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. The models showed a moderate prediction ability, with random forest emerging as the optimal model. Mean absolute errors were 0.71 and 0.73 in females and males, respectively, using general models and 0.69 and 0.73 using sex-specific models. Variables most highly correlated with the ISE, including activity pursuits, cognition, and physical health and functioning, differed little by sex. Factors associated with social engagement were similar in female and male residents.

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. The aim of this study was to assess the feasibility and impact of a soundscape intervention on people with dementia and behavioral symptoms. Research Design and Methods: Pilot single-blind repeated-measures randomized controlled trial of an augmented soundscape intervention. Participants were people with dementia in a hospital-based specialized dementia unit. Participants were randomized to an augmented soundscape intervention delivered in their room in the morning and evening or treatment as usual, with 2 baseline weeks and 4 weekly post-randomization assessments of the primary and secondary behavioral outcomes. Results: The soundscape intervention was feasible in terms of recruitment, retention, and delivery of the intervention. There were improvements in the neuropsychiatric inventory total scores over time in both groups (-5.89, 95%CI -8.45 to -3.28, p < .001), but no differences between groups. There were no significant group, time, or group × time differences for the Pittsburgh Agitation Scale (PAS) total score. For the PAS-resisting care subscale, there was a significant group × time difference, with a greater reduction in the soundscape group over the study period (-0.81, 95% CI -1.59 to -0.03, p = .042). Discussion and Implications: In this pilot study, soundscape augmentation was a feasible and effective nonpharmacological approach to reducing resistance to care in people with dementia, although it did not improve neuropsychiatric symptoms more globally. Further studies with larger samples and of longer duration are needed to investigate the long-term effects of augmented sonic environments on people with dementia. Clinical Trials Registration Number: NCT04809545.

Trends in Hyperpolypharmacy Before and After Nursing Home Admission Among Older Adults in Ontario, Canada.

OBJECTIVES: To examine trends in the prevalence of hyperpolypharmacy prior to and following nursing home admission in Ontario, Canada. METHODS: We conducted a cohort study of adults aged 75+ years admitted to nursing homes between 2017 and 2020 using health administrative data (n = 61,470). The prevalence of hyperpolypharmacy (≥10 dispensed drugs) was assessed quarterly from ten years prior to 1.5 years following admission. RESULTS: Over ten years, the prevalence of hyperpolypharmacy increased from 4.4% to 12.0% (+0.2% per quarter, [p <0.001]) and further increased after admission (13.8%). Antidepressants (three-fold), antipsychotics (seven-fold) and cholinesterase inhibitors (14-fold) increased significantly over ten years prior to admission, while cardiovascular medications peaked 4 to 5 years prior to admission. CONCLUSIONS: While hyperpolypharmacy increased nearly three-fold in the ten years prior to nursing home admission, patterns varied by drug class. Increasing hyperpolypharmacy throughout the life course suggests opportunities exist for medication reconciliation in community and nursing home settings.

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. Included articles assessed anxiety in community-dwelling older adults using an index anxiety tool and a gold standard form of anxiety assessment and reported resulting diagnostic accuracy outcomes. Estimates of pooled diagnostic accuracy outcomes were completed. RESULTS: Twenty-three anxiety tools were identified from the 32 included articles. Pooled diagnostic accuracy outcomes were estimated for the Geriatric Anxiety Inventory (GAI)-20 [n = 3, sensitivity = 0.89, 95% confidence interval (CI) = 0.70-0.97, specificity = 0.80, 95% CI = 0.67-0.89] to detect generalized anxiety disorder (GAD) and for the GAI-20 (n = 3, cut off ≥ 9, sensitivity = 0.74, 95% CI = 0.62-0.83, specificity = 0.96, 95% CI = 0.74-1.00), Beck Anxiety Inventory (n = 3, sensitivity = 0.70, 95% CI = 0.58-0.79, specificity = 0.60, 95% CI = 0.51-0.68) and Hospital Anxiety and Depression Scale (HADS-A) (n = 3, sensitivity = 0.78, 95% CI = 0.60-0.89, specificity = 0.76, 95% CI = 0.60-0.87) to detect anxiety disorders in clinical samples. CONCLUSION: The GAI-20 was the most studied tool and had adequate sensitivity while maintaining acceptable specificity when identifying GAD and anxiety disorders. The GAI-20, GAI-Short Form and HADS-A tools are supported for use in detecting anxiety in community-dwelling older adults. Brief, self-rated and easy-to-use tools may be the best options for anxiety detection in community-dwelling older adults given resource limitations. Clinicians may consider factors including patient comorbidities and anxiety prevalence when selecting a tool and cut off.

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. SETTING AND PARTICIPANTS: Residents of Ontario nursing homes aged 66-105 years with an admission assessment between April 1, 2010, and March 31, 2019, who were receiving an antipsychotic and had no antidepressant medication use at admission to the nursing home. METHODS: We used linked health administrative data to examine changes in medication use over three quarterly assessments following admission. Antipsychotic and trazodone use were measured at each assessment. The rate of trazodone initiation was compared between residents no longer dispensed an antipsychotic (discontinued) and those with an ongoing antipsychotic (continued) using discrete time survival analysis, controlling for baseline resident characteristics. RESULTS: We identified 13,306 residents dispensed an antipsychotic with no antidepressant use at admission (mean age 84 years, 61.5% women, 82.8% with dementia). As of the first quarterly assessment, nearly 20% of residents no longer received an antipsychotic and 9% received a new trazodone medication. Over time, residents who discontinued antipsychotics had a rate of trazodone initiation that was 82% higher compared to residents who continued (adjusted hazard ratio 1.82, 95% CI 1.66-2.00). CONCLUSIONS AND IMPLICATIONS: Residents admitted to a nursing home with antipsychotic use had a higher rate of trazodone initiation if they discontinued (vs continued) an antipsychotic. These findings suggest antipsychotic substitution with trazodone after entering a nursing home.

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. DESIGN: A pragmatic, mixed-methods multicenter, open-label, uncontrolled interventional study with assessment of outcomes at baseline and 3 months. SETTING AND PARTICIPANTS: A total of 58 resident-care partner dyads were recruited across 7 LTCHs in Ontario, Canada. The main inclusion criterion was having a care partner willing to participate, and we excluded residents who already had an internet-connected device. METHODS: Resident demographics, functional status assessments, and recreational engagement were captured using items from the Resident Assessment Instrument/Minimum Data Set. Care partners completed a questionnaire about relational closeness and site leads assessed resident quality of life before and approximately 3 months after tablet distribution. Interviews with 23 care partners and 7 residents post-implementation were completed and analyzed. RESULTS: The median tablet use by participants was 7 minutes (interquartile range 27) per day on average over the study period. Predictors of higher tablet use were younger age, higher cognitive functioning, absence of hearing impairment, and having a care partner who lives farther away. There was no improvement on quantitative measures of quality of life, recreation, or relational closeness. In interviews, participants identified many different opportunities afforded by access to personalized tablets. CONCLUSIONS AND IMPLICATIONS: Some LTCH residents without current access to the internet benefit from being provided a personal tablet and use it in a variety of ways to enrich their lives. There is a critical need to bridge the digital divide for this population.

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.

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.

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.

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.