Standing Balance Conditions and Digital Sway Measures for Clinical Trials of Friedreich's Ataxia.

BACKGROUND: Progressive loss of standing balance is a feature of Friedreich's ataxia (FRDA). OBJECTIVES: This study aimed to identify standing balance conditions and digital postural sway measures that best discriminate between FRDA and healthy controls (HC). We assessed test-retest reliability and correlations between sway measures and clinical scores. METHODS: Twenty-eight subjects with FRDA and 20 HC completed six standing conditions: feet apart, feet together, and feet tandem, both with eyes opened (EO) and eyes closed. Sway was measured using a wearable sensor on the lumbar spine for 30 seconds. Test completion rate, test-retest reliability with intraclass correlation coefficients, and areas under the receiver operating characteristic curves (AUCs) for each measure were compared to identify distinguishable FRDA sway characteristics from HC. Pearson correlations were used to evaluate the relationships between discriminative measures and clinical scores. RESULTS: Three of the six standing conditions had completion rates over 70%. Of these three conditions, natural stance and feet together with EO showed the greatest completion rates. All six of the sway measures' mean values were significantly different between FRDA and HC. Four of these six measures discriminated between groups with >0.9 AUC in all three conditions. The Friedreich Ataxia Rating Scale Upright Stability and Total scores correlated with sway measures with P-values <0.05 and r-values (0.63-0.86) and (0.65-0.81), respectively. CONCLUSION: Digital postural sway measures using wearable sensors are discriminative and reliable for assessing standing balance in individuals with FRDA. Natural stance and feet together stance with EO conditions suggest use in clinical trials for FRDA. © 2024 International Parkinson and Movement Disorder Society.

Digital Measures of Postural Sway Quantify Balance Deficits in Spinocerebellar Ataxia.

BACKGROUND: Maintaining balance is crucial for independence and quality of life. Loss of balance is a hallmark of spinocerebellar ataxia (SCA). OBJECTIVE: The aim of this study was to identify which standing balance conditions and digital measures of body sway were most discriminative, reliable, and valid for quantifying balance in SCA. METHODS: Fifty-three people with SCA (13 SCA1, 13 SCA2, 14 SCA3, and 13 SCA6) and Scale for Assessment and Rating of Ataxia (SARA) scores 9.28 ± 4.36 and 31 healthy controls were recruited. Subjects stood in six test conditions (natural stance, feet together and tandem, each with eyes open [EO] and eyes closed [EC]) with an inertial sensor on their lower back for 30 seconds (×2). We compared test completion rate, test-retest reliability, and areas under the receiver operating characteristic curve (AUC) for seven digital sway measures. Pearson's correlations related sway with the SARA and the Patient-Reported Outcome Measure of Ataxia (PROM ataxia). RESULTS: Most individuals with SCA (85%-100%) could stand for 30 seconds with natural stance EO or EC, and with feet together EO. The most discriminative digital sway measures (path length, range, area, and root mean square) from the two most reliable and discriminative conditions (natural stance EC and feet together EO) showed intraclass correlation coefficients from 0.70 to 0.91 and AUCs from 0.83 to 0.93. Correlations of sway with SARA were significant (maximum r = 0.65 and 0.73). Correlations with PROM ataxia were mild to moderate (maximum r = 0.56 and 0.34). CONCLUSION: Inertial sensor measures of extent of postural sway in conditions of natural stance EC and feet together stance EO were discriminative, reliable, and valid for monitoring SCA. © 2024 International Parkinson and Movement Disorder Society.

Assessment of Mobility Trajectories Using Wearable Inertial Sensors During Autologous Hematopoietic Cell Transplant.

OBJECTIVE: This study aimed to characterize mobility patterns using wearable inertial sensors and serial assessment across autologous hematopoietic cell transplant (autoHCT) and investigate the relation between mobility and perceived function in patients with hematologic cancer. DESIGN: Prospective longitudinal study. SETTING: Hospital adult transplant clinic followed by discharge. PARTICIPANTS: 78 patients with hematological cancer receiving autoHCT. MAIN OUTCOME MEASURES: Mobility was measured across 3 clinical phases (pretransplant, pre-engraftment, and post-engraftment) in using inertial sensors worn during prescribed performance tests in the hospital. Perceived function was assessed using validated provider-reported (Eastern Cooperative Oncology Group [ECOG] Performance Status Scale) and patient-reported [European Organization for Research and Treatment of Cancer Quality of Life Questionnaire [EORTC QLQ-C30]) measures. Trajectories of 5 selected mobility characteristics (turn duration, gait speed, stride time variability, double support time, and heel strike angle) across the clinical phases were also evaluated using piecewise linear mixed-effects models. RESULTS: Using Principal Components Analysis, 4 mobility patterns were identified pretransplant: Gait Limitation, Sagittal Sway, Coronal Sway, and Balance Control. Gait Limitation measured pretransplant was significantly inversely associated with perceived function reported by the provider- (ß = -0.11; 95% CI: -0.19, -0.02) and patient- (ß = -4.85; 95% CI: -7.72, -1.99) post-engraftment in age-adjusted linear regression models. Mobility characteristics demonstrated immediate declines early pre-engraftment with stabilization by late pre-engraftment. CONCLUSION: Patients with hematological cancer experiencing gait limitations pretransplant are likely to have worse perceived function post-engraftment. Mobility declines in early phases post-transplant and may not fully recover, indicating an opportunity for timely rehabilitation referrals. Wearable inertial sensors can be used to identify early mobility problems and patients who may be at risk for future functional decline who may be candidates for early physical rehabilitation.

Mobility Rehab visual feedback system for gait rehabilitation in older adults.

BACKGROUND: Gait and balance impairments are among the main causes of falls in older adults. The feasibility and effectiveness of adding sensor-based feedback to physical therapy (PT) in an outpatient PT setting is unknown. We evaluated the feasibility and effectiveness of PT intervention combined with a therapist-assisted visual feedback system, called Mobility Rehab, (PT + MR) in older adults. METHODS: Twenty-eight older adults with and without neurological diseases were assigned either PT + MR (n = 22) or PT alone (n = 6). Both groups performed 8 sessions (individualized) of 45 min long (30 min for gait training and 15 min for endurance, strength, and balance exercises) in an outpatient clinic. Mobility Rehab uses unobtrusive, inertial sensors on both wrists and feet, and at the sternum level with real-time algorithms to provide real-time feedback on five gait metrics (step duration, stride length, elevation at mid-swing, arm swing range-of-motion [ROM], and trunk coronal ROM), which are displayed on a tablet. The primary outcome was the Activities-specific Balance Confidence scale (ABC). The secondary outcome was gait speed measured with wearable inertial sensors during 2 min of walking. RESULTS: There were no between-group differences at baseline for any variable (P > 0.05). Neither PT + MR nor PT alone showed significant changes on the ABC scores. PT + MR, but not PT alone, showed significant improvements in gait speed and arm swing ROM. The system was evaluated as 'easy to use' by the PT. CONCLUSIONS: Our preliminary results show that PT + MR improves gait speed in older adults with and without neurological diseases in an outpatient clinic. CLINICAL TRIAL REGISTRATION: www. CLINICALTRIALS: gov , identifier: NCT03869879.

Opal Actigraphy (Activity and Sleep) Measures Compared to ActiGraph: A Validation Study.

Physical activity and sleep monitoring in daily life provide vital information to track health status and physical fitness. The aim of this study was to establish concurrent validity for the new Opal Actigraphy solution in relation to the widely used ActiGraph GT9X for measuring physical activity from accelerometry epic counts (sedentary to vigorous levels) and sleep periods in daily life. Twenty participants (age 56 + 22 years) wore two wearable devices on each wrist for 7 days and nights, recording 3-D accelerations at 30 Hz. Bland-Altman plots and intraclass correlation coefficients (ICCs) assessed validity (agreement) and test-retest reliability between ActiGraph and Opal Actigraphy sleep durations and activity levels, as well as between the two different versions of the ActiGraph. ICCs showed excellent reliability for physical activity measures and moderate-to-excellent reliability for sleep measures between Opal versus Actigraph GT9X and between GT3X versus GT9X. Bland-Altman plots and mean absolute percentage error (MAPE) also show a comparable performance (within 10%) between Opal and ActiGraph and between the two ActiGraph monitors across activity and sleep measures. In conclusion, physical activity and sleep measures using Opal Actigraphy demonstrate performance comparable to that of ActiGraph, supporting concurrent validation. Opal Actigraphy can be used to quantify activity and monitor sleep patterns in research and clinical studies.

Inertial Sensor Algorithm to Estimate Walk Distance.

The "total distance walked" obtained during a standardized walking test is an integral component of physical fitness and health status tracking in a range of consumer and clinical applications. Wearable inertial sensors offer the advantages of providing accurate, objective, and reliable measures of gait while streamlining walk test administration. The aim of this study was to develop an inertial sensor-based algorithm to estimate the total distance walked using older subjects with impaired fasting glucose (Study I), and to test the generalizability of the proposed algorithm in patients with Multiple Sclerosis (Study II). All subjects wore two inertial sensors (Opals by Clario-APDM Wearable Technologies) on their feet. The walking distance algorithm was developed based on 108 older adults in Study I performing a 400 m walk test along a 20 m straight walkway. The validity of the algorithm was tested using a 6-minute walk test (6MWT) in two sub-studies of Study II with different lengths of a walkway, 15 m (Study II-A, n = 24) and 20 m (Study II-B, n = 22), respectively. The start and turn around points were marked with lines on the floor while smaller horizontal lines placed every 1 m served to calculate the manual distance walked (ground truth). The proposed algorithm calculates the forward distance traveled during each step as the change in the horizontal position from each foot-flat period to the subsequent foot-flat period. The total distance walked is then computed as the sum of walk distances for each stride, including turns. The proposed algorithm achieved an average absolute error rate of 1.92% with respect to a fixed 400 m distance for Study I. The same algorithm achieved an absolute error rate of 4.17% and 3.21% with respect to an averaged manual distance for 6MWT in Study II-A and Study II-B, respectively. These results demonstrate the potential of an inertial sensor-based algorithm to estimate a total distance walked with good accuracy with respect to the manual, clinical standard. Further work is needed to test the generalizability of the proposed algorithm with different administrators and populations, as well as larger diverse cohorts.

Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis.

This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk.

Feasibility of a Novel Therapist-Assisted Feedback System for Gait Training in Parkinson's Disease.

We tested the feasibility of one session of treadmill training using a novel physical therapist assisted system (Mobility Rehab) using wearable sensors on the upper and lower limbs of 10 people with Parkinson's disease (PD). Participants performed a 2-min walk overground before and after 15 min of treadmill training with Mobility Rehab, which included an electronic tablet (to visualize gait metrics) and five Opal sensors placed on both the wrists and feet and on the sternum area to measure gait and provide feedback on six gait metrics (foot-strike angle, trunk coronal range-of-motion (ROM), arm swing ROM, double-support duration, gait-cycle duration, and step asymmetry). The physical therapist used Mobility Rehab to select one or two gait metrics (from the six) to focus on during the treadmill training. Foot-strike angle (effect size (ES) = 0.56, 95% Confidence Interval (CI) = 0.14 to 0.97), trunk coronal RoM (ES = 1.39, 95% CI = 0.73 to 2.06), and arm swing RoM (ES = 1.64, 95% CI = 0.71 to 2.58) during overground walking showed significant and moderate-to-large ES following treadmill training with Mobility Rehab. Participants perceived moderate (60%) and excellent (30%) effects of Mobility Rehab on their gait. No adverse events were reported. One session of treadmill training with Mobility Rehab is feasible for people with mild-to-moderate PD.

Gait Variability in Spinocerebellar Ataxia Assessed Using Wearable Inertial Sensors.

BACKGROUND: Quantitative assessment of severity of ataxia-specific gait impairments from wearable technology could provide sensitive performance outcome measures with high face validity to power clinical trials. OBJECTIVES: The aim of this study was to identify a set of gait measures from body-worn inertial sensors that best discriminate between people with prodromal or manifest spinocerebellar ataxia (SCA) and age-matched, healthy control subjects (HC) and determine how these measures relate to disease severity. METHODS: One hundred and sixty-three people with SCA (subtypes 1, 2, 3, and 6), 42 people with prodromal SCA, and 96 HC wore 6 inertial sensors while performing a natural pace, 2-minute walk. Areas under the receiver operating characteristic curves (AUC) were compared for 25 gait measures, including standard deviations as variability, to discriminate between ataxic and normal gait. Pearson's correlation coefficient assessed the relationships between the gait measures and severity of ataxia. RESULTS: Increased gait variability was the most discriminative gait feature of SCA; toe-out angle variability (AUC = 0.936; sensitivity = 0.871; specificity = 0.896) and double-support time variability (AUC = 0.932; sensitivity = 0.834; specificity = 0.865) were the most sensitive and specific measures. These variability measures were also significantly correlated with the scale for the assessment and rating of ataxia (SARA) and disease duration. The same gait measures discriminated gait of people with prodromal SCA from the gait of HC (AUC = 0.610, and 0.670, respectively). CONCLUSIONS: Wearable inertial sensors provide sensitive and specific measures of excessive gait variability in both manifest and prodromal SCAs that are reliable and related to the severity of the disease, suggesting they may be useful as clinical trial performance outcome measures. © 2021 International Parkinson and Movement Disorder Society.

Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson's disease, and matched controls.

BACKGROUND AND PURPOSE: Recent findings suggest that a gait assessment at a discrete moment in a clinic or laboratory setting may not reflect functional, everyday mobility. As a step towards better understanding gait during daily life in neurological populations, we compared gait measures that best discriminated people with multiple sclerosis (MS) and people with Parkinson's Disease (PD) from their respective, age-matched, healthy control subjects (MS-Ctl, PD-Ctl) in laboratory tests versus a week of daily life monitoring. METHODS: We recruited 15 people with MS (age mean ± SD: 49 ± 10 years), 16 MS-Ctl (45 ± 11 years), 16 people with idiopathic PD (71 ± 5 years), and 15 PD-Ctl (69 ± 7 years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory followed by 7 days during daily life. Mann-Whitney U test and area under the curve (AUC) compared differences between PD and PD-Ctl, and between MS and MS-Ctl in the laboratory and in daily life. RESULTS: Participants wore sensors for 60-68 h in daily life. Measures that best discriminated gait characteristics in people with MS and PD from their respective control groups were different between the laboratory gait test and a week of daily life. Specifically, the toe-off angle best discriminated MS versus MS-Ctl in the laboratory (AUC [95% CI] = 0.80 [0.63-0.96]) whereas gait speed in daily life (AUC = 0.84 [0.69-1.00]). In contrast, the lumbar coronal range of motion best discriminated PD versus PD-Ctl in the laboratory (AUC = 0.78 [0.59-0.96]) whereas foot-strike angle in daily life (AUC = 0.84 [0.70-0.98]). AUCs were larger in daily life compared to the laboratory. CONCLUSIONS: Larger AUC for daily life gait measures compared to the laboratory gait measures suggest that daily life monitoring may be more sensitive to impairments from neurological disease, but each neurological disease may require different gait outcome measures.

Effect of Bout Length on Gait Measures in People with and without Parkinson's Disease during Daily Life.

Although the use of wearable technology to characterize gait disorders in daily life is increasing, there is no consensus on which specific gait bout length should be used to characterize gait. Clinical trialists using daily life gait quality as study outcomes need to understand how gait bout length affects the sensitivity and specificity of measures to discriminate pathological gait as well as the reliability of gait measures across gait bout lengths. We investigated whether Parkinson's disease (PD) affects how gait characteristics change as bout length changes, and how gait bout length affects the reliability and discriminative ability of gait measures to identify gait impairments in people with PD compared to neurotypical Old Adults (OA). We recruited 29 people with PD and 20 neurotypical OA of similar age for this study. Subjects wore 3 inertial sensors, one on each foot and one over the lumbar spine all day, for 7 days. To investigate which gait bout lengths should be included to extract gait measures, we determined the range of gait bout lengths available across all subjects. To investigate if the effect of bout length on each gait measure is similar or not between subjects with PD and OA, we used a growth curve analysis. For reliability and discriminative ability of each gait measure as a function of gait bout length, we used the intraclass correlation coefficient (ICC) and area under the curve (AUC), respectively. Ninety percent of subjects walked with a bout length of less than 53 strides during the week, and the majority (>50%) of gait bouts consisted of less than 12 strides. Although bout length affected all gait measures, the effects depended on the specific measure and sometimes differed for PD versus OA. Specifically, people with PD did not increase/decrease cadence and swing duration with bout length in the same way as OA. ICC and AUC characteristics tended to be larger for shorter than longer gait bouts. Our findings suggest that PD interferes with the scaling of cadence and swing duration with gait bout length. Whereas control subjects gradually increased cadence and decreased swing duration as bout length increased, participants with PD started with higher than normal cadence and shorter than normal stride duration for the smallest bouts, and cadence and stride duration changed little as bout length increased, so differences between PD and OA disappeared for the longer bout lengths. Gait measures extracted from shorter bouts are more common, more reliable, and more discriminative, suggesting that shorter gait bouts should be used to extract potential digital biomarkers for people with PD.

Validation of an Inertial Sensor Algorithm to Quantify Head and Trunk Movement in Healthy Young Adults and Individuals with Mild Traumatic Brain Injury.

Wearable inertial measurement units (IMUs) may provide useful, objective information to clinicians interested in quantifying head movements as patients' progress through vestibular rehabilitation. The purpose of this study was to validate an IMU-based algorithm against criterion data (motion capture) to estimate average head and trunk range of motion (ROM) and average peak velocity. Ten participants completed two trials of standing and walking tasks while moving the head with and without moving the trunk. Validity was assessed using a combination of Intra-class Correlation Coefficients (ICC), root mean square error (RMSE), and percent error. Bland-Altman plots were used to assess bias. Excellent agreement was found between the IMU and criterion data for head ROM and peak rotational velocity (average ICC > 0.9). The trunk showed good agreement for most conditions (average ICC > 0.8). Average RMSE for both ROM (head = 2.64°; trunk = 2.48°) and peak rotational velocity (head = 11.76 °/s; trunk = 7.37 °/s) was low. The average percent error was below 5% for head and trunk ROM and peak rotational velocity. No clear pattern of bias was found for any measure across conditions. Findings suggest IMUs may provide a promising solution for estimating head and trunk movement, and a practical solution for tracking progression throughout rehabilitation or home exercise monitoring.

Validation of an Inertial Sensor System for Physical Therapists to Quantify Movement Coordination During Functional Tasks.

Physical therapists evaluate patients' movement patterns during functional tasks; yet, their ability to interpret these observations consistently and accurately is unclear. Physical therapists would benefit from a clinic-friendly method for accurately quantifying movement patterns during functional tasks. Inertial sensors, which are inexpensive, portable sensors capable of monitoring multiple body segments simultaneously, are a relatively new rehabilitation technology. We sought to validate an inertial sensor system by comparing lower limb and lumbar spine kinematic data collected simultaneously with a commercial inertial sensor system and a motion camera system while 10 subjects performed functional tasks. Mean and peak segment angular displacement data were calculated and compared between systems. Mean angular displacement root mean square error between the systems across all tasks and segments was <5°. Mean differences in peak displacements were generally acceptable (<5°) for the femur, tibia, and pelvis segments for all tasks; however, the inertial system overestimated lumbar flexion compared to the motion camera system. These data suggest that the inertial system is capable of measuring angular displacements within 5° of a system widely accepted for its accuracy. Standardization of sensor placement, better attachment methods, and improvement of inertial sensor algorithms will further increase the accuracy of the system.

Validity of the Instrumented Push and Release Test to Quantify Postural Responses in Persons With Multiple Sclerosis.

OBJECTIVE: To test the validity of wearable inertial sensors to provide objective measures of postural stepping responses to the push and release clinical test in people with multiple sclerosis. DESIGN: Cross-sectional study. SETTING: University medical center balance disorder laboratory. PARTICIPANTS: Total sample N=73; persons with multiple sclerosis (PwMS) n=52; healthy controls n=21. MAIN OUTCOME MEASURES: Stepping latency, time and number of steps required to reach stability, and initial step length were calculated using 3 inertial measurement units placed on participants' lumbar spine and feet. RESULTS: Correlations between inertial sensor measures and measures obtained from the laboratory-based systems were moderate to strong and statistically significant for all variables: time to release (r=.992), latency (r=.655), time to stability (r=.847), time of first heel strike (r=.665), number of steps (r=.825), and first step length (r=.592). Compared with healthy controls, PwMS demonstrated a longer time to stability and required a larger number of steps to reach stability. CONCLUSIONS: The instrumented push and release test is a valid measure of postural responses in PwMS and could be used as a clinical outcome measures for patient care decisions or for clinical trials aimed at improving postural control in PwMS.

Wearable Inertial Sensors Allow for Quantitative Assessment of Shoulder and Elbow Kinematics in a Cadaveric Knee Arthroscopy Model.

PURPOSE: To develop a model using wearable inertial sensors to assess the performance of orthopaedic residents while performing a diagnostic knee arthroscopy. METHODS: Fourteen subjects performed a diagnostic arthroscopy on a cadaveric right knee. Participants were divided into novices (5 postgraduate year 3 residents), intermediates (5 postgraduate year 4 residents), and experts (4 faculty) based on experience. Arm movement data were collected by inertial measurement units (Opal sensors) by securing 2 sensors to each upper extremity (dorsal forearm and lateral arm) and 2 sensors to the trunk (sternum and lumbar spine). Kinematics of the elbow and shoulder joints were calculated from the inertial data by biomechanical modeling based on a sequence of links connected by joints. Range of motion required to complete the procedure was calculated for each group. Histograms were used to compare the distribution of joint positions for an expert, intermediate, and novice. RESULTS: For both the right and left upper extremities, skill level corresponded well with shoulder abduction-adduction and elbow prono-supination. Novices required on average 17.2° more motion in the right shoulder abduction-adduction plane than experts to complete the diagnostic arthroscopy (P = .03). For right elbow prono-supination (probe hand), novices required on average 23.7° more motion than experts to complete the procedure (P = .03). Histogram data showed novices had markedly more variability in shoulder abduction-adduction and elbow prono-supination compared with the other groups. CONCLUSIONS: Our data show wearable inertial sensors can measure joint kinematics during diagnostic knee arthroscopy. Range-of-motion data in the shoulder and elbow correlated inversely with arthroscopic experience. Motion pattern-based analysis shows promise as a metric of resident skill acquisition and development in arthroscopy. CLINICAL RELEVANCE: Wearable inertial sensors show promise as metrics of arthroscopic skill acquisition among residents.

Daily life mobility detects frailty, falls, and functioning in ADT-treated prostate cancer survivors.

Background: Androgen deprivation therapy (ADT) increases the risk of frailty, falls, and, poor physical functioning in prostate cancer survivors. Detection of frailty is limited to self-report instruments and performance measures, so unbiased tools are needed. We investigated relationships between an unbiased measure - daily life mobility - and ADT history, frailty, falls, and functioning in ADT-treated prostate cancer survivors. Methods: ADT-treated prostate cancer survivors (N=99) were recruited from an exercise clinical trial, an academic medical center, and the community. Participants completed performance measures and surveys to assess frailty, fall history, and physical functioning, then wore instrumented socks to continuously monitor daily life mobility. We performed a principal component analysis on daily life mobility metrics and used regression analyses to investigate relationships between domains of daily life mobility and frailty, fall history, and physical functioning. Results: Daily life mobility metrics clustered into four domains: Gait Pace, Rhythm, Activity, and Balance. Worse scores on Rhythm and Activity were associated with increased odds of frailty (OR 1.59, 95% CI: 1.04, 2.49 and OR 1.81, 95% CI: 1.19, 2.83, respectively). A worse score on Rhythm was associated with increased odds of ≥1 falls in the previous year (OR 1.60, 95% CI: 1.05, 2.47). Worse scores on Gait Pace, Rhythm, and Activity were associated with worse physical functioning. Mobility metrics were similar between current and past users of ADT. Conclusions: Continuous passive monitoring of daily life mobility may identify prostate cancer survivors who have or are developing risk for frailty, falls, and declines in physical functioning.

The Use of Novel Instrumented Socks to Detect Changes in Daily Life Mobility During an Exercise Intervention in Prostate Cancer Survivors Treated with Androgen Deprivation Therapy.

OBJECTIVES: To describe changes in daily life mobility in prostate cancer survivors treated with androgen deprivation therapy (ADT) after a 6-month exercise intervention using novel instrumented socks and to identify characteristics of participants who exhibited changes in daily life mobility. METHODS: A subset of participants in a fall prevention exercise trial completed objective tests and patient-reported surveys of physical functioning, and wore instrumented socks for up to 7 days to measure daily life mobility. Changes in cadence, double support proportion, and pitch angle of the foot at toe-off were selected as measures of daily life mobility previously found to be different in men exposed to ADT for prostate cancer versus controls. Daily life mobility was compared from baseline to 6 months using paired t-tests. Characteristics of responders who improved their daily life mobility were compared to nonresponders using two-sample t-tests, Chi-squared proportion tests, or Fisher's Exact Tests. RESULTS: Our sample included 35 prostate cancer survivors (mean age 71.6 ± 7.8 years). Mean cadence, double support proportion, and pitch angle at toe-off did not change significantly over 6 months of exercise, but 14 participants (40%) improved in at least two of three daily life mobility measures ("responders"). Responders were characterized by lower physical functioning, lower cadence in daily life, fewer comorbidities, and better social and mental/emotional functioning. CONCLUSIONS: Certain daily life mobility measures potentially impacted by ADT could be measured with instrumented socks and improved by exercise. Men who start with lower physical functioning and better social and mental/emotional functioning appear most likely to benefit, possibly because they have more to gain from exercise and are able to engage in a 6-month intervention. IMPLICATIONS FOR NURSING PRACTICE: Technology-based approaches could provide nurses with an objective measure of daily life mobility for patients with chronic illness and detect who is responding to rehabilitation.

Continuous monitoring of turning in patients with movement disability.

Difficulty with turning is a major contributor to mobility disability and falls in people with movement disorders, such as Parkinson's disease (PD). Turning often results in freezing and/or falling in patients with PD. However, asking a patient to execute a turn in the clinic often does not reveal their impairments. Continuous monitoring of turning with wearable sensors during spontaneous daily activities may help clinicians and patients determine who is at risk of falls and could benefit from preventative interventions. In this study, we show that continuous monitoring of natural turning with wearable sensors during daily activities inside and outside the home is feasible for people with PD and elderly people. We developed an algorithm to detect and characterize turns during gait, using wearable inertial sensors. First, we validate the turning algorithm in the laboratory against a Motion Analysis system and against a video analysis of 21 PD patients and 19 control (CT) subjects wearing an inertial sensor on the pelvis. Compared to Motion Analysis and video, the algorithm maintained a sensitivity of 0.90 and 0.76 and a specificity of 0.75 and 0.65, respectively. Second, we apply the turning algorithm to data collected in the home from 12 PD and 18 CT subjects. The algorithm successfully detects turn characteristics, and the results show that, compared to controls, PD subjects tend to take shorter turns with smaller turn angles and more steps. Furthermore, PD subjects show more variability in all turn metrics throughout the day and the week.

Effect of Levodopa and Environmental Setting on Gait and Turning Digital Markers Related to Falls in People with Parkinson's Disease.

Background: It is unknown whether medication status (off and on levodopa) or laboratory versus home settings plays a role in discriminating fallers and non-fallers in people with Parkinson's disease (PD). Objectives: To investigate which specific digital gait and turning measures, obtained with body-worn sensors, best discriminated fallers from non-fallers with PD in the clinic and during daily life. Methods: We recruited 34 subjects with PD (17 fallers and 17 non-fallers based on the past 6 month's falls). Subjects wore three inertial sensors attached to both feet and the lumbar region in the laboratory for a 3-minute walking task (both off and on levodopa) and during daily life activities for a week. We derived 24 digital (18 gait and 6 turn) measures from the 3-minute walk and from daily life. Results: In clinic, none of the gait and turning measures collected during on levodopa state were significantly different between fallers and non-fallers. In contrast, digital measures collected in the off levodopa state were significantly different between groups, (average turn velocity, average number of steps to complete a turn, and variability of gait speed, P < 0.03). During daily life, the variability of average turn velocity (P = 0.023) was significantly different in fallers than non-fallers. Last, the average number of steps to complete a turn was significantly correlated with the patient-reported outcomes. Conclusions: Digital measures of turning, but not gait, were different in fallers compared to non-fallers with PD, in the laboratory when off medication and during a daily life.

Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease.

Objectives: To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. Methods: We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls. Results: Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10). Conclusions: These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.