Validity of the Fitbit One for Measuring Activity in Community-Dwelling Stroke Survivors

Psychometric properties of wearable technologies to assess post-stroke gait parameters: A systematic review

BACKGROUND

Wearable technologies using inertial sensors are an alternative for gait assessment. However, their psychometric properties in evaluating post-stroke patients are still being determined. This systematic review aimed to evaluate the psychometric properties of wearable technologies used to assess post-stroke gait and analyze their reliability and measurement error. The review also investigated which wearable technologies have been used to assess angular changes in post-stroke gait.

METHODS

The present review included studies in English with no publication date restrictions that evaluated the psychometric properties (e.g., validity, reliability, responsiveness, and measurement error) of wearable technologies used to assess post-stroke gait. Searches were conducted from February to March 2023 in the following databases: Cochrane Central Registry of Controlled Trials (CENTRAL), Medline/PubMed, EMBASE Ovid, CINAHL EBSCO, PsycINFO Ovid, IEEE Xplore Digital Library (IEEE), and Physiotherapy Evidence Database (PEDro); the gray literature was also verified. The Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) risk-of-bias tool was used to assess the quality of the studies that analyzed reliability and measurement error.

RESULTS

Forty-two studies investigating validity (37 studies), reliability (16 studies), and measurement error (6 studies) of wearable technologies were included. Devices presented good reliability in measuring gait speed and step count; however, the quality of the evidence supporting this was low. The evidence of measurement error in step counts was indeterminate. Moreover, only two studies obtained angular results using wearable technology.

SIGNIFICANCE

Wearable technologies have demonstrated reliability in analyzing gait parameters (gait speed and step count) among post-stroke patients. However, higher-quality studies should be conducted to improve the quality of evidence and to address the measurement error assessment. Also, few studies used wearable technology to analyze angular changes during post-stroke gait.

Measurement properties of activity monitoring for a rehabilitation (AMoR) platform in post-stroke individuals in a simulated home environment

AIM

The aim of this study was to evaluate the measurement properties of activity monitoring for a rehabilitation (AMoR) platform for step counting, time spent in sedentary behavior, and postural changes during activities of daily living (ADLs) in a simulated home environment.

METHODS

Twenty-one individuals in the post-stroke chronic phase used the AMoR platform during an ADL protocol and were monitored by a video camera. Spearman's correlation coefficient, mean absolute percent error (MAPE), intraclass correlation coefficient (ICC), and Bland-Altman plot analyses were used to estimate the validity and reliability between the AMoR platform and the video for step counting, time spent sitting/lying, and postural changes from sit-to-stand (SI-ST) and sit-to-stand (ST-SI).

RESULTS

Validity of the platform was observed with very high correlation values for step counting (rs = 0.998) and time spent sitting/lying (rs = 0.992) and high correlation for postural change of SI-ST (rs = 0.850) and ST-SI (rs = 0.851) when compared to the video. An error percentage above 5% was observed only for the SI-ST postural change (7.13%). The ICC values show excellent agreement for step counting (ICC3, k = 0.999) and time spent sitting/lying (ICC3, k = 0.992), and good agreement for SI-ST (ICC3, k = 0.859) and ST-SI (ICC3, k = 0.936) postural change. Values of the differences for step counting, sitting/lying time, and postural change were within the limits of agreement according to the analysis of the Bland-Altman graph.

CONCLUSION

The AMoR platform presented validity and reliability for step counting, time spent sitting/lying, and identification of SI-ST and ST-SI postural changes during tests in a simulated environment in post-stroke individuals.

Interventions to Change Movement Behaviors After Stroke: A Systematic Review and Meta-analysis

OBJECTIVE

This systematic review aimed to determine which interventions increase physical activity (PA) and decrease sedentary behavior (SB) based on objective measures of movement behavior in individuals with stroke.

DATA SOURCES

The PubMed (Medline), EMBASE, Scopus, CINAHL (EBSCO), and Web of Science databases were searched for articles published up to January 3, 2023.

STUDY SELECTION

The StArt 3.0.3 BETA software was used to screen titles, abstracts, and full texts for studies with randomized controlled trial designs; individuals with stroke (≥18 years of age); interventions aimed at increasing PA or decreasing SB; and objective measurement instruments.

DATA EXTRACTION

Data extraction was standardized, considering participants and assessments of interest. The risk of bias and quality of evidence of the included studies were assessed.

DATA SYNTHESIS

Twenty-eight studies involving 1855 patients were included. Meta-analyses revealed that in the post-stroke acute/subacute phase, exercise interventions combined with behavior change techniques (BCTs) increased both daily steps (standardized mean difference [SMD]=0.65, P=.0002) and time spent on moderate-to-vigorous intensity physical activities (MVPAs) duration of PA (SMD=0.68, P=.0004) with moderate-quality evidence. In addition, interventions based only on BCTs increased PA levels with very low-quality evidence (SMD (low-intensity physical activity)=0.36, P=.02; SMD (MVPA)=0.56, P=.0004) and decreased SB with low-quality evidence (SMD=0.48, P=.03). In the post-stroke chronic phase, there is statistical significance in favor of exercise-only interventions in PA frequency (steps/day) with moderate-quality evidence (SMD=0.68, P=.002). In general, the risk of bias in the included studies was low.

CONCLUSIONS

In the acute/subacute phase after stroke, the use of BCTs combined with exercise can increase the number of daily steps and time spent on MVPA. In contrast, in the post-stroke chronic phase, exercise-only interventions resulted in a significant increase in daily steps.

Increasing Activity After Stroke: A Randomized Controlled Trial of High-Intensity Walking and Step Activity Intervention

BACKGROUND

Physical inactivity in people with chronic stroke profoundly affects daily function and increases recurrent stroke risk and mortality, making physical activity improvements an important target of intervention. We compared the effects of a high-intensity walking intervention (FAST), a step activity monitoring behavioral intervention (SAM), or a combined intervention (FAST+SAM) on physical activity (ie, steps/day). We hypothesized the combined intervention would yield the greatest increase in steps/day.

METHODS

This assessor-blinded multisite randomized controlled trial was conducted at 4 university/hospital-based laboratories. Participants were 21 to 85 years old, walking without physical assistance following a single, unilateral noncerebellar stroke of ≥6 months duration, and randomly assigned to FAST, SAM, or FAST+SAM for 12 weeks (2-3 sessions/week). FAST training consisted of walking-related activities at 70% to 80% heart rate reserve, while SAM received daily feedback and goal setting of walking activity (steps/day). Assessors and study statistician were masked to group assignment. The a priori-determined primary outcome and end point was a comparison of the change in steps/day between the 3 intervention groups from pre- to post-intervention. Adverse events were tracked after randomization. All randomized participants were included in the intent-to-treat analysis.

RESULTS

Participants were enrolled from July 18, 2016, to November 16, 2021. Of 2385 participants initially screened, 250 participants were randomized (mean [SE] age, 63 [0.80] years; 116 females/134 males), with 89 assigned to FAST, 81 to SAM, and 80 to FAST+SAM. Steps/day significantly increased in both the SAM (mean [SE], 1542 [267; 95% CI, 1014-2069] P<0.001) and FAST+SAM group (1307 [280; 95% CI, 752-1861] P<0.001) but not in the FAST group (406 [238; 95% CI, -63 to 876] P=0.09). There were no deaths or serious study-related adverse events.

CONCLUSIONS

Only individuals with chronic stroke who completed a step activity monitoring behavioral intervention with skilled coaching and goal progression demonstrated improvements in physical activity (steps/day).

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02835313.

Measurement properties of device-based physical activity instruments in ambulatory adults with physical disabilities and/or chronic diseases: a scoping review

BACKGROUND

People with physical disabilities and/or chronic diseases tend to have an inactive lifestyle. Monitoring physical activity levels is important to provide insight on how much and what types of activities people with physical disabilities and/or chronic diseases engage in. This information can be used as input for interventions to promote a physically active lifestyle. Therefore, valid and reliable physical activity measurement instruments are needed. This scoping review aims 1) to provide a critical mapping of the existing literature and 2) directions for future research on measurement properties of device-based instruments assessing physical activity behavior in ambulant adults with physical disabilities and/or chronic diseases.

METHODS

Four databases (MEDLINE, CINAHL, Web of Science, Embase) were systematically searched from 2015 to April 16th 2023 for articles investigating measurement properties of device-based instruments assessing physical activity in ambulatory adults with physical disabilities and/or chronic diseases. For the majority, screening and selection of eligible studies were done in duplicate. Extracted data were publication data, study data, study population, device, studied measurement properties and study outcome. Data were synthesized per device.

RESULTS

One hundred three of 21566 Studies were included. 55 Consumer-grade and 23 research-grade devices were studied on measurement properties, using 14 different physical activity outcomes, in 23 different physical disabilities and/or chronic diseases. ActiGraph (n = 28) and Fitbit (n = 39) devices were most frequently studied. Steps (n = 68) was the most common used physical activity outcome. 97 studies determined validity, 11 studies reliability and 6 studies responsiveness.

CONCLUSION

This scoping review shows a large variability in research on measurement properties of device-based instruments in ambulatory adults with physical disabilities and/or chronic diseases. The variability highlights a need for standardization of and consensus on research in this field. The review provides directions for future research.

Predictors of non-stepping time in people with chronic stroke

BACKGROUND

Sedentary time is an independent construct from active time. Previous studies have examined variables associated with sedentary time to inform behavior change programs; however, these studies have lacked data sets that encompass potentially important domains.

OBJECTIVES

The purpose of this study was to build a more comprehensive model containing previously theorized important predictors of sedentary time and new predictors that have not been explored. We hypothesized that variables representing the domains of physical capacity, psychosocial, physical health, cognition, and environmental would be significantly related to sedentary time in individuals post-stroke.

METHODS

This was a cross-sectional analysis of 280 individuals with chronic stroke. An activity monitor was used to measure sedentary (i.e. non-stepping) time. Five domains (8 predictors) were entered into a sequential linear regression model: physical capacity (6-Minute Walk Test, assistive device use), psychosocial (Activities Specific Balance Confidence Scale and Patient Health Questionnaire-9), physical health (Charlson Comorbidity Index and body mass index), cognition (Montreal Cognitive Assessment), and environmental (Area Deprivation Index).

RESULTS

The 6-Minute Walk Test (β = -0.39, p < .001), assistive device use (β = 0.15, p = .03), Patient Health Questionnaire-9 (β = 0.16, p = .01), and body mass index (β = 0.11, p = .04) were significantly related to non-stepping time in individuals with chronic stroke. The model explained 28.5% of the variability in non-stepping time.

CONCLUSIONS

This work provides new perspective on which variables may need to be addressed in programs targeting sedentary time in stroke. Such programs should consider physical capacity, depressive symptoms, and physical health.

Increasing activity after stroke: a randomized controlled trial of highintensity walking and step activity intervention

Background

Physical inactivity in people with chronic stroke profoundly affects daily function and increases recurrent stroke risk and mortality, making physical activity improvements an important target of intervention. We compared the effects of a highintensity walking intervention (FAST), a step activity monitoring behavioral intervention (SAM), or a combined intervention (FAST+SAM) on physical activity (i.e., steps per day). We hypothesized the combined intervention would yield the greatest increase in steps per day.

Methods

This assessor-blinded multi-site randomized controlled trial was conducted at four university/hospital-based laboratories. Participants were 21-85 years old, walking without physical assistance following a single, unilateral non-cerebellar stroke of ≥6 months duration, and randomly assigned to FAST, SAM, or FAST+SAM for 12 weeks (2-3 sessions/week). FAST training consisted of walking-related activities for 40 minutes/session at 70-80% heart rate reserve, while SAM received daily feedback and goal-setting of walking activity (steps per day). Assessors and study statistician were masked to group assignment.The a priori-determined primary outcome and primary endpoint was change in steps per day from pre- to post-intervention. Adverse events (AEs) were tracked after randomization. All randomized participants were included in the intent-to-treat analysis.This study is registered at ClinicalTrials.gov, NCT02835313.

Findings

Participants were enrolled from July 18, 2016-November 16, 2021. Of 250 randomized participants (mean[SE] age 63[0.80], 116F/134M), 89 were assigned to FAST, 81 to SAM, and 80 to FAST+SAM. Steps per day significantly increased in both the SAM (mean[SE] 1542[267], 95%CI:1014-2069, p<0.001) and FAST+SAM groups (1307[280], 752-1861, p<0.001), but not in the FAST group (406[238], 63-876, p=0.09). There were no deaths or serious study-related AEs and all other minor AEs were similar between groups.

Interpretation

Only individuals with chronic stroke who completed a step activity monitoring behavioral intervention with skilled coaching and goal progression demonstrated improvements in physical activity (steps per day).

Daily steps are associated with walking ability in hospitalized patients with sub-acute stroke

Increased physical activity is required in patients with stroke that are hospitalized in the rehabilitation unit. This study investigated the association between the daily number of steps and walking independence in order to determine the cutoff value of daily number of steps that can predict walking independence in hospitalized patients with sub-acute stroke. This cross-sectional observational study included 85 stroke patients admitted to the rehabilitation unit. The average daily number of steps was measured using Fitbit One for 4 days starting at 30 days after stroke onset. 6-min walk test, and Fugl-Meyer assessment of the lower extremities were measured The category of walking independence was classified using the Functional Ambulation Category (FAC). The subjects were divided into two groups according to the FAC score: a walking independence group (FAC ≥ 4) and a walking non-independence group (FAC ≤ 3). Logistic regression analysis was conducted to investigate the association of daily number of steps with walking independence and a receiver operating characteristic curve was used to identify the cutoff value of daily number of steps for predicting walking independence. The daily number of steps (per 1000 steps) was independently associated with walking independence (odds ratio (OR); 2.53, 95% confidence interval (CI); 1.40–5.73, p = 0.009). The cutoff value of daily number of steps for predicting independent walking was 4286 steps (area under the curve = 0.914, sensitivity of 0.731, and specificity of 0.949). The daily number of steps was associated with independent walking in hospitalized patients with sub-acute stroke. The daily number of steps may be a useful target in rehabilitation for patients with sub-acute stroke.

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.

Relationships Among Environmental Variables, Physical Capacity, Balance Self-Efficacy, and Real-World Walking Activity Post-Stroke

BACKGROUND

Social and physical environmental factors affect real-world walking activity in individuals with stroke. However, environmental factors are often non-modifiable, presenting a challenge for clinicians working with individuals with stroke whose real-world walking is limited due to environmental barriers.

OBJECTIVE

The purpose of this work was to test a model hypothesizing the relationships among environmental factors (specifically, living situation and area deprivation), modifiable factors, and real-world walking activity to understand opportunities for intervention. We hypothesized that balance self-efficacy would mediate the relationship between the environment and real-world walking and that physical capacity would moderate this mediation.

METHODS

This was a cross-sectional study of 282 individuals with chronic (≥6 months) stroke. We tested the indirect effect to determine if mediation was present. Multiple group structural equation modeling was used to test if physical capacity moderated this mediation. A χ² difference test was used to compare the moderation model against the null (no moderation) model.

RESULTS

Balance self-efficacy mediated the relationship between area deprivation and real-world walking (indirect effect: β = -0.04, P = .04). Both the moderation and null models fit the data equally well statistically (χ²(5) = 6.9, P = .23). We therefore accepted the simpler (null) model and concluded that the mediation was not moderated.

CONCLUSIONS

Targeting balance self-efficacy may be an effective approach to improving real-world walking in persons with stroke who experience barriers within the physical environment. A stroke survivor's physical capacity may not impact this approach. Future work should consider utilizing more specific measures of the social and physical environment to better understand their influences on real-world walking activity in individuals with stroke. However, the results of this work provide excellent targets for future longitudinal studies targeting real-world walking activity in stroke.

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.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Background

Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

Objective

This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

Methods

Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

Results

Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

Conclusions

Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

Objectively measured physical activity was not associated with neighborhood walkability attributes in community-dwelling patients with stroke

Although the built environment may affect physical activity, there is little evidence on how neighborhood walkability attributes influence post-stroke physical activity. This study aimed to explore associations between objectively measured physical activity and neighborhood walkability attributes in community-dwelling patients with stroke. This cross-sectional study recruited patients who could ambulate outside free of assistance. We assessed objectively measured physical activity comprising the number of steps taken and time spent in moderate-to-vigorous physical activity (MVPA) with an accelerometer. Neighborhood walkability attributes were evaluated using the Walk Score. Multiple linear regression analyses were used to determine whether the Walk Score was independently associated with the number of steps taken or MVPA. Eighty participants with a mean age of 65.9 ± 11.1 years were included. The participants took an average of 5900.6 ± 2947.3 steps/day and spent an average of 19.7 ± 21.7 min/day in MVPA. The mean Walk Score was 71.4 ± 17.2. Multiple linear regression analyses showed that no significant associations were found between the Walk Score and the number of steps taken or MVPA. No associations were found between objectively measured physical activity and neighborhood walkability attributes in community-dwelling patients with stroke in an Asian area.

Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis

Background

Wearable accelerometer-containing devices have become a mainstay in clinical studies which attempt to classify the gait patterns in various diseases. A gait profile for lumbar spinal stenosis (LSS) has not been developed, and no study has validated a simple wearable system for the clinical assessment of gait in lumbar stenosis. This study identifies the changes to gait patterns that occur in LSS to create a preliminary disease-specific gait profile. In addition, this study compares a chest-based wearable sensor, the MetaMotionC^© device and inertial measurement unit python script (MMC/IMUPY) system, against a reference-standard, videography, to preliminarily assess its accuracy in measuring the gait features of patients with LSS.

Methods

We conduct a cross-sectional observational study examining the walking patterns of 25 LSS patients and 33 healthy controls. To construct a preliminary disease-specific gait profile for LSS, the gait patterns of the 25 LSS patients and 25 healthy controls with similar ages were compared. To assess the accuracy of the MMC/IMUPY system in measuring the gait features of patients with LSS, its results were compared with videography for the 21 LSS and 33 healthy controls whose walking bouts exceeded 30 m.

Results

Patients suffering from LSS walked significantly slower, with shorter, less frequent steps and higher asymmetry compared to healthy controls. The MMC/IMUPY system had >90% agreement with videography for all spatiotemporal gait metrics that both methods could measure.

Conclusions

The MMC/IMUPY system is a simple and feasible system for the construction of a preliminary disease-specific gait profile for LSS. Before clinical application in everyday living conditions is possible, further studies involving the construction of a more detailed disease-specific gait profile for LSS by disease severity, and the validation of the MMC/IMUPY system in the home environment, are required.

Leveraging insights from behavioral economics to improve mobility for adults with stroke: Design and rationale of the BE Mobile clinical trial

Physical inactivity post-stroke can negatively impact long-term health outcomes and contribute to cardiovascular deconditioning, muscle loss, and increased risk for recurrent stroke. The limited number of interventions designed to improve daily physical activity post-stroke have lacked precision in step goals, are resource intensive, and difficult to scale. The purpose of the Leveraging Insights from Behavioral Economics to Improve Mobility for Adults with Stroke (BE Mobile) trial is to examine the preliminary effectiveness of a novel gamification with social incentives intervention for improving physical activity post-stroke. This trial includes adults who have experienced an ischemic or hemorrhagic stroke ≥3 months prior to the time of recruitment who are randomized to a control or gamification arm. All participants receive a Fitbit Inspire 2 wearable device to quantify daily steps and complete a 2-week baseline run-in period followed by an 8-week intervention period. All participants select a daily step goal and the gamification arm is enrolled in a game with loss-framed points and levels to help participants achieve their daily step goal. Participants in the gamification arm also select a support partner who receives weekly updates on their progress in the game. The primary outcome is change in daily steps from baseline during the intervention period. The secondary outcome is difference in the proportion of days participants achieved their daily step goal. Results from this trial will inform future, larger studies that leverage insights from behavioral economics to help improve daily physical activity post-stroke. Trial registration: NCT #04607811.

Biomonitoring and Digital Data Technology as an Opportunity for Enhancing Animal Study Translation

The failure of animal studies to translate to effective clinical therapeutics has driven efforts to identify underlying cause and develop solutions that improve the reproducibility and translatability of preclinical research. Common issues revolve around study design, analysis, and reporting as well as standardization between preclinical and clinical endpoints. To address these needs, recent advancements in digital technology, including biomonitoring of digital biomarkers, development of software systems and database technologies, as well as application of artificial intelligence to preclinical datasets can be used to increase the translational relevance of preclinical animal research. In this review, we will describe how a number of innovative digital technologies are being applied to overcome recurring challenges in study design, execution, and data sharing as well as improving scientific outcome measures. Examples of how these technologies are applied to specific therapeutic areas are provided. Digital technologies can enhance the quality of preclinical research and encourage scientific collaboration, thus accelerating the development of novel therapeutics.

Beyond Physical Capacity: Factors Associated With Real-world Walking Activity After Stroke

OBJECTIVE

To identify homogeneous subsets of survivors of chronic stroke who share similar characteristics across several domains and test if these groups differ in real-world walking activity. We hypothesized that variables representing the domains of walking ability, psychosocial, environment, and cognition would be important contributors in differentiating real-world walking activity in survivors of chronic stroke.

DESIGN

Cross-sectional, secondary data analysis.

SETTING

University/laboratory.

PARTICIPANTS

A total of 283 individuals with chronic (≥6mo) stroke (N=238).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Thirteen variables representing 5 domains were included: (1) walking ability: 6-minute walk test (6MWT), self-selected speed (SSS) of gait; (2) psychosocial: Patient Health Questionnaire-9, Activities-specific Balance Confidence (ABC) scale; (3) physical health: low-density lipoprotein cholesterol, body mass index, Charlson Comorbidity Index (CCI); (4) cognition: Montreal Cognitive Assessment (MoCA); and (5) environment: living situation and marital status, work status, Area Deprivation Index (ADI), Walk Score. Mixture modeling was used to identify latent classes of survivors of stroke. After identifying the latent classes, walking activity, measured as steps per day (SPD), was included as a distal outcome to understand if classes were meaningfully different in their real-world walking RESULTS: A model with 3 latent classes was selected. The 6MWT, SSS, ABC scale, and Walk Score were significantly different among all 3 classes. Differences were also seen for the MoCA, ADI, and CCI between 2 of the 3 classes. Importantly, the distal outcome of SPD was significantly different in all classes, indicating that real-world walking activity differs among the groups identified by the mixture model.

CONCLUSIONS

Survivors of stroke with lower walking ability, lower self-efficacy, lower cognitive abilities, and greater area deprivation had lower SPD. These results demonstrate that the physical and social environment (including socioeconomic factors) and cognitive function should also be considered when developing interventions to improve real-world walking activity after stroke.

Readiness to Change is Related to Real-World Walking and Depressive Symptoms in Chronic Stroke

BACKGROUND AND PURPOSE

The transtheoretical model is a health behavior model used to understand an individual's readiness to change their behavior. This study aims to apply the transtheoretical model in understanding a person with stroke's readiness to change their activity level, as it relates to physical capacity, physical health, depressive symptoms, self-efficacy, and daily stepping activity.

METHODS

This was a cross-sectional analysis of baseline data from a clinical trial. Participants' readiness to change their activity levels was measured via self-report and daily stepping activity was measured using a step activity monitor. Robust regression (M-estimation with robust standard errors) was used to test the relationship between readiness to change and measures of physical capacity (6-minute walk test, self-selected walking speed), physical health (body mass index, age-adjusted Charlson Comorbidity Index), depressive symptoms (Patient Health Questionnaire-9), self-efficacy (Activities-Specific Balance Confidence Scale), and daily stepping (steps per day).

RESULTS

A total of 274 individuals were included in the analysis. Adjusted for age, readiness to change was positively related to daily stepping (β = 0.29, P < 0.001) and negatively related to depressive symptoms (β = -0.13, P = 0.01). Readiness to change was not significantly associated with measures of physical capacity, physical health, or self-efficacy.

DISCUSSION

These results suggest that individuals with stroke in the later stages of change may demonstrate greater daily stepping activity and lower depressive symptoms compared with those in earlier stages.

CONCLUSIONS

Understanding the relationship between readiness to change, daily stepping, and depressive symptoms will help clinicians implement appropriate stage-specific intervention strategies and facilitate greater improvement in activity levels.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A333).

Relationships of Linear and Non-linear Measurements of Post-stroke Walking Activity and Their Relationship to Weather

Background: Stroke survivors are more sedentary than the general public. Previous research on stroke activity focuses on linear quantities. Non-linear measures, such as Jensen-Shannon Divergence and Lempel-Ziv Complexity, may help explain when and how stroke survivors move so that interventions to increase activity may be designed more effectively.

Objectives: Our objective was to understand what factors affect a stroke survivor's physical activity, including weather, by characterizing activity by step counts, structure, and complexity.

Methods: A custom MATLAB code was used to analyze clinical trial (NCT02835313, https://clinicaltrials.gov/ct2/show/NCT02835313) data presented as minute by minute step counts. Six days of data were analyzed for 142 participants to determine the regularity of activity structure across days and complexity patterns of varied cadences. The effect of steps on structure and complexity, the season's effect on steps, structure, and complexity, and the presence of precipitation's effect on steps and complexity were all analyzed.

Results: Step counts and regularity were linearly related (p < 0.001). Steps and complexity were quadratically related (r² = 0.70 for mean values, 0.64 for daily values). Season affected complexity between spring and winter (p = 0. 019). Season had no effect on steps or structure. Precipitation had no effect on steps or complexity.

Conclusions: Stroke survivors with high step counts are active at similar times each day and have higher activity complexities as measured through patterns of movement at different intensity levels. Non-linear measures, such as Jensen-Shannon Divergence and Lempel-Ziv Complexity, are valuable in describing a person's activity. Weather affects our activity parameters in terms of complexity between spring and winter.

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Background

Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures.

Objective

The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure.

Methods

We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability.

Results

We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand.

Conclusions

Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.

Social and physical environmental factors in daily stepping activity in those with chronic stroke

BACKGROUND, PURPOSE/OBJECTIVE

Walking behavior in the chronic stroke population is multi-factorial. Previous work focused on the role of physical and biopsychosocial factors in understanding daily stepping post stroke. However, qualitative evidence suggests that social and physical environmental factors also affect daily stepping in those with stroke. The purpose of this study was to understand the role of social and physical environmental factors in daily stepping after stroke.

METHODS

A total of 249 individuals ≥6 months post stroke were included in this cross-sectional analysis (129 females, mean age 62.98 years, SD 11.94). The social environment included living situation, work status, and marital status. The physical environment included the Area Deprivation Index (ADI) and Walk Score. At least 3 days of stepping was collected using an accelerometry-based device. Predictors were entered sequentially into a regression model: demographic characteristics, social environmental factors, and physical environmental factors.

RESULTS

After adjusting for demographic factors, social environmental factors explained 6.2% (p =.017) of the variance in post stroke daily stepping. The addition of physical environmental factors improved the model (ΔR² =.029, p =.024). The final model explained 9.2% (p =.003) of the variance in daily stepping. Lower area deprivation (ADI β = -0.178, p =.015) and working (working vs. retired β = -0.187, p = .029 and working vs. unemployed β = -0.227, p =.008) were associated with greater daily stepping.

DISCUSSION/CONCLUSION

Social and physical environmental factors predicted daily stepping and should be considered when setting expectations relative to the effects of rehabilitation on daily stepping in individuals poststroke.

Performance-based outcome measures are associated with cadence variability during community ambulation among individuals with a transtibial amputation

BACKGROUND

In the United States, Medicare Functional Classification Level (K-level) guidelines require demonstration of cadence variability to justify higher-level prosthetic componentry prescription; however, clinical assessment of cadence variability is subjective. Currently, no clinical outcome measures are associated with cadence variability during community ambulation.

OBJECTIVES

Evaluate whether physical performance, i.e. 10-meter Walk Test (10mWT)-based walking speeds, L-Test, and Figure-of-8 Walk Test scores, is associated with community-based cadence variability among individuals with a transtibial amputation.

STUDY DESIGN

Cross-sectional.

METHODS

Forty-nine participants, aged 18-85 years, with a unilateral transtibial amputation were included. Linear regression models were conducted to determine whether physical performance was associated with cadence variability (a unitless calculation from FitBit® One^TM minute-by-minute step counts), while controlling for sex, age, and time since amputation (p ⩽ .013).

RESULTS

Beyond covariates, self-selected gait speed explained the greatest amount of variance in cadence variability (19.2%, p < .001). Other outcome measures explained smaller, but significant, amounts of the variance (11.1-17.1%, p = .001-.008). For each 0.1 m/s-increase in self-selected and fast gait speeds, or each 1-s decrease in L-Test and F8WT time, community-based cadence variability increased by 1.76, 1.07, 0.39, and 0.79, respectively (p < .013).

CONCLUSIONS

In clinical settings, faster self-selected gait speed best predicted increased cadence variability during community ambulation.

CLINICAL RELEVANCE

The 10-meter Walk Test may be prioritized during prosthetic evaluations to provide objective self-selected walking speed data, which informs the assessment of cadence variability potential outside of clinical settings.

Using an Accelerometer-Based Step Counter in Post-Stroke Patients: Validation of a Low-Cost Tool

Monitoring the real-life mobility of stroke patients could be extremely useful for clinicians. Step counters are a widely accessible, portable, and cheap technology that can be used to monitor patients in different environments. The aim of this study was to validate a low-cost commercial tri-axial accelerometer-based step counter for stroke patients and to determine the best positioning of the step counter (wrists, ankles, and waist). Ten healthy subjects and 43 post-stroke patients were enrolled and performed four validated clinical tests (10 m, 50 m, and 6 min walking tests and timed up and go tests) while wearing five step counters in different positions while a trained operator counted the number of steps executed in each test manually. Data from step counters and those collected manually were compared using the intraclass coefficient correlation and mean average percentage error. The Bland–Altman plot was also used to describe agreement between the two quantitative measurements (step counter vs. manual counting). During walking tests in healthy subjects, the best reliability was found for lower limbs and waist placement (intraclass coefficient correlations (ICCs) from 0.46 to 0.99), and weak reliability was observed for upper limb placement in every test (ICCs from 0.06 to 0.38). On the contrary, in post-stroke patients, moderate reliability was found only for the lower limbs in the 6 min walking test (healthy ankle ICC: 0.69; pathological ankle ICC: 0.70). Furthermore, the Bland–Altman plot highlighted large average discrepancies between methods for the pathological group. However, while the step counter was not able to reliably determine steps for slow patients, when applied to the healthy ankle of patients who walked faster than 0.8 m/s, it counted steps with excellent precision, similar to that seen in the healthy subjects (ICCs from 0.36 to 0.99). These findings show that a low-cost accelerometer-based step counter could be useful for measuring mobility in select high-performance patients and could be used in clinical and real-world settings.

Locomotor training intensity after stroke: Effects of interval type and mode

Background and Objectives: High-intensity interval training (HIIT) is a promising strategy for improving gait and fitness after stroke, but optimal parameters remain unknown. We tested the effects of short vs long interval type and over-ground vs treadmill mode on training intensity. Methods: Using a repeated measures design, 10 participants with chronic hemiparesis performed 12 HIIT sessions over 4 weeks, alternating between short and long-interval HIIT sessions. Both protocols included 10 minutes of over-ground HIIT, 20 minutes of treadmill HIIT and another 10 minutes over-ground. Short-interval HIIT involved 30 second bursts at maximum safe speed and 30-60 second rest periods. Long-interval HIIT involved 4-minute bursts at ~90% of peak heart rate (HR_peak) and 3-minute recovery periods at ~70% HR_peak. Results: Compared with long-interval HIIT, short-interval HIIT had significantly faster mean overground speeds (0.75 vs 0.67 m/s) and treadmill speeds (0.90 vs 0.51 m/s), with similar mean treadmill HR (82.9 vs 81.8%HR_peak) and session perceived exertion (16.3 vs 16.3), but lower overground HR (78.4 vs 81.1%HR_peak) and session step counts (1481 vs 1672). For short-interval HIIT, training speeds and HR were significantly higher on the treadmill vs. overground. For long-interval HIIT, the treadmill elicited HR similar to overground training at significantly slower speeds. Conclusions: Both short and long-interval HIIT elicit high intensities but emphasize different dosing parameters. From these preliminary findings and previous studies, we hypothesize that overground and treadmill short-interval HIIT could be optimal for improving gait speed and overground long-interval HIIT could be optimal for improving gait endurance.

Activity monitor use among persons with multiple sclerosis: Report on rate, pattern, and association with physical activity levels

BACKGROUND

Commercially available activity monitors are a promising approach for tracking and changing physical activity in multiple sclerosis.

OBJECTIVE

This study reports on the rate and pattern of activity monitor use in persons with multiple sclerosis, and compares self-reported physical activity levels between persons who do wear and those who do not wear activity monitors.

METHODS

Participants completed a cross-sectional survey that included a demographic and clinical characteristics scale, activity monitor use questionnaire, and Godin Leisure-Time Exercise Questionnaire (GLTEQ) for measuring total and health-promoting physical activity.

RESULTS

Of the 629 participants who completed the full survey, 249 (40%) reported using an activity monitor. The most common activity monitors were Fitbit, Apple watch, iPhone, and Garmin. There was a significant (p < 0.05), moderate difference (d = 0.5) in GLTEQ total scores between activity monitor users (36.6 ± 23.9) and non-users (25.0 ± 22.2), and in GLTEQ Health Contribution Score between activity monitor users (25.6 ± 22.3) and non-users (14.6 ± 18.9) (p < 0.05, d = 0.5). Self-reported steps from the activity monitor were significantly correlated with GLTEQ total score (ρ = 0.45; r = 0.36) and GLTEQ Health Contribution Score (ρ = 0.41; r = 0.35).

CONCLUSION

Activity monitor use is common among persons with multiple sclerosis, and activity monitor users report more total and health-promoting physical activity; this warrants further research investigating how devices may be used as a behavioral intervention tool.

Exploring the Association Between Physical Activity, Sedentary Behavior, and High-Sensitivity C-Reactive Protein Among Stroke Survivors

Stroke results in low physical activity, high sedentary behavior (SB), and elevated C-reactive protein (CRP), which are associated with stroke recurrence. This study examined the association between physical activity, SB, and CRP in stroke. CRP data from 185 stroke survivors (M_age = 65 years) from the National Health and Nutritional Examination Survey 2009-2010 were analyzed using weighted regression analysis. There was an interaction between physical activity and SB on CRP (estimated-β = -0.079, 95% confidence interval [-0.14, -0.02], p = .011). SB was associated with CRP among those who did not engage in physical activity (estimated-β = 0.068, 95% confidence interval [0.02, 0.11], p = .005), but not among those who did (estimated-β = 0.0003, 95% confidence interval [-0.03, 0.03], p = .988). Physical activity and SB are important modifiable risk factors to lower the risk of recurrent stroke. These findings provide insight into the inflammatory consequences of SB after stroke, particularly among those who also do not engage in physical activity.

Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data

Background

Although designed as a consumer product to help motivate individuals to be physically active, Fitbit activity trackers are becoming increasingly popular as measurement tools in physical activity and health promotion research and are also commonly used to inform health care decisions.

Objective

The objective of this review was to systematically evaluate and report measurement accuracy for Fitbit activity trackers in controlled and free-living settings.

Methods

We conducted electronic searches using PubMed, EMBASE, CINAHL, and SPORTDiscus databases with a supplementary Google Scholar search. We considered original research published in English comparing Fitbit versus a reference- or research-standard criterion in healthy adults and those living with any health condition or disability. We assessed risk of bias using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. We explored measurement accuracy for steps, energy expenditure, sleep, time in activity, and distance using group percentage differences as the common rubric for error comparisons. We conducted descriptive analyses for frequency of accuracy comparisons within a ±3% error in controlled and ±10% error in free-living settings and assessed for potential bias of over- or underestimation. We secondarily explored how variations in body placement, ambulation speed, or type of activity influenced accuracy.

Results

We included 67 studies. Consistent evidence indicated that Fitbit devices were likely to meet acceptable accuracy for step count approximately half the time, with a tendency to underestimate steps in controlled testing and overestimate steps in free-living settings. Findings also suggested a greater tendency to provide accurate measures for steps during normal or self-paced walking with torso placement, during jogging with wrist placement, and during slow or very slow walking with ankle placement in adults with no mobility limitations. Consistent evidence indicated that Fitbit devices were unlikely to provide accurate measures for energy expenditure in any testing condition. Evidence from a few studies also suggested that, compared with research-grade accelerometers, Fitbit devices may provide similar measures for time in bed and time sleeping, while likely markedly overestimating time spent in higher-intensity activities and underestimating distance during faster-paced ambulation. However, further accuracy studies are warranted. Our point estimations for mean or median percentage error gave equal weighting to all accuracy comparisons, possibly misrepresenting the true point estimate for measurement bias for some of the testing conditions we examined.

Conclusions

Other than for measures of steps in adults with no limitations in mobility, discretion should be used when considering the use of Fitbit devices as an outcome measurement tool in research or to inform health care decisions, as there are seemingly a limited number of situations where the device is likely to provide accurate measurement.