Can accelerometry identify/monitor neuropsychiatric symptoms correlated with motor activity in persons living with dementia? Results from a diagnostic test accuracy systematic review.

Background: Wearable sensor technology shows promise for neuropsychiatric symptoms (NPS) identification/monitoring in persons living with dementia (PLWD). Accelerometry measures acceleration of body segments and physical activity. This study explores the diagnostic test accuracy (DTA) of accelerometry to identify the following 6 NPS associated with motor activity: aberrant motor behavior (AMB), aggression, agitation, anxiety, apathy, and wandering.

Drug Substitution as a Potential Unintended Consequence of Antipsychotic Deprescribing in Long‐Term Care: A Retrospective Cohort Study in Ontario, Canada.

Background: The overuse of antipsychotics in persons with dementia in long‐term care (LTC) has been a source of clinical concern, public attention, and policy intervention for over 30 years. Targeted quality improvement, broader awareness of risks, and other initiatives have resulted in substantial reductions in antipsychotic use in LTC settings in North America and elsewhere. Limited evidence suggests that reductions in antipsychotic use may be resulting in unintended consequences, such as substitution with alternate, but similarly harmful, psychotropic medications.

Factors associated with anxiety and fear of falling in older adults: A rapid systematic review of reviews.

Background: Anxiety disorders are prevalent amongst older adults and negatively impact their quality-of-life and health. Anxiety disorders often go undetected or are misattributed to age-related changes. The aim of this systematic review of reviews, was to synthesize existing evidence on risk factors associated with anxiety in older adults to improve opportunities for early detection and intervention.

Prediction of Social Engagement in Long-Term Care Homes by Sex: A Population-Based Analysis Using Machine Learning.

The objective of this study was to use population-based clinical assessment data to build and evaluate machine-learning models for predicting social engagement among female and male residents of long-term care (LTC) homes. Routine clinical assessments from 203,970 unique residents in 647 LTC homes in Ontario, Canada, collected between April 1, 2010, and March 31, 2020, were used to build predictive models for the Index of Social Engagement (ISE) using a data-driven machine-learning approach. General and sex-specific models were built to predict the ISE.

Effect of Soundscape Augmentation on Behavioral Symptoms in People With Dementia: A Pilot Randomized Controlled Trial.

Background And Objectives: Sound is an important environmental factor that influences the expression of behavioral and psychological symptoms of dementia. Recent research on the effect of soundscape has shown promising results in improving environmental impact on people with dementia. However, no controlled studies have aimed to quantify the effects of soundscape intervention on resident outcomes.

Detection of anxiety symptoms and disorders in older adults: a diagnostic accuracy systematic review.

Background: Anxiety symptoms and disorders are common in older adults and often go undetected. A systematic review was completed to identify tools that can be used to detect anxiety symptoms and disorders in community-dwelling older adults.

Methods: MEDLINE, Embase and PsycINFO were searched using the search concepts anxiety, older adults and diagnostic accuracy in March 2023.

Antipsychotic Discontinuation and New Trazodone Use in Ontario Nursing Homes: Evidence of Medication Substitution.

Objectives: An unintended consequence of efforts to reduce antipsychotic medications in nursing homes is the increase in use of other psychotropic medications; however, evidence of substitution remains limited. Our objective was to measure individual-level prescribing patterns consistent with substitution of trazodone for antipsychotics.

Design: Retrospective cohort study.

Personalized Tablets for Residents in Long-Term Care to Support Recreation and Mitigate Isolation.

Objectives: There is a digital divide in long-term care homes (LTCHs), with few residents having regular access to internet-connected devices. In this study, we provided long-term care residents with personalized and adapted tablets. We aimed to understand what factors influenced tablet use and the impact of tablet access on opportunities for social connection and recreation.

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.

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.

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

Unlabelled: 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.

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.

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.

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.

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.

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.

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.

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.

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

Unlabelled: 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.

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.

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.

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.