Stride width and postural stability in frontal gait disorders and Parkinson's disease.

Older adults, as well as those with certain neurological disorders, may compensate for poor neural control of postural stability by widening their base of foot support while walking. However, the extent to which this wide-based gait improves postural stability or affects postural control strategies has not been explored. People with idiopathic Parkinson's disease (iPD, n = 72), frontal gait disorders (FGD, n = 16), and healthy older adults (n = 32) performed walking trials at their preferred speed over an 8-m-long, instrumented walkway. People with iPD were tested in their OFF medication state. Analyses of covariance were performed to determine the associations between stride width and measures of lateral stability control. People with FGD exhibited a wide-based gait compared to both healthy older adults and iPD. An increased stride width was associated with an increase in lateral margin of stability in FGD. Unlike healthy older adults or iPD, people with FGD did not externally rotate their feet (toe-out angle) or shift their center of pressure laterally to aid lateral dynamic stability during walking but slowed their gait instead to increase stability. By adopting a slow, wide-based gait, people with FGD take advantage of the passive, pendular mechanics of walking.

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.

Digital gait measures, but not the 400-meter walk time, detect abnormal gait characteristics in people with Prediabetes.

BACKGROUND AND AIM: Abnormal gait characteristics have been observed in people with diabetic neuropathy, but it is unclear if subtle changes in gait occur in prediabetic people with impaired fasting glucose (IFG). The aims of this study were: (1) to investigate if digital gait measures discriminate people with prediabetes from healthy control participants (HC) and (2) to investigate the relationship between gait measures and clinical scores (concurrent validity). METHODS: 108 people with prediabetes (71.20 ± 5.11 years) and 63 HC subjects (70.40 ± 6.25 years) wore 6 inertial sensors (Opals by APDM, Clario) while performing the 400-meter fast walk test. Fifty-five measures across 5 domains of gait (Lower Body, Upper Body, Turning, and Variability) were averaged. Analysis of Covariance was used to investigate the group differences, with body mass index as a covariate. Pearson's correlation coefficient assessed the association between the gait measures and the Short Physical Performance Battery (SPPB) score. RESULTS: Nine gait measures were significantly different (p < 10-4) between IFG and HC groups. Step duration, cadence, and turn velocity were the most discriminative measures. In contrast, traditional stop-watch time was not significantly different between groups (p = 0.13), after controlling for BMI. Cadence (r = -0.37, p < 0.001), step duration (r = -0.39, p < 0.001), and turn velocity (r = 0.47, p < 0.001) showed a significant correlation with the SPPB score. CONCLUSION: Body-worn inertial sensors detected gait impairments in people with prediabetes that related to clinical balance test performance, even when the traditional stop-watch time was not prolonged for the 400-meter walk test.

Quantitative Gait and Balance Outcomes for Ataxia Trials: Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers.

With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials.This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics in natural history studies and clinical trials, and discuss relevant issues for their use as performance outcomes.

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.

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.

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.

Should we use both clinical and mobility measures to identify fallers in Parkinson's disease?

BACKGROUND: Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear. OBJECTIVE: To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD. METHODS: People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a 'best subsets selection strategy' with a 5-fold cross validation, and calculated the area under the curve (AUC). RESULTS: The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x). CONCLUSIONS: Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD.

The Instrumented Stand and Walk (ISAW) test to predict falls in older men.

Objective measures of balance and gait have the potential to improve prediction of future fallers because balance and gait impairments are common precursors. We used the Instrumented Stand and Walk Test (ISAW) with wearable, inertial sensors to maximize the domains of balance and gait evaluated in a short test. We hypothesized that ISAW objective measures across a variety of gait and balance domains would improve fall prediction beyond history of falls and better than gait speed or dual-task cost on gait-speed. We recruited 214 high-functioning older men (mean 82 years), of whom 91 participants (42.5%) had one or more falls in the 12 months following the ISAW test. The ISAW test involved 30 s of stance followed by a 7-m walk, turn, and return. We examined regression models for falling using 17 ISAW metrics, with and without age and fall history, and characterize top-performing models by AUC and metrics included. The ISAW test improved distinguishing between future fallers and non-fallers compared to age and history of falls, alone (AUC improved from 0.69 to 0.75). Models with 1 ISAW metric usually included a postural sway measure, models with 2 ISAW measures included a turning measure, models with 3 ISAW measures included a gait variability measure, and models with 4 or 5 measures added a gait initiation measure. Gait speed and dual-task cost did not distinguish between fallers and non-fallers in this high-functioning cohort. The best fall-prediction models support the notion that older people may fall due to a variety of balance and gait impairments.

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.

Effects of a Cognitively Challenging Agility Boot Camp Program on Balance and Gait in People With Parkinson's Disease: Does Freezing of Gait Status Matter?

BACKGROUND AND AIM: Individuals with Parkinson's disease (PD) with and without freezing of Gait (FoG) may respond differently to exercise interventions for several reasons, including disease duration. This study aimed to determine whether both people with and without FoG benefit from the Agility Boot Camp with Cognitive Challenges (ABC-C) program. METHODS: This secondary analysis of our ABC-C trial included 86 PD subjects: 44 without FoG (PD-FoG) and 42 with FoG (PD + FoG). We collected measures of standing sway balance, anticipatory postural adjustments, postural responses, and a 2-minute walk with and without a cognitive task. Two-way repeated analysis of variance, with disease duration as covariate, was used to investigate the effects of ABC-C program. Effect sizes were calculated using standardized response mean (SRM) for PD-FoG and PD + FoG, separately. RESULTS: The ABC-C program was effective in improving gait performance in both PD-FoG and PD + FoG, even after controlling for disease duration. Specifically, dual-task gait speed (P < .0001), dual-cost stride length (P = .012), and these single-task measures: arm range of motion (P < .0001), toe-off angle (P = .005), gait cycle duration variability (P = .019), trunk coronal range of motion (P = .042), and stance time (P = .046) improved in both PD-FoG and PD + FoG. There was no interaction effect between time (before and after exercise) and group (PD-FoG/PD + FoG) in all 24 objective measures of balance and gait. Dual-task gait speed improved the most in PD + FoG (SRM = 1.01), whereas single-task arm range of motion improved the most in PD-FoG (SRM = 1.01). CONCLUSION: The ABC-C program was similarly effective in improving gait (and not balance) performance in both PD-FoG and PD + FoG.

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.

Surrogates for rigidity and PIGD MDS-UPDRS subscores using wearable sensors.

BACKGROUND: Telemedicine has the advantage of expanding access to care for patients with Parkinson's Disease (PD). However, rigidity and postural instability in PD are difficult to measure remotely, and are important measures of functional impairment and fall risk. RESEARCH QUESTION: Can measures from wearable sensors be used as future surrogates for the MDS-UPDRS rigidity and Postural Instability and Gait Difficulty (PIGD) subscores? METHODS: Thirty-one individuals with mild to moderate PD wore 3 inertial sensors at home for one week to measure quantity and quality of gait and turning in daily life. Separately, we performed a clinical assessment and balance characterization of postural sway with the same wearable sensors in the laboratory (On medication). We then first performed a traditional correlation analysis between clinical scores and objective measures of gait and balance followed by multivariable linear regression employing a best subset selection strategy. RESULTS: The number of walking bouts and turns correlated significantly with the rigidity subscore, while the number of turns, foot pitch angle, and sway area while standing correlated significantly with the PIGD subscore (p < 0.05). The multivariable linear regression showed that rigidity subscore was best predicted by the number of walking bouts while the PIGD subscore was best predicted by a combination of number of walking bouts, gait speed, and postural sway. SIGNIFICANCE: The correlation between objective sensor data and MDS-UPDRS rigidity and PIGD scores paves the way for future larger studies that evaluate use of objective sensor data to supplement remote MDS-UPDRS assessment.

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.

Functional limits of stability and standing balance in people with Parkinson's disease with and without freezing of gait using wearable sensors.

BACKGROUND: People with from Parkinson's disease (PD) and freezing of gait (FoG) have more frequent falls compared to those who do not freeze but there is no consensus on which, specific objective measures of postural instability are worse in freezers (PD + FoG) than non-freezers (PD-FoG). RESEARCH QUESTION: Are functional limits of stability (fLoS) or postural sway during stance measured with wearable inertial sensors different between PD + FoG versus PD-FoG, as well as between PD versus healthy control subjects (HC)? METHODS: Sixty-four PD subjects with FoG (MDS-UPDRS Part III: 45.9 ±â€¯12.5) and 80 PD subjects without FoG (MDS-UPDRS Part III: 36.2 ±â€¯10.9) were tested Off medication and compared with 79 HC. Balance was quantified with inertial sensors worn on the lumbar spine while performing the following balance tasks: 1) fLoS as defined by the maximum displacement in the forward and backward directions and 2) postural sway area while standing with eyes open on a firm and foam surface. An ANOVA, controlling for disease duration, compared postural control between groups. RESULTS: PD + FoG had significantly smaller fLoS compared to PD-FoG (p =  0.004) and to healthy controls (p <  0.001). However, PD-FoG showed similar fLoS compared to healthy controls (p =  0.48). Both PD+FoG and PD-FoG showed larger postural sway on a foam surface compared to healthy controls (p =  0.001) but there was no significant difference in postural sway between PD+FoG and PD-FoG. SIGNIFICANCE: People with PD and FoG showed task-specific, postural impairments with smaller fLoS compared to non-freezers, even when controlling for disease duration. However, individuals with PD with or without FoG had similar difficulties standing quietly on an unreliable surface compared to healthy controls. Wearable inertial sensors can reveal worse fLoS in freezers than non-freezers that may contribute to FoG and help explain their more frequent falls.

Dual-Task Costs of Quantitative Gait Parameters While Walking and Turning in People with Parkinson's Disease: Beyond Gait Speed.

BACKGROUND: There is a lack of recommendations for selecting the most appropriate gait measures of Parkinson's disease (PD)-specific dual-task costs to use in clinical practice and research. OBJECTIVE: We aimed to identify measures of dual-task costs of gait and turning that best discriminate performance in people with PD from healthy individuals. We also investigated the relationship between the most discriminative measures of dual-task costs of gait and turning with disease severity and disease duration. METHODS: People with mild-to-moderate PD (n = 144) and age-matched healthy individuals (n = 79) wore 8 inertial sensors while walking under single and dual-task (reciting every other letter of the alphabet) conditions. Outcome measures included 26 objective measures within four gait domains (upper/lower body, turning and variability). The area under the curve (AUC) from the receiver-operator characteristic plot was calculated to compare discriminative ability of dual-task costs on gait across outcome measures. RESULTS: PD-specific, dual-task interference was identified for arm range of motion, foot strike angle, turn velocity and turn duration. Arm range of motion (AUC = 0.73) and foot strike angle (AUC = 0.68) had the largest AUCs across dual-task costs measures and they were associated with disease severity and/or disease duration. In contrast, the most commonly used dual-task gait measure, gait speed, showed an AUC of only 0.54. CONCLUSION: Findings suggest that people with PD rely more than healthy individuals on executive-attentional resources to control arm swing, foot strike, and turning, but not gait speed. The dual-task costs of arm range of motion best discriminated people with PD from healthy individuals.

Measuring freezing of gait during daily-life: an open-source, wearable sensors approach.

BACKGROUND: Although a growing number of studies focus on the measurement and detection of freezing of gait (FoG) in laboratory settings, only a few studies have attempted to measure FoG during daily life with body-worn sensors. Here, we presented a novel algorithm to detect FoG in a group of people with Parkinson's disease (PD) in the laboratory (Study I) and extended the algorithm in a second cohort of people with PD at home during daily life (Study II). METHODS: In Study I, we described of our novel FoG detection algorithm based on five inertial sensors attached to the feet, shins and lumbar region while walking in 40 participants with PD. We compared the performance of the algorithm with two expert clinical raters who scored the number of FoG episodes from video recordings of walking and turning based on duration of the episodes: very short (< 1 s), short (2-5 s), and long (> 5 s). In Study II, a different cohort of 48 people with PD (with and without FoG) wore 3 wearable sensors on their feet and lumbar region for 7 days. Our primary outcome measures for freezing were the % time spent freezing and its variability. RESULTS: We showed moderate to good agreement in the number of FoG episodes detected in the laboratory (Study I) between clinical raters and the algorithm (if wearable sensors were placed on the feet) for short and long FoG episodes, but not for very short FoG episodes. When extending this methodology to unsupervised home monitoring (Study II), we found that percent time spent freezing and the variability of time spent freezing differentiated between people with and without FoG (p < 0.05), and that short FoG episodes account for 69% of the total FoG episodes. CONCLUSION: Our findings showed that objective measures of freezing in PD using inertial sensors on the feet in the laboratory are matching well with clinical scores. Although results found during daily life are promising, they need to be validated. Objective measures of FoG with wearable technology during community-living would be useful for managing this distressing feature of mobility disability in PD.

Inertial Sensor Algorithms to Characterize Turning in Neurological Patients With Turn Hesitations.

BACKGROUND: One difficulty in turning algorithm design for inertial sensors is detecting two discrete turns in the same direction, close in time. A second difficulty is under-estimation of turn angle due to short-duration hesitations by people with neurological disorders. We aimed to validate and determine the generalizability of a: I. Discrete Turn Algorithm for variable and sequential turns close in time and II: Merged Turn Algorithm for a single turn angle in the presence of hesitations. METHODS: We validated the Discrete Turn Algorithm with motion capture in healthy controls (HC, n = 10) performing a spectrum of turn angles. Subsequently, the generalizability of the Discrete Turn Algorithm and associated, Merged Turn Algorithm were tested in people with Parkinson's disease (PD, n = 124), spinocerebellar ataxia (SCA, n = 51), and HC (n = 125). RESULTS: The Discrete Turn Algorithm shows improved agreement with optical motion capture and with known turn angles, compared to our previous algorithm by El-Gohary et al. The Merged Turn algorithm that merges consecutive turns in the same direction with short hesitations resulted in turn angle estimates closer to a fixed 180-degree turn angle in the PD, SCA, and HC subjects compared to our previous turn algorithm. Additional metrics were proposed to capture turn hesitations in PD and SCA. CONCLUSION: The Discrete Turn Algorithm may be particularly useful to characterize turns when the turn angle is unknown, i.e., during free-living conditions. The Merged Turn algorithm is recommended for clinical tasks in which the single-turn angle is known, especially for patients who hesitate while turning.

Does gait bout definition influence the ability to discriminate gait quality between people with and without multiple sclerosis during daily life?

BACKGROUND: There is currently no consensus about standardized gait bout definitions when passively monitoring walking during normal daily life activities. It is also not known how different definitions of a gait bout in daily life monitoring affects the ability to distinguish pathological gait quality. Specifically, how many seconds of a pause with no walking indicates an end to one gait bout and the start of another bout? In this study, we investigated the effect of 3 gait bout definitions on the discriminative ability to distinguish quality of walking in people with multiple sclerosis (MS) from healthy control subjects (HC) during a week of daily living. METHODS: 15 subjects with MS and 16 HC wore instrumented socks on each foot and one Opal sensor over the lower lumbar area for a week of daily activities for at least 8 h/day. Three gait bout definitions were based on the length of the pause between the end of one gait bout and start of another bout (1.25 s, 2.50 s, and 5.0 s pause). Area under the curve (AUC) was used to compare gait quality measures in MS versus HC. RESULTS: Total number of gait bouts over the week were statistically significantly different across bout definitions, as expected. However, AUCs of gait quality measures (such as gait speed, stride length, stride time) discriminating people with MS from HC were not different despite the 3 bout definitions. SIGNIFICANCE: Quality of gait measures that discriminate MS from HC during daily life are not influenced by the length of a gait bout, despite large differences in quantity of gait across bout definitions. Thus, gait quality measures in people with MS versus controls can be compared across studies using different gait bout definitions with pause lengths ≤5 s.