The suggested innovative upper extremity frailty assessment method integrates low-cost sensors, and the physical assessment is easily performed in less than 1 minute. The uniqueness of the proposed technology is its applicability in older nonambulatory individuals, such as those in emergency settings. Further improvement is warrant to make it suitable for routine clinical applications.

Advances in wearable technology allow for the objective assessment of motor performance in both in-home and in-clinic environments and were used to explore motor impairments in Parkinson’s disease (PD). The aims of this study were to: 1) assess differences between in-clinic and in-home gait speed, and sit-to-stand and stand-to-sit duration in PD patients (in comparison with healthy controls); and 2) determine the objective physical activity measures, including gait, postural balance, instrumented Timed-up-and-go (iTUG), and in-home spontaneous physical activity (SPA), with the highest correlation with subjective/semi-objective measures, including health survey, fall history (fallers vs. non-fallers), fear of falling, pain, Unified Parkinson's Disease Rating Scale, and PD stage (Hoehn and Yahr). Objective assessments of motor performance were made by measuring physical activities in the same sample of PD patients (n = 15, Age: 71.2±6.3 years) and age-matched healthy controls (n = 35, Age: 71.9±3.8 years). The association between in-clinic and in-home parameters, and between objective parameters and subjective/semi-objective evaluations in the PD group was assessed using linear regression-analysis of variance models and reported as Pearson correlations (R). Both in-home SPA and in-clinic assessments demonstrated strong discriminatory power in detecting impaired motor function in PD. However, mean effect size (0.94±0.37) for in-home measures was smaller compared to in-clinic assessments (1.30±0.34) for parameters that were significantly different between PD and healthy groups. No significant correlation was observed between identical in-clinic and in-home parameters in the PD group (R = 0.10–0.25; p>0.40), while the healthy showed stronger correlation in gait speed, sit-to-stand duration, and stand-to-sit duration (R = 0.36–0.56; p<0.03). This suggests a better correlation between supervised and unsupervised motor function assessments in healthy controls compared to PD group. In the PD group, parameters related to velocity and range-of-motion of lower extremity within gait assessment (R = 0.58–0.84), and turning duration and velocity within iTUG test (R = 0.62–0.77) demonstrated strong correlations with PD stage (p<0.01).

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Background: Difficulties in orchestrating simultaneous tasks (i.e., dual-tasking) have been associated with cognitive impairments in older adults. Gait tests have been commonly used as the motor task component for dual-task assessments; however, many older adults have mobility impairments or there is a lack of space in busy clinical settings. We assessed an upper-extremity function (UEF) test as an alternative motor task to study the dual-task motor performance in older adults.Methods: Older adults (≥65 years) were recruited, and cognitive ability was measured using the Montreal cognitive assessment (MoCA). Participants performed repetitive elbow flexion with their maximum pace, once single-task, and once while counting backward by one (dual-task). Single- and dual-task gait tests were also performed with normal speed. Three-dimensional kinematics was measured both from upper-extremity and lower-extremity using wearable sensors to determine UEF and gait parameters. Parameters were compared between the cognitively impaired and healthy groups using analysis of variance tests, while controlling for age, gender, and body mass index (BMI). Correlations between UEF and gait parameters for dual-task and dual-task cost were assessed using linear regression models.Results: Sixty-seven older adults were recruited (age = 83 ± 10 years). Based on MoCA, 10 (15%) were cognitively impaired. While no significant differences were observed in the single-task condition, within the dual-task condition, the cognitively impaired group showed significantly less arm flexion speed (62%, d = 1.51, p = 0.02) and range of motion (27%, d = 0.93, p = 0.04), and higher speed variability (88%, d = 1.82, p < 0.0001) compared to the cognitively intact group, when adjusted with age, gender, and BMI. Significant correlations were observed between UEF speed parameters and gait stride velocity for dual-task condition (r = 0.55, p < 0.0001) and dual-task cost (r = 0.28, p = 0.03).Conclusion: We introduced a novel test for assessing dual-task performance in older adults that lasts 20 s and is based on upper-extremity function. Our results confirm significant associations between upper-extremity speed, range of motion, and speed variability with both the MoCA score and the gait performance within the dual-task condition.

Background Despite increasing evidence that assessing frailty facilitates medical decision-making, a quick and clinically simple frailty assessment tool is not available for trauma settings. Study Design This study examined accuracy and acceptability of a novel wearable technology (upper-extremity frailty: UEF) to objectively assess frailty status in older adults (≥65 years) admitted to the hospital due to traumatic ground-level falls. Frailty was measured using a validated modified Rockwood questionnaire, the Trauma-Specific Frailty Index (TSFI), as the gold standard. Participants performed a ~20-second trial of rapid elbow flexion with the dominant elbow in a supine posture while wearing the UEF system. Results We recruited 101 eligible older adults (Age: 79±9 years). UEF parameters indicative of slowness, weakness, and exhaustion during elbow flexion were independent predictors of the TSFI score, while adjusted for age, gender, and body mass index. A high agreement (r=0.72, p<.0001) was observed between TSFI score and UEF model; sensitivity and specificity for predicting the frailty status were 78% and 82%, respectively. Of recruited participants 57% were not able to walk at the time of measurements, suggesting a limitation for walking-based frailty assessments. Significant correlations were observed between UFE parameters and number of falls within a prior year, with highest correlation observed for elbow flexion slowness (r=−0.41). Conclusions The results suggest that a simple test of 20-second elbow flexion may be practical and sensitive to identify frailty among hospitalized older adults. The UEF test is independent of walking assessments, reflects several frailty markers, and it is practical for bed-bound patients.

Poor balance control and increased fall risk have been reported in people with diabetic peripheral neuropathy (DPN). Traditional body sway measures are unable to describe underlying postural control mechanism. In the current study, we used stabilogram diffusion analysis to examine the mechanism under which balance is altered in DPN patients under local-control (postural muscle control) and central-control (postural control using sensory cueing). DPN patients and healthy age-matched adults over 55 years performed two 15-second Romberg balance trials. Center of gravity sway was measured using a motion tracker system based on wearable inertial sensors, and used to derive body sway and local/central control balance parameters. Eighteen DPN patients (age = 65.4±7.6 years; BMI = 29.3±5.3 kg/m2) and 18 age-matched healthy controls (age = 69.8±2.9; BMI = 27.0±4.1 kg/m2) with no major mobility disorder were recruited. The rate of sway within local-control was significantly higher in the DPN group by 49% (healthy local-controlslope = 1.23±1.06×10-2 cm2/sec, P<0.01), which suggests a compromised local-control balance behavior in DPN patients. Unlike local-control, the rate of sway within central-control was 60% smaller in the DPN group (healthy central-controlslope-Log = 0.39±0.23, P<0.02), which suggests an adaptation mechanism to reduce the overall body sway in DPN patients. Interestingly, significant negative correlations were observed between central-control rate of sway with neuropathy severity (r Pearson = 0.65-085, P<0.05) and the history of diabetes (r Pearson = 0.58-071, P<0.05). Results suggest that in the lack of sensory feedback cueing, DPN participants were highly unstable compared to controls. However, as soon as they perceived the magnitude of sway using sensory feedback, they chose a high rigid postural control strategy, probably due to high concerns for fall, which may increase the energy cost during extended period of standing; the adaptation mechanism using sensory feedback depends on the level of neuropathy and the history of diabetes.

Background: Impairment of physical function is a major indicator of frailty. Functional performance tests have been shown to be useful for identification of frailty in older adults. However, these tests are often not translatable into unsupervised and remote monitoring of frailty status at home and/or community settings. Objective: In this study, we explored daily postural transition quantified using a chest-worn wearable technology to identify frailty in community-dwelling older adults. Methods: Spontaneous daily physical activity was monitored over 24 h in 120 community-dwelling elderly (age: 78 ± 8 years) using an unobtrusive wearable sensor (PAMSys™, BioSensics LLC, Watertown, MA, USA). Participants were classified as non-frail and pre-frail/frail using Fried's criteria. A validated software package was used to identify body postures and postural transition between each independent postural activity such as sit-to-stand, stand-to-sit, stand-to-walk, and walk-to-stand. The transition from walking to sitting was further classified as quick sitting and cautious sitting based on presence/absence of a standing posture pause between sitting and walking. A general linear model univariate test was used for between-group comparison. Pearson's correlation was used to determine the association between sensor-derived parameters and age. Logistic regression model was used to identify independent predictors of frailty. Results: According to Fried's criteria, 63% of participants were pre-frail/frail. The total number of postural transitions, stand-to-walk, and walk-to-stand were, respectively, 25.2, 30.2, and 30.6% lower in the pre-frail/frail group when compared to the non-frail group (p < 0.05, Cohen's d = 0.73-0.79). Furthermore, the ratio of cautious sitting was significantly higher by 6.2% in pre-frail/frail compared to non-frail (p = 0.025, Cohen's d = 0.22). Total number of postural transitions and the ratio of cautious sitting also showed significant negative and positive correlations with age, respectively (r = -0.51 and 0.29, p < 0.05). After applying a logistic regression model, among tested parameters, walk-to-stand (odds ratio [OR] = 0.997 p = 0.013), quick sitting (OR = 1.036, p = 0.05), and age (OR = 1.073, p = 0.016) were recognized as independent variables to identify frailty status. Conclusions: This study demonstrated that daily number of specific postural transitions such as walk-to-stand and quick sitting could be used for monitoring frailty status by unsupervised monitoring of daily physical activity. Further study is warranted to explore whether tracking the daily number of specific postural transitions is also sensitive to track change in the status of frailty over time.

Background: Few studies of the association between prospective falls and sensor-based measures of motor performance and physical activity (PA) have evaluated subgroups of frailty status separately. Objective: To evaluate wearable sensor-based measures of gait, balance, and PA that are predictive of future falls in community-dwelling older adults. Methods: The Arizona Frailty Cohort Study in Tucson, Arizona, followed community-dwelling adults aged 65 years and over (without baseline cognitive deficit, severe movement disorders, or recent stroke) for falls over 6 months. Baseline measures included Fried frailty criteria: in-home and sensor-based gait (normal and fast walk), balance (bipedal eyes open and eyes closed), and spontaneous daily PA over 48 h, measured using validated wearable technologies. Results: Of the 119 participants (36% non-frail, 48% pre-frail, and 16% frail), 48 reported one or more fall (47% of non-frail, 33% of pre-frail, and 47% of frail). Although balance deficit and PA were independent fall predictors in pre-frail and frail groups, they were not sensitive to predict prospective falls in the non-frail group. Even though gait performance deteriorated as frailty increased, gait was not a predictor of prospective falls when participants were stratified based on frailty status. In pre-frail and frail participants combined, center of mass sway [odds ratio (OR) = 5.9, 95% confidence interval (CI) 2.6-13.7], PA mean walking bout duration (OR = 1.1, 95% CI 1.0-1.2), PA mean standing bout duration (OR = 0.94, 95% CI 0.91-0.99), and a fall in previous 6 months (OR = 7.3, 95% CI 1.5-36.4) were independent predictors of prospective falls (area under the curve: 0.882). Conclusion: This study suggests that independent predictors of falls are dependent on frailty status. Among sensor-derived parameters, balance deficit, longer typical walking episodes, and shorter typical standing episodes were the most sensitive predictors of prospective falls in the combined pre-frail and frail sample. Gait deficit was not a sensitive fall predictor in the context of frailty status.