Intraindividual Changes in Ambulation Associated With Falls in a Population of Vulnerable Older Adults in Long-Term Care

Mobility Assessment Instruments

OBJECTIVES

Review commonly used mobility assessment instruments and discuss their use in multidisciplinary research and clinical practice.

METHODS

Data sources include peer-reviewed articles sourced in electronic databases (PubMed, CINAHL), government websites, national, and international best practice guidelines to describe frequently used mobility assessment instruments.

RESULTS

Numerous clinician-, observer-, patient-reported, and performance outcome instruments and evidence-based implementation program resources exist, though these vary in their intended purpose and setting. Wearable and ambient sensors provide new opportunities to collect passive, objective physical activity data and observe changes in mobility across settings.

CONCLUSIONS

Selection among multiple assessment tools requires consideration of the available evidence for use in the desired population, the outcomes of interest, whether use is feasible for the setting, and the strength of validity and reliability data for the tool.

IMPLICATIONS FOR NURSING PRACTICE

Nurses, especially in the inpatient setting, are typically in most frequent contact with patients and are well-positioned to assess mobility and ensure that safe, progressive mobility care plans are in place. Development of an organization-wide mobility culture requires a systematic, multidisciplinary approach and long-term commitment.

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.

Change in gait speed and fall risk among community-dwelling older adults with and without mild cognitive impairment: a retrospective cohort analysis

BACKGROUND

Although slow gait speed is an established risk factor for falls, few studies have evaluated change in gait speed as a predictor of falls or considered variability in effects by cognitive status. Change in gait speed may be a more useful metric because of its potential to identify decline in function. In addition, older adults with mild cognitive impairment are at an elevated risk of falls. The purpose of this research was to quantify the association between 12-month change in gait speed and falls in the subsequent 6 months among older adults with and without mild cognitive impairment.

METHODS

Falls were self-reported every six months, and gait speed was ascertained annually among 2,776 participants in the Ginkgo Evaluation of Memory Study (2000-2008). Adjusted Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for fall risk relative to a 12-month change in gait speed.

RESULTS

Slowing gait speed over 12 months was associated with increased risk of one or more falls (HR:1.13; 95% CI: 1.02 to 1.25) and multiple falls (HR:1.44; 95% CI: 1.18 to 1.75). Quickening gait speed was not associated with risk of one or more falls (HR 0.97; 95% CI: 0.87 to 1.08) or multiple falls (HR 1.04; 95% CI: 0.84 to 1.28), relative to those with a less than 0.10 m/s change in gait speed. Associations did not vary by cognitive status (pinteraction = 0.95 all falls, 0.25 multiple falls).

CONCLUSIONS

Decline in gait speed over 12 months is associated with an increased likelihood of falls among community-dwelling older adults, regardless of cognitive status. Routine checks of gait speed at outpatient visits may be warranted as a means to focus fall risk reduction efforts.

Noticing Acute Changes in Health in Long-Term Care Residents

PURPOSE

Early signs of acute conditions and increased fall risk often go unrecognized in patients in long-term care facilities. The aim of this study was to examine how healthcare staff identify and act on changes in health status in this patient population.

DESIGN

A qualitative study design was used for this study.

METHODS

Six focus groups across two Department of Veterans Affairs long-term care facilities were conducted with 26 interdisciplinary healthcare staff members. Using thematic content analysis, the team preliminarily coded based on interview questions, reviewed and discussed emerging themes, and agreed on the resultant coding scheme for each category with additional independent scientist review.

RESULTS

Themes included describing and explaining how "normal" or expected behavior is identified by staff, noticing changes in a resident, determining the significance of the change, hypothesizing reasons for an observed change, response to an observed change, and resolution of the clinical change.

CONCLUSIONS

Despite limited training in formal assessment methods, long-term care staff have developed methods to conduct ongoing assessments of the residents. This technique, individual phenotyping, often identifies acute changes; however, the lack of formal methods, language, or tools to communicate the changes means that these assessments are not often formalized in a manner that informs the residents' changing care needs.

CLINICAL RELEVANCE TO THE PRACTICE OF REHABILITATION NURSING

More formal objective measures of health change are needed to assist long-term care staff in expressing and interpreting the subjective phenotype changes into objective, easily communicated health status changes. This is particularly important for acute health changes and impending falls, both of which are associated with acute hospitalization.

Developing Acute Event Risk Profiles for Older Adults with Dementia in Long-Term Care Using Motor Behavior Clusters Derived from Deep Learning

OBJECTIVES

This paper uses deep (machine) learning techniques to develop and test how motor behaviors, derived from location and movement sensor tracking data, may be associated with falls, delirium, and urinary tract infections (UTIs) in long-term care (LTC) residents.

DESIGN

Longitudinal observational study.

SETTING AND PARTICIPANTS

A total of 23 LTC residents (81,323 observations) with cognitive impairment or dementia in 2 northeast Department of Veterans Affairs LTC facilities.

METHODS

More than 18 months of continuous (24/7) monitoring of motor behavior and activity levels used objective radiofrequency identification sensor data to track and record movement data. Occurrence of acute events was recorded each week. Unsupervised deep learning models were used to classify motor behaviors into 5 clusters; supervised decision tree algorithms used these clusters to predict acute health events (falls, delirium, and UTIs) the week before the week of the event.

RESULTS

Motor behaviors were classified into 5 categories (Silhouette score = 0.67), and these were significantly different from each other. Motor behavior classifications were sensitive and specific to falls, delirium, and UTI predictions 1 week before the week of the event (sensitivity range = 0.88-0.91; specificity range = 0.71-0.88).

CONCLUSION AND IMPLICATIONS

Intraindividual changes in motor behaviors predict some of the most common and detrimental acute events in LTC populations. Study findings suggest real-time locating system sensor data and machine learning techniques may be used in clinical applications to effectively prevent falls and lead to the earlier recognition of risk for delirium and UTIs in this vulnerable population.

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.

Quantifying physical activity in aged residential care facilities: A structured review

BACKGROUND

Engaging aged residential care (ARC) residents with physical activity (PA) may be a useful strategy to decelerate dependence and disability. It is unclear what volume, intensity and patterns of PA ARC residents participate in. This review aims to synthesize the literature to quantify the volume, intensity and pattern of PA that ARC residents participate in across differing care levels (e.g. low, intermediate, high, mixed), and make recommendations for future research.

METHODS

30 studies of 48,760 yielded were reviewed using systematic review strategies.

RESULTS

Questionnaires and technological tools were used to assess PA, with accelerometers employed in 70% of studies. Overall, studies reported low volumes and intensities of PA across all care levels, and suggested limited variation in patterns of PA (e.g. little day-to-day variation in total PA). There was limited inclusion of people with cognitive impairment, potentially causing representativeness bias. Findings were limited by lack of consistency in methodological approaches and PA outcomes.

DISCUSSION

Based on findings and limitations of current research, we recommend that total volume or low-light intensity PA are more useful interventional outcomes than higher-intensity PA. Researchers also need to consider which methodology and PA outcomes are most useful to quantify PA in ARC residents.

Factors Affecting the Implementation, Use, and Adoption of Real-Time Location System Technology for Persons Living With Cognitive Disabilities in Long-term Care Homes: Systematic Review

BACKGROUND

As the aging population continues to grow, the number of adults living with dementia or other cognitive disabilities in residential long-term care homes is expected to increase. Technologies such as real-time locating systems (RTLS) are being investigated for their potential to improve the health and safety of residents and the quality of care and efficiency of long-term care facilities.

OBJECTIVE

The aim of this study is to identify factors that affect the implementation, adoption, and use of RTLS for use with persons living with dementia or other cognitive disabilities in long-term care homes.

METHODS

We conducted a systematic review of the peer-reviewed English language literature indexed in MEDLINE, Embase, PsycINFO, and CINAHL from inception up to and including May 5, 2020. Search strategies included keywords and subject headings related to cognitive disability, residential long-term care settings, and RTLS. Study characteristics, methodologies, and data were extracted and analyzed using constant comparative techniques.

RESULTS

A total of 12 publications were included in the review. Most studies were conducted in the Netherlands (7/12, 58%) and used a descriptive qualitative study design. We identified 3 themes from our analysis of the studies: barriers to implementation, enablers of implementation, and agency and context. Barriers to implementation included lack of motivation for engagement; technology ecosystem and infrastructure challenges; and myths, stories, and shared understanding. Enablers of implementation included understanding local workflows, policies, and technologies; usability and user-centered design; communication with providers; and establishing policies, frameworks, governance, and evaluation. Agency and context were examined from the perspective of residents, family members, care providers, and the long-term care organizations.

CONCLUSIONS

There is a striking lack of evidence to justify the use of RTLS to improve the lives of residents and care providers in long-term care settings. More research related to RTLS use with cognitively impaired residents is required; this research should include longitudinal evaluation of end-to-end implementations that are developed using scientific theory and rigorous analysis of the functionality, efficiency, and effectiveness of these systems. Future research is required on the ethics of monitoring residents using RTLS and its impact on the privacy of residents and health care workers.

Monitoring functional status using a wearable real-time locating technology

Sensor technologies enable real-time, continuous, and objective monitoring of activity and functioning in later life. In long-term care, timely assessment of functional status is needed to prevent falls and other acute events. However, the electronic forms and paper and pencil tools currently used are time-consuming and conducted too infrequently (e.g., every 6 months) to provide the sensitivity and specificity required. Staff are also unable to detect subtle changes in functioning through observation alone. The purpose of this paper is to discuss the use of a wearable real-time locating system that utilizes ultra wideband radio technology to continuously and objectively measure activity and aspects of functional status. This paper discusses the associated conceptualization and development of the scoring algorithms, raw data transformation, use of traditional paper and pencil tools and electronic health record data to validate sensor data, and other tips for those interested in this type of wearable sensor technology.

A research proposal testing a new model of ambulation activity among long-term care residents with dementia/cognitive impairment: the study protocol of a prospective longitudinal natural history study

Background

Excessive and patterned ambulation is associated with falls, urinary tract infections, co-occurring delirium and other acute events among long-term care residents with cognitive impairment/dementia. This study will test a predictive longitudinal data model that may lead to the preservation of function of this vulnerable population.

Methods/design

This is a single group, longitudinal study with natural observations. Data from a real-time locating system (RTLS) will be used to objectively and continuously measure ambulation activity for up to 2 years. These data will be combined with longitudinal acute event and functional status data to capture patterns of change in health status over time. Theory-driven multilevel models will be used to test the trajectories of falls and other acute conditions as a function of the ambulation activity and demographic, functional status, gait quality and balance ability including potential mediation and/or moderation effects. Data-driven machine learning algorithms will be applied to run screening of the high dimensional RTLS data together with other variables to discover new and robust predictors of acute events.

Discussion

The findings from this study will lead to the early identification of older adults at risk for falls and the onset of acute medical conditions and interventions for individualized care.

Balance ability and cognitive impairment influence sustained walking in an assisted living facility

PURPOSE OF STUDY

The purpose of this study was to determine the influence of cognitive impairment (CI),¹ gait quality, and balance ability on walking distance and speed in an assisted living facility.

MATERIALS AND METHODS

This was a longitudinal cohort study of institutionalized older adults (N = 26; 555 observations) followed for up to 8 months. Hierarchical linear modeling statistical techniques were used to examine the effects of gait quality and balance ability (using the Tinetti Gait and Balance Test) and cognitive status (using the Montreal Cognitive Assessment) on walking activity (distance, sustained distance, sustained speed). The latter were measured objectively and continuously by a real-time locating system (RTLS).

RESULTS

A one-point increase in balance ability was associated with an 8% increase in sustained walking distance (p = 0.03) and a 4% increase in sustained gait speed (p = 0.00). Gait quality was associated with decreased sustained gait speed (p = 0.03). Residents with moderate (ERR = 2.34;p = 0.01) or severe CI (trend with an ERR = 1.62; p = 0.06) had longer sustained walking distances at slower speeds when compared to residents with no CI.

CONCLUSIONS

After accounting for cognitive status, it was balance ability, not gait quality, that was a determinant of sustained walking distances and speeds. Therefore, balance interventions for older adults in assisted living may enable sustained walking activity. Given that CI was associated with more sustained walking, limiting sustained walking in the form of wandering behavior, especially for those with balance impairments, may prevent adverse events, including fall-related injury.

Wandering Behaviors and Activities of Daily Living Among Older Adults With Cognitive Impairment

PURPOSE

The aim of the study was to examine the characteristics of wandering associated with preserved versus worsened activities of daily living (ADL) function.

DESIGN

Longitudinal prospective design. Twenty-two cognitively impaired residents of an assisted living facility with over 450 observations were followed up to 8 months.

METHODS

Hierarchical linear modeling techniques examine how wandering activity (episodes, distance traveled, gait speed), measured by a real-time locating system, may affect ADL (the Barthel index, the Functional Independence Measure [FIM]).

FINDINGS

Wandering episodes were associated with increased ADL (B = 0.11, p ≤ .05, FIM); wandering distance (B = -4.52, p ≤ .05, the Barthel index; B = -2.14, p ≤ .05, FIM) was associated with decreased ADL.

CONCLUSION

Walking an average of 0.81 miles per week with 18 or fewer wandering episodes is associated with decreased ability to perform ADL.

CLINICAL RELEVANCE

Tailored protocols that allow productive wandering with ongoing assessment for fatigue/other physiological needs to appropriately limit distance walked within wandering episodes are needed for this population.