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BackgroundWrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. ObjectiveThe aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. MethodsParticipants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. ResultsThe best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. ConclusionsAlgorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial RegistrationISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2021-050785

Abstract Background Juvenile myoclonic epilepsy (JME) is characterized by generalized seizures. Nearly 30% of JME patients are drug‐resistant (DR‐JME), indicating a widespread cortical dysfunction. Walking is an important function that necessitates orchestrated coordination of frontocentral cortical regions. However, gait alterations in JME have been scarcely investigated. Our aim was to assess changes in gait and motor‐evoked responses in DR‐JME patients. Methods Twenty‐nine subjects (11 JME drug‐responder, 8 DR‐JME, and 10 healthy controls) underwent a gait analyses during usual walking and dual‐task walking. Later, subjects underwent 64‐channel EEG recordings while performing a simple motor task. We calculated the motor‐evoked current source densities (CSD) at a priori chosen cortical regions. Gait and CSD measures were compared between groups and tasks using mixed model analysis. Results DR‐JME patients demonstrated an altered gait pattern that included slower gait speed (p = .018), reduced cadence (p = .003), and smaller arm‐swing amplitude (p = .011). The DR‐JME group showed higher motor‐evoked CSD in the postcentral gyri compared to responders (p = .049) and both JME groups showed higher CSD in the superior frontal gyri compared to healthy controls (p |–0.494|, p

Stress tests, e.g., the cardiac stress test, are standard clinical screening tools aimed to unmask clinical pathology. As such stress tests indirectly measure physiological reserves. The term reserve has been developed to account for the dis-junction, often observed, between pathology and clinical manifestation. It describes a physiological capacity that is utilized in demanding situations. However, developing a new and reliable stress test based screening tool is complex, prolonged, and relies extensively on domain knowledge. We propose a novel distributional-free machine-learning framework, the Stress Test Performance Scoring (STEPS) framework, to model expected performance in a stress test. A performance scoring function is trained with measures taken during the performance in a given task while exploiting information regarding the stress test set-up and subjects' medical state. Multiple ways of aggregating performance scores at different stress levels are suggested and are examined with an extensive simulation study. When applied to a real-world data example, an AUC of 84.35[95%CI: 70.68 - 95.13] was obtained for the STEPS framework to distinguish subjects with neurodegeneration from controls. In summary, STEPS improved screening by exploiting existing domain knowledge and state-of-the-art clinical measures. The STEPS framework can ease and speed up the production of new stress tests.

BackgroundThe development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions.Methods/designThe Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson's Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability.DiscussionThe results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility.Trial registrationISRCTN12051706.

We tested the hypothesis that dividing attention would strengthen the ability to detect mild cognitive impairment (MCI) and specific cognitive abilities from Timed Up and Go (TUG) performance in the community setting. While wearing a belt-worn sensor, 757 dementia-free older adults completed TUG during two conditions, with and without a concurrent verbal serial subtraction task. We segmented TUG into its four subtasks (i.e., walking, turning, and two postural transitions), and extracted 18 measures that were summarized into nine validated sensor metrics. Participants also underwent a detailed cognitive assessment during the same visit. We then employed a series of regression models to determine the combinations of subtask sensor metrics most strongly associated with MCI and specific cognitive abilities for each condition. We also compared subtask performances with and without dividing attention to determine whether the costs of divided attention were associated with cognition. While slower TUG walking and turning were associated with higher odds of MCI under normal conditions, these and other subtask associations became more strongly linked to MCI when TUG was performed under divided attention. Walking and turns were also most strongly associated with executive function and attention, particularly under divided attention. These differential associations with cognition were mirrored by performance costs. However, since several TUG subtasks were more strongly associated with MCI and cognitive abilities when performed under divided attention, future work is needed to determine how instrumented dual-task TUG testing can more accurately estimate risk for late-life cognitive impairment in older adults.

BackgroundTai Chi (TC) mind-body exercise has been shown to reduce falls and improve balance and gait, however, few studies have evaluated the role of lower extremity muscle activation patterns in the observed benefits of TC on mobility.PurposeTo perform an exploratory analysis of the association between TC training and levels of lower extremity muscle co-contraction in healthy adults during walking under single-task (ST) and cognitive dual-task (DT) conditions.MethodsSurface electromyography of the anterior tibialis and lateral gastrocnemius muscles was recorded during 90 sec trials of overground ST (walking normally) and DT (walking with verbalized serial subtractions) walking. A mean co-contraction index (CCI), across all strides, was calculated based on the percentage of total muscle activity when antagonist muscles were simultaneously activated. A hybrid study design investigated long-term effects of TC via a cross-sectional comparison of 27 TC experts and 60 age-matched TC-naïve older adults. A longitudinal comparison assessed the shorter-term effects of TC; TC-naïve participants were randomly allocated to either 6 months of TC training or to usual care.ResultsAcross all participants at baseline, greater CCI was correlated with slower gait speed under DT (β(95% CI) = -26.1(-48.6, -3.7)) but not ST (β(95% CI) = -15.4(-38.2, 7.4)) walking. Linear models adjusting for age, gender, BMI and other factors that differed at baseline indicated that TC experts exhibited lower CCI compared to TC naives under DT, but not ST conditions (ST: mean difference (95% CI) = -7.1(-15.2, 0.97); DT: mean difference (95% CI) = -10.1(-18.1, -2.4)). No differences were observed in CCI for TC-naive adults randomly assigned to 6 months of TC vs. usual care.ConclusionLower extremity muscle co-contraction may play a role in the observed benefit of longer-term TC training on gait and postural control. Longer-duration and adequately powered randomized trials are needed to evaluate the effect of TC on neuromuscular coordination and its impact on postural control.Trial registrationThe randomized trial component of this study was registered at ClinicalTrials.gov (NCT01340365).

BackgroundCurrently, it is not feasible to obtain laboratory-based measures of joint motion in large numbers of older adults. We assessed the utility of a portable depth-sensing camera for quantifying hip and knee joint motion of older adults during mobility testing in the community.MethodsParticipants were 52 older adults enrolled in the Rush Memory and Aging Project, a community-based cohort study of aging. In a subset, we compared dynamic hip and knee flexion/extension obtained via the depth-sensing camera with that obtained concurrently using a laboratory-based optoelectronic motion capture system. Then we recorded participants' annual instrumented gait assessment in the community setting with the depth-sensing camera and examined the inter-relationships of hip and knee range of motion (ROM) with mobility metrics derived from a wearable sensor and other mobility-related health measures.ResultsIn the community, we successfully acquired joint motion from 49/52 participants using the depth-sensing camera. Hip and knee ROMs were related to diverse sensor-derived metrics of mobility performance (hip: Pearson's r = 0.31 to 0.58; knee: Pearson's r = 0.29 to 0.51), as well as daily physical activity, conventional motor measures, self-report hip and knee pain and dysfunction, mobility disability, and falls.ConclusionsThe depth-sensing camera's high rate of successful data acquisition and correlations of its hip and knee ROMs with other mobility measures suggest that this device can provide a cost-efficient means of quantifying joint motion in large numbers of community-dwelling older adults who span the health spectrum.

Objectives To assess the feasibility and inform design features of a fully powered randomized controlled trial (RCT) evaluating the effects of Tai Chi (TC) in Parkinson’s disease (PD) and to select outcomes most responsive to TC assessed during off-medication states. Design Two-arm, wait-list controlled RCT. Settings Tertiary care hospital. Subjects Thirty-two subjects aged 40–75 diagnosed with idiopathic PD within 10 years. Interventions Six-month TC intervention added to usual care (UC) versus UC alone. Outcome Measures Primary outcomes were feasibility-related (recruitment rate, adherence, and compliance). Change in dual-task (DT) gait stride-time variability (STV) from baseline to 6 months was defined, a priori, as the clinical outcome measure of primary interest. Other outcomes included: PD motor symptom progression (Unified Parkinson’s Disease Rating Scale [UPDRS]), PD-related quality of life (PDQ-39), executive function (Trail Making Test), balance confidence (Activity-Specific Balance Confidence Scale, ABC), and Timed Up and Go test (TUG). All clinical assessments were made in the off-state for PD medications. Results Thirty-two subjects were enrolled into 3 sequential cohorts over 417 days at an average rate of 0.08 subjects per day. Seventy-five percent (12/16) in the TC group vs 94% (15/16) in the UC group completed the primary 6-month follow-up assessment. Mean TC exposure hours overall: 52. No AEs occurred during or as a direct result of TC exercise. Statistically nonsignificant improvements were observed in the TC group at 6 months in DT gait STV (TC [20.1%] vs UC [−0.1%] group [effect size 0.49; P = .47]), ABC, TUG, and PDQ-39. UPDRS progression was modest and very similar in TC and UC groups. Conclusions Conducting an RCT of TC for PD is feasible, though measures to improve recruitment and adherence rates are needed. DT gait STV is a sensitive and logical outcome for evaluating the combined cognitive-motor effects of TC in PD.

PURPOSE:To determine if Tai Chi (TC) has an impact on long-range correlations and fractal-like scaling in gait stride time dynamics, previously shown to be associated with aging, neurodegenerative disease, and fall risk. METHODS:Using Detrended Fluctuation Analysis (DFA), this study evaluated the impact of TC mind-body exercise training on stride time dynamics assessed during 10 minute bouts of overground walking. A hybrid study design investigated long-term effects of TC via a cross-sectional comparison of 27 TC experts (24.5 ± 11.8 yrs experience) and 60 age- and gender matched TC-naïve older adults (50-70 yrs). Shorter-term effects of TC were assessed by randomly allocating TC-naïve participants to either 6 months of TC training or to a waitlist control. The alpha (α) long-range scaling coefficient derived from DFA and gait speed were evaluated as outcomes. RESULTS:Cross-sectional comparisons using confounder adjusted linear models suggest that TC experts exhibited significantly greater long-range scaling of gait stride time dynamics compared with TC-naïve adults. Longitudinal random-slopes with shared baseline models accounting for multiple confounders suggest that the effects of shorter-term TC training on gait dynamics were not statistically significant, but trended in the same direction as longer-term effects although effect sizes were very small. In contrast, gait speed was unaffected in both cross-sectional and longitudinal comparisons. CONCLUSION:These preliminary findings suggest that fractal-like measures of gait health may be sufficiently precise to capture the positive effects of exercise in the form of Tai Chi, thus warranting further investigation. These results motivate larger and longer-duration trials, in both healthy and health-challenged populations, to further evaluate the potential of Tai Chi to restore age-related declines in gait dynamics. TRIAL REGISTRATION:The randomized trial component of this study was registered at ClinicalTrials.gov (NCT01340365).

Healthy walking is characterized by pronounced arm swing and axial rotation. Aging effects on gait speed, stride length and stride time variability have been previously reported, however, less is known about aging effects on arm swing and axial rotation and their relationship to age-associated gait changes during usual walking and during more challenging conditions like dual tasking. Sixty healthy adults between the ages of 30-77 were included in this study designed to address this gap. Lightweight body fixed sensors were placed on each wrist and lower back. Participants walked under 3 walking conditions each of 1 minute: 1) comfortable speed, 2) walking while serially subtracting 3's (Dual Task), 3) walking at fast speed. Aging effects on arm swing amplitude, range, symmetry, jerk and axial rotation amplitude and jerk were compared between decades of age (30-40; 41-50; 51-60; 61-77 years). As expected, older adults walked slower (p = 0.03) and with increased stride variability (p = 0.02). Arm swing amplitude decreased with age under all conditions (p = 0.04). In the oldest group, arm swing decreased during dual task and increased during the fast walking condition (p

BackgroundDiminished control of standing balance, traditionally indicated by greater postural sway magnitude and speed, is associated with falls in older adults. Tai Chi (TC) is a multisystem intervention that reduces fall risk, yet its impact on sway measures vary considerably. We hypothesized that TC improves the integrated function of multiple control systems influencing balance, quantifiable by the multi-scale "complexity" of postural sway fluctuations.ObjectivesTo evaluate both traditional and complexity-based measures of sway to characterize the short- and potential long-term effects of TC training on postural control and the relationships between sway measures and physical function in healthy older adults.MethodsA cross-sectional comparison of standing postural sway in healthy TC-naïve and TC-expert (24.5±12 yrs experience) adults. TC-naïve participants then completed a 6-month, two-arm, wait-list randomized clinical trial of TC training. Postural sway was assessed before and after the training during standing on a force-plate with eyes-open (EO) and eyes-closed (EC). Anterior-posterior (AP) and medio-lateral (ML) sway speed, magnitude, and complexity (quantified by multiscale entropy) were calculated. Single-legged standing time and Timed-Up-and-Go tests characterized physical function.ResultsAt baseline, compared to TC-naïve adults (n = 60, age 64.5±7.5 yrs), TC-experts (n = 27, age 62.8±7.5 yrs) exhibited greater complexity of sway in the AP EC (P = 0.023), ML EO (PConclusionMultiscale entropy offers a complementary approach to traditional COP measures for characterizing sway during quiet standing, and may be more sensitive to the effects of TC in healthy adults.Trial registrationClinicalTrials.gov NCT01340365.

To provide objective measures which characterize mobility in older adults assessed in the community setting and to examine the extent to which these measures are associated with parkinsonian gait.During conventional mobility testing in the community-setting, 351 ambulatory non-demented Memory and Aging Project participants wore a belt with a whole body sensor that recorded both acceleration and angular velocity in 3 directions. We used measures derived from these recordings to quantify 5 subtasks including a) walking, b) transition from sit to stand, c) transition from stand to sit, d) turning and e) standing posture. Parkinsonian gait and other mild parkinsonian signs were assessed with a modified version of the original Unified Parkinson's Disease Rating Scale (mUPDRS).In a series of separate regression models which adjusted for age and sex, all 5 mobility subtask measures were associated with parkinsonian gait and accounted for 2% to 32% of its variance. When all 5 subtask measures were considered in a single model, backward elimination showed that measures of walking sit to stand and turning showed independent associations with parkinsonian gait and together accounted for more than 35% of its variance. Cross-validation using data from a 2(nd) group of 258 older adults showed similar results. In similar analyses, only walking was associated with bradykinesia and sway with tremor.Quantitative mobility subtask measures vary in their associations with parkinsonian gait scores and other parkinsonian signs in older adults. Quantifying the different facets of mobility has the potential to facilitate the clinical characterization and understanding the biologic basis for impaired mobility in older adults.

Patients with Parkinson's disease (PD) suffer from a high fall risk. Previous approaches for evaluating fall risk are based on self-report or testing at a given time point and may, therefore, be insufficient to optimally capture fall risk. We tested, for the first time, whether metrics derived from 3 day continuous recordings are associated with fall risk in PD.107 patients (Hoehn & Yahr Stage: 2.6±0.7) wore a small, body-fixed sensor (3D accelerometer) on lower back for 3 days. Walking quantity (e.g., steps per 3-days) and quality (e.g., frequency-derived measures of gait variability) were determined. Subjects were classified as fallers or non-fallers based on fall history. Subjects were also followed for one year to evaluate predictors of the transition from non-faller to faller.The 3 day acceleration derived measures were significantly different in fallers and non-fallers and were significantly correlated with previously validated measures of fall risk. Walking quantity was similar in the two groups. In contrast, the fallers walked with higher step-to-step variability, e.g., anterior-posterior width of the dominant frequency was larger (p = 0.012) in the fallers (0.78 ± 0.17 Hz) compared to the non-fallers (0.71 ± 0.07 Hz). Among subjects who reported no falls in the year prior to testing, sensor-derived measures predicted the time to first fall (p = 0.0034), whereas many traditional measures did not. Cox regression analysis showed that anterior-posterior width was significantly (p = 0.0039) associated with time to fall during the follow-up period, even after adjusting for traditional measures.These findings indicate that a body-fixed sensor worn continuously can evaluate fall risk in PD. This sensor-based approach was able to identify transition from non-faller to faller, whereas many traditional metrics were not successful. This approach may facilitate earlier detection of fall risk and may in the future, help reduce high costs associated with falls.

The identification and documentation of subclinical gait impairments in older adults may facilitate the appropriate use of interventions for preventing or delaying mobility disability. We tested whether measures derived from a single body-fixed sensor worn during traditional Timed Up and Go (TUG) testing could identify subclinical gait impairments in community dwelling older adults without mobility disability.We used data from 432 older adults without dementia (mean age 83.30 ± 7.04 yrs, 76.62% female) participating in the Rush Memory and Aging Project. The traditional TUG was conducted while subjects wore a body-fixed sensor. We derived measures of overall TUG performance and different subtasks including transitions (sit-to-stand, stand-to-sit), walking, and turning. Multivariate analysis was used to compare persons with and without mobility disability and to compare individuals with and without Instrumental Activities of Daily Living disability (IADL-disability), all of whom did not have mobility disability.As expected, individuals with mobility disability performed worse on all TUG subtasks (p

BACKGROUND: Brain white matter hyperintensities (WMHs) commonly observed on brain imaging of older adults are associated with balance and gait impairment and have also been linked to cognitive deficits. Parkinson's disease (PD) is traditionally sub-classified into the postural instability gait difficulty (PIGD) sub-type, and the tremor dominant (TD) sub-type. Considering the known association between WMHs and axial symptoms like gait disturbances and postural instability, one can hypothesize that WMHs might contribute to the disparate clinical sub-types of patients with PD. METHODS: 110 patients with PD underwent a clinical evaluation and a 3T MRI exam. Based on the Unified Parkinson Disease Rating Scale, the patients were classified into motor sub-types, i.e., TD or PIGD, and scores reflecting PIGD and TD symptoms were computed. We compared white matter burden using three previously validated methods: one using a semi-quantitative visual rating scale in specific brain regions and two automated methods. RESULTS: Overall, MRI data were obtained in 104 patients. The mean WMHs scores and the percent of subjects with lesions in specific brain regions were similar in the two subtypes, p = 0.678. The PIGD and the TD scores did not differ even when comparing patients with a relatively high burden of WMHs to patients with a relatively low burden. Across most of the brain regions, mild to moderate correlations between WMHs and age were found (r = 0.23 to 0.41; p

Recent findings suggest that executive function (EF) plays a critical role in the regulation of gait in older adults, especially under complex and challenging conditions, and that EF deficits may, therefore, contribute to fall risk. The objective of this study was to evaluate if reduced EF is a risk factor for future falls over the course of 5 years of follow-up. Secondary objectives were to assess whether single and dual task walking abilities, an alternative window into EF, were associated with fall risk.We longitudinally followed 256 community-living older adults (age: 76.4±4.5 yrs; 61% women) who were dementia free and had good mobility upon entrance into the study. At baseline, a computerized cognitive battery generated an index of EF, attention, a closely related construct, and other cognitive domains. Gait was assessed during single and dual task conditions. Falls data were collected prospectively using monthly calendars. Negative binomial regression quantified risk ratios (RR). After adjusting for age, gender and the number of falls in the year prior to the study, only the EF index (RR: .85; CI: .74-.98, p = .021), the attention index (RR: .84; CI: .75-.94, p = .002) and dual tasking gait variability (RR: 1.11; CI: 1.01-1.23; p = .027) were associated with future fall risk. Other cognitive function measures were not related to falls. Survival analyses indicated that subjects with the lowest EF scores were more likely to fall sooner and more likely to experience multiple falls during the 66 months of follow-up (p