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

Examining the validity of smart glasses in measuring spatiotemporal parameters of gait among people with Parkinson's disease

BACKGROUND

Gait impairment is an early marker of Parkinson's disease (PD) and is frequently monitored to evaluate disease progression. Wearable sensors are increasingly being used to quantify gait in the real-world setting among people with PD (pwPD). Particularly, embedding wearables on devices or clothing that are worn daily may represent a useful strategy to improve compliance and regular monitoring of gait.

RESEARCH QUESTION

The current investigation examined the validity of innovative smart glasses to measure gait among pwPD.

METHODS

Participants wore the smart glasses and 6 APDM gait sensors simultaneously, while performing two walking tasks: the 3-meters Timed Up and Go test (TUG) and the 7-meters Stand and Walk (SAW) test. The following spatiotemporal gait parameters were calculated from the data collected using the two different devices: step time, step length, swing percentage, TUG duration, turn duration, and turn velocity.

RESULTS

A total of 31 pwPD (mean age=68.6±8.5 years; 35.48 % female(N=11), mean Unified Parkinson's Disease Rating Scale (UPDRS) total score=32.1±14.7) participated in the study. Smart glasses achieved high validity in measuring step time (ICC=0.92, p=0.01) and TUG duration (ICC=0.96, p=0.03) compared to APDM sensors. On the other hand, the smart glasses did not achieve adequate validity when measuring step length, swing percentage, turn duration or turn velocity.

SIGNIFICANCE

The current study suggests that smart glasses has the potential to measure TUG and step time in individuals living with PD. However, further research is needed to improve algorithms for sensors worn on the head.

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.

Natural Walking Intensity in Persons With Parkinson Disease

BACKGROUND AND PURPOSE

Few persons with Parkinson disease (PD) appear to engage in moderate-intensity walking associated with disease-modifying health benefits. How much time is spent walking at lower, yet still potentially beneficial, intensities is poorly understood. The purpose of this exploratory, observational study was to describe natural walking intensity in ambulatory persons with PD.

METHODS

Accelerometer-derived real-world walking data were collected for more than 7 days at baseline from 82 participants enrolled in a PD clinical trial. Walking intensity was defined according to the number of steps in each active minute (1-19, 20-39, 40-59, 60-79, 80-99, or ≥100 steps). Daily minutes of walking and duration of the longest sustained walking bout were calculated at each intensity. Number of sustained 10 to 19, 20 to 29, and 30-minute bouts and greater at any intensity also were calculated. Values were analyzed in the context of physical activity guidelines.

RESULTS

Most daily walking occurred at lower intensities (157.3 ± 58.1 min of 1-19 steps; 81.3 ± 32.6 min of 20-39 steps; 38.2 ± 21.3 min of 40-59 steps; 15.1 ± 11.5 min of 60-79 steps; 7.4 ± 7.0 min of 80-99 steps; 7.3 ± 9.6 min of ≥100 steps). The longest daily sustained walking bout occurred at the lowest intensity level (15.9 ± 5.2 min of 1-19 steps). Few bouts lasting 20 minutes and greater occurred at any intensity.

DISCUSSION AND CONCLUSIONS

Despite relatively high daily step counts, participants tended to walk at remarkably low intensity, in bouts of generally short duration, with relatively few instances of sustained walking. The findings reinforced the need for health promotion interventions designed specifically to increase walking intensity.Video Abstract available for more insight from authors (see the Video, Supplemental Digital Content 1 available at: http://links.lww.com/JNPT/A426 ).

Improvement in the Capacity for Activity Versus Improvement in Performance of Activity in Daily Life During Outpatient Rehabilitation

We addressed questions about the potential discrepancy between improvements in activity capacity and improvements in activity performance in daily life. We asked whether this discrepancy is: Common in routine, outpatient care, or an artifact of intervention studies? Unique to upper limb (UL) rehabilitation, or is it seen in walking rehabilitation too? Only seen in persons with stroke, or a broader neurorehabilitation problem?

Beyond steps per day: other measures of real-world walking after stroke related to cardiovascular risk

Background

Significant variability exists in how real-world walking has been measured in prior studies in individuals with stroke and it is unknown which measures are most important for cardiovascular risk. It is also unknown whether real-world monitoring is more informative than laboratory-based measures of walking capacity in the context of cardiovascular risk. The purpose of this study was to determine a subset of real-world walking activity measures most strongly associated with systolic blood pressure (SBP), a measure of cardiovascular risk, in people with stroke and if these measures are associated with SBP after accounting for laboratory-based measures of walking capacity.

Methods

This was a cross-sectional analysis of 276 individuals with chronic (≥ 6 months) stroke. Participants wore an activity monitor for ≥ 3 days. Measures of activity volume, activity frequency, activity intensity, and sedentary behavior were calculated. Best subset selection and lasso regression were used to determine which activity measures were most strongly associated with systolic blood pressure. Sequential linear regression was used to determine if these activity measures were associated with systolic blood pressure after accounting for walking capacity (6-Minute Walk Test).

Results

Average bout cadence (i.e., the average steps/minute across all bouts of walking) and the number of long (≥ 30 min) sedentary bouts were most strongly associated with systolic blood pressure. After accounting for covariates (ΔR² = 0.089, p < 0.001) and walking capacity (ΔR² = 0.002, p = 0.48), these activity measures were significantly associated with systolic blood pressure (ΔR² = 0.027, p = 0.02). Higher systolic blood pressure was associated with older age (β = 0.219, p < 0.001), male gender (β = − 0.121, p = 0.046), black race (β = 0.165, p = 0.008), and a slower average bout cadence (β = − 0.159, p = 0.022).

Conclusions

Measures of activity intensity and sedentary behavior may be superior to commonly used measures, such as steps/day, when the outcome of interest is cardiovascular risk. The relationship between walking activity and cardiovascular risk cannot be inferred through laboratory-based assessments of walking capacity.

A machine learning approach to identifying important features for achieving step thresholds in individuals with chronic stroke

BACKGROUND

While many factors are associated with stepping activity after stroke, there is significant variability across studies. One potential reason to explain this variability is that there are certain characteristics that are necessary to achieve greater stepping activity that differ from others that may need to be targeted to improve stepping activity.

OBJECTIVE

Using two step thresholds (2500 steps/day, corresponding to home vs. community ambulation and 5500 steps/day, corresponding to achieving physical activity guidelines through walking), we applied 3 different algorithms to determine which predictors are most important to achieve these thresholds.

METHODS

We analyzed data from 268 participants with stroke that included 25 demographic, performance-based and self-report variables. Step 1 of our analysis involved dimensionality reduction using lasso regularization. Step 2 applied drop column feature importance to compute the mean importance of each variable. We then assessed which predictors were important to all 3 mathematically unique algorithms.

RESULTS

The number of relevant predictors was reduced from 25 to 7 for home vs. community and from 25 to 16 for aerobic thresholds. Drop column feature importance revealed that 6 Minute Walk Test and speed modulation were the only variables found to be important to all 3 algorithms (primary characteristics) for each respective threshold. Other variables related to readiness to change activity behavior and physical health, among others, were found to be important to one or two algorithms (ancillary characteristics).

CONCLUSIONS

Addressing physical capacity is necessary but not sufficient to achieve important step thresholds, as ancillary characteristics, such as readiness to change activity behavior and physical health may also need to be targeted. This delineation may explain heterogeneity across studies examining predictors of stepping activity in stroke.

High Level Mobility Training in Ambulatory Patients with Acquired Non-Progressive Central Neurological Injury: a Feasibility Study

The purpose of this study was to test the feasibility and safety of High-Level Mobility (HLM) training on adults with Acquired Brain Injury (ABI). Our hypotheses were that HLM training would be feasible and safe. This study was a pilot randomized control trial with a Simple Skill Group (SSG) and a Complex Skill Group (CSG). Both groups received 12 sessions over 8 weeks and completed 4 testing sessions over 16 weeks. The SSG focused on locomotion, while CSG focused on the acquisition of running. Feasibility was assessed in terms of process, resources, management, and scientific metrics, including safety. Among the 41 participants meeting inclusion criteria, 28 consented (CSG, n = 13, SSG, n = 15), 20 completed the assigned protocol and 8 withdrew (CSG n = 4, SSG n = 4). Adherence rate to assigned protocol was 100%. There were two Adverse Events (AEs), 1 over 142 SSG sessions and 1 over 120 CSG sessions. The AE Odd Ratio (OR) (CSG:SSG) was 1.18 (95% CI: 0.07, 19.15). The data support our hypotheses that HLM training is feasible and safe on ambulatory adults with ABI.

Physical Activity in De Novo Parkinson Disease: Daily Step Recommendation and Effects of Treadmill Exercise on Physical Activity

OBJECTIVE

People with Parkinson disease (PD) have low physical activity (PA) levels and are at risk for cardiovascular events. The 3 purposes of this study were to determine a step threshold that corresponds to meeting aerobic PA guidelines, determine effects of treadmill exercise on PA, and quantify the relationship between changes in daily steps and fitness.

METHODS

This was a secondary analysis of the Study in Parkinson's Disease of Exercise trial, which randomized participants to high-intensity treadmill exercise, moderate-intensity treadmill exercise, or usual care for 6 months. Daily steps and moderate- to vigorous-intensity PA (MVPA) were assessed at baseline and once each month using an activity monitor. Fitness was assessed via graded exercise test at baseline and at 6 months. A step threshold that corresponds to meeting PA guidelines was determined by receiver operating characteristic curves. The effect of treadmill exercise on PA was examined in those below the step threshold (ie, the least active participants). Pearson r correlations determined the relationship between daily steps and fitness.

RESULTS

Individuals with de novo PD (n = 110) were included. Those with ≥4200 steps were 23 times more likely (95% CI = 7.72 to 68) to meet PA guidelines than those with <4200 steps. For those with <4200 steps at baseline (n = 33), only those in the high-intensity exercise group increased daily steps (median of differences = 1250 steps, z = -2.35) and MVPA (median of differences = 12.5 minutes, z = -2.67) at 6 months. For those with <4200 steps, changes in daily steps were not associated with changes in fitness (r = .183).

CONCLUSION

In people with PD and <4200 daily steps at baseline, high-intensity treadmill exercise increased daily steps and MVPA, but these changes were not associated with changes in fitness.

IMPACT

People with PD should be encouraged to take ≥4200 daily steps to meet PA guidelines through walking.

Predictors of Daily Steps at 1-Year Poststroke: A Secondary Analysis of a Randomized Controlled Trial

[Figure: see text].

Sensor-based categorization of upper limb performance in daily life of persons with and without neurological upper limb deficits

Background: The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life.

Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits.

Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters.

Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit.

Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.

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.