitoring Using Smart Home Technologies: A Scoping Review (Preprint)

BACKGROUND

The Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring.

OBJECTIVE

This scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring.

METHODS

Peer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed.

RESULTS

Most of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy.

CONCLUSIONS

This scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps-in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector.

Factors influencing community-dwelling older adults' readiness to adopt smart home technology: A qualitative exploratory study

AIMS

Ageing-in-place for older people could be more feasible with the support of smart home technology. Ageing in-place may maximize the independence of older adults and enhance their well-being and quality of life, while decreasing the financial burden of residential care costs, and addressing workforce shortages. However, the uptake of smart home technology is very low among older adults. Accordingly, the aim of this study was to explore factors influencing community-dwelling older adults' readiness to adopt smart home technology.

DESIGN

A qualitative exploratory study design was utilized.

METHODS

Descriptive data were collected between 2019 and 2020 to provide context of sample characteristics for community-dwelling older adults aged ≥65 years. Qualitative data were collected via semi-structured interviews and focus groups, to generate an understanding of older adult's perspectives. Thematic analysis of interviews and focus group transcripts was completed. The Elderadopt model was the conceptual framework used in the analysis of the findings.

RESULTS

Several factors influenced community-dwelling older adults' (N = 19) readiness to adopt smart home technology. Five qualitative themes were identified: knowledge, health and safety, independence, security and cost.

CONCLUSION

Community-dwelling older adults were open to adopting smart home technology to support independence despite some concerns about security and loss of privacy. Opportunities to share information about smart home technology need to be increased to promote awareness and discussion.

IMPACT

Wider adoption of smart home technology globally into the model of aged care can have positive impacts on caregiver burden, clinical workforce, health care utilization and health care economics. Nurses, as the main providers of healthcare in this sector need to be knowledgeable about the options available and be able to provide information and respond to questions know about ageing-in-place technologies to best support older adults and their families.

Application of Intelligent Nursing Information System in Emergency Nursing Management

This paper is combined with the intelligent nursing information system to build the emergency nursing platform architecture, from the system emergency procedures, system functionality, network environment deployment, and database design aspects of the discussion. Based on hospital information security, the nursing monitoring system of the intelligent nursing information system is constructed to realize network communication, which is clear and intuitive. The intelligent information system is applied to safety control, medical order information, condition information, and information inquiry, which can save working time and complete the rapid transmission and accurate execution of medical order, making the network communication of medical care more quick and convenient and maximizing the overall efficiency. Based on the disordered phenomenon of registration triage, the Relief algorithm is used to classify the aetiology and triage, and the combination of medical advice, information query, and IT technology is optimized, so as to eliminate the phenomenon of round diagnosis, insert number, and improve the medical environment of waiting for diagnosis, taking medicine, examination, and testing. Finally, through the testing of system information security, information traceability, and rapid information query, the problems in nursing management have been basically solved.

A Concept Analysis on the Use of Artificial Intelligence in Nursing

Artificial intelligence (AI) has a considerable present and future influence on healthcare. Nurses, representing the largest proportion of healthcare workers, are set to immensely benefit from this technology. However, the overall adoption of new technologies by nurses is quite slow, and the use of AI in nursing is considered to be in its infancy. The current literature on AI in nursing lacks conceptual clarity and consensus, which is affecting clinical practice, research activities, and theory development. Therefore, to set the foundations for nursing AI knowledge development, the purpose of this concept analysis is to clarify the conceptual components of AI in nursing and to determine its conceptual maturity. A concept analysis following Morse's approach was conducted, which examined definitions, characteristics, preconditions, outcomes, and boundaries on the state of AI in nursing. A total of 18 quantitative, qualitative, mixed-methods, and reviews related to AI in nursing were retrieved from the CINAHL and EMBASE databases using a Boolean search. Presently, the concept of AI in nursing is immature. The characteristics and preconditions of the use of AI in nursing are mixed between and within each other. The preconditions and outcomes on the use of AI in nursing are diverse and indiscriminately reported. As for boundaries, they can be more distinguished between robots, sensors, and clinical decision support systems, but these lines can become more blurred in the future. As of 2021, the use of AI in nursing holds much promise for the profession, but conceptual and theoretical issues remain.

The opportunities and challenges of using mobile health in elderly self-care

Population aging is a phenomenon expanding around the world and will be increase the incidence of chronic diseases and health costs. This study was conducted according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA). A comprehensive literature search was performed on 4 databases (Web of Science, PubMed, Science Direct, and ProQuest) for English language studies from January 1, 2000, to December 31, 2019. The keywords used to extract relevant contents were "e-health," "Elderly care," "Self-care," "challenge," "Opportunity" etc., The search strategy led to a total of 638 potentially relevant papers, of which 19 papers met the inclusion criteria. The results showed that the challenges of using mobile health in elderly self-care can be divided into technical, human and managerial challenges. The resulting opportunities include reducing health care costs; no need to visit verbal and remote access to elderly information. The use of mobile health in the elderly has advantages and disadvantages. One of the advantages of that is improving physical activity and reducing care costs, but it may break the privacy. The disadvantages of that can be resolved by educating the elder men.

Interdisciplinary collaboration between nursing and engineering in health care: A scoping review

BACKGROUND

Due to the rapid advancements in precision medicine and artificial intelligence, interdisciplinary collaborations between nursing and engineering have emerged. Although engineering is vital in solving complex nursing problems and advancing healthcare, the collaboration between the two fields has not been fully elucidated.

OBJECTIVES

To identify the study areas of interdisciplinary collaboration between nursing and engineering in health care, particularly focusing on the role of nurses in the collaboration.

METHODS

In this study, a scoping review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews was performed. A comprehensive search for published literature was conducted using the PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, Embase, Web of Science, ScienceDirect, Institute of Electrical and Electronics Engineers Digital Library, and Association for Computing Machinery Digital Library from inception to November 22, 2020. Data screening and extraction were performed independently by two reviewers. Any discrepancies in results were resolved through discussions or in consultation with a third reviewer. Data were analyzed by descriptive statistics and content analysis. Results were visualized in an interdisciplinary collaboration model.

RESULTS

We identified 6,752 studies through the literature search, and 60 studies met the inclusion criteria. The study areas of interdisciplinary collaboration concentrated on patient safety (n = 18), symptom monitoring and health management (n = 18), information system and nursing human resource management (n = 16), health education (n = 5), and nurse-patient communication (n = 3). The roles of nurses in the interdisciplinary collaboration were divided into four themes: requirement analyst (n = 21), designer (n = 22), tester(n = 37) and evaluator (n = 49). Based on these results, an interdisciplinary collaboration model was constructed.

CONCLUSIONS

Interdisciplinary collaborations between nursing and engineering promote nursing innovation and practice. However, these collaborations are still emerging and in the early stages. In the future, nurses should be more involved in the early stages of solving healthcare problems, particularly in the requirement analysis and designing phases. Furthermore, there is an urgent need to develop interprofessional education, strengthen nursing connections with the healthcare engineering industry, and provide more platforms and resources to bring nursing and engineering disciplines together.

Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review

BACKGROUND

It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. Increasingly, researchers are exploring the potential influences of AI health technologies (AIHTs) on nursing in general and on nursing education more specifically. However, little emphasis has been placed on synthesizing this body of literature.

OBJECTIVE

A scoping review was conducted to summarize the current and predicted influences of AIHTs on nursing education over the next 10 years and beyond.

METHODS

This scoping review followed a previously published protocol from April 2020. Using an established scoping review methodology, the databases of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Centre, Scopus, Web of Science, and Proquest were searched. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI. Data were charted using a structured form and narratively summarized into categories.

RESULTS

A total of 27 articles were identified (20 expository papers, six studies with quantitative or prototyping methods, and one qualitative study). The population included nurses, nurse educators, and nursing students at the entry-to-practice, undergraduate, graduate, and doctoral levels. A variety of AIHTs were discussed, including virtual avatar apps, smart homes, predictive analytics, virtual or augmented reality, and robots. The two key categories derived from the literature were (1) influences of AI on nursing education in academic institutions and (2) influences of AI on nursing education in clinical practice.

CONCLUSIONS

Curricular reform is urgently needed within nursing education programs in academic institutions and clinical practice settings to prepare nurses and nursing students to practice safely and efficiently in the age of AI. Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education. Finally, nursing students and practicing nurses must be equipped with the requisite knowledge and skills to effectively assess AIHTs and safely integrate those deemed appropriate to support person-centered compassionate nursing care in practice settings.

INTERNATIONAL REGISTERED REPORT IDENTIFIER IRRID

RR2-10.2196/17490.

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

BACKGROUND

Artificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses-the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers.

OBJECTIVE

This review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond.

METHODS

Using an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized.

RESULTS

A total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI.

CONCLUSIONS

Nurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/17490.

Profiling Patients by Intensity of Nursing Care: An Operative Approach Using Machine Learning

Physical function is a patient-oriented indicator and can be considered a proxy for the assignment of healthcare personnel. The study aims to create an algorithm that classifies patients into homogeneous groups according to physical function. A two-step machine-learning algorithm was applied to administrative data recorded between 2015 and 2018 at the University Hospital of Padova. A clustering-large-applications (CLARA) algorithm was used to partition patients into homogeneous groups. Then, machine learning technique (MLT) classifiers were used to categorize the doubtful records. Based on the results of the CLARA algorithm, records were divided into three groups according to the Barthel index: <45, >65, ≥45 and ≤65. The support vector machine was the MLT showing the best performance among doubtful records, reaching an accuracy of 66%. The two-step algorithm, since it splits patients into low and high resource consumption, could be a useful tool for organizing healthcare personnel allocation according to the patients' assistance needs.

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

BACKGROUND

Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients' natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain.

OBJECTIVE

This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain.

METHODS

A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem.

RESULTS

We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42.

CONCLUSIONS

Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians' real-world knowledge when developing pain-assessing machine learning models improves the model's performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

Guardians of humanity? The challenges of nursing practice in the digital age

Digital technologies have become a crucial factor in nursing. Given the fact that many tasks could also be done by robots or AI systems, the place for the nurse in this scenario is unclear. In what way and to what extent will the implementation of ever more sophisticated technology affect nursing practice? It is the aim of this paper to analyse the potential challenges of nursing practice in the digital age. The analysis is conducted through the lens of new materialism, a set of theoretical models that understand the relationship between humans and technology as dynamic and performative. According to this view, there is no prefixed essence of technology. Rather, the meaning of technology is enacted in concrete practice. The analysis shows that in past debates on technology use in nursing, the nurses' role has been defined as guardians of humanity, defending the patient against the dehumanizing effects of technology. This role has been transferred to the digital age, where it is the duty of nurses to cushion the negative effects of digital technology. As an alternative to this outdated role, nurses should be included in processes of technology design and policymaking. Enabling nursing professionals to shape the circumstances of a digitally enhanced holistic practice may empower their status within the healthcare system and also benefit the patient by contributing to a more person-centred care.

Illuminating the dark spaces of healthcare with ambient intelligence

Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.

Nursing in the Age of Artificial Intelligence: Protocol for a Scoping Review

BACKGROUND

It is predicted that digital health technologies that incorporate artificial intelligence will transform health care delivery in the next decade. Little research has explored how emerging trends in artificial intelligence-driven digital health technologies may influence the relationship between nurses and patients.

OBJECTIVE

The purpose of this scoping review is to summarize the findings from 4 research questions regarding emerging trends in artificial intelligence-driven digital health technologies and their influence on nursing practice across the 5 domains outlined by the Canadian Nurses Association framework: administration, clinical care, education, policy, and research. Specifically, this scoping review will examine how emerging trends will transform the roles and functions of nurses over the next 10 years and beyond.

METHODS

Using an established scoping review methodology, MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Centre, Scopus, Web of Science, and Proquest databases were searched. In addition to the electronic database searches, a targeted website search will be performed to access relevant grey literature. Abstracts and full-text studies will be independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included literature will focus on nursing and digital health technologies that incorporate artificial intelligence. Data will be charted using a structured form and narratively summarized.

RESULTS

Electronic database searches have retrieved 10,318 results. The scoping review and subsequent briefing paper will be completed by the fall of 2020.

CONCLUSIONS

A symposium will be held to share insights gained from this scoping review with key thought leaders and a cross section of stakeholders from administration, clinical care, education, policy, and research as well as patient advocates. The symposium will provide a forum to explore opportunities for action to advance the future of nursing in a technological world and, more specifically, nurses' delivery of compassionate care in the age of artificial intelligence. Results from the symposium will be summarized in the form of a briefing paper and widely disseminated to relevant stakeholders.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/17490.

Interpreting Health Events in Big Data Using Qualitative Traditions

The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant's description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. We make the case for clinicians with qualitative research expertise to be included at the design table to ensure optimal efficacy of smart health artificial intelligence and a positive end-user experience.

Interactive CO-learning for Research Engagement and Education (I-COREE) Curriculum to Build Capacity Between Community Partners and Academic Researchers

The voice of diverse communities continues to be minimal in academic research. Few models exist for education and training of new research topics and terminology and building partnership capacity in community-engaged research. Little is known about integrative education and training when building participatory research partnerships for sustainability and developing trust and rapport. Community partners at an Asian community-based health and social services center in a large metropolitan area wanted to explore the cultural context of a health-assistive smart home that monitors and auto-alerts with changes in health. With historical and recent rising trends in culturally insensitive research in several diverse communities, the concept of technology-enabled monitoring in the privacy of one's home brings uncertainty. Academic nurse researchers and community partners co-created a culturally safe integrative education and training curriculum, the Interactive CO-learning for Research Engagement and Education (I-COREE). The purpose was to design, implement, and evaluate the curriculum to respond to the community partners' needs to create a culturally safe space through an integrative education and training to facilitate building partnership capacity for research engagement including developing trust and rapport and addressing uncertainties in health-assistive technologies. Popular education tenets informed the curriculum. Twelve academic and community partners participated, four were team teachers who co-led the session. Implementation of the experiential, multimodal co-learning activities were conducted within ahalf-day. The curriculum evaluation indicated that it helped bridge critical conversations about partners' fears of the unknown, approach culturally sensitive topics safely, and trust and rapport. Key elements may be translatable to other partnerships.