Upper-Extremity Function Predicts Adverse Health Outcomes among Older Adults Hospitalized for Ground-Level Falls

Geriatric Trauma: Identifying Research Gaps and Opportunities for Improvement

Geriatric trauma is a critical area of research owing to the increasing elderly population and the unique challenges associated with their injuries. This study aimed to explore the intellectual structure, growth, spatial analysis, seminal publications, most frequent keywords, trending topics, conceptual structure, and thematic evolution of geriatric trauma research (GTR). A comprehensive analysis was conducted using VOSviewer (Centre for Science and Technology Studies, Leiden University, The Netherlands) and Biblioshiny (K-Synth Srl, University of Naples Federico II, Italy) to examine a corpus of scholarly publications (N = 682) related to GTR (2004-2024). Bibliographic data were collected from the Scopus database and analyzed to highlight the key findings and trends. The analysis revealed the leading contributors to GTR. Over the years, there has been an increased interest in geriatric trauma, as demonstrated by the increasing trend in research publications. Collaboration patterns among nations were determined through spatial analysis, and insights into significant topics and their influence were offered by influential publications. Keywords used frequently as well as current issues formed part of this study's findings, which give insight into what was most focused on within the GTR. Themes and their development over time were made explicit by revealing their conceptual structures and thematic evolutions. GTR has increased significantly. Interdisciplinary approaches are suggested for future research. Furthermore, gaps need to be addressed using technological advancements that will help improve geriatric trauma management and lead to better patient outcomes.

Towards a Remote Patient Monitoring Platform for Comprehensive Risk Evaluations for People with Diabetic Foot Ulcers

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stays and amputation. We suggest a remote monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, characteristics of the wound, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10); categorized as low, medium, and high risk for unfavorable outcomes; and visually represented using color-coded radar plots. The initial results of the case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

The Application of Digital Frailty Screening to Triage Nonhealing and Complex Wounds

OBJECTIVE

We investigated the association between the complexity of diabetic foot ulcers (DFUs) and frailty.

RESEARCH DESIGN AND METHODS

Individuals (n = 38) with Grade 2 Wagner DFU were classified into 3 groups based on the Society for Vascular Surgery risk-stratification for major limb amputation as Stage 1 at very low risk (n = 19), Stage 2 at low risk (n = 9), and Stage 3 to 4 at moderate-to-high risk (n = 10) of major limb amputation. Frailty status was objectively assessed using a validated digital frailty meter (FM). The FM works by quantifying weakness, slowness, rigidity, and exhaustion over a 20-second repetitive elbow flexion-extension exercise using a wrist-worn sensor. FM generates a frailty index (FI) ranging from 0 to 1; higher values indicate progressively greater severity of frailty. Skin perfusion pressure (SPP), albumin, and tissue oxygenation level (SatO2) were also measured. One-way analysis of variance (ANOVA) was used to identify group effect for wound complexity. Pearson's correlation coefficient was used to assess the associations with frailty and clinical endpoints.

RESULTS

Frailty index was higher in Stage 3 and 4 as compared to Stage 1 (d = 1.4, P < .01) and Stage 2 (d = 1.2, P < .01). Among assessed frailty phenotypes, exhaustion was correlated with SPP (r = -0.63, P < .01) and albumin (r = -0.5, P < .01).

CONCLUSION

Digital biomarkers of frailty may predict complexity of DFU and thus triage individuals who can be treated more simply in their primary clinic versus higher risk patients who require prompt referral to multidisciplinary, more complex care.

A computational musculoskeletal arm model for assessing muscle dysfunction in chronic obstructive pulmonary disease

Computational models have been used extensively to assess diseases and disabilities effects on musculoskeletal system dysfunction. In the current study, we developed a two degree-of-freedom subject-specific second-order task-specific arm model for characterizing upper-extremity function (UEF) to assess muscle dysfunction due to chronic obstructive pulmonary disease (COPD). Older adults (65 years or older) with and without COPD and healthy young control participants (18 to 30 years) were recruited. First, we evaluated the musculoskeletal arm model using electromyography (EMG) data. Second, we compared the computational musculoskeletal arm model parameters along with EMG-based time lag and kinematics parameters (such as elbow angular velocity) between participants. The developed model showed strong cross-correlation with EMG data for biceps (0.905, 0.915) and moderate cross-correlation for triceps (0.717, 0.672) within both fast and normal pace tasks among older adults with COPD. We also showed that parameters obtained from the musculoskeletal model were significantly different between COPD and healthy participants. On average, higher effect sizes were achieved for parameters obtained from the musculoskeletal model, especially for co-contraction measures (effect size = 1.650 ± 0.606, p < 0.001), which was the only parameter that showed significant differences between all pairwise comparisons across the three groups. These findings suggest that studying the muscle performance and co-contraction, may provide better information regarding neuromuscular deficiencies compared to kinematics data. The presented model has potential for assessing functional capacity and studying longitudinal outcomes in COPD.

Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff's Views on Utility and Effectiveness

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Frailty: Pathophysiology, Theoretical and Operational Definition(s), Impact, Prevalence, Management and Prevention, in an Increasingly Economically Developed and Ageing World

The world's population is ageing, and most older adults experience a later life burdened with disease and disability. Frailty is a multidimensional and dynamic condition characterized by declines in reserve and function across multiple physiological systems, such that the ability to cope with every day or acute stressors becomes compromised. It is projected to become one of the most serious public health challenges economically developed societies will face in the coming century. This review provides a comprehensive overview of frailty, exploring its pathophysiology, theoretical and operational definition(s), impact, prevalence, management, and prevention, within the context of its emergence as a major public health challenge, in an increasingly economically developed and ageing world. Further, this review discusses the major limitations, deficiencies, and knowledge gaps presently within the field, and future research directions pertinent to the advancement of frailty research and the promotion of healthy longevity among the increasing global population of older adults.

Use of the Kinect sensor measured three-dimensional reachable workspace to assess the upper extremity function in older adults

BACKGROUND

We explored the utility of Kinect sensor-based upper extremity reachable workspace measure in healthy adults aged over 65 years.

METHODS

Forty-three healthy older subjects (19 men and 24 women) aged over 65 years and 22 healthy young subjects (11 men and 11 women) were included. All participants were ambulatory and perform the activities of daily living independently. Three-dimensional reachable workspace data were acquired for both arms using the Kinect sensor. We evaluated hand grip strength, manual muscle shoulder strength, and the active shoulder ranges of motion of the dominant and non-dominant sides. We assessed upper limb function using the Disabilities of Arm, Shoulder, and Hand (DASH) instrument and the health-related quality of life employing the descriptive EQ-5D-5L system.

FINDINGS

The quadrant 3 relative surface area in older adults was significantly smaller than that of young adults (both dominant and non-dominant sides), while the total and quadrants 1, 2, and 4 relative surface areas did not differ between older and young adults. However, the quadrant 3 relative surface area did not correlate with the DASH or EQ5D scores. The total and quadrant 1, 2, and 4 relative surface areas of the dominant side significantly correlated with the DASH score. The quadrant 4 relative surface area of the dominant side significantly correlated with the EQ5D score.

INTERPRETATION

Kinect sensor-based, three-dimensional, reachable workspace analysis may be useful to evaluate upper limb function in older adults.

Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review

Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.

Exergames to Prevent the Secondary Functional Deterioration of Older Adults during Hospitalization and Isolation Periods during the COVID-19 Pandemic

The COVID-19 pandemic is having an intense impact on the functional capacity of older adults, making them more vulnerable to frailty and dependency. The development of preventive and rehabilitative measures which counteract the consequences of confinement or hospitalization is an urgent need. Exergaming can promote physical activity, prevent falls, and maintain functional and cognitive capacity. However, although the use of exergames in health programs for the elderly is promising, their widespread use should not be considered without the supervision of a social health professional. Therefore, the objective of this work was to evaluate and analyze three video game consoles (Nintendo Wii®, Xbox-Kinect® and Play Station 4®) and 26 commercial exergames with the aim of identifying their usefulness for the prevention of functional deterioration. Three occupational therapists analyzed the data independently, and subsequently agreed on the results. The examination of the commercial consoles met three criteria: components, interaction channels and the type of the exergame. Each exergame was analyzed taking into account its ability to train postural control, balance, upper limb functionality and cognitive function. The results of the evaluation showed that exergames contain game activities that can be part of the rehabilitative treatment aimed at the prevention of the functional impairment of older people affected by COVID.

Physical and Cognitive Function Assessment to Predict Postoperative Outcomes of Abdominal Surgery

BACKGROUND

Current evaluation methods to assess physical and cognitive function are limited and often not feasible in emergency settings. The upper-extremity function (UEF) test to assess physical and cognitive performance using wearable sensors. The purpose of this study was to examine the (1) relationship between preoperative UEF scores with in-hospital outcomes; and (2) association between postoperative UEF scores with 30-d adverse outcomes among adults undergoing emergent abdominal surgery.

METHODS

We performed an observational, longitudinal study among adults older than 40 y who presented with intra-abdominal symptoms. The UEF tests included a 20-sec rapid repetitive elbow flexion (physical function), and a 60-sec repetitive elbow flexion at a self-selected pace while counting backwards by threes (cognitive function), administered within 24-h of admission and within 24-h prior to discharge. Multiple logistic regression models assessed the association between UEF and outcomes. Each model consisted of the in-hospital or 30-d post-discharge outcome as the dependent variable, preoperative UEF physical and cognitive scores as hypothesis covariates, and age and sex as adjuster covariates.

RESULTS

Using UEF physical and cognitive scores to predict in-hospital outcomes, an area under curve (AUC) of 0.76 was achieved, which was 17% more sensitive when compared to age independently. For 30-d outcomes, the AUC increased to 0.89 when UEF physical and cognitive scores were included in the model with age and sex.

DISCUSSION

Sensor-based measures of physical and cognitive function enhance outcome prediction providing an objective practicable tool for risk stratification in emergency surgery settings among aging adults presenting with intra-abdominal symptoms.

ICT technologies as new promising tools for the managing of frailty: a systematic review

OBJECTIVES

Frailty is a major health issue as it encompasses functional decline, physical dependence, and increased mortality risk. Recent studies explored Information and Communication Technology (ICT) interventions as alternatives to manage frailty in older persons. The aim of the present systematic review was to synthesize current evidence on ICT application within the complex models of frailty care in older people.

METHODS

Data sources included PubMed, PsycINFO, EMBASE and Web of Science, considering eligible those reviews on ICT application in samples of older persons formally assessed as frail. Records were screened by two independent researchers, who extracted data and appraised methodological quality of reviews and studies.

RESULTS

Among the 764 retrieved papers, two systematic reviews were included. Most of the studies analyzed defined frailty considering only few components of the phenotype and used ICT to stratify different levels of frailty or to support traditional screening strategies. Assessment of frailty was the context in which ICT has been mostly tested as compared to intervention. Cost effectiveness evaluations of the ICT technologies were not reported.

CONCLUSIONS

The research investigating the use of ICT in the context of frailty is still at the very beginning. Few studies strictly focused on the assessment of frailty, while intervention on frailty using ICT was rarely reported. The lack of a proper characterization of the frail condition along with the methodological limitations prevented the investigation of ICT within complex care models. Future studies are needed to effectively integrate ICT in the care of frailty in orders.

Gait Impairment and Upper Extremity Disturbance Are Associated With Total Magnetic Resonance Imaging Cerebral Small Vessel Disease Burden

Background and Purpose: Cerebral small vessel disease (cSVD)—including white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), lacunes, and enlarged perivascular spaces (EPVS)—is related to gait impairment. However, the association between the total magnetic resonance imaging (MRI) cSVD burden and gait and upper extremity function remains insufficiently investigated. This study aimed to assess the correlation between the total MRI cSVD burden score and gait impairment as well as upper extremity impairment.

Method: A total of 224 participants underwent MRI scans, and the presence of lacunes, WMHs, CMBs, and EPVS was evaluated and recorded as a total MRI cSVD burden score (range 0–4). Gait was assessed by 4-m walkway, Tinetti, Timed Up and Go (TUG), and Short Physical Performance Battery (SPPB) tests. Upper extremity function was assessed by 10-repeat hand pronation-supination time, 10-repeat finger-tapping time, and 10-repeat hand opening and closing time.

Result: The mean age of the 224 participants was 60.6 ± 10.5 years, and 64.3% were men. Independent of age, sex, height, and vascular risk factors, multivariable linear regression analyses showed that a higher total MRI cSVD burden score was related to a shorter stride length, wider step width, higher cadence, and poorer performance on the Tinetti, TUG, and SPPB tests and upper extremity tests (all P < 0.05).

Conclusion: Total MRI cSVD burden was associated with gait impairment and upper extremity disturbances, suggesting that total MRI cSVD burden might contribute to motor function decline. Longitudinal studies are required to determine whether there is a causal relationship between total MRI cSVD burden and motor function decline.

Increased morbidity and mortality in elderly patients with lower extremity trauma and associated injuries: A review of 420,066 patients from the national trauma database

INTRODUCTION

There is a paucity of research addressing the morbidity and mortality associated with polytrauma in elderly patients. This study aimed to compare the outcomes of elderly trauma patients with an isolated lower extremity fracture, to patients lower extremity fractures and associated musculoskeletal injuries.

METHODS

This study is a retrospective review from the National Trauma Database (NTDB) between 2008 and 2014. ICD 9 codes were used to identify patients 65 years and older with lower extremity fractures. Patients were categorize patients into three sub groups: patients with isolated lower extremity fractures (ILE), patients with two or more (multiple) lower extremity fractures (MLE) and, patients with at least one upper and at least one lower extremity fracture (ULE). Groups were stratified into patients age 65-80 and patients >80 years of age.

RESULTS

A total 420,066 patients were included in analysis with 356,120 ILE fracture patients, 27,958 MLE fracture patients, and 35,988 ULE fracture patients. The MLE group reported the highest dispatch to ACS level 1 trauma centers at 31.8% followed by the ULE group at 28.5% and the ILE group at 24.7% of patients (p<0.001). The overall rate of complications was highest in the MLE group followed by the ULE and then the ILE group (41.4%, 40.3%, 36.1%, respectively p<0.001). Motility rates in patients >80 years old in the MLE group and ULE group were similar (1.483 vs 1.4432). However, in the 65-80 year group the odds of mortality was 1.260 in the MLE group and 1.450 in the ULE group (p<0.001), such that the odds of mortality after sustaining a MLE fracture increases with age, whereas this effect was not seen in the ULE group.

CONCLUSION

Patients who sustained MLE and ULE fractures, had increased mortality, complications and in hospital care requirements as compared to patients with isolated lower extremity injuries. These outcomes are comparable between ULE and MLE fracture patients over the age of 80 however patients 65-80 with ULE fractures had increased mortality as compared patients 65-80 with MLE fractures. Understanding the unique considerations and requirements of elderly trauma patients is vital to providing successful outcomes.

Digital Predictors of Morbidity, Hospitalization, and Mortality Among Older Adults: A Systematic Review and Meta-Analysis

The widespread adoption of digital health technologies such as smartphone-based mobile applications, wearable activity trackers and Internet of Things systems has rapidly enabled new opportunities for predictive health monitoring. Leveraging digital health tools to track parameters relevant to human health is particularly important for the older segments of the population as old age is associated with multimorbidity and higher care needs. In order to assess the potential of these digital health technologies to improve health outcomes, it is paramount to investigate which digitally measurable parameters can effectively improve health outcomes among the elderly population. Currently, there is a lack of systematic evidence on this topic due to the inherent heterogeneity of the digital health domain and the lack of clinical validation of both novel prototypes and marketed devices. For this reason, the aim of the current study is to synthesize and systematically analyse which digitally measurable data may be effectively collected through digital health devices to improve health outcomes for older people. Using a modified PICO process and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, we provide the results of a systematic review and subsequent meta-analysis of digitally measurable predictors of morbidity, hospitalization, and mortality among older adults aged 65 or older. These findings can inform both technology developers and clinicians involved in the design, development and clinical implementation of digital health technologies for elderly citizens.

Frailty and heart response to physical activity

BACKGROUND

Although previous studies showed that frail older adults are more susceptible to develop cardiovascular diseases, the underlying effect of frailty on heart rate dynamics is still unclear. The goal of the current study was to measure heart rate changes due to normal speed and rapid walking among non-frail and pre-frail/frail older adults, and to implement heart rate dynamic measures to identify frailty status.

METHODS

Eighty-eight older adults (≥65 years) were recruited and stratified into frailty groups based on the five-component Fried frailty phenotype. While performing gait tests, heart rate was recorded using a wearable ECG and accelerometer sensors. Groups consisted of 27 non-frail (age = 78.70 ± 7.32) and 61 pre-frail/frail individuals (age = 81.00 ± 8.14). The parameters of interest included baseline heart rate measures (mean heart rate and heart rate variability), and heart rate dynamics due to walking (percentage change in heart rate and required time to reach the maximum heart rate).

RESULTS

Respectively for normal and rapid walking conditions, pre-frail/frail participants had 46% and 44% less increase in heart rate, and 49% and 27% slower occurrence of heart rate peak, when compared to non-frail older adults (p < 0.04, effect size = 0.71 ± 0.12). Measures of heart rate dynamics showed stronger associations with frailty status compared to baseline resting-state measures (sensitivity = 0.75 and specificity = 0.65 using heart rate dynamics measures, compared to sensitivity = 0.64 and specificity = 0.62 using baseline parameters).

CONCLUSIONS

These findings suggest that measures of heart rate dynamics in response to daily activities may provide meaningful markers for frailty screening.

Remote Physical Frailty Monitoring-The Application of Deep Learning-Based Image Processing in Tele-Health

Remote screening physical frailty (PF) may assist in triaging patients with chronic obstructive pulmonary disease (COPD) who are in clinical priorities to visit a clinical center for preventive care. Conventional PF assessment tools have however limited feasibility for remote patient monitoring applications. To improve the safety of PF assessment, we previously developed and validated a quick and safe PF screening tool called Frailty Meter (FM). FM works by quantifying weakness, slowness, rigidity, and exhaustion during a 20-second repetitive elbow flexion/extension task using a wrist-worn sensor and generates a frailty index (FI) ranging from zero to one; higher values indicate progressively greater severity of frailty. However, the use of wrist-sensor limits its applications in telemedicine and remote patient monitoring. In this study, we developed a sensor-less FM based on deep learning-based image processing, which can be easily integrated into mobile health and enables remote assessment of physical frailty. The sensor-less FM extracts kinematic features of the forearm motion from the video of 20-second elbow flexion and extension recorded by a tablet camera, and then calculates frailty phenotypes and FI. To test the validity of sensor-less FM, 11 COPD patients admitted to a Telehealth pulmonary rehabilitation clinic and 10 healthy young volunteers (controls) were recruited. All participants completed the test indicating high feasibility. Strong correlations (0.72 < r < 0.99) were observed between the sensor-based FM and sensor-less FM to extract all frailty phenotypes and FI. After adjusting with age and body mass index(BMI), sensor-less FM enables distinguishing COPD group from controls (p<0.050) with the largest effect sizes observed for weakness (Cohen's effect size d=2.24), frailty index (d=1.70), and slowness (d=1.70). These pilot findings suggest feasibility and proof of concept validity of this sensor-less FM toward remote assessment of PF in COPD patients.

Association Between Wearable Device-Based Measures of Physical Frailty and Major Adverse Events Following Lower Extremity Revascularization

IMPORTANCE

Physical frailty is a key risk factor associated with higher rates of major adverse events (MAEs) after surgery. Assessing physical frailty is often challenging among patients with chronic limb-threatening ischemia (CLTI) who are often unable to perform gait-based assessments because of the presence of plantar wounds.

OBJECTIVE

To test a frailty meter (FM) that does not rely on gait to determine the risk of occurrence of MAEs after revascularization for patients with CLTI.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 184 consecutively recruited patients with CLTI at 2 tertiary care centers. After 32 individuals were excluded, 152 participants were included in the study. Data collection was conducted between May 2018 and June 2019.

EXPOSURES

Physical frailty measurement within 1 week before limb revascularization and incidence of MAEs for as long as 1 month after surgery.

MAIN OUTCOMES AND MEASURES

The FM works by quantifying weakness, slowness, rigidity, and exhaustion during a 20-second repetitive elbow flexion-extension exercise using a wrist-worn sensor. The FM generates a frailty index (FI) ranging from 0 to 1; higher values indicate progressively greater severity of physical frailty.

RESULTS

Of 152 eligible participants (mean [SD] age, 67.0 [11.8] years; 59 [38.8%] women), 119 (78.2%) were unable to perform the gait test, while all could perform the FM test. Overall, 53 (34.9%), 58 (38.1%), and 41 (27.0%) were classified as robust (FI <0.20), prefrail (FI ≥0.20 to <0.35), or frail (FI ≥0.35), respectively. Within 30 days after surgery, 24 (15.7%) developed MAEs, either major adverse cardiovascular events (MACE; 8 [5.2%]) or major adverse limb events (MALE; 16 [10.5%]). Baseline demographic characteristics were not significantly different between frailty groups. In contrast, the FI was approximately 30% higher in the group that developed MAEs (mean [SD] score, 0.36 [0.14]) than those who were MAE free (mean [SD] score, 0.26 [0.13]; P = .001), with observed MAE rates of 4 patients (7.5%), 7 patients (12.1%), and 13 patients (31.7%) in the robust, prefrail and frail groups, respectively (P = .004). The FI distinguished individuals who developed MACE and MALE from those who were MAE free (MACE: mean [SD] FI score, 0.38 [0.16]; P = .03; MALE: mean [SD] FI score, 0.35 [0.13]; P = .004) after adjusting by body mass index.

CONCLUSIONS AND RELEVANCE

In this cohort study, measuring physical frailty using a wrist-worn sensor during a short upper extremity test was a practical method for stratifying the risk of MAEs following revascularization for CLTI when the administration of gait-based tests is often challenging.

Toward Using Wearables to Remotely Monitor Cognitive Frailty in Community-Living Older Adults: An Observational Study

Physical frailty together with cognitive impairment (Cog), known as cognitive frailty, is emerging as a strong and independent predictor of cognitive decline over time. We examined whether remote physical activity (PA) monitoring could be used to identify those with cognitive frailty. A validated algorithm was used to quantify PA behaviors, PA patterns, and nocturnal sleep using accelerometer data collected by a chest-worn sensor for 48-h. Participants (N = 163, 75 ± 10 years, 79% female) were classified into four groups based on presence or absence of physical frailty and Cog: PR-Cog-, PR+Cog-, PR-Cog+, and PR+Cog+. Presence of physical frailty (PR-) was defined as underperformance in any of the five frailty phenotype criteria based on Fried criteria. Presence of Cog (Cog-) was defined as a Mini-Mental State Examination (MMSE) score of less than 27. A decision tree classifier was used to identify the PR-Cog- individuals. In a univariate model, sleep (time-in-bed, total sleep time, percentage of sleeping on prone, supine, or sides), PA behavior (sedentary and light activities), and PA pattern (percentage of walk and step counts) were significant metrics for identifying PR-Cog- (p < 0.050). The decision tree classifier reached an area under the curve of 0.75 to identify PR-Cog-. Results support remote patient monitoring using wearables to determine cognitive frailty.

Using wearable technology to predict health outcomes: a literature review

Objective

To review and analyze the literature to determine whether wearable technologies can predict health outcomes.

Materials and methods

We queried Ovid Medline 1946 -, Embase 1947 -, Scopus 1823 -, the Cochrane Library, clinicaltrials.gov 1997 – April 17, 2018, and IEEE Xplore Digital Library and Engineering Village through April 18, 2018, for studies utilizing wearable technology in clinical outcome prediction. Studies were deemed relevant to the research question if they involved human subjects, used wearable technology that tracked a health-related parameter, and incorporated data from wearable technology into a predictive model of mortality, readmission, and/or emergency department (ED) visits.

Results

Eight unique studies were directly related to the research question, and all were of at least moderate quality. Six studies developed models for readmission and two for mortality. In each of the eight studies, data obtained from wearable technology were predictive of or significantly associated with the tracked outcome.

Discussion

Only eight unique studies incorporated wearable technology data into predictive models. The eight studies were of moderate quality or higher and thereby provide proof of concept for the use of wearable technology in developing models that predict clinical outcomes.

Conclusion

Wearable technology has significant potential to assist in predicting clinical outcomes, but needs further study. Well-designed clinical trials that incorporate data from wearable technology into clinical outcome prediction models are required to realize the opportunities of this advancing technology.

Upper-extremity function prospectively predicts adverse discharge and all-cause COPD readmissions: a pilot study

Background

Frailty can inform management approaches for individuals with COPD. However, inpatient measures of frailty are seldom employed because they are time-consuming or inapplicable for bed-bound patients. We investigated the feasibility and potential of an innovative sensor-based upper-extremity function (UEF) test for frailty assessment in predicting adverse outcomes.

Methods

Hospitalized patients with COPD-related exacerbations (aged ≥55 years) were recruited and performed the UEF test within 24 hours of admission. UEF parameters were obtained and fed into our previously developed frailty model to calculate frailty status (non-frail, pre-frail, and frail) and frailty score (0: extreme resilience to 1: extreme frailty). In-hospital (length of stay) and post-discharge (discharge disposition, 30-day exacerbation with treatment, and all-cause 30-day readmission) outcomes were collected. Associations between UEF frailty and outcomes were investigated using ANOVA and logistic models adjusted for demographic data.

Results

In total, 42 patients were recruited. All participants were able to perform the UEF test. Based on UEF, participants were stratified into three groups of non-frail (n=6, frailty score =0.18±0.09), pre-frail (n=14, frailty score =0.45±0.09), and frail (n=22, frailty score =0.78±0.11). Both frailty status and frailty score were significantly associated with unfavorable discharge disposition (P<0.005) and all-cause 30-day readmission (P<0.05). On the other hand, UEF frailty measures were associated with neither hospital length of stay (P>0.5) nor 30-day exacerbation with treatment (P>0.70). Age was only significantly associated with unfavorable discharge disposition (P=0.048).

Conclusion

In agreement with previous work, the current findings underline the importance of measuring frailty for risk-stratification of COPD patients. The UEF was feasible and easily performed among all hospitalized COPD patients. In this study, we have shown that, using our quick and objective frailty measures, COPD patients can be prospectively risk-stratified in terms of unfavorable discharge disposition and all-cause 30-day readmissions.

Instrumented Trail-Making Task: Application of Wearable Sensor to Determine Physical Frailty Phenotypes

BACKGROUND

The physical frailty assessment tools that are currently available are often time consuming to use with limited feasibility.

OBJECTIVE

To address these limitations, an instrumented trail-making task (iTMT) platform was developed using wearable technology to automate quantification of frailty phenotypes without the need of a frailty walking test.

METHODS

Sixty-one older adults (age = 72.8 ± 9.9 years, body mass index [BMI] = 27.4 ± 4.9 kg/m2) were recruited. According to the Fried Frailty Criteria, 39% of participants were determined as robust and 61% as non-robust (pre-frail or frail). In addition, 17 young subjects (age = 29.0 ± 7.2 years, BMI = 26.2 ± 4.6 kg/m2) were recruited to determine the healthy benchmark. The iTMT included reaching 5 indexed circles (including numbers 1-to-3 and letters A&amp;B placed in random orders), which virtually appeared on a computer-screen, by rotating one's ankle-joint while standing. By using an ankle-worn inertial sensor, 3D ankle-rotation was estimated and mapped into navigation of a computer-cursor in real-time (100 Hz), allowing subjects to navigate the computer-cursor to perform the iTMT. The ankle-sensor was also used for quantifying ankle-rotation velocity (representing slowness), its decline during the test (representing exhaustion), and ankle-velocity variability (representing movement inefficiency), as well as the power (representing weakness) generated during the test. Comparative assessments included Fried frailty phenotypes and gait assessment.

RESULTS

All subjects were able to complete the iTMT, with an average completion time of 125 ± 85 s. The iTMT-derived parameters were able to identify the presence and absence of slowness, exhaustion, weakness, and inactivity phenotypes (Cohen's d effect size = 0.90-1.40). The iTMT Velocity was significantly different between groups (d = 0.62-1.47). Significant correlation was observed between the iTMT Velocity and gait speed (r = 0.684 p < 0.001). The iTMT-derived parameters and age together enabled significant distinguishing of non-robust cases with area under curve of 0.834, sensitivity of 83%, and specificity of 67%.

CONCLUSION

This study demonstrated a non-gait-based wearable platform to objectively quantify frailty phenotypes and determine physical frailty, using a quick and practical test. This platform may address the hurdles of conventional physical frailty phenotypes methods by replacing the conventional frailty walking test with an automated and objective process that reduces the time of assessment and is more practical for those with mobility limitations.

Toward Smart Footwear to Track Frailty Phenotypes-Using Propulsion Performance to Determine Frailty

Frailty assessment is dependent on the availability of trained personnel and it is currently limited to clinic and supervised setting. The growing aging population has made it necessary to find phenotypes of frailty that can be measured in an unsupervised setting for translational application in continuous, remote, and in-place monitoring during daily living activity, such as walking. We analyzed gait performance of 161 older adults using a shin-worn inertial sensor to investigate the feasibility of developing a foot-worn sensor to assess frailty. Sensor-derived gait parameters were extracted and modeled to distinguish different frailty stages, including non-frail, pre-frail, and frail, as determined by Fried Criteria. An artificial neural network model was implemented to evaluate the accuracy of an algorithm using a proposed set of gait parameters in predicting frailty stages. Changes in discriminating power was compared between sensor data extracted from the left and right shin sensor. The aim was to investigate the feasibility of developing a foot-worn sensor to assess frailty. The results yielded a highly accurate model in predicting frailty stages, irrespective of sensor location. The independent predictors of frailty stages were propulsion duration and acceleration, heel-off and toe-off speed, mid stance and mid swing speed, and speed norm. The proposed model enables discriminating different frailty stages with area under curve ranging between 83.2–95.8%. Furthermore, results from the neural network suggest the potential of developing a single-shin worn sensor that would be ideal for unsupervised application and footwear integration for continuous monitoring during walking.

Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study

Background: The geriatric syndrome of frailty is one of the greatest challenges facing the U.S. aging population. Frailty in older adults is associated with higher adverse outcomes, such as mortality and hospitalization. Identifying precise early indicators of pre-frailty and measures of specific frailty components are of key importance to enable targeted interventions and remediation. We hypothesize that sensor-derived parameters, measured by a pendant accelerometer device in the home setting, are sensitive to identifying pre-frailty. Methods: Using the Fried frailty phenotype criteria, 153 community-dwelling, ambulatory older adults were classified as pre-frail (51%), frail (22%), or non-frail (27%). A pendant sensor was used to monitor the at home physical activity, using a chest acceleration over 48 h. An algorithm was developed to quantify physical activity pattern (PAP), physical activity behavior (PAB), and sleep quality parameters. Statistically significant parameters were selected to discriminate the pre-frail from frail and non-frail adults. Results: The stepping parameters, walking parameters, PAB parameters (sedentary and moderate-to-vigorous activity), and the combined parameters reached and area under the curve of 0.87, 0.85, 0.85, and 0.88, respectively, for identifying pre-frail adults. No sleep parameters discriminated the pre-frail from the rest of the adults. Conclusions: This study demonstrates that a pendant sensor can identify pre-frailty via daily home monitoring. These findings may open new opportunities in order to remotely measure and track frailty via telehealth technologies.

Toward Using a Smartwatch to Monitor Frailty in a Hospital Setting: Using a Single Wrist-Wearable Sensor to Assess Frailty in Bedbound Inpatients

BACKGROUND

While various objective tools have been validated for assessing physical frailty in the geriatric population, these are often unsuitable for busy clinics and mobility-impaired patients. Recently, we have developed a frailty meter (FM) using two wearable sensors, which allows capturing key frailty phenotypes (weakness, slowness, and exhaustion), by testing 20-s rapid elbow flexion-extension test.

OBJECTIVE

In this study, we proposed an enhanced automated algorithm to identify frailty using a single wrist-worn sensor.

METHODS

The data collected from 100 geriatric inpatients (age: 78.9 ± 9.1 years, 49% frail) were reanalyzed to validate the new algorithm. The frailty status of the participants was determined using a validated modified frailty index. Different FM phenotypes (31 features) including velocity of elbow rotation, decline in velocity of elbow rotation over 20 s, range of motion, etc. were extracted. A regression model, bootstrap with 2,000 iterations, and recursive feature elimination technique were used for optimizing the FM parameters and identifying frailty using a single wrist-worn sensor.

RESULTS

A strong agreement was observed between two-sensor and wrist-worn sensor configuration (r = 0.87, p < 0.001). Results suggest that the wrist-worn FM with no demographic information still yields a high accuracy of 80.0% (95% CI: 79.7-80.3%) and an area under the curve of 87.7% (95% CI: 87.4-87.9%) to identify frailty status. Results are comparable with two-sensor configuration, where the observed accuracy and area under the curve were 80.6% (95% CI: 80.4-80.9%) and 87.4% (95% CI: 87.1-87.6%), respectively.

CONCLUSION

The simplicity of FM may open new avenues to integrate wearable technology and mobile health to capture frailty status in a busy hospital setting. Furthermore, the reduction of needed sensors to a single wrist-worn sensor allows deployment of the proposed algorithm in the form of a smartwatch application. From the application standpoint, the proposed FM is superior to traditional physical frailty-screening tools in which the walking test is a key frailty phenotype, and thus they cannot be used for bedbound patients or in busy clinics where administration of gait test as a part of routine assessment is impractical.

Frail phenotype might herald bone health worsening among end-stage renal disease patients

BACKGROUND

Frailty exhibits a high prevalence in end-stage renal disease (ESRD) patients and is associated with adverse health-related outcomes, including falls and fractures. Available studies do not address whether frailty is associated with temporal changes in BMD. We evaluated this issue by analyzing the follow-up dual energy X-ray absorptiometry (DXA) results in an ESRD cohort.

METHODS

In 2015, we enrolled forty-three ESRD patients, divided into frail, pre-frail, and robust ones based on a validated simple FRAIL scale, all receiving DXA at baseline. After one year of follow-up, survivors received another DXA, and we calculated the absolute and percentage changes in area, bone mineral density (BMD), T-, and Z-scores of lumbar spine and femoral neck (FN) between baseline and follow-up examinations.

RESULTS

Among all, frail individuals with ESRD had significantly lower average lumbar spine area, lower L4, FN, and total BMD and T-scores, lower FN and total Z-scores than non-frail ones, without differences in gender, body mass index, dialysis duration, and comorbidities. Furthermore, we discovered frail ESRD patients had significantly more prominent decrease in average lumbar spine area, percentage changes in L1 Z-scores and average lumbar spine area, and a trend toward more prominent decrease in L4 area than non-frail ones after one year of follow-up.

CONCLUSIONS

Baseline frailty might be associated with deteriorating bone health, including shrinking L-spine areas and a more rapid decrease in L-spine Z scores, among ESRD patients. This frailty-bone association should be highlighted during our care of frail individuals with ESRD.