Co-creating Humanistic AI AgeTech to Support Dynamic Care Ecosystems: A Preliminary Guiding Model.

As society rapidly digitizes, successful aging necessitates using technology for health and social care and social engagement. Technologies aimed to support older adults (e.g.

Designing the User Interface of a Nitroglycerin Dose Titration Decision Support System: User-Centered Design Study.

Background:  Nurses adjust intravenous nitroglycerin infusions to provide acute relief for angina by manually increasing or decreasing the dosage. However, titration can pose challenges, as excessively high doses can lead to hypotension, and low doses may result in inadequate pain relief. Clinical decision support systems (CDSSs) that predict changes in blood pressure for nitroglycerin dose adjustments may assist nurses with titration.

Nurse-led remote digital support for adults with chronic conditions: A systematic synthesis without meta-analysis.

Aim: The systematic review aims to synthesize the literature examining the effectiveness of nurse-led remote digital support on health outcomes in adults with chronic conditions.

Background: Adults with chronic diseases have increased rates of mortality and morbidity and use health care resources at a higher intensity than those without chronic conditions-placing strain on the patient, their caregivers and health systems. Nurse-led digital health disease self-management interventions have potential to improve outcomes for patients with chronic conditions by facilitating care in environments other that the hospital setting.

Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review.

Background: Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. Despite the recognition of the importance of this problem, there is a lack of research that specifically examines the strategies used to mitigate age-related bias in ML models and the effectiveness of these strategies.

Objective: To address this gap, we conducted a scoping review of mitigation strategies to reduce age-related bias in ML.

User Perception of Smart Home Surveillance Among Adults Aged 50 Years and Older: Scoping Review.

Background: Smart home technology (SHT) can be useful for aging in place or health-related purposes. However, surveillance studies have highlighted ethical issues with SHTs, including user privacy, security, and autonomy.

Objective: As digital technology is most often designed for younger adults, this review summarizes perceptions of SHTs among users aged 50 years and older to explore their understanding of privacy, the purpose of data collection, risks and benefits, and safety.

Predicting Blood Pressure After Nitroglycerin Infusion Dose Titration in Critical Care Units: A Multicenter Retrospective Study.

Critical care nurses use physiological indicators, such as blood pressure, to guide their decision-making regarding the titration of nitroglycerin infusions. A retrospective study was conducted to determine the accuracy of systolic blood pressure predictions during nitroglycerin infusions. Data were extracted from the publicly accessible eICU program database.

Researched Apps Used in Dementia Care for People Living With Dementia and Their Informal Caregivers: Systematic Review on App Features, Security, and Usability.

Background: Studies have shown that mobile apps have the potential to serve as nonpharmacological interventions for dementia care, improving the quality of life of people living with dementia and their informal caregivers. However, little is known about the needs for and privacy aspects of these mobile apps in dementia care.

Objective: This review seeks to understand the landscape of existing mobile apps in dementia care for people living with dementia and their caregivers with respect to app features, usability testing, privacy, and security.

A New Age of Health Promotion: The Use of Digital Infographics in Nursing Education.

The COVID-19 pandemic has shifted how nursing education and information are delivered, with many classes being moved to an online platform. This opened opportunities to find creative ways to engage students. As a result, an entirely online infographic assignment for final-year baccalaureate nursing students was created.

Explainable Machine Learning Model to Predict COVID-19 Severity Among Older Adults in the Province of Quebec.

Context: Patients over the age of 65 years are more likely to experience higher severity and mortality rates than other populations from COVID-19. Clinicians need assistance in supporting their decisions regarding the management of these patients. Artificial Intelligence (AI) can help with this regard.

Evaluating knowledge and attitudes scales for the care of older adults among nursing students in Ghana.

Background: Understanding nursing students' knowledge about and attitudes toward older adults' using context-specific survey instruments can help to identify and design effective learning and teaching materials to improve the care for persons 60 years and above. However, there are no validated instruments to examine nursing students' knowledge and attitudes toward the care for older adults in the African context. The study aimed to evaluate the items on the Knowledge about Older Patients Quiz and Kogan's Attitudes towards Old People Scale suitable for the African context.

The effectiveness and cost-effectiveness of clinical fracture-risk assessment tools in reducing future osteoporotic fractures among older adults: a structured scoping review.

Unlabelled: This scoping review described the use, effectiveness, and cost-effectiveness of clinical fracture-risk assessment tools to prevent future osteoporotic fractures among older adults. Results show that the screening was not superior in preventing all osteoporosis-related fractures to usual care. However, it positively influenced participants' perspectives on osteoporosis, may have reduced hip fractures, and seemed cost-effective.

Real-time location systems technology in the care of older adults with cognitive impairment living in residential care: A scoping review.

Introduction: There has been growing interest in using real-time location systems (RTLS) in residential care settings. This technology has clinical applications for locating residents within a care unit and as a nurse call system, and can also be used to gather information about movement, location, and activity over time. RTLS thus provides health data to track markers of health and wellbeing and augment healthcare decisions.

"We've All Lost So Much": The Long-Term Care Home Experiences of Essential Family Caregivers During COVID-19.

Background: During the coronavirus (COVID-19) pandemic, long-term care homes (LTCHs) imposed visitor restrictions that prevented essential family caregivers (EFCs) from entering the homes. Under these policies, EFCs had to engage in virtual, window, and outdoor visits, prior to the re-initiation of indoor visits.

Objective: To understand EFCs' visitation experiences with LTCH residents during COVID-19.

Evolving with technology: Machine learning as an opportunity for operating room nurses to improve surgical care-A commentary.

Aims: To describe machine learning applications in an operating room setting, raise awareness of the lack of nursing inclusion on machine learning algorithm development, and show how operating room nurses can co-create this new technology.

Background: Operating room nurses and managers perform anticipatory work on a daily basis to manage intrinsic and extrinsic factors that can cause surgical delays.

Evaluation: Recent literature on machine learning and its potential use in operating room settings was reviewed along with literature on the role of the nurse in co-creating novel technology.

Ageism and Artificial Intelligence: Protocol for a Scoping Review.

Background: Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention has been paid to algorithmic biases toward older adults.

Objective: This paper documents the search strategy and process for a scoping review exploring how age-related bias is encoded or amplified in AI systems as well as the corresponding legal and ethical implications.

Methods: The scoping review follows a 6-stage methodology framework developed by Arksey and O'Malley.

"It's the worst thing I've ever been put through in my life": the trauma experienced by essential family caregivers of loved ones in long-term care during the COVID-19 pandemic in Canada.

Background: Essential family caregivers (EFCs) of relatives living in long-term care homes (LTCHs) experienced restricted access to their relatives due to COVID-19 visitation policies. Residents' experiences of separation have been widely documented; yet, few have focused on EFCs' traumatic experiences during the pandemic. Objective: This study aims to explore the EFCs' trauma of being locked out of LTCHs and unable to visit their loved ones in-person during COVID-19.

Who's in the House? Staffing in Long-Term Care Homes Before and During COVID-19 Pandemic.

Critical gaps exist in our knowledge on how best to provide quality person-centered care to long-term care (LTC) home residents which is closely tied to not knowing what the ideal staff is complement in the home. A survey was created on staffing in LTC homes before and during the COVID-19 pandemic to determine how the staff complement changed. Perspectives were garnered from researchers, clinicians, and policy experts in eight countries and the data provides a first approximation of staffing before and during the pandemic.

Poor and Lost Connections: Essential Family Caregivers' Experiences Using Technology with Family Living in Long-Term Care Homes during COVID-19.

Long-term care homes (LTCHs) restricted essential family caregivers' (EFCs) visitations during COVID-19, and virtual visits using technology were used. To understand EFCs' virtual visitations experiences during COVID-19 in two Canadian provinces. Seven focus groups were conducted with EFCs.

Effects of Exergaming on Physical and Cognitive Outcomes of Older Adults Living in Long-Term Care Homes: A Systematic Review.

Background: Aging is often associated with increasing functional decline as measured by deterioration in mobility and activities of daily living. Older adults (OAs) living in residential long-term care (LTC) homes in particular may not engage in regular physical exercise, significantly increasing their risk of further cognitive and functional decline. Exergaming may hold promise for OAs by combining exercise and technology-based gaming systems, but evidence for its use in LTC is unknown.

Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence.

Background: Research on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare.

Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults.

Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, education, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature.

Development and dissemination of an ethical guidance and person-centred isolation care planning tool to support the care of people with dementia during the COVID-19 pandemic.

Long-term care (LTC) residents have been disproportionately impacted by the COVID-19 pandemic, both from the virus itself and the restrictions in effect for infection prevention and control. Many barriers exist in LTC to prevent the effective isolation of suspect or confirmed COVID-19 cases. Furthermore, these measures have a severe impact on the well-being of LTC residents.