Impact of automated data flow and reminders on adherence and resource utilization for remotely monitoring physical activity in individuals with stroke or chronic obstructive pulmonary disease.

As rehabilitation advances into the era of digital health, remote monitoring of physical activity via wearable devices has the potential to change how we provide care. However, uncertainties about patient adherence and the significant resource requirements needed create challenges to adoption of remote monitoring into clinical care. Here we aim to determine the impact of a novel digital application to overcome these barriers. The Rehabilitation Remote Monitoring Application (RRMA) automatically extracts data about physical activity collected via a Fitbit device, screens the data for adherence, and contacts the participant if adherence is low. We compare adherence and estimate the resources required (i.e., time and financial) to perform remote monitoring of physical activity with and without the RRMA in two patient groups. Seventy-three individuals with stroke or chronic obstructive pulmonary disease completed 28 days of monitoring physical activity with the RRMA, while 62 individuals completed 28 days with the data flow processes being completed manually. Adherence (i.e., the average percentage of the day that the device was worn) was similar between groups (p=0.85). However, the RRMA saved an estimated 123.8 minutes or $50.24 per participant month when compared to manual processes. These results demonstrate that automated technologies like the RRMA can maintain patient adherence to remote monitoring of physical activity while reducing the time and financial resources needed. Applications like the RRMA can facilitate the adoption of remote monitoring in rehabilitation by reducing barriers related to adherence and resource requirements.

Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change.

Gait dysfunction is common in many clinical populations and often has a profound and deleterious impact on independence and quality of life. Gait analysis is a foundational component of rehabilitation because it is critical to identify and understand the specific deficits that should be targeted prior to the initiation of treatment. Unfortunately, current state-of-the-art approaches to gait analysis (e.g., marker-based motion capture systems, instrumented gait mats) are largely inaccessible due to prohibitive costs of time, money, and effort required to perform the assessments. Here, we demonstrate the ability to perform quantitative gait analyses in multiple clinical populations using only simple videos recorded using low-cost devices (tablets). We report four primary advances: 1) a novel, versatile workflow that leverages an open-source human pose estimation algorithm (OpenPose) to perform gait analyses using videos recorded from multiple different perspectives (e.g., frontal, sagittal), 2) validation of this workflow in three different populations of participants (adults without gait impairment, persons post-stroke, and persons with Parkinson's disease) via comparison to ground-truth three-dimensional motion capture, 3) demonstration of the ability to capture clinically relevant, condition-specific gait parameters, and 4) tracking of within-participant changes in gait, as is required to measure progress in rehabilitation and recovery. Importantly, our workflow has been made freely available and does not require prior gait analysis expertise. The ability to perform quantitative gait analyses in nearly any setting using only low-cost devices and computer vision offers significant potential for dramatic improvement in the accessibility of clinical gait analysis across different patient populations.

The effect of post-acute rehabilitation setting on 90-day mobility after stroke: A difference-in-difference analysis.

Background: After discharged from the hospital for acute stroke, individuals typically receive rehabilitation in one of three settings: inpatient rehabilitation facilities (IRFs), skilled nursing facilities (SNFs), or home with community services (i.e., home health or outpatient clinics). The initial setting of post-acute care (i.e., discharge location) is related to mortality and hospital readmission; however, the impact of this setting on the change in functional mobility at 90-days after discharge is still poorly understood. The purpose of this work was to examine the impact of discharge location on the change in functional mobility between hospital discharge and 90-days post-discharge. Methods: In this retrospective cohort study, we used the electronic health record to identify individuals admitted to Johns Hopkins Medicine with an acute stroke and who had measurements of mobility [Activity Measure for Post Acute Care Basic Mobility (AM-PAC BM)] at discharge from the acute hospital and 90-days post-discharge. Individuals were grouped by discharge location (IRF=190 [40%], SNF=103 [22%], Home with community services=182 [(38%]). We compared the change in mobility from time of discharge to 90-days post-discharge in each group using a difference-in-differences analysis and controlling for demographics, clinical characteristics, and social determinants of health. Results: We included 475 individuals (age 64.4 [14.8] years; female: 248 [52.2%]). After adjusting for covariates, individuals who were discharged to an IRF had a significantly greater improvement in AM-PAC BM from time of discharge to 90-days post-discharge compared to individuals discharged to a SNF or home with community services (ß=-3.5 (1.4), p=0.01 and ß=-8.2 (1.3), p=<0.001, respectively). Conclusions: These findings suggest that the initial post-acute rehabilitation setting impacts the magnitude of functional recovery at 90-days after discharge from the acute hospital. These findings support the need for high-intensity rehabilitation and for policies that facilitate the delivery of high-intensity rehabilitation after stroke.

Accuracy of Video-Based Gait Analysis Using Pose Estimation During Treadmill Walking Versus Overground Walking in Persons After Stroke.

OBJECTIVE: Video-based pose estimation is an emerging technology that shows significant promise for improving clinical gait analysis by enabling quantitative movement analysis with little costs of money, time, or effort. The objective of this study is to determine the accuracy of pose estimation-based gait analysis when video recordings are constrained to 3 common clinical or in-home settings (ie, frontal and sagittal views of overground walking and sagittal views of treadmill walking). METHODS: Simultaneous video and motion capture recordings were collected from 30 persons after stroke during overground and treadmill walking. Spatiotemporal and kinematic gait parameters were calculated from videos using an open-source human pose estimation algorithm and from motion capture data using traditional gait analysis. Repeated-measures analyses of variance were then used to assess the accuracy of the pose estimation-based gait analysis across the different settings, and the authors examined Pearson and intraclass correlations with ground-truth motion capture data. RESULTS: Sagittal videos of overground and treadmill walking led to more accurate measurements of spatiotemporal gait parameters versus frontal videos of overground walking. Sagittal videos of overground walking resulted in the strongest correlations between video-based and motion capture measurements of lower extremity joint kinematics. Video-based measurements of hip and knee kinematics showed stronger correlations with motion capture versus ankle kinematics for both overground and treadmill walking. CONCLUSION: Video-based gait analysis using pose estimation provides accurate measurements of step length, step time, and hip and knee kinematics during overground and treadmill walking in persons after stroke. Generally, sagittal videos of overground gait provide the most accurate results. IMPACT: Many clinicians lack access to expensive gait analysis tools that can help identify patient-specific gait deviations and guide therapy decisions. These findings show that video-based methods that require only common household devices provide accurate measurements of a variety of gait parameters in persons after stroke and could make quantitative gait analysis significantly more accessible.

Multi-Site Identification and Generalization of Clusters of Walking Behaviors in Individuals With Chronic Stroke and Neurotypical Controls.

BACKGROUND: Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions. OBJECTIVES: We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: (1) identify clusters of walking behaviors in people post-stroke and neurotypical controls and (2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants. METHODS: We gathered data from 81 post-stroke participants across 4 research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach. RESULTS: We identified 4 stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites. CONCLUSIONS: Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.

Automatic learning mechanisms for flexible human locomotion.

Movement flexibility and automaticity are necessary to successfully navigate different environments. When encountering difficult terrains such as a muddy trail, we can change how we step almost immediately so that we can continue walking. This flexibility comes at a cost since we initially must pay deliberate attention to how we are moving. Gradually, after a few minutes on the trail, stepping becomes automatic so that we do not need to think about our movements. Canonical theory indicates that different adaptive motor learning mechanisms confer these essential properties to movement: explicit control confers flexibility, while forward model recalibration confers automaticity. Here we uncover a distinct mechanism of treadmill walking adaptation - an automatic stimulus-response mapping - that confers both properties to movement. The mechanism is flexible as it learns stepping patterns that can be rapidly changed to suit a range of treadmill configurations. It is also automatic as it can operate without deliberate control or explicit awareness by the participants. Our findings reveal a tandem architecture of forward model recalibration and automatic stimulus-response mapping mechanisms for walking, reconciling different findings of motor adaptation and perceptual realignment.

Simple within-stride changes in treadmill speed can drive selective changes in human gait symmetry.

Millions of people walk with asymmetric gait patterns, highlighting a need for customizable rehabilitation approaches that can flexibly target different aspects of gait asymmetry. Here, we studied how simple within-stride changes in treadmill speed could drive selective changes in gait symmetry. In Experiment 1, healthy adults (n = 10) walked on an instrumented treadmill with and without a closed-loop controller engaged. This controller changed the treadmill speed to 1.50 or 0.75 m/s depending on whether the right or left leg generated propulsive ground reaction forces, respectively. Participants walked asymmetrically when the controller was engaged: the leg that accelerated during propulsion (right) showed smaller leading limb angles, larger trailing limb angles, and smaller propulsive forces than the leg that decelerated (left). In Experiment 2, healthy adults (n = 10) walked on the treadmill with and without an open-loop controller engaged. This controller changed the treadmill speed to 1.50 or 0.75 m/s at a prescribed time interval while a metronome guided participants to step at different time points relative to the speed change. Different patterns of gait asymmetry emerged depending on the timing of the speed change: step times, leading limb angles, and peak propulsion were asymmetric when the speed changed early in stance while step lengths, step times, and propulsion impulses were asymmetric when the speed changed later in stance. In sum, we show that simple manipulations of treadmill speed can drive selective changes in gait symmetry. Future work will explore the potential for this technique to restore gait symmetry in clinical populations.

Identifying Unique Subgroups of Individuals With Stroke Using Heart Rate and Steps to Characterize Physical Activity.

Background Low physical activity (PA) is associated with poor health outcomes after stroke. Step counts are a common metric of PA; however, other physiologic signals (eg, heart rate) may help to identify subgroups of individuals poststroke at varying levels of risk of poor health outcomes. Here, we aimed to identify clinically relevant subgroups of individuals poststroke based on PA profiles that leverage multiple data sources, including step count and heart rate data, from wearable devices. Methods and Results Seventy individuals poststroke participated. Participants wore a Fitbit Inspire 2 for 1 year and completed clinical assessments. We defined a group-based steps-per-minute threshold and an individual heart rate threshold to categorize each minute of PA into 1 of 4 states: high steps/high heart rate, low steps/low heart rate, high steps/low heart rate, and low steps/high heart rate. We used the proportion of time spent in each state along with steps per day, sedentary time, mean steps among minutes with high steps and high heart rate, and resting heart rate in a k-means clustering algorithm to identify subgroups and compared Activity Measure for Post-Acute Care Mobility T Score, Stroke Impact Scale, and gait speed among subgroups. We identified 3 subgroups, Active (n=8), Sedentary (n=29), and Deconditioned (n=33), which differed significantly on all clustering variables except resting heart rate. We observed significant differences in Activity Measure for Post-Acute Care Mobility T scores between subgroups, with the Deconditioned subgroup exhibiting the lowest score. Conclusions Quantifying PA with heart rate and step count using readily available wearable devices can identify clinically meaningful subgroups of individuals poststroke.

The feasibility of remotely monitoring physical, cognitive, and psychosocial function in individuals with stroke or chronic obstructive pulmonary disease.

Objective: Clinical implementation of remote monitoring of human function requires an understanding of its feasibility. We evaluated adherence and the resources required to monitor physical, cognitive, and psychosocial function in individuals with either chronic obstructive pulmonary disease or stroke during a three-month period. Methods: Seventy-three individuals agreed to wear a Fitbit to monitor physical function and to complete monthly online assessments of cognitive and psychosocial function. During a three-month period, we measured adherence to monitoring (1) physical function using average daily wear time, and (2) cognition and psychosocial function using the percentage of assessments completed. We measured the resources needed to promote adherence as (1) the number of participants requiring at least one reminder to synchronize their Fitbit, and (2) the number of reminders needed for each completed cognitive and psychosocial assessment. Results: After accounting for withdrawals, the average daily wear time was 77.5 ± 19.9% of the day and did not differ significantly between months 1, 2, and 3 (p = 0.30). To achieve this level of adherence, 64.9% of participants required at least one reminder to synchronize their device. Participants completed 61.0% of the cognitive and psychosocial assessments; the portion of assessments completed each month didnot significantly differ (p = 0.44). Participants required 1.13 ± 0.57 reminders for each completed assessment. Results did not differ by disease diagnosis. Conclusions: Remote monitoring of human function in individuals with either chronic obstructive pulmonary disease or stroke is feasible as demonstrated by high adherence. However, the number of reminders required indicates that careful consideration must be given to the resources available to obtain high adherence.

Multi-site identification and generalization of clusters of walking behaviors in individuals with chronic stroke and neurotypical controls.

Background: Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions. Objective: We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: 1) identify clusters of walking behaviors in people post-stroke and neurotypical controls, and 2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants. Methods: We gathered data from 81 post-stroke participants across four research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach. Results: We identified four stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites. Conclusions: Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.

Dopamine facilitates the translation of physical exertion into assessments of effort.

Our assessments of effort are critically shaped by experiences of exertion. However, it is unclear how the nervous system transforms physical exertion into assessments of effort. Availability of the neuromodulator dopamine influences features of motor performance and effort-based decision-making. To test dopamine's role in the translation of effortful exertion into assessments of effort, we had participants with Parkinson's disease, in dopamine depleted (OFF dopaminergic medication) and elevated (ON dopaminergic medication) states, exert levels of physical exertion and retrospectively assess how much effort they exerted. In a dopamine-depleted state, participants exhibited increased exertion variability and over-reported their levels of exertion, compared to the dopamine-supplemented state. Increased exertion variability was associated with less accurate effort assessment and dopamine had a protective influence on this effect, reducing the extent to which exertion variability corrupted assessments of effort. Our findings provide an account of dopamine's role in the translation of features of motor performance into judgments of effort, and a potential therapeutic target for the increased sense of effort observed across a range of neurologic and psychiatric conditions.

Joint-level coordination patterns for split-belt walking across different speed ratios.

Locomotion is a highly flexible process, requiring rapid changes to gait due to changes in the environment or goals. Here, we used a split-belt treadmill to examine how the central nervous system coordinates a novel gait pattern. Existing research has focused on summary measures, most often step lengths, when describing changes induced while walking on the split-belt treadmill and during subsequent aftereffects. Here, we asked how the nervous system adjusts individual joint motions and the coordination pattern of the legs when people walk with one leg moving at either 2×, 3×, or 4× the speed of the other leg. We found that relative to tied-belt walking, split-belt perturbations change the timing relationships between the legs while most joint angle peaks and range of motion change little. The kinematic changes over the course of adaptation (i.e., from the beginning to end of a single split-belt walking bout) were subtle, particularly when comparing individual joint motions. The magnitude of the belt speed differences impacted intralimb coordination but did not produce consistent differences in most other measures. Most significant changes in kinematics occurred in the fast leg. Overall, interlimb timing changes drove a large proportion of the differences observed between tied-belt and split-belt gaits. Thus, it appears that the central nervous system can produce novel gait patterns through changes in coordination between legs that lead to new configurations at significant time points. These patterns can use within-limb and within-joint patterns that closely resemble those of normal walking.NEW & NOTEWORTHY We studied how the nervous system coordinates limb movements during asymmetric gait. Using a split-belt treadmill, we found that most changes in motion occurred when comparing motions between limbs, rather than among joints within a limb. Individual joint patterns resembled speed-matched comparisons, but this meant that joint movements became asymmetric during split-belt walking. These findings demonstrate that the nervous system can use consistent joint motions that are reconfigured in time to achieve new gait patterns.

A Learning Health System Infrastructure for Precision Rehabilitation After Stroke.

ABSTRACT: Functional recovery and the response to rehabilitation interventions after stroke are highly variable. Understanding this variability will promote precision rehabilitation for stroke, allowing us to deliver targeted interventions to the right person at the right time. Capitalizing on large, heterogeneous data sets, such as those generated through clinical care and housed within the electronic health record, can lead to understanding of poststroke variability. However, accessing data from the electronic health record can be challenging because of data quality, privacy concerns, and the resources required for data extraction. Therefore, creating infrastructure that overcomes these challenges and contributes to a learning health system is needed to achieve precision rehabilitation after stroke. We describe the creation of a Precision Rehabilitation Data Repository that facilitates access to systematically collected data from the electronic health record as part of a learning health system to drive precision rehabilitation. Specifically, we describe the process of (1) standardizing the documentation of functional assessments, (2) obtaining regulatory approval, (3) defining the patient cohort, and (4) extracting data for the Precision Rehabilitation Data Repository. The development of similar infrastructures at other institutions can help generate large, heterogeneous data sets to drive poststroke care toward precision rehabilitation, thereby maximizing poststroke function within an efficient healthcare system.

Opportunities for Improving Motor Assessment and Rehabilitation After Stroke by Leveraging Video-Based Pose Estimation.

ABSTRACT: Stroke is a leading cause of long-term disability in adults in the United States. As the healthcare system moves further into an era of digital medicine and remote monitoring, technology continues to play an increasingly important role in post-stroke care. In this Analysis and Perspective article, opportunities for using human pose estimation-an emerging technology that uses artificial intelligence to track human movement kinematics from simple videos recorded using household devices (e.g., smartphones, tablets)-to improve motor assessment and rehabilitation after stroke are discussed. The focus is on the potential of two key applications: (1) improving access to quantitative, objective motor assessment and (2) advancing telerehabilitation for persons post-stroke.

Neurologic music therapy combined with EEG-tDCS for upper motor extremity performance in patients with corticobasal syndrome: Study protocol for a novel approach.

BACKGROUND: Corticobasal syndrome (CBS) is an atypical parkinsonian disorder that involves degeneration of brain regions associated with motor coordination and sensory processing. Combining transcranial direct current stimulation (tDCS) with rehabilitation training has been shown to improve upper-limb performance in other disease models. Here, we describe the protocol investigating whether tDCS with neurologic music therapy (NMT) (patterned sensory enhancement and therapeutic instrumental music performance) enhances functional arm/hand performance in individuals with CBS. METHODS: Study participants are randomly assigned to six 30-min sessions (twice per week for 3 weeks) of NMT + either sham tDCS or active tDCS. We aim to stimulate the frontoparietal cortex, which is associated with movement execution/coordination and sensory processing. The hemisphere contralateral to the more affected arm is stimulated (total stimulation current of 2 mA from 5 dime-sized electrodes). Individualized NMT sessions designed to exercise the upper limb are provided. Participants undergo gross/fine motor, cognitive and emotional assessments at baseline and follow-up (one month after the final session). To investigate the immediate effects of tDCS and NMT training, gross /fine motor, affective level, and kinematic parameter measurements using motion sensors are collected before and after each session. Electroencephalography is used to collect electrical neurophysiological responses before, during, and after tDCS+NMT sessions. The study participants, neurologic music therapist and outcome assessor are blinded to whether participants are in the sham or active tDCS group. CONCLUSION: This noninvasive and patient-centered clinical trial for CBS may provide insight into rehabilitation options that are sorely lacking in this population.

Evaluation of physical health status beyond daily step count using a wearable activity sensor.

Physical health status defines an individual's ability to perform normal activities of daily living and is usually assessed in clinical settings by questionnaires and/or by validated tests, e.g. timed walk tests. These measurements have relatively low information content and are usually limited in frequency. Wearable sensors, such as activity monitors, enable remote measurement of parameters associated with physical activity but have not been widely explored beyond measurement of daily step count. Here we report on results from a cohort of 22 individuals with Pulmonary Arterial Hypertension (PAH) who were provided with a Fitbit activity monitor (Fitbit Charge HR®) between two clinic visits (18.4 ± 12.2 weeks). At each clinical visit, a maximum of 26 measurements were recorded (19 categorical and 7 continuous). From analysis of the minute-to-minute step rate and heart rate we derive several metrics associated with physical activity and cardiovascular function. These metrics are used to identify subgroups within the cohort and to compare to clinical parameters. Several Fitbit metrics are strongly correlated to continuous clinical parameters. Using a thresholding approach, we show that many Fitbit metrics result in statistically significant differences in clinical parameters between subgroups, including those associated with physical status, cardiovascular function, pulmonary function, as well as biomarkers from blood tests. These results highlight the fact that daily step count is only one of many metrics that can be derived from activity monitors.

Motor control: In constant pursuit of optimality.

Humans learn through exploration. A new study suggests that this may be how we learn to save energy when we walk.

Precision Rehabilitation: Optimizing Function, Adding Value to Health Care.

Precision medicine efforts are underway in many medical disciplines; however, the power of precision rehabilitation has not yet been explored. Precision medicine aims to deliver the right intervention, at the right time, in the right setting, for the right person, ultimately bolstering the value of the care that we provide. To date, precision medicine efforts have rarely focused on function at the level of a person, but precision rehabilitation is poised to change this and bring the focus on function to the broader precision medicine enterprise. To do this, subgroups of individuals must be identified based on their level of function via precise measurement of their abilities in the physical, cognitive, and psychosocial domains. Adoption of electronic health records, advances in data storage and analytics, and improved measurement technology make this shift possible. Here we detail critical components of the precision rehabilitation framework, including (1) the synergistic use of various study designs, (2) the need for standardized functional measurements, (3) the importance of precise and longitudinal measures of function, (4) the utility of comprehensive databases, (5) the importance of predictive analyses, and (6) the need for system and team science. Precision rehabilitation has the potential to revolutionize clinical care, optimize function for all individuals, and magnify the value of rehabilitation in health care; however, to reap the benefits of precision rehabilitation, the rehabilitation community must actively pursue this shift.