Factors That Influence Successful Adoption of Real-Time Location Systems for Use in a Dementia Care Setting: Mixed Methods Study.

BACKGROUND: Technology has been identified as a potential solution to alleviate resource gaps and augment care delivery in dementia care settings such as hospitals, long-term care, and retirement homes. There has been an increasing interest in using real-time location systems (RTLS) across health care settings for older adults with dementia, specifically related to the ability to track a person's movement and location. OBJECTIVE: In this study, we aimed to explore the factors that influence the adoption or nonadoption of an RTLS during its implementation in a specialized inpatient dementia unit in a tertiary care rehabilitation hospital. METHODS: The study included data from a brief quantitative survey and interviews from a convenience sample of frontline participants. Our deductive analysis of the interview used the 3 categories of the Fit Between Individuals, Task, and Technology framework as follows: individual and task, individual and technology, and task and technology. The purpose of using this framework was to assess the quality of the fit between technology attributes and an individual's self-reported intentions to adopt RTLS technology. RESULTS: A total of 20 health care providers (HCPs) completed the survey, of which 16 (80%) participated in interviews. Coding and subsequent analysis identified 2 conceptual subthemes in the individual-task fit category, including the identification of the task and the perception that participants were missing at-risk patient events. The task-technology fit category consisted of 3 subthemes, including reorganization of the task, personal control in relation to the task, and efficiency or resource allocation. A total of 4 subthemes were identified in the individual-technology fit category, including privacy and personal agency, trust in the technology, user interfaces, and perceptions of increased safety. CONCLUSIONS: By the end of the study, most of the unit's HCPs were using the tablet app based on their perception of its usefulness, its alignment with their comfort level with technology, and its ability to help them perform job responsibilities. HCPs perceived that they were able to reduce patient search time dramatically, yet any improvements in care were noted to be implied, as this was not measured. There was limited anecdotal evidence of reduced patient risk or adverse events, but greater reported peace of mind for HCPs overseeing patients' activity levels.

Implementation of the Dementia Isolation Toolkit in long-term care improves awareness but does not reduce moral distress amongst healthcare providers.

BACKGROUND: Healthcare providers may experience moral distress when they are unable to take the ethically or morally appropriate action due to real or perceived constraints in delivering care, and this psychological stressor can negatively impact their mental health, leading to burnout and compassion fatigue. This study describes healthcare providers experiences of moral distress working in long-term care settings during the COVID-19 pandemic and measures self-reported levels of moral distress pre- and post-implementation of the Dementia Isolation Toolkit (DIT), a person-centred care intervention designed for use by healthcare providers to alleviate moral distress. METHODS: Subjective levels of moral distress amongst providers (e.g., managerial, administrative, and front-line employees) working in three long-term care homes was measured pre- and post-implementation of the DIT using the Moral Distress in Dementia Care Survey and semi-structured interviews. Interviews explored participants' experiences of moral distress in the workplace and the perceived impact of the intervention on moral distress. RESULTS: A total of 23 providers between the three long-term care homes participated. Following implementation of the DIT, subjective levels of moral distress measured by the survey did not change. When interviewed, participants reported frequent experiences of moral distress from implementing public health directives, staff shortages, and professional burnout that remained unchanged following implementation. However, in the post-implementation interviews, participants who used the DIT reported improved self-awareness of moral distress and reductions in the experience of moral distress. Participants related this to feeling that the quality of resident care was improved by integrating principals of person-centered care and information gathered from the DIT. CONCLUSIONS: This study highlights the prevalence and exacerbation of moral distress amongst providers during the pandemic and the myriad of systemic factors that contribute to experiences of moral distress in long-term care settings. We report divergent findings with no quantitative improvement in moral distress post-intervention, but evidence from interviews that the DIT may ease some sources of moral distress and improve the perceived quality of care delivered. This study demonstrates that an intervention to support person-centred isolation care in this setting had limited impact on overall moral distress during the COVID-19 pandemic.

Undersampling and cumulative class re-decision methods to improve detection of agitation in people with dementia.

Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk. Developing objective agitation detection approaches is important to support health and safety of PwD living in a residential setting. In a previous study, we collected multimodal wearable sensor data from 17 participants for 600 days and developed machine learning models for detecting agitation in 1-min windows. However, there are significant limitations in the dataset, such as imbalance problem and potential imprecise labels as the occurrence of agitation is much rarer in comparison to the normal behaviours. In this paper, we first implemented different undersampling methods to eliminate the imbalance problem, and came to the conclusion that only 20% of normal behaviour data were adequate to train a competitive agitation detection model. Then, we designed a weighted undersampling method to evaluate the manual labeling mechanism given the ambiguous time interval assumption. After that, the postprocessing method of cumulative class re-decision (CCR) was proposed based on the historical sequential information and continuity characteristic of agitation, improving the decision-making performance for the potential application of agitation detection system. The results showed that a combination of undersampling and CCR improved F1-score and other metrics to varying degrees with less training time and data. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00313-8.

Factors influencing the clinical adoption of quantitative gait analysis technology with a focus on clinical efficacy and clinician perspectives: A scoping review.

INTRODUCTION: Quantitative gait analysis (QGA) has the potential to support clinician decision-making. However, it is not yet widely accepted in practice. Evidence for clinical efficacy (i.e., efficacy and effectiveness), as well as a users' perspective on using the technology in clinical practice (e.g., ease of use and usefulness) can help impact their widespread adoption. OBJECTIVE: To synthesize the literature on the clinical efficacy and clinician perspectives on the use of gait analysis technologies in the clinical care of adult populations. METHODS: This scoping review followed the Joanna Briggs Institute (JBI) methodology for scoping reviews. We included peer-reviewed and gray literature (i.e., conference abstracts). A search was conducted in MEDLINE (Ovid), CENTRAL (Ovid), EMBASE (Ovid), CINAHL (EBSCO) and SPORTDiscus (EBSCO). Included full-text studies were critically appraised using the JBI critical appraisal tools. RESULTS: A total of 15 full-text studies and two conference abstracts were included in this review. Results suggest that QGA technologies can influence decision-making with some evidence to suggest their role in improving patient outcomes. The main barrier to ease of use was a clinician's lack of data expertise, and main facilitator was receiving support from staff. Barriers to usefulness included challenges finding suitable reference data and data accuracy, while facilitators were enhancing patient care and supporting clinical decision-making. SIGNIFICANCE: This review is the first step to understanding how QGA technologies can optimize clinical practice. Many gaps in the literature exist and reveal opportunities to improve the clinical adoption of gait analysis technologies. Further research is needed in two main areas: 1) examining the clinical efficacy of gait analysis technologies and 2) gathering clinician perspectives using a theoretical model like the Technology Acceptance Model to guide study design. Results will inform research aimed at evaluating, developing, or implementing these technologies. FUNDING: This work was supported by the Walter and Maria Schroeder Institute for Brain Innovation and Recovery and AGE-WELL Graduate Student Award in Technology and Aging [2021,2022].

Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson's disease.

INTRODUCTION: Gait impairments in Parkinson's disease (PD) are treated with dopaminergic medication or deep-brain stimulation (DBS), although the magnitude of the response is variable between individuals. Computer vision-based approaches have previously been evaluated for measuring the severity of parkinsonian gait in videos, but have not been evaluated for their ability to identify changes within individuals in response to treatment. This pilot study examines whether a vision-based model, trained on videos of parkinsonism, is able to detect improvement in parkinsonian gait in people with PD in response to medication and DBS use. METHODS: A spatial-temporal graph convolutional model was trained to predict MDS-UPDRS-gait scores in 362 videos from 14 older adults with drug-induced parkinsonism. This model was then used to predict MDS-UPDRS-gait scores on a different dataset of 42 paired videos from 13 individuals with PD, recorded while ON and OFF medication and DBS treatment during the same clinical visit. Statistical methods were used to assess whether the model was responsive to changes in gait in the ON and OFF states. RESULTS: The MDS-UPDRS-gait scores predicted by the model were lower on average (representing improved gait; p = 0.017, Cohen's d = 0.495) during the ON medication and DBS treatment conditions. The magnitude of the differences between ON and OFF state was significantly correlated between model predictions and clinician annotations (p = 0.004). The predicted scores were significantly correlated with the clinician scores (Kendall's tau-b = 0.301, p = 0.010), but were distributed in a smaller range as compared to the clinician scores. CONCLUSION: A vision-based model trained on parkinsonian gait did not accurately predict MDS-UPDRS-gait scores in a different PD cohort, but detected weak, but statistically significant proportional changes in response to medication and DBS use. Large, clinically validated datasets of videos captured in many different settings and treatment conditions are required to develop accurate vision-based models of parkinsonian gait.

A pilot observational study of gait changes over time before and after an unplanned hospital visit in long-term care residents with dementia.

BACKGROUND: Older adults with dementia living in long-term care (LTC) have high rates of hospitalization. Two common causes of unplanned hospital visits for LTC residents are deterioration in health status and falls. Early detection of health deterioration or increasing falls risk may present an opportunity to intervene and prevent hospitalization. There is some evidence that impairments in older adults' gait, such as reduced gait speed, increased variability, and poor balance may be associated with hospitalization. However, it is not clear whether changes in gait are observable and measurable before an unplanned hospital visit and whether these changes persist after the acute medical issue has been resolved. The objective of this study was to examine gait changes before and after an unplanned acute care hospital visit in people with dementia. METHODS: We performed a secondary analysis of quantitative gait measures extracted from videos of natural gait captured over time on a dementia care unit and collected information about unplanned hospitalization from health records. RESULTS: Gait changes in study participants before hospital visits were characterized by decreasing stability and step length, and increasing step variability, although these changes were also observed in participants without hospital visits. In an age and sex-adjusted mixed effects model, gait speed and step length declined more quickly in those with a hospital visit compared to those without. CONCLUSIONS: These results provide preliminary evidence that clinically meaningful longitudinal gait changes may be captured by repeated non-invasive gait monitoring, although a larger study is needed to identify changes specific to future medical events.

Nurse practitioner led implementation of huddles for staff in long term care homes during the COVID-19 pandemic.

BACKGROUND: Staff working in long-term care (LTC) homes during COVID-19 frequently reported a lack of communication, collaboration, and teamwork, all of which are associated with staff dissatisfaction, health concerns, lack of support and moral distress. Our study introduced regular huddles to support LTC staff during COVID-19, led by a Nurse Practitioner (NP). The objectives were to evaluate the process of huddle implementation and to examine differences in outcomes between categories of staff (direct care staff, allied care and support staff, and management) who attended huddles and those who did not. METHODS: All staff and management at one LTC home (< 150 beds) in Ontario, Canada were included in this pre-experimental design study. The process evaluation used a huddle observation tool and focused on the dose (duration, frequency) and fidelity (NP's adherence to the huddle guide) of implementation. The staff attending and non-attending huddles were compared on outcomes measured at post-test: job satisfaction, physical and mental health, perception of support received, and levels of moral distress. The outcomes were assessed with validated measures and compared between categories of staff using Bayesian models. RESULTS: A total of 42 staff enrolled in the study (20 attending and 22 non-attending huddles). Forty-eight huddles were implemented by the NP over 15 weeks and lasted 15 min on average. Huddles were most commonly attended by direct care staff, followed by allied care/support, and management staff. All huddles adhered to the huddle guide as designed by the research team. Topics most often addressed during the huddles were related to resident care (46%) and staff well-being (34%). Differences were found between staff attending and non-attending huddles: direct care staff attending huddles reported lower levels of overall moral distress, and allied care and support staff attending huddles perceived higher levels of support from the NP. CONCLUSIONS: NP-led huddles in LTC homes may positively influence staff outcomes. The process evaluation provided some understanding of why the huddles may have been beneficial: the NP addressed resident care issues which were important to staff, encouraged a collaborative approach to solving issues on the unit, and discussed their well-being. TRIAL REGISTRATION NUMBER: NCT05387213, registered on 24/05/2022.

Walking path images from real-time location data predict degree of cognitive impairment.

BACKGROUND: We propose a novel approach that uses spatial walking patterns produced by real-time location systems to classify the severity of cognitive impairment (CI) among residents of a memory care unit. METHODS: Each participant was classified as "No-CI", "Mild-Moderate CI" or "Severe CI" based on their Mini-Mental State Examination scores. The location data was distributed into windows of various durations (5, 10, 15 and 30 min) and transformed into images used to train a custom convolutional neural network (CNN) at each window size. Class Activation Mapping was applied to the top-performing models to determine the features of images associated with each class. RESULTS: The best performing model achieved an accuracy of 87.38 % (30-min window length) with an overall pattern that larger window sizes perform better. The class activation maps were effectively consolidated into a Cognitive Impairment Classification Value (CICV) score that distinguishes between No-CI, Mild-Moderate CI, and Severe CI. CONCLUSION: The class activation maps show that the CNN made relevant and intuitive distinctions for paths corresponding to each class. Future work should validate the proposed techniques with participants who are well-characterized clinically, over larger and diversified settings, and towards classification of neuropsychiatric symptoms such as motor agitation, mood, or apathy.

Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia.

Falls are a leading cause of morbidity and mortality in older adults with dementia residing in long-term care. Having access to a frequently updated and accurate estimate of the likelihood of a fall over a short time frame for each resident will enable care staff to provide targeted interventions to prevent falls and resulting injuries. To this end, machine learning models to estimate and frequently update the risk of a fall within the next 4 weeks were trained on longitudinal data from 54 older adult participants with dementia. Data from each participant included baseline clinical assessments of gait, mobility, and fall risk at the time of admission, daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system. Systematic ablations investigated the effects of various hyperparameters and feature sets and experimentally identified differential contributions from baseline clinical assessments, ambient gait analysis, and daily medication intake. In leave-one-subject-out cross-validation, the best performing model predicts the likelihood of a fall over the next 4 weeks with a sensitivity and specificity of 72.8 and 73.2, respectively, and achieved an area under the receiver operating characteristic curve (AUROC) of 76.2. By contrast, the best model excluding ambient gait features achieved an AUROC of 56.2 with a sensitivity and specificity of 51.9 and 54.0, respectively. Future research will focus on externally validating these findings to prepare for the implementation of this technology to reduce fall and fall-related injuries in long-term care.

Can Patients with Dementia Be Identified in Primary Care Electronic Medical Records Using Natural Language Processing?

Dementia and mild cognitive impairment can be underrecognized in primary care practice and research. Free-text fields in electronic medical records (EMRs) are a rich source of information which might support increased detection and enable a better understanding of populations at risk of dementia. We used natural language processing (NLP) to identify dementia-related features in EMRs and compared the performance of supervised machine learning models to classify patients with dementia. We assembled a cohort of primary care patients aged 66 + years in Ontario, Canada, from EMR notes collected until December 2016: 526 with dementia and 44,148 without dementia. We identified dementia-related features by applying published lists, clinician input, and NLP with word embeddings to free-text progress and consult notes and organized features into thematic groups. Using machine learning models, we compared the performance of features to detect dementia, overall and during time periods relative to dementia case ascertainment in health administrative databases. Over 900 dementia-related features were identified and grouped into eight themes (including symptoms, social, function, cognition). Using notes from all time periods, LASSO had the best performance (F1 score: 77.2%, sensitivity: 71.5%, specificity: 99.8%). Model performance was poor when notes written before case ascertainment were included (F1 score: 14.4%, sensitivity: 8.3%, specificity 99.9%) but improved as later notes were added. While similar models may eventually improve recognition of cognitive issues and dementia in primary care EMRs, our findings suggest that further research is needed to identify which additional EMR components might be useful to promote early detection of dementia. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-023-00125-6.

Factors Influencing the Clinical Adoption of Quantitative Gait Analysis Technologies for Adult Patient Populations With a Focus on Clinical Efficacy and Clinician Perspectives: Protocol for a Scoping Review.

BACKGROUND: Quantitative gait analysis can support clinical decision-making. These analyses can be performed using wearable sensors, nonwearable sensors, or a combination of both. However, to date, they have not been widely adopted in clinical practice. Technology adoption literature has highlighted the clinical efficacy of technology and the users' perspective on the technology (eg, ease of use and usefulness) as some factors that influence their widespread adoption. OBJECTIVE: To assist with the clinical adoption of quantitative gait technologies, this scoping review will synthesize the literature on their clinical efficacy and clinician perspectives on their use in the clinical care of adult patient populations. METHODS: This scoping review protocol follows the Joanna Briggs Institute methodology for scoping reviews. The review will include both peer-reviewed and gray literature (ie, conference abstracts) regarding the clinical efficacy of quantitative gait technologies and clinician perspectives on their use in the clinical care of adult patient populations. A comprehensive search strategy was created in MEDLINE (Ovid), which was then translated to 4 other databases: CENTRAL (Ovid), Embase (Ovid), CINAHL (EBSCO), and SPORTDiscus (EBSCO). The title and abstract screening, full-text review, and data extraction of relevant articles will be performed independently by 2 reviewers, with a third reviewer involved to support the resolution of conflicts. Data will be analyzed using content analysis and summarized in tabular and diagram formats. RESULTS: A search of relevant articles will be conducted in all 5 databases, and through hand-searching in Google Scholar and PEDro, including articles published up until December 2022. The research team plans to submit the final scoping review for publication in a peer-reviewed journal in 2023. CONCLUSIONS: The findings of this review will be presented at clinical science conferences and published in a peer-reviewed journal. This review will inform future studies designed to develop, evaluate, or implement quantitative gait analysis technologies in clinical practice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39767.

Trends and correlates of concurrent opioid and benzodiazepine and/or gabapentinoid use among Ontario nursing home residents.

BACKGROUND: A concern with long-term opioid use is the increased risk arising when opioids are used concurrently with drugs that can potentiate their associated adverse effects. The drugs most often encountered are benzodiazepines (BZDs) and gabapentinoids. Our study objectives were to examine trends in the concurrent use of opioids and BZDs, or gabapentinoids, in a Canadian nursing home population over an 11-year period, and current resident-level correlates of this concurrent use. METHODS: We conducted a population-based, repeated cross-sectional study among Ontario nursing home residents (>65 years) dispensed opioids between April 2009 and February 2020. For the last study year, we examined cross-sectional associations between resident characteristics and concurrent use of opioids with BZDs or gabapentinoids. Linked data on nursing home residents from clinical and health administrative databases was used. The yearly proportions of residents who were dispensed an opioid concurrently with a BZD or gabapentinoid were plotted with percent change derived from log-binomial regression models. Separate modified Poisson regression models estimated resident-level correlates of concurrent use of opioids with BZDs or gabapentinoids. RESULTS: Over the study period, among residents dispensed an opioid there was a 53.2% relative decrease (30.7% to 14.4%) in concurrent BZD and a 505.4% relative increase (4.4% to 26.6%) in concurrent gabapentinoid use. In adjusted models, increasing age and worsening cognition were inversely associated with the concurrent use of both classes, but most other significantly related covariates were unique to each drug class (e.g., sex and anxiety disorders for BZD, pain severity and presence of pain-related conditions for gabapentinoids). CONCLUSIONS: Co-administration of BZDs or gabapentinoids in Ontario nursing home residents dispensed opioids remains common, but the pattern of co-use has changed over time. Observed covariates of concurrent use in 2019/20 suggest distinct but overlapping resident populations requiring consideration of the relative risks versus benefits of this co-use and monitoring for potential harm.

Variation and Correlation of Potential Unintended Consequences of Antipsychotic Reduction in Ontario Nursing Homes Over Time.

BACKGROUND: Potentially inappropriate antipsychotic use has declined in nursing homes over the past decade; however, increases in the documentation of relevant clinical indications (eg, delusions) and the use of other psychotropic medications have raised concerns about diagnosis upcoding and medication substitution. Few studies have examined how these trends over time vary across and within nursing homes, information that may help to support antipsychotic reduction efforts. OBJECTIVE: To jointly model facility-level time trends in potentially inappropriate antipsychotic use, antidepressant use, and the indications used to define appropriate antipsychotic use. RESEARCH DESIGN: We conducted a repeated cross-sectional study of all nursing homes in Ontario, Canada between April 1, 2010 and December 31, 2019 using linked health administrative data (N=649). Each nursing home's quarterly prevalence of potentially inappropriate antipsychotic use, antidepressant use, and relevant indications were measured as outcome variables. With time as the independent variable, multivariate random effects models jointly estimated time trends for each outcome across nursing homes and the correlations between time trends within nursing homes. RESULTS: We observed notable variations in the time trends for each outcome across nursing homes, especially for the relevant indications. Within facilities, we found no correlation between time trends for potentially inappropriate antipsychotic and antidepressant use ( r =-0.0160), but a strong negative correlation between time trends for potentially inappropriate antipsychotic use and relevant indications ( r =-0.5036). CONCLUSIONS: Nursing homes with greater reductions in potentially inappropriate antipsychotics tended to show greater increases in the indications used to define appropriate antipsychotic use-possibly leading to unmonitored use of antipsychotics.

Changes in the Initiation of Antipsychotics and Trazodone Over Time: A Cohort Study of New Admissions to Nursing Homes in Ontario, Canada.

OBJECTIVES: To investigate whether trazodone is being initiated in lieu of antipsychotics following antipsychotic reduction efforts, this study described changes in medication initiation over time. METHODS: We conducted a retrospective cohort study of new admissions to nursing homes in Ontario, Canada between April 2010 and December 2019 using health administrative data (N = 61,068). The initiation of antipsychotic and trazodone use was compared by year of admission using discrete time survival analysis and stratified by history of dementia. RESULTS: Relative to residents admitted in 2014, antipsychotic initiation significantly decreased in later years (e.g., 2017 admission year hazard odds ratio [HOR2017]=0.72 [95% confidence interval (95%CI)=0.62-0.82]) while trazodone initiation modestly increased (e.g., HOR2017=1.09 [95%CI=0.98-1.21]). The relative increase in trazodone initiation was larger among residents with dementia (e.g., HOR2017Dem =1.22 [95%CI=1.07-1.39]). CONCLUSIONS: Differences in which medications were started following nursing home admission were observed and suggest trazodone may be initiated in lieu of antipsychotics.

Privacy-protecting behaviours of risk detection in people with dementia using videos.

BACKGROUND: People living with dementia often exhibit behavioural and psychological symptoms of dementia that can put their and others' safety at risk. Existing video surveillance systems in long-term care facilities can be used to monitor such behaviours of risk to alert the staff to prevent potential injuries or death in some cases. However, these behaviours of risk events are heterogeneous and infrequent in comparison to normal events. Moreover, analysing raw videos can also raise privacy concerns. PURPOSE: In this paper, we present two novel privacy-protecting video-based anomaly detection approaches to detect behaviours of risks in people with dementia. METHODS: We either extracted body pose information as skeletons or used semantic segmentation masks to replace multiple humans in the scene with their semantic boundaries. Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction. We used anonymized videos of normal activities to train customized spatio-temporal convolutional autoencoders and identify behaviours of risk as anomalies. RESULTS: We showed our results on a real-world study conducted in a dementia care unit with patients with dementia, containing approximately 21 h of normal activities data for training and 9 h of data containing normal and behaviours of risk events for testing. We compared our approaches with the original RGB videos and obtained a similar area under the receiver operating characteristic curve performance of 0.807 for the skeleton-based approach and 0.823 for the segmentation mask-based approach. CONCLUSIONS: This is one of the first studies to incorporate privacy for the detection of behaviours of risks in people with dementia. Our research opens up new avenues to reduce injuries in long-term care homes, improve the quality of life of residents, and design privacy-aware approaches for people living in the community.

Voice your values, a tailored advance care planning intervention in persons living with mild dementia: A pilot study.

BACKGROUND: In the early stage of dementia, persons living with dementia (PLwD) can identify their values and wishes for future care with a high degree of accuracy and reliability. However, there is a paucity of research to guide best practices on how best to incorporate advance care planning (ACP) in older adults diagnosed with mild dementia and therefore only a minority of these individuals participate in any ACP discussions. We developed an intervention called Voice Your Values (VYV) that healthcare professionals can implement to identify and document the values of PLwD and their trusted individuals such as friends or family. PURPOSE: This single-group pre-test and post-test design aimed to determine the feasibility, acceptability, and preliminary efficacy of the VYV intervention. METHODS: A convenience sample of 21 dyads of PLwD and their trusted individuals were recruited from five outpatient geriatric clinics. The tailored VYV intervention was delivered to the dyads over two sessions using videoconferencing. RESULTS: In terms of feasibility, the recruitment rate was lower (52%) than the expected 60%; the retention rate was high at 94%, and the intervention fidelity was high based on the audit of 20% of the sessions. In terms of preliminary efficacy, PLwD demonstrated improvement in ACP engagement (p = <0.01); trusted individuals showed improvements in decision-making confidence (p = 0.01) and psychological distress (p = 0.02); whereas a minimal change was noted in their dementia knowledge (p = 0.22). CONCLUSION: Most of the feasibility parameters were met. A larger sample along with a control group, as well as a longitudinal study, are requisite to rigorously evaluate the efficacy of the promising VYV intervention. There is emerging evidence that people living with mild dementia can effectively participate in identifying and expressing their values and wishes for future care.

Prevalence, causes, and consequences of moral distress in healthcare providers caring for people living with dementia in long-term care during a pandemic.

Healthcare providers caring for people living with dementia may experience moral distress when faced with ethically challenging situations, such as the inability to provide care that is consistent with their values. The COVID-19 pandemic produced conditions in long-term care homes (hereafter referred to as 'care homes') that could potentially contribute to moral distress. We conducted an online survey to examine changes in moral distress during the pandemic, its contributing factors and correlates, and its impact on the well-being of care home staff. Survey participants (n = 227) working in care homes across Ontario, Canada were recruited through provincial care home organizations. Using a Bayesian approach, we examined the association between moral distress and staff demographics and roles, and characteristics of the long-term care home. We performed a qualitative analysis of the survey's free-text responses. More than 80% of care home healthcare providers working with people with dementia reported an increase in moral distress since the start of the pandemic. There was no difference in the severity of distress by age, sex, role, or years of experience. The most common factors associated with moral distress were lack of activities and family visits, insufficient staffing and high turnover, and having to follow policies and procedures that were perceived to harm residents with dementia. At least two-thirds of respondents reported feelings of physical exhaustion, sadness/anxiety, frustration, powerlessness, and guilt due to the moral distress experienced during the pandemic. Respondents working in not-for-profit or municipal homes reported less sadness/anxiety and feelings of not wanting to go to work than those in for-profit homes. Front-line staff were more likely to report not wanting to work than those in management or administrative positions. Overall, we found that increases in moral distress during the pandemic negatively affected the well-being of healthcare providers in care homes, with preliminary evidence suggesting that individual and systemic factors may intensify the negative effect.

Pandemic Preparedness and Beyond: Person-Centred Care for Older Adults Living in Long-Term Care during the COVID-19 Pandemic.

The increasing complexity of residents' needs, emphasis on social distancing and limited access to high-quality support presented challenges to patient-centred care during the pandemic. Yet the pandemic created an opportunity to explore novel approaches to achieving person-centred care within long-term care (LTC). We share three projects designed to enhance care delivery in the context of the pandemic: to address personhood needs during outbreaks, to improve the quality of medical care and to deliver personalized palliative and end-of-life care using a prediction algorithm. These projects enabled better care during the pandemic and will continue to advance person-centred care beyond the pandemic.

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. To date, no reviews have examined the potential use of RTLS data in caring for older adults with cognitive impairment living in a residential care setting. Objective: This scoping review aims to explore the use of data from real-time locating systems (RTLS) technology to inform clinical measures and augment healthcare decision-making in the care of older adults with cognitive impairment who live in residential care settings. Methods: Embase (Ovid), CINAHL (EBSCO), APA PsycINFO (Ovid) and IEEE Xplore databases were searched for published English-language articles that reported the results of studies that investigated RTLS technologies in persons aged 50 years or older with cognitive impairment who were living in a residential care setting. Included studies were summarized, compared and synthesized according to the study outcomes. Results: A total of 27 studies were included. RTLS data were used to assess activity levels, characterization of wandering, cognition, social interaction, and to monitor a resident's health and wellbeing. These RTLS-based measures were not consistently validated against clinical measurements or clinically important outcomes, and no studies have examined their effectiveness or impact on decision-making. Conclusion: This scoping review describes how data from RTLS technology has been used to support clinical care of older adults with dementia. Research efforts have progressed from using the data to track activity levels to, most recently, using the data to inform clinical decision-making and as a predictor of delirium. Future studies are needed to validate RTLS-based health indices and examine how these indices can be used to inform decision-making.