Quantifying the effects of sleep on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment.

BACKGROUND: Despite the promise of wearable sensors for both rehabilitation research and clinical care, these technologies pose significant burden on data collectors and analysts. Investigations of factors that may influence the wearable sensor data processing pipeline are needed to support continued use of these technologies in rehabilitation research and integration into clinical care settings. The purpose of this study was to investigate the effect of one such factor, sleep, on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment and across a two-day wearing period. METHODS: This was a secondary analysis of data collected during a prospective, longitudinal cohort study (n = 127 individuals, 62 with upper limb impairment and 65 without). Participants wore a wearable sensor on each wrist for 48 h. Five upper limb sensor variables were calculated over the full wear period (sleep included) and with sleep time removed (sleep excluded): preferred time, non-preferred time, use ratio, non-preferred magnitude and its standard deviation. Linear mixed effects regression was used to quantify the effect of sleep on each sensor variable and determine if the effect differed between people with and without upper limb impairment and across a two-day wearing period. RESULTS: There were significant differences between sleep included and excluded for the variables preferred time (p < 0.001), non-preferred time (p < 0.001), and non-preferred magnitude standard deviation (p = 0.001). The effect of sleep was significantly different between people with and without upper limb impairment for one variable, non-preferred magnitude (p = 0.02). The effect of sleep was not substantially different across wearing days for any of the variables. CONCLUSIONS: Overall, the effects of sleep on sensor-derived variables of upper limb accelerometry are small, similar between people with and without upper limb impairment and across a two-day wearing period, and can likely be ignored in most contexts. Ignoring the effect of sleep would simplify the data processing pipeline, facilitating the use of wearable sensors in both research and clinical practice.

Predicting later categories of upper limb activity from earlier clinical assessments following stroke: an exploratory analysis.

BACKGROUND: Accelerometers allow for direct measurement of upper limb (UL) activity. Recently, multi-dimensional categories of UL performance have been formed to provide a more complete measure of UL use in daily life. Prediction of motor outcomes after stroke have tremendous clinical utility and a next step is to explore what factors might predict someone's subsequent UL performance category. PURPOSE: To explore how different machine learning techniques can be used to understand how clinical measures and participant demographics captured early after stroke are associated with the subsequent UL performance categories. METHODS: This study analyzed data from two time points from a previous cohort (n = 54). Data used was participant characteristics and clinical measures from early after stroke and a previously established category of UL performance at a later post stroke time point. Different machine learning techniques (a single decision tree, bagged trees, and random forests) were used to build predictive models with different input variables. Model performance was quantified with the explanatory power (in-sample accuracy), predictive power (out-of-bag estimate of error), and variable importance. RESULTS: A total of seven models were built, including one single decision tree, three bagged trees, and three random forests. Measures of UL impairment and capacity were the most important predictors of the subsequent UL performance category, regardless of the machine learning algorithm used. Other non-motor clinical measures emerged as key predictors, while participant demographics predictors (with the exception of age) were generally less important across the models. Models built with the bagging algorithms outperformed the single decision tree for in-sample accuracy (26-30% better classification) but had only modest cross-validation accuracy (48-55% out of bag classification). CONCLUSIONS: UL clinical measures were the most important predictors of the subsequent UL performance category in this exploratory analysis regardless of the machine learning algorithm used. Interestingly, cognitive and affective measures emerged as important predictors when the number of input variables was expanded. These results reinforce that UL performance, in vivo, is not a simple product of body functions nor the capacity for movement, instead being a complex phenomenon dependent on many physiological and psychological factors. Utilizing machine learning, this exploratory analysis is a productive step toward the prediction of UL performance. Trial registration NA.

Accuracy of an Algorithm in Predicting Upper Limb Functional Capacity in a United States Population.

OBJECTIVE: To determine the accuracy of an algorithm, using clinical measures only, on a sample of persons with first-ever stroke in the United States (US). It was hypothesized that algorithm accuracy would fall in a range of 70%-80%. DESIGN: Secondary analysis of prospective, observational, longitudinal cohort; 2 assessments were done: (1) within 48 hours to 1 week poststroke and (2) at 12 weeks poststroke. SETTING: Recruited from a large acute care hospital and followed over the first 6 months after stroke. PARTICIPANTS: Adults with first-ever stroke (N=49) with paresis of the upper limb (UL) at ≤48 hours who could follow 2-step commands and were expected to return to independent living at 6 months. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: The overall accuracy of the algorithm with clinical measures was quantified by comparing predicted (expected) and actual (observed) categories using a correct classification rate. RESULTS: The overall accuracy (61%) and weighted κ (62%) were significant. Sensitivity was high for the Excellent (95%) and Poor (81%) algorithm categories. Specificity was high for the Good (82%), Limited (98%), and Poor (95%) categories. Positive predictive value (PPV) was high for Poor (82%) and negative predictive value (NPV) was high for all categories. No differences in participant characteristics were found between those with accurate or inaccurate predictions. CONCLUSIONS: The results of the present study found that use of an algorithm with clinical measures only is better than chance alone (chance=25% for each of the 4 categories) at predicting a category of UL capacity at 3 months post troke. The moderate to high values of sensitivity, specificity, PPV, and NPV demonstrates some clinical utility of the algorithm within health care settings in the US.

Day-to-Day Variability of Walking Performance Measures in Individuals Poststroke and Individuals With Parkinson Disease.

BACKGROUND AND PURPOSE: Improvement of walking performance is a primary goal for individuals poststroke or with Parkinson disease (PD) who receive physical therapy. More data about day-to-day variability of walking performance are critical for determining if changes in performance have occurred. METHODS: Baseline assessments were utilized from an ongoing, observational, prospective cohort study including 84 individuals poststroke (n = 37) or with PD (n = 47) receiving outpatient physical therapy services to improve mobility. Participants wore step activity monitors for up to 7 days to measure walking performance (steps per day, walking duration, maximum 30-minute output, and peak activity index) in daily life. Correlation analyses evaluated relationships between both capacity and performance measures as well as the relationships between mean performance variables and day-to-day variability. Regression analyses explored factors that contribute to variability in day-to-day performance variables. RESULTS: Mean steps per day for participants poststroke (5376 ± 2804) and with PD (8149 ± 4490) were consistent with previously reported cohorts. Greater amounts of walking were related to more day-to-day variability, with moderate correlations found between the mean and day-to-day variability of each performance measure, regardless of medical diagnosis or walking speed. Day-to-day variability is large (upwards of 50% of the mean), with the amount of walking performance serving as the primary predictor of day-to-day variability in walking performance. DISCUSSION AND CONCLUSIONS: The results of this study elucidate the factors that are related to and predict day-to-day variability of performance. Walking performance metrics should be evaluated over multiple days and greater variability should be anticipated with greater amounts of performance.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A319).

Implementation of Wearable Sensing Technology for Movement: Pushing Forward into the Routine Physical Rehabilitation Care Field.

While the promise of wearable sensor technology to transform physical rehabilitation has been around for a number of years, the reality is that wearable sensor technology for the measurement of human movement has remained largely confined to rehabilitation research labs with limited ventures into clinical practice. The purposes of this paper are to: (1) discuss the major barriers in clinical practice and available wearable sensing technology; (2) propose benchmarks for wearable device systems that would make it feasible to implement them in clinical practice across the world and (3) evaluate a current wearable device system against the benchmarks as an example. If we can overcome the barriers and achieve the benchmarks collectively, the field of rehabilitation will move forward towards better movement interventions that produce improved function not just in the clinic or lab, but out in peoples' homes and communities.

Dose of remote limb ischemic conditioning for enhancing learning in healthy young adults.

Remote limb ischemic conditioning (RLIC) is a technique in which tissues distant from the target organ are exposed to brief, sub-lethal bouts of ischemia. The effects of remotely applied ischemic conditioning are systemically transferred to the target organ, and typically manifested as protection from subsequent ischemic injury. Previous studies in our lab have found and confirmed that RLIC enhances learning and retention during motor training on a balance task. The current study tested the effect of RLIC dose (number of cycles) on learning enhancement in young, healthy adults. Forty healthy participants age 18-40 years were randomized to receive 5 cycles of sham conditioning (n = 9), 3 cycles of RLIC (n = 11), 4 cycles of RLIC (n = 10), or 5 cycles of RLIC (n = 10) using a blood pressure cuff around the upper arm once a day for 7 consecutive weekdays (Days 1-7). Participants concurrently trained on a balance task, bimanual cup stacking task, and a discrete sequence production task on Days 3-7. Change in performance on each of the three tasks was compared across groups. Participants in all four groups improved their performance on each of the three tasks over time. However, RLIC at any dose did not enhance learning on any of the three tasks. While RLIC is safe, inexpensive, and clinically feasible, reproducibility may be challenged by unidentified factors, raising critical challenges to the straightforward translation of RLIC for improving rehabilitation outcomes in individuals recovering from neurological injury.

Dose response of task-specific upper limb training in people at least 6 months poststroke: A phase II, single-blind, randomized, controlled trial.

OBJECTIVE: The objectives of this work were to (1) determine whether higher doses of motor therapy in chronic poststroke hemiparesis result in better outcomes, compared to lower doses, and (2) evaluate potential modifiers of the dose-response relationship. METHODS: Eighty-five adults with upper extremity paresis ≥6 months poststroke were randomized to one of four dose groups in this single-blind, parallel, randomized, control trial. The dosing parameter manipulated was amount of task-specific training, as indexed by the number of task repetitions. Groups received 3,200, 6,400, 9,600, or individualized maximum (IM) repetitions, during 1-hour sessions, 4 days/week for 8 weeks. The intervention was an individualized, progressive, task-specific upper-limb training program designed to improve upper-limb functional motor capacity. The primary outcome was the slope of the Action Research Arm Test (ARAT) during the intervention. Effects of dose and potential modifiers of the dose-response relationship were evaluated with hierarchical linear models. RESULTS: ARAT scores for the 3,200, 9,600, and IM groups improved over time as indicated by slopes (ΔARAT/week, mean ± standard errors) of 0.40 ± 0.15, 0.31 ± 0.16, and 0.66 ± 0.14, respectively (p < 0.05). The slope of the 6,400 group was smaller (-0.05 ± 0.15) and significantly different from the 3,200 and IM groups (p < 0.001). Initial motor capacity, neglect, and other tested characteristics did not modify the dose-response relationship. INTERPRETATION: Overall, treatment effects were small. There was no evidence of a dose-response effect of task-specific training on functional capacity in people with long-standing upper-limb paresis poststroke. Ann Neurol 2016;80:342-354.

Quantifying Change During Outpatient Stroke Rehabilitation: A Retrospective Regression Analysis.

OBJECTIVE: To examine change and individual trajectories for balance, upper extremity motor capacity, and mobility in people poststroke during the time they received outpatient therapies. DESIGN: Retrospective analyses of an observational cohort using hierarchical linear modeling. SETTING: Outpatient rehabilitation. PARTICIPANTS: Persons poststroke (N=366). INTERVENTIONS: Usual outpatient physical and occupational therapy. MAIN OUTCOMES MEASURES: Berg Balance Scale (BBS), Action Research Arm Test (ARAT), and walking speed were used to assess the 3 domains. Initial scores at the start of outpatient therapy (intercepts), rate of change during outpatient therapy (slopes), and covariance between slopes and intercepts were modeled as random effects. Additional variables modeled as fixed effects were duration (months of outpatient therapy), time (days poststroke), age (y), and inpatient status (if the patient went to an inpatient rehabilitation facility [IRF]). RESULTS: A patient with average age and time started at 37 points on the BBS with a change of 1.8 points per month, at 35 points on the ARAT with a change of 2 points per month, and with a walking speed of .59m/s with a change of .09m/s per month. When controlling for other variables, patients started with lower scores on the BBS and ARAT or had slower walking speeds at admission if they started outpatient therapy later than average or went to an IRF. CONCLUSIONS: Patients generally improved over the course of outpatient therapy, but there was considerable variability in individual trajectories. Average rates of change across all 3 domains were small.

A case-control study of the effectiveness of tissue plasminogen activator on 6 month patients--reported outcomes and health care utilization.

We examined the benefit of tissue plasminogen activator (tPA), delivered as part of usual stroke management, on patient-reported outcomes and health care utilization. Using a case control design, patients who received tPA as part of usual stroke management were compared with patients who would have received tPA had they arrived to the hospital within the therapeutic time window. Data were collected from surveys 6 months after stroke using standardized patient-reported outcome measures and questions about health care utilization. Demographic and medical data were acquired from hospital records. Patients were matched on stroke severity, age, race, and gender. Matching was done with 1:2 ratio of tPA to controls. Results were compared between groups with 1-tailed tests because of a directionally specific hypothesis in favor of the tPA group. The tPA (n = 78) and control (n = 156) groups were matched across variables, except for stroke severity, which was better in the control group; subsequent analyses controlled for this mismatch. The tPA group reported better physical function, communication, cognitive ability, depressive symptomatology, and quality of life/participation compared with the control group. Fewer people in the tPA group reported skilled nursing facility stays, emergency department visits, and rehospitalizations after their stroke compared with controls. Reports of other postacute services were not different between groups. Although it is known that tPA reduces disability, this is the first study to demonstrate the effectiveness of tPA in improving meaningful, patient-reported outcomes. Thus, use of tPA provides a large benefit to the daily lives of people with ischemic stroke.

A Large Harmonized Upper and Lower Limb Accelerometry Dataset: A Resource for Rehabilitation Scientists.

Wearable sensors can measure movement in daily life, an outcome that is salient to patients, and have been critical to accelerating progress in rehabilitation research and practice. However, collecting and processing sensor data is burdensome, leaving many scientists with limited access to such data. To address these challenges, we present a harmonized, wearable sensor dataset that combines 2,885 recording days of sensor data from the upper and lower limbs from eight studies. The dataset includes 790 individuals ages 0 - 90, nearly equal sex proportions (53% male, 47% female), and representation from a range of demographic backgrounds (69.4% White, 24.9% Black, 1.8% Asian) and clinical conditions (46% neurotypical, 31% stroke, 7% Parkinson's disease, 6% orthopedic conditions, and others). The dataset is publicly available and accompanied by open source code and an app that allows for interaction with the data. This dataset will facilitate the use of sensor data to advance rehabilitation research and practice, improve the reproducibility and replicability of wearable sensor studies, and minimize costs and duplicated scientific efforts.

Knowledge Translation Task Force for core measures clinical practice guideline: a short report on the process and utilization.

BACKGROUND: As part of the 2018 Clinical Practice Guideline (CPG): A Core Set of Outcome Measures for Adults with Neurologic Conditions Undergoing Rehabilitation, a Knowledge Translation (KT) Task Force was convened. The purpose of this short report was to (1) demonstrate the potential impact of a CPG KT Task Force through a practical example of efforts to implement a CPG into neurologic physical therapy practice and (2) describe the process to convene a KT Task Force and develop products (KT Toolkit) to facilitate implementation of the CPG. METHODS: To describe the process used by the KT Task Force to develop and review a KT Toolkit for implementation of the CPG. RESULTS: Utilizing the Knowledge-To-Action Cycle framework, eight tools were developed as part of the KT Toolkit and are available with open access to the public. Findings indicate that the Core Outcome Measures Homepage, which houses the KT Toolkit, has had greater than 70,000 views since its publication. CONCLUSIONS: This short report serves as an example of the efforts made to implement a CPG into physical therapy practice. The processes to facilitate KT and the tools developed can inform future implementation efforts and underscore the importance of having a KT Task Force to implement a CPG. Moving forward, KT Task Forces should be convened to implement new or revised guidelines. TRIAL REGISTRATION: N/A.

Referent data for investigations of upper limb accelerometry: harmonized data from three cohorts of typically-developing children.

Aim: The rise of wearable sensing technology shows promise for addressing the challenges of measuring motor behavior in pediatric populations. The current pediatric wearable sensing literature is highly variable with respect to the number of sensors used, sensor placement, wearing time, and how data extracted from the sensors are analyzed. Many studies derive conceptually similar variables via different calculation methods, making it hard to compare across studies and clinical populations. In hopes of moving the field forward, this report provides referent upper limb wearable sensor data from accelerometers on 25 variables in typically-developing children, ages 3-17 years. Methods: This is a secondary analysis of data from three pediatric cohorts of children 3-17 years of age. Participants (n = 222) in the cohorts wore bilateral wrist accelerometers for 2-4 days for a total of 622 recording days. Accelerometer data were reprocessed to compute 25 variables that quantified upper limb movement duration, intensity, symmetry, and complexity. Analyses examined the influence of hand dominance, age, gender, reliability, day-to-day stability, and the relationships between variables. Results: The majority of variables were similar on the dominant and non-dominant sides, declined slightly with age, and were not different between boys and girls. ICC values were moderate to excellent. Variation within individuals across days generally ranged from 3% to 32%. A web-based R shiny object is available for data viewing. Interpretation: With the use of wearable movement sensors increasing rapidly, these data provide key, referent information for researchers as they design studies, and analyze and interpret data from neurodevelopmental and other pediatric clinical populations. These data may be of particularly high value for pediatric rare diseases.

Corrigendum: Referent data for investigations of upper limb accelerometry: harmonized data from three cohorts of typically-developing children.

[This corrects the article DOI: 10.3389/fped.2024.1361757.].

Clinician adherence to a standardized assessment battery across settings and disciplines in a poststroke rehabilitation population.

OBJECTIVES: (1) To examine clinician adherence to a standardized assessment battery across settings (acute hospital, inpatient rehabilitation facilities [IRFs], outpatient facility), professional disciplines (physical therapy [PT], occupational therapy, speech-language pathology), and time of assessment (admission, discharge/monthly), and (2) to evaluate how specific implementation events affected adherence. DESIGN: Retrospective cohort study. SETTING: Acute hospital, IRF, and outpatient facility with approximately 118 clinicians (physical therapists, occupational therapists, speech-language pathologists). PARTICIPANTS: Participants (N=2194) with stroke who were admitted to at least 1 of the above settings. All persons with stroke underwent standardized clinical assessments. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Adherence to Brain Recovery Core assessment battery across settings, professional disciplines, and time. Visual inspections of 17 months of time-series data were conducted to see if the events (eg, staff meetings) increased adherence ≥5% and if so, how long the increase lasted. RESULTS: Median adherence ranged from .52 to .88 across all settings and professional disciplines. Both the acute hospital and the IRF had higher adherence than the outpatient setting (P≤.001), with PT having the highest adherence across all 3 disciplines (P<.004). Of the 25 events conducted across the 17-month period to improve adherence, 10 (40%) resulted in a ≥5% increase in adherence the following month, with 6 services (60%) maintaining their increased level of adherence for at least 1 additional month. CONCLUSIONS: Actual adherence to a standardized assessment battery in clinical practice varied across settings, disciplines, and time. Specific events increased adherence 40% of the time with those gains maintained for >1 month 60% of the time.

Assessment of upper extremity impairment, function, and activity after stroke: foundations for clinical decision making.

The purpose of this review is to provide a comprehensive approach for assessing the upper extremity (UE) after stroke. First, common UE impairments and how to assess them are briefly discussed. Although multiple UE impairments are typically present after stroke, the severity of one's impairment, paresis, is the primary determinant of UE functional loss. Second, UE function is operationally defined and a number of clinical measures are discussed. It is important to consider how impairment and loss of function affect UE activity outside of the clinical environment. Thus, this review also identifies accelerometry as an objective method for assessing UE activity in daily life. Finally, the role that each of these levels of assessment should play in clinical decision making is discussed to optimize the provision of stroke rehabilitation services.

Comprehensive Assessment of the Activity Level of the ICF Using Both Capacity and Performance Measures: A Case Report.

Individuals with neurologic conditions seek physical therapy services to improve mobility in their daily lives. While clinicians commonly track activity capacity, measurement of activity performance in daily life is an emerging yet unstandardized practice within routine clinical physical therapy. The purpose of this case report is to (1) provide an example of the structure, clinical reasoning, and implementation of both activity capacity and activity performance level assessments across an episode of outpatient physical therapy and (2) to describe how objective activity performance in daily life tracking supported the physical therapy intervention and education plan. A 42-year-old woman presented to outpatient neurologic physical therapy with a rare autoimmune-mediated disorder with primary goals of independently caring for her youngest child and grandchild, walking without limitations in the home and community, participating in exercise, and returning to work due to deconditioning and dizziness. The patient participated in 12 visits across a span of 4.5 months targeting performance in daily life (steps per day), aerobic conditioning, and vestibular habituation. Activity capacity measurement served as a standardized assessment of what the patient was able to do in the clinic, and activity performance in daily life tracking via a Samsung wrist worn consumer-grade device provided a quantitative assessment of real-world daily stepping activity. Tracking of activity performance in daily life was an essential component of physical therapy management that provided an objective quantification of daily stepping activity to identify barriers and facilitators to increasing daily performance in an individual with a medical diagnosis of Susac syndrome.

ICF classification of therapeutic goals for outpatient episodes of neurorehabilitation in post-stroke and Parkinson disease.

PURPOSE: To understand therapeutic priorities, a secondary data analysis on a retrospective cohort was conducted to classify rehabilitation goals according to the International Classification of Functioning, Disability, and Health (ICF). MATERIALS AND METHODS: Therapeutic goals from an initial outpatient physical or occupational therapy evaluation for patients post-stroke or with Parkinson disease, were classified into Level 1 of the ICF. Goals in the Activity and Participation component were further sub-classified as activity capacity or activity performance (self-report or direct) in daily life. RESULTS: 776 goals across 104 participants were classified into Level 1 of the ICF. The majority, 73% (563/776) were classified as Activity and Participation, 20% (155/776) as Body Function and 2% (17/776) as Environmental Factors. Fifty-two percent (400/776) of all goals were classified as activity capacity and 21% (163/776) as activity performance in daily life, with 21% (160/776) of goals measuring self-report activity performance in daily life and less than 1% (3/776) of goals measuring direct activity performance in daily life. CONCLUSIONS: While the majority of therapeutic goals were classified into the Activity and Participation component, less than 1% of goals measured direct activity performance in daily life. If people seek outpatient rehabilitation to improve functioning in their real-world environment, therapeutic goal setting should reflect this.

The majority of therapeutic goals for an episode of outpatient neurorehabilitation were classified into the Activity and Participation component of the International Classification of Functioning, Disability, and Health.However, less than 1% of therapeutic goals measured direct activity performance in daily life.If people with neurological diagnoses seek out outpatient rehabilitation to improve functioning in their real-world environment, than therapeutic goal setting should reflect this.

Prediction of discharge walking ability from initial assessment in a stroke inpatient rehabilitation facility population.

OBJECTIVES: To (1) determine which clinical assessments at admission to an inpatient rehabilitation facility (IRF) most simply predict discharge walking ability, and (2) identify a clinical decision rule to differentiate household versus community ambulators at discharge from an IRF. DESIGN: Retrospective cohort study. SETTING: IRF. PARTICIPANTS: Two samples of participants (n=110 and 159) admitted with stroke. INTERVENTIONS: A multiple regression determined which variables obtained at admission (age, time from stroke to assessment, Motricity Index, somatosensation, Modified Ashworth Scale, FIM, Berg Balance Scale, 10-m walk speed) could most simply predict discharge walking ability (10-m walk speed). A logistic regression determined the likelihood of a participant achieving household (<0.4m/s) versus community (≥0.4-0.8m/s; >0.8m/s) ambulation at the time of discharge. Validity of the results was evaluated on a second sample of participants. MAIN OUTCOME MEASURE: Discharge 10-m walk speed. RESULTS: Admission Berg Balance Scale and FIM walk item scores explained most of the variance in discharge walk speed. The odds ratio of achieving only household ambulation at discharge was 20 (95% confidence interval [CI], 6-63) for sample 1 and 32 (95% CI, 10-96) for sample 2 when the combination of having a Berg Balance Scale score of ≤20 and a FIM walk item score of 1 or 2 was present. CONCLUSIONS: A Berg Balance Scale score of ≤20 and a FIM walk item score of 1 or 2 at admission indicates that a person with stroke is highly likely to only achieve household ambulation speeds at discharge from an IRF.