Consumer-Based Physical Activity Monitor as a Practical Way to Measure Walking Intensity During Inpatient Stroke Rehabilitation

Construct validity of the TCI mobile app in chronic stroke

BACKGROUND

The TCI Mobile application (TCI Mobile app) has tools to help assess the mobility of people with chronic stroke. Objective: To investigate the construct validity of the TCI Mobile app to assess the mobility of people with chronic stroke.

METHODS

This cross-sectional study included 30 participants with chronic stroke (>6 months), able to walk 8 meters, three times a day with or without an assistive device. Data collection spans 5 days, starting with sociodemographic data and assessment scales: the Timed Up and Go Test, Life Space Assessment, 6-meter Walk Test, 10-meter Walk Test, and Lower Extremity Motor Activity Log. After baseline data, participants wore the TCI Mobile app and an activity monitor in their homes for 3 consecutive days. The Cohen correlation test was used to assess correlations, categorized as small (0.10-0.29), moderate (0.30-0.49), and large (>0.50).

RESULTS

There was a positive and large correlation between the TCI Mobile and activity monitors (0.63 p < .01), 10-meter Walk Test (0.54 p < .01) and Life Space Assessment (0.54 p < .01); a negative and large correlation with the Timed Up and Go Test (-0.50 p < .01); and a positive and moderate correlation with the 6-meter Walk Test (0.42 p = .02) and Lower Extremity Motor Activity Log (0.45 p = .01).

CONCLUSION

The TCI Mobile app demonstrates strong construct validity for assessing mobility in chronic stroke patients. By offering accessible and objective data, it supports clinical decision-making and promotes greater patient engagement in real-world rehabilitation settings.

Perspectives of key stakeholders on integrating wearable sensor technology into rehabilitation care: a mixed-methods analysis

Introduction

Rehabilitation is facing a critical practice gap: Patients seek out rehabilitation services to improve their activity in daily life, yet recent work demonstrates that rehabilitation may be having a limited impact on improving this outcome due to lack of objective data on patients' activity in daily life. Remote monitoring using wearable sensor technology is a promising solution to this address this gap. The purpose of this study was to understand patient and clinician awareness of the practice gap and preferences for integrating wearable sensor technology into rehabilitation care.

Methods

This study used a mixed-methods approach consisting of surveys and 1:1 interviews with clinicians (physical and occupational therapists or assistants) employed at an outpatient rehabilitation clinic within an academic medical center and patients seeking care at this clinic. Data were analyzed using descriptive statistics and thematic analysis.

Results

Data saturation was reached from nineteen clinicians and ten patients. Both clinicians and patients recognized the importance of measuring activity outside the clinic and viewed wearable sensor technology as an objective measurement tool. Most clinicians (63%) preferred continuous (vs. intermittent) monitoring within a care episode and most patients (60%) were willing to sync their sensor data as often as instructed by their provider. To maximize integration into clinical workflows, clinicians voiced a preference for availability of sensor data in the electronic health record.

Conclusions

Clinicians and patients value the use of wearable sensor technology to improve measurement of activity outside the clinic environment and expressed preferences for how this technology could best be integrated into routine rehabilitation care.

Perspectives of Key Stakeholders on Integrating Wearable Sensor Technology into Rehabilitation Care: A Mixed-Methods Analysis

Introduction

Rehabilitation is facing a critical practice gap: Patients seek out rehabilitation services to improve their activity in daily life, yet recent work demonstrates that rehabilitation may be having a limited impact on improving this outcome due to lack of objective data on patients' activity in daily life. Remote monitoring using wearable sensor technology is a promising solution to this address this gap. The purpose of this study was to understand patient and clinician awareness of the practice gap and preferences for integrating wearable sensor technology into rehabilitation care.

Methods

This study used a mixed-methods approach consisting of surveys and 1:1 interviews with clinicians (physical and occupational therapists or assistants) employed at an outpatient rehabilitation clinic within an academic medical center and patients seeking care at this clinic. Data were analyzed using descriptive statistics and thematic analysis.

Results

Data saturation was reached from recruiting nineteen clinicians and ten patients. Both clinicians and patients recognized the importance of measuring activity outside the clinic and viewed wearable sensor technology as an objective measurement tool. Most clinicians (63%) preferred continuous (vs. intermittent) monitoring within a care episode and most patients (60%) were willing to sync their sensor data as often as instructed by their provider. To maximize integration into clinical workflows, clinicians voiced a preference for availability of sensor data in the electronic health record.

Conclusions

Clinicians and patients value the use of wearable sensor technology to improve measurement of activity outside the clinic environment and expressed preferences for how this technology could best be integrated into routine rehabilitation care.

Use of commercially available wearable devices for physical rehabilitation in healthcare: a systematic review

OBJECTIVES

To evaluate whether commercially available 'off-the-shelf' wearable technology can improve patient rehabilitation outcomes, and to categorise all wearables currently being used to augment rehabilitation, including the disciplines and conditions under investigation.

DESIGN

Systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 statement checklist, and using the Grading of Recommendations, Assessment, Development and Evaluation approach.

DATA SOURCES

Embase, MEDLINE, Web of Science and the Cochrane Library were searched up to and including July 2023.

ELIGIBILITY CRITERIA

We included trials and observational studies evaluating the use of consumer-grade wearables, in real patient cohorts, to aid physical therapy or rehabilitation. Only studies investigating rehabilitation of acute events with defined recovery affecting adult patients were included.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers used a standardised protocol to search, screen and extract data from the included studies. Risk of bias was assessed using the Cochrane Methods Risk of Bias in Randomised Trials V.2 and Risk of Bias in Non-Randomised Studies of Interventions tools for randomised controlled trials (RCTs) and observational studies, respectively.

RESULTS

Eighteen studies encompassing 1754 patients met eligibility criteria, including six RCTs, six quasi-experimental studies and six observational studies. Eight studies used wearables in Orthopaedics, seven in Stroke Medicine, two in Oncology and one in General Surgery. All six RCTs demonstrated that wearable-driven feedback increases physical activity. Step count was the most common measure of physical activity. Two RCTs in orthopaedics demonstrated non-inferiority of wearable self-directed rehabilitation compared with traditional physiotherapy, highlighting the potential of wearables as alternatives to traditional physiotherapy. All 12 non-randomised studies demonstrated the feasibility and acceptability of wearable-driven self-directed rehabilitation.

CONCLUSION

This review demonstrates that consumer-grade wearables can be used as adjuncts to traditional physiotherapy, and potentially as alternatives for self-directed rehabilitation of non-chronic conditions. Better designed studies, and larger RCTs, with a focus on economic evaluations are needed before a case can be made for their widespread adoption in healthcare settings.

PROSPERO REGISTRATION ID

CRD42023459567.

Validity and stability of the international physical activity questionnaire short-form for stroke survivors with preserved walking ability

BACKGROUND

Stroke survivors usually present sedentary lifestyles and fail to comply with the World Health Organization physical activity recommendations. Reliable, low-cost, and fast tools are needed to monitor physical activity levels in this population.

OBJECTIVES

This study aimed to evaluate the content and face validity, construct validity, and test-retest stability of the International Physical Activity Questionnaire Short-Form (IPAQ-SF) in stroke survivors.

METHODS

One hundred and twenty stroke survivors able to walk independently and preserved comprehension and communication abilities (61 ± 12 years, 35% female) were involved in this psychometric study. Participants completed the interview form of the IPAQ-SF via standardized videoconference twice, one week apart, under identical conditions, to evaluate test-retest stability. Correlations between IPAQ-SF and the caloric expenditure during the minutes of activity registered with the Fitbit Inspire 2 activity tracker wristband and 6-Metre Timed Walk (6MTW) were explored to assess construct validity.

RESULTS

The IPAQ-SF showed good content and face validity. "Moderate" to "strong" correlations were found with the Fitbit Inspire 2 (rho: 0.40 to 0.63), while "weak" to "moderate" correlations were found with the 6MTW (rho: 0.35 to 0.50). Test-retest stability was "moderate" to "excellent" (κ: 0.844 to 0.881; ICC: 0.533 to 0.917).

CONCLUSIONS

The IPAQ-SF demonstrated satisfactory content and construct validity, and stability in stroke survivors, supporting its clinical and research utility when the data collection is conducted by trained evaluators using a standardized interview protocol in large samples.

Improving patient outcomes in acute and subacute stroke using a wearable device-assisted rehabilitation system: a randomized controlled trial

OBJECTIVE

Multidisciplinary rehabilitation facilitates post-stroke functional recovery, but is associated with resource and accessibility barriers. This study evaluated the combination of a wearable device-assisted system (WEAR) and conventional therapy for post-stroke rehabilitation.

METHODS

This randomized, controlled, parallel group, clinical trial was conducted at two rehabilitation centers. A WEAR system was developed featuring sensors and application program-embedded smartphones. Stroke patients within 12 weeks of onset and modified Rankin Scale (mRS) scores of 2 to 4 were randomized into a wearable group (WG, WEAR + conventional rehabilitation) or control group (CG, conventional rehabilitation) for 90 days. The primary outcome was mRS score changes within 90 days.

RESULTS

Among 127 stroke patients enrolled (76 men [59.8%]; mean age: 57.5 years), 63 and 64 patients were randomized to WG and CG, respectively. Both groups showed significant improvements in mRS scores. Between-group repeated measures analysis adjusted for sex, age and number of rehabilitation sessions showed greater improvement in mRS scores within 90 days in the WG than in the CG (estimate: 0.73).

CONCLUSIONS

This combined WEAR and conventional rehabilitation approach may improve post-stroke functional recovery compared with conventional rehabilitation alone. The WEAR system permits remote monitoring and recording of rehabilitation in various settings.This clinical trial was retrospectively registered at www.clinicaltrials.gov with the Unique Identifier NCT04997408.

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.

Agreement of activity monitors for assessment of patients with sub-acute stroke in an inpatient rehabilitation facility

PURPOSE

Determine the level of agreement of three activity monitors compared with the gold standard (video review) on the activity level of patients with stroke.

METHODS

A prospective, observational, agreement study was performed on 47 individuals with sub-acute stroke in an inpatient rehabilitation facility. Data was collected during one physical therapy session. Individuals wore three device types; Actigraph (AG), Activpal (AP), and stepwatch activity monitor (SAM). Variables assessed were step counts for each limb (hemiparetic and non-hemiparetic) and percent time standing and other.

ANALYSIS

Results from the activity monitors were compared to the video review and assessed for agreement using the intraclass correlation coefficient (ICC) and accuracy of mean difference from video observation.

RESULTS

The step counts with the SAM on the non-hemiparetic limb had the highest ICC for step counts (ICC = 0.98, p < 0.001) and were overestimated with 21% accuracy. The SAM on the hemiparetic limb had 9.7% accuracy (ICC = 0.92, p < 0.001). For percent standing time all devices overestimated with poor reliability. For percent other activity time, the AP had the best accuracy and underestimated for both the hemiparetic limb (9.9% accuracy; ICC = 0.90, p < 0.001) and non-hemiparetic limb (8.3% accuracy; ICC = 0.84, p < 0.001).

CONCLUSIONS

The use of multiple devices may be warranted to capture an accurate understanding of activity levels in this population of individuals with sub-acute stroke. There are concerns with all monitors and clinicians and researchers should be aware of what measures they are wanting to understand about their population.

A Novel, Wearable Inertial Measurement Unit for Stroke Survivors: Validity, Acceptability, and Usability

OBJECTIVE

To establish the concurrent validity, acceptability, and sensor optimization of a consumer-grade, wearable, multi-sensor system to capture quantity and quality metrics of mobility and upper limb movements in stroke survivors.

DESIGN

Single-session, cross-sectional.

SETTING

Clinical research laboratory.

PARTICIPANTS

Thirty chronic stroke survivors (age 57 (10) years; 33% female) with mild to severe motor impairments participated.

INTERVENTIONS

Not Applicable.

MAIN OUTCOME MEASURES

Participants donned 5 sensors and performed standardized assessments of mobility and upper limb (UL) movement. True/false, positive/negative time in active movement for the UL were calculated and compared to criterion-standards using an accuracy rate. Bland-Altman plots and linear regression models were used to establish concurrent validity of UL movement counts, step counts, and stance time symmetry of MiGo against established criterion-standard measures. Acceptability and sensor optimization were assessed through an end-user survey and decision matrix.

RESULTS

Mobility metrics showed excellent association with criterion-standards for step counts (video: r=0.988, P<.001, IMU: r=0.921, P<.001) and stance-time symmetry (r=0.722, P<.001). In the UL, movement counts showed excellent to good agreement (paretic: r=0.849, P<.001, nonparetic: r=0.672, P<.001). Accuracy of active movement time was 85.2% (paretic) and 88.0% (nonparetic) UL. Most participants (63.3%) had difficulty donning/doffing the sensors. Acceptability was high (4.2/5).

CONCLUSIONS

The sensors demonstrated excellent concurrent validity for mobility metrics and UL movements of stroke survivors. Acceptability of the system was high, but alternative wristbands should be considered.

Validity and reliability of inertial measurement units for gait assessment within a post stroke population

BACKGROUND

The ability to objectively measure spatiotemporal metrics within individuals post-stroke is integral to plan appropriate intervention, track recovery, and ultimately improve efficacy of rehabilitation programs. Inertial measurement units (IMUs) provide a means to systematically collect gait-specific metrics that could not otherwise be obtained from clinical outcomes. However, the use of IMUs to measure spatiotemporal parameters in stroke survivors has yet to be validated. The purpose of this study is to determine the validity and reliability of IMU-recorded spatiotemporal gait metrics as compared to a motion capture camera system (MCCS) in individuals post-stroke.

METHODS

Participants (n = 23, M/F = 12/11, mean (SD) age = 50.2(11.1) spatiotemporal data were collected simultaneously from a MCCS and APDM Opal IMUs during a five-minute treadmill walking task at a self-selected speed. Criterion validity and test-retest reliability were assessed using Lin's concordance correlation coefficients (CCCs) and intraclass correlation coefficients (ICCs), respectively. Spatiotemporal values from MCCS and IMU were used to calculate gait asymmetry, and a t-test was used to assess the difference between asymmetry values.

RESULTS

There were fair-to-excellent agreement between IMU and MCCS of temporal parameters (CCC 0.56-0.98), excellent agreement of spatial parameters (CCC >0.90), and excellent test-retest reliability for all parameters (ICC >0.90).

CONCLUSIONS

Compared to motion capture, the APDM Opal IMUs produced accurate and reliable measures of spatiotemporal parameters. Findings support the use of IMUs to assess spatiotemporal parameters in individual's post-stroke.

Reliability and validity of remote Life Space Assessment: LSA in persons with chronic stroke

BACKGROUND

 StepWatch Activity Monitor (SAM) is used to measure the mobility of chronic hemiparetic patients and the Life Space Assessment (LSA) scale was developed to assess the displacement of hemiparetic patients in different contexts through self-reporting. Studies that apply the LSA remotely and correlate it with the number of steps measured by the SAM were not found.

OBJECTIVE

 To evaluate the measurement properties of the LSA applied remotely and to evaluate the correlation between the LSA scale score and the number of steps measured by the SAM in post-stroke chronic hemiparetic patients.

METHODS

 Nineteen patients participated in the study. The LSA scale was applied remotely and later, face to face. The SAM measured the steps taken by the participants over a period of three consecutive days. The correlation between the LSA and the SAM was performed using Pearson's correlation. The measurement properties calculated of remote LSA were the intraclass correlation coefficient (ICC), Cronbrach's alpha, standard error of measurement (SEM), and smallest real difference (SRD).

RESULTS

 The reproducibility of the LSA scale between remote and face-to-face applications was considered excellent with ICC = 0.85 (IC 95% 0.62-0.94); SEM = 8.4; SRD = 23.2, and Cronbach's alpha = 0.85. The correlation between SAM and LSA was positive, considered moderate (r = 0.51) and significant (p = 0.025).

CONCLUSION

 The LSA is a reproducible measure for post-stroke chronic hemiparetic patients even if applied remotely and can be used as a remote measurement for mobility in a real-world environment for people with chronic hemiparesis after stroke.

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.

Validity of the activPAL and ActiGraph for measuring sitting time and steps in hospitalised orthopaedic patients with altered weight bearing

PURPOSE

To determine the criterion validity of the activPAL and ActiGraph for measuring steps and sitting/sedentary time, compared to observation, in people hospitalised following orthopaedic lower limb injury who were weight bearing (WB) (i.e., walking) or non-weight bearing (NWB) (i.e., hopping).

MATERIALS AND METHODS

Participants wore an activPAL and ActiGraph on the hip/thigh/unaffected (UA)/affected ankle (AA) while completing bouts of walking and sitting. Lin's concordance correlation coefficient, Bland-Altman methods, and ratio of agreement were used to compare device-measured to observed (videoed) step count, sitting/sedentary time.

RESULTS

In 42 participants, the ActiGraph demonstrated excellent concordance with the observed step count when worn on the ankle (LCC 0.91-0.92) compared to the hip (LCC 0.56) in participants that were WB. The ActiGraph AA achieved the highest concordance (LCC 0.71) with observed steps in participants NWB. The activPAL had poor concordance with observed steps, particularly at slow gait speeds, in participants that were WB (LCC 0.38-0.46), however was less influenced by gait speed and had good concordance in NWB participants (LCC 0.52-0.69). The activPAL (LCC 0.79-0.88) and ActiGraph UA (LCC 0.94) showed excellent concordance with observed sitting and sedentary time, respectively.

CONCLUSIONS

The ActiGraph worn at the ankle provided the most valid measure of steps in people who are WB and NWB following orthopaedic injury, while the activPAL was best for measuring sitting time.Implications for rehabilitationTo accurately measure both steps and sitting time in people with lower limb orthopaedic injuries, a combination of activity monitors should be used (i.e., ActiGraph for steps, activPAL for sitting time).The ActiGraph device when worn on the ankle demonstrated the strongest agreement with observed step count in people who were weight bearing and non-weight bearing.Caution is needed when using thigh- or hip-worn devices in people who walk slowly.

How should we measure physical activity after stroke? An international consensus

BACKGROUND

Physical activity is important for secondary stroke prevention. Currently, there is inconsistency of outcomes and tools used to measure physical activity following stroke.

AIM

To establish internationally agreed recommendations to enable consistent measurement of post-stroke physical activity.

METHODS

Stroke survivors and carers were surveyed online once regarding what is important in physical activity measurement. Three survey rounds with expert stroke researchers and clinicians were conducted using Keeney's Value-Focused Thinking Methodology. Survey 1 identified physical activity tools, outcomes, and measurement considerations which were ranked in Survey 2. Consensus recommendations on tools were then formulated by the consensus group based on survey responses. In Survey 3, participants reviewed ranked results and evidence gathered to determine their support for consensus recommendations.

RESULTS

Twenty-five stroke survivors, 5 carers, 18 researchers, and 17 clinicians from 16 countries participated. Time in moderate-vigorous physical activity and step count were identified as the most important outcomes to measure. Key measurement considerations included the ability to measure across frequency, intensity, duration domains in real-world settings; user-friendliness, comfort, and ability to detect changes. Consensus recommendations included using the Actigraph, Actical, and Activ8 devices for physical activity intensity; ActivPAL for duration and Step Activity Monitor for frequency; and the IPAQ and PASE questionnaires. Survey 3 indicated 100% support for device and 96% for questionnaire recommendations.

CONCLUSIONS

These consensus recommendations can guide selection of physical activity measurement tools and outcomes. Tool selection will depend on measurement purpose, user-knowledge, and resources. Comprehensive measurement requires the use of devices and questionnaires.

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.

Multidimensional analysis of sedentary behaviour and participation in Spanish stroke survivors (Part&Sed-Stroke): a protocol for a longitudinal multicentre study

INTRODUCTION

Stroke survivors usually experience long-lasting functional, emotional and social consequences that might contribute to sedentary behaviour and participation restrictions, which are important targets to address during rehabilitation. However, the trajectory and inter-relationship between these factors are unknown.

METHODS AND ANALYSIS

Part&Sed is a research project based on an observational study design with 6 and 12 months of follow-ups in stroke survivors. In addition, a qualitative analysis of the impact of the stroke on the stroke survivor, validation of the Satisfaction with Daily Occupation-Occupational Balance assessment tool and analysis of the reliability of the Fitbit Inspire 2 activity tracker wristband will be carried out. Participants will be chronic stroke survivors with independent walking capacity. Sociodemographic and clinical data, physical activity, ambulation, sleep, quality of life, anxiety and depression, community participation, and occupational satisfaction and balance, as well as data provided by the activity tracker wristband, will be collected. In addition, if the participant has a primary caregiver, the caregiver will also be monitored. A minimum of 130 participants will be recruited to conduct a random-effects multiple regression model. Mixed models for repeated measures will assess the variation over time of the different variables associated with participation and sedentary behaviour. Psychometric properties (eg, internal consistency, construct validity, test-retest reliability) of the Satisfaction with Daily Occupation-Occupational Balance will be determined. Additionally, intraclass correlation coefficients and minimum detectable change will be calculated to assess intrasubject reliability of physical activity and sleep parameters recorded by the Fitbit Inspire 2. The qualitative analysis process will be carried out using the analysis proposed by Giorgi.

ETHICS AND DISSEMINATION

The study received ethical approval from the Spanish Regional Ethics Committee 'Comité de Ética de la Investigación de la Comunidad de Aragón' (PI21/333). The results will be made available via peer-reviewed publications, international conferences and official channels.

Feasibility and efficacy of an activity-monitoring approach using pedometer in patients undergoing subacute rehabilitation: A pilot study

Wearable devices for the quantification of walking have recently been adopted for gait rehabilitation. To apply this method in subacute rehabilitation settings, this approach must be effective in these populations and implemented as a feasible method in terms of adherence and safety, especially the risk of falling. This study aimed to investigate the feasibility and efficacy of an activity monitoring approach in subacute rehabilitation using a commercially available pedometer validated with slow walking. This randomized controlled study with blinded assessors recruited 29 patients admitted to a rehabilitation ward. The participants were randomly assigned to either the feedback (intervention) or the no-feedback (control) group. Participants in both groups received at least 120 min of therapy sessions every day for 6 or 7 days per week while wearing pedometers on their unaffected ankles from the day they were permitted to walk independently till discharge. Only participants in the feedback group received weekly encouragement and the next goals. The primary outcome was the change in the 6-minute walking distance (Δ6MD). Feasibility (percentage of pedometer data acquisition days in the total observational period and the number of falls) and other efficacy outcomes (step counts, gait speed, 30-seconds chair stand test, Berg Balance Scale, and Timed Up and Go Test) were also evaluated. Regarding feasibility outcomes, the data acquisition rate was 94.1% and the number of falls during the observation period was one in the feedback group. Regarding efficacy outcomes, Δ6MD was not significantly greater in the feedback group [mean (standard deviation): 79.1 (51.7) m] than in the no-feedback group [86.1 (65.4) m] (p = 0.774) and the other five secondary outcomes showed no between-group difference. Considering the large number of steps per day in both groups [6,912 (4,751) and 5,600 (5,108) steps in the feedback and no-feedback group, respectively], the effect of the intended intervention might have been masked by the effect of simply wearing pedometers in the control group. This study revealed that the activity monitoring approach using an ankle-worn pedometer was practical in terms of adherence and safety. Further clinical trials are required to elucidate ways to effectively use wearable devices in subacute rehabilitation.

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.

Walk the Talk: Current Evidence for Walking Recovery After Stroke, Future Pathways and a Mission for Research and Clinical Practice

Achieving safe, independent, and efficient walking is a top priority for stroke survivors to enable quality of life and future health. This narrative review explores the state of the science in walking recovery after stroke and potential for development. The importance of targeting walking capacity and performance is explored in relation to individual stroke survivor gait recovery, applying a common language, measurement, classification, prediction, current and future intervention development, and health care delivery. Findings are summarized in a model of current and future stroke walking recovery research and a mission statement is set for researchers and clinicians to drive the field forward to improve the lives of stroke survivors and their carers.

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.

mHealth technologies used to capture walking and arm use behavior in adult stroke survivors: a scoping review beyond measurement properties

PURPOSE

We aimed to provide a critical review of measurement properties of mHealth technologies used for stroke survivors to measure the amount and intensity of functional skills, and to identify facilitators and barriers toward adoption in research and clinical practice.

MATERIALS AND METHODS

Using Arksey and O'Malley's framework, two independent reviewers determined eligibility and performed data extraction. We conducted an online consultation survey exercise with 37 experts.

RESULTS

Sixty-four out of 1380 studies were included. A majority reported on lower limb behavior (n = 32), primarily step count (n = 21). Seventeen studies reported on arm-hand behaviors. Twenty-two studies reported metrics of intensity, 10 reported on energy expenditure. Reliability and validity were the most frequently reported properties, both for commercial and non-commercial devices. Facilitators and barriers included: resource costs, technical aspects, perceived usability, and ecological legitimacy. Two additional categories emerged from the survey: safety and knowledge, attitude, and clinical skill.

CONCLUSIONS

This provides an initial foundation for a field experiencing rapid growth, new opportunities and the promise that mHealth technologies affords for envisioning a better future for stroke survivors. We synthesized findings into a set of recommendations for clinicians and clinician-scientists about how best to choose mHealth technologies for one's individual objective.Implications for RehabilitationRehabilitation professionals are encouraged to consider the measurement properties of those technologies that are used to monitor functional locomotor and object-interaction skills in the stroke survivors they serve.Multi-modal knowledge translation strategies (research synthesis, educational courses or videos, mentorship from experts, etc.) are available to rehabilitation professionals to improve knowledge, attitude, and skills pertaining to mHealth technologies.Consider the selection of commercially available devices that are proven to be valid, reliable, accurate, and responsive to the targeted clinical population.Consider usability and privacy, confidentiality and safety when choosing a specific device or smartphone application.

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.

Influence of upper limb activity on the step count and accuracy of sleep time of a wristband-type physical activity tracker

Background

A wristband-type consumer physical activity tracker (PAT) is commonly used in rehabilitation to assess an individual’s physical activity. However, under the free-living setting, the wristband-type PAT tends to overestimate step counts when compared with the research-standard criterion. Also, daily rhythm characteristics, such as sleep time, are difficult to monitor accurately based solely on self-reporting.

Purpose

To identify the conditions measured as step counts by a wristband-type consumer PAT when using the upper limbs in daily living, and the measurement accuracy of the sleeping time estimated from the wristband-type PAT.

Methods

Forty participants (20 females, mean age 32.65 ± 9.52 years) were enrolled in two experiments in this study. In Experiment 1, we measured the influence of upper limbs activity (movement speed and distance) on step counts of wristband-type and waist holder-type PAT in two upper limb tasks. In Experiment 2, we verified the measurement accuracy of two sleep times by wristband-type PAT using a self-reported survey for 3 days.

Results

The results of Experiment 1 revealed that the step counts using wristband-type PAT were influenced by upper limbs activity depending on movement distance (F (1, 19) = 31.705, p < 0.001) but not speed (F (1, 19) = 2.669, p < 0.117). Whereas, there was no relationship between step counts and upper limb activity in waist holder-type PAT. The results of Experiment 2 showed that the sleep times of wristband-type and self-report had a strong correlation (coefficient value = 0.93, p < 0.001).

Conclusions

This PAT is useful for capturing changes in the amount of physical activity and the daily rhythm within the individual. It can be expected to be used for rehabilitation support centered on upper limb activity and daily rhythm.

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.

Step Monitor Accuracy During PostStroke Physical Therapy and Simulated Activities

Introduction/Purpose

The amount of stepping activity during rehabilitation post-stroke can predict walking outcomes, although the most accurate methods to evaluate stepping activity are uncertain with conflicting findings on available stepping monitors during walking assessments. Rehabilitation sessions also include non-stepping activities and the ability of activity monitors to differentiate these activities from stepping is unclear. The objective of this study was to examine the accuracy of different activity monitors worn by individuals post-stroke with variable walking speeds during clinical physical therapy (PT) and research interventions focused on walking.

Methods

In Part I, 28 participants post-stroke wore a StepWatch, ActiGraph with and without a Low Frequency Extension (LFE) filter, and Fitbit on paretic and non-paretic distal shanks at or above the ankle during clinical PT or research interventions with steps simultaneously hand counted. Mean absolute percent errors were compared between limbs and tasks performed. In Part II, 12 healthy adults completed 8 walking and 9 non-walking tasks observed during clinical PT or research. Data were descriptively analyzed and used to assist interpretation of Part I results.

Results

Part I results indicate most devices did not demonstrate an optimal limb configuration during research sessions focused on walking, with larger errors during clinical PT on the non-paretic limb. Using the limb that minimized errors for each device, the StepWatch had smaller errors than the ActiGraph and Fitbit (p<0.01), particularly in those who walked < 0.8 m/s. Conversely, errors from the ActiGraph-LFE demonstrated inconsistent differences in step counts between Fitbit and ActiGraph. Part II results indicate that errors observed during different stepping and non-stepping activities were often device-specific, with non-stepping tasks frequently detected as stepping.

Conclusions

The StepWatch and ActiGraph-LFE had smaller errors than the Fitbit or ActiGraph, with greater errors in those walking at slower speeds. Inclusion of non-stepping activities affected step counts and should be considered when measuring stepping activity in individuals post-stroke to predict locomotor outcomes following rehabilitation.

Step Number and Aerobic Minute Exercise Prescription and Progression in Stroke: A Roadmap

Background

While higher therapeutic intensity improves motor recovery after stroke, translating findings from successful studies is challenging without clear exercise intensity targets. We show in the DOSE trial¹ more than double the steps and aerobic minutes within a session can be achieved compared with usual care and translates to improved long-term walking outcomes.

Objective

We modeled data from this successful higher intensity multi-site RCT to develop targets for prescribing and progressing exercise for varying levels of walking impairment after stroke.

Methods

In twenty-five individuals in inpatient rehabilitation, twenty sessions were monitored for a total of 500 one-hour physical therapy sessions. For the 500 sessions, step number and aerobic minute progression were modeled using linear mixed effects regression. Using formulas from the linear mixed effects regression, targets were calculated.

Results

The model for step number included session number and baseline walking speed, and for aerobic minutes, session number and age. For steps, there was an increase of 73 steps per session. With baseline walking speed, for every 0.1 m/s increase, a corresponding increase of 302 steps was predicted. For aerobic minutes, there was an increase of .56 minutes of aerobic activity (ie, 34 seconds) per session. For every year increase in age, a decrease of .39 minutes (ie, 23 seconds) was predicted.

Conclusions

Using data associated with better walking outcomes, we provide step number and aerobic minute targets that future studies can cross-validate. As walking speed and age are collected at admission, these models allow for uptake of routine measurement of therapeutic intensity.

Registration: www.clinicaltrials.gov; NCT01915368.

Effectiveness of Self-Monitoring Approach Using Fitness Trackers to Improve Walking Ability in Rehabilitation Settings: A Systematic Review

Background: A self-monitoring approach utilizing fitness trackers that provide feedback regarding physical activities has been recently applied to rehabilitation patients to promote voluntary walking activities. Although this approach has been proven to increase physical activity, it is uncertain whether the intervention improves walking ability. Aim: This review investigated whether the additional self-monitoring approach using activity trackers would improve walking ability in any type of rehabilitation setting. Methods: A systematic search was performed in four databases [PubMed (MEDLINE), The Cochrane Library, SPORTDiscus, and Cumulative Index to Nursing and Allied Health Literature] to identify studies that examined the self-monitoring approach combined with rehabilitative intervention vs. the same rehabilitative intervention only in participants with any unhealthy conditions. Two review authors independently assessed the eligibility of all the retrieved English literature published from 2009 to 2019, then discussed the final inclusion. The risk of bias was assessed referring to the criteria of the Cochrane Risk of Bias tool. The key findings were synthesized using narrative synthesis. In addition, a quantitative synthesis was conducted when more than two studies investigating the same disease were identified. Results: Eleven randomized controlled trials satisfied the eligibility criteria, nine of which had a lower risk of bias. The types of diseases included stroke, chronic obstructive pulmonary disease (COPD), cancer, Parkinson's disease, hemophilia, peripheral artery disease, post-total knee arthroplasty, and geriatric rehabilitation. Eight studies reported measures of walking endurance and four reported measures of gait speed. In the quantitative synthesis of two studies investigating COPD, there was a significant between-group difference in terms of changes in the 6-min walking distance from the baseline, which was favorable to the additional self-monitoring intervention group (mean difference: 13.1 m; 95% confidence interval, 1.8-24.5; 2 studies, 124 participants; p = 0.02; I 2 = 0%). Other available data revealed no consistent evidence regarding effectiveness of the intervention. Conclusions: The findings indicate that there is little evidence suggesting the effectiveness of the self-monitoring approach in improving walking ability in rehabilitation settings. However, a weak recommendation for patients with stable COPD was implicated in the quantitative synthesis. Further research would be required to explore the best indications for this self-monitoring approach. Systematic Review Registration: CRD 42020157695.

Using Intervention Mapping to develop and implement a home-based sedentary behavior change intervention after stroke

Prolonged sedentary behavior is a problem immediately following a stroke and it persists from the acute to chronic phases of recovery. Frequently interrupting sedentary time might help to mitigate the associated detrimental health consequences. The purpose of this article was to describe the process followed in the systematic development and implementation of a theory- and home-based sedentary behavior change intervention for people with stroke. The Intervention Mapping protocol was used to design, develop and implement a STand Up Frequently From Stroke (STUFFS) program aimed at reducing prolonged sedentary behavior after stroke. A qualitative inquiry with people with stroke (n = 13) informed the development of the intervention. The systematic approach followed in the development process including the theoretical basis, content development, and evaluation planning were described. Program reception was assessed using thematic content analysis of exit-interview data (n = 25). The 8-weeks STUFFS intervention is a home-based sedentary behavior change program that provides education about the risks of prolonged sedentary behavior and facilitates behavior change, including the use of motivational tools to empower people with stroke to reduce sedentary behavior. The program was well-received and acceptable to people with stroke (89% satisfaction). This article provides information that furthers knowledge about theory-based strategies to reduce sedentary behavior in the home environment after stroke and facilitates implementation of this type of intervention. Trial registration: This study is registered at www.clinicaltrials.gov (No. NCT02980744).

Telehealth coaching to improve self-management for secondary prevention after stroke: A randomized controlled trial of Stroke Coach

BACKGROUND

Stroke Coach is a lifestyle coaching telehealth program to improve self-management of stroke risk factors.

AIMS

To examine the efficacy of Stroke Coach on lifestyle behavior and risk factor control among community-living stroke survivors within one-year post stroke.

METHODS

Participants were randomized to Stroke Coach or an attention control Memory Training group. Lifestyle behavior was measured using the Health Promoting Lifestyle Profile II. Secondary outcomes included specific behavioral and cardiometabolic risk factors, health-related quality of life (HRQoL), cognitive status, and depressive symptoms. Measurements were taken at baseline, post-intervention (6 months), and retention (12 month). Linear mixed-effects models were used to test the study hypotheses (p < 0.05). All analyses were intention-to-treat.

RESULTS

The mean age of the Stroke Coach (n = 64) and Memory Training (n = 62) groups was 67.2 and 69.1 years, respectively. The majority of participants (n = 100) had mild stroke (modified Rankin Scale = 1 or 2), were active, with controlled blood pressure (mean = 129/79 mmHg) at baseline. At post-intervention, there were no significant differences in lifestyle (b = -2.87; 95%CI - 8.03 to 2.29; p = 0.28). Glucose control, as measured by HbA1c (b = 0.17; 95%CI 0.17 to 0.32; p = 0.03), and HRQoL, measured using SF-36 Physical Component Summary (b = -3.05; 95%CI -5.88 to -0.21; p = 0.04), were significantly improved in Stroke Coach compared to Memory Training, and the improvements were maintained at retention.

CONCLUSION

Stroke Coach did not improve lifestyle behavior; however, there were improvements to HbA1c and HRQoL among community-living stroke survivors with mild stroke-related disability. (ClinicalTrials.gov identifier: NCT02207023).

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.

Higher Doses Improve Walking Recovery During Stroke Inpatient Rehabilitation

BACKGROUND AND PURPOSE

We investigated the effect of higher therapeutic exercise doses on walking during inpatient rehabilitation, typically commencing 1 to 4 weeks poststroke.

METHODS

This phase II, blinded-assessor, randomized controlled trial recruited from 6 Canadian inpatient rehabilitation units, between 2014 and 2018. Subjects (n=75; 25/group) were randomized into: control (usual care) physical therapy: typically, 1 hour, 5 days/week; Determining Optimal Post-Stroke Exercise (DOSE1): 1 hour, 5 days/week, more than double the intensity of Control (based on aerobic minutes and walking steps); and DOSE2: 2 hours, 5 days/week, more than quadruple the intensity of Control, each for 4 weeks duration. The primary outcome, walking endurance at completion of the 4-week intervention (post-evaluation), was compared across these groups using linear regression. Secondary outcomes at post-evaluation, and longitudinal outcomes at 6 and 12-month evaluations, were also analyzed.

RESULTS

Both DOSE1 (mean change 61 m [95% CI, 9-113], P=0.02) and DOSE2 (mean change 58 m, 6-110, P=0.03) demonstrated greater walking endurance compared with Control at the post-evaluation. Significant improvements were also observed with DOSE2 in gait speed (5-m walk), and both DOSE groups in quality of life (EQ-5D-5 L) compared with Control. Longitudinal analyses revealed that improvements in walking endurance from the DOSE intervention were retained during the 1-year follow-up period over usual care.

CONCLUSIONS

This study provides the first preliminary evidence that patients with stroke can improve their walking recovery and quality of life with higher doses of aerobic and stepping activity within a critical time period for neurological recovery. Furthermore, walking endurance benefits achieved from a 4-week intervention are retained over the first-year poststroke. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01915368.

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.

Perspectives on the prospective development of stroke-specific lower extremity wearable monitoring technology: a qualitative focus group study with physical therapists and individuals with stroke

BACKGROUND

Wearable activity monitors that track step count can increase the wearer's physical activity and motivation but are infrequently designed for the slower gait speed and compensatory patterns after stroke. New and available technology may allow for the design of stroke-specific wearable monitoring devices, capable of detecting more than just step counts, which may enhance how rehabilitation is delivered. The objective of this study was to identify important considerations in the development of stroke-specific lower extremity wearable monitoring technology for rehabilitation, from the perspective of physical therapists and individuals with stroke.

METHODS

A qualitative research design with focus groups was used to collect data. Five focus groups were conducted, audio recorded, and transcribed verbatim. Data were analyzed using content analysis to generate overarching categories representing the stakeholder considerations for the development of stroke-specific wearable monitor technology for the lower extremity.

RESULTS

A total of 17 physical therapists took part in four focus group discussions and three individuals with stroke participated in the fifth focus group. Our analysis identified four main categories for consideration: 1) 'Variability' described the heterogeneity of patient presentation, therapy approaches, and therapeutic goals that are taken into account for stroke rehabilitation; 2) 'Context of use' described the different settings and purposes for which stakeholders could foresee employing stroke-specific wearable technology; 3) 'Crucial design features' identified the measures, functions, and device characteristics that should be considered for incorporation into prospective technology to enhance uptake; and 4) 'Barriers to adopting technology' highlighted challenges, including personal attitudes and design flaws, that may limit the integration of current and future wearable monitoring technology into clinical practice.

CONCLUSIONS

The findings from this qualitative study suggest that the development of stroke-specific lower extremity wearable monitoring technology is viewed positively by physical therapists and individuals with stroke. While a single, specific device or function may not accommodate all the variable needs of therapists and their clients, it was agreed that wearable monitoring technology could enhance how physical therapists assess and treat their clients. Future wearable devices should be developed in consideration of the highlighted design features and potential barriers for uptake.

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.

Quantifying dosage of physical therapy using lower body kinematics: a longitudinal pilot study on early post-stroke individuals

Background

While therapy is an important part of the recovery process, there is a lack of quantitative data detailing the “dosage” of therapy received due to the limitations on in/outpatient accessibility and mobility. Advances in wearable sensor technology have allowed us to obtain an unprecedented glimpse into joint-level kinematics in an unobtrusive manner. The objective of this observational longitudinal pilot study was to evaluate the relations between lower body joint kinematics during therapy and functional gait recovery over the first three months after stroke.

Methods

Six individuals with subacute stroke (< 1 month) were monitored for a total of 59 one-hour physical therapy sessions including gait and non-gait activities. Participants donned a heart rate monitor and an inertial motion capture system to measure full lower body joint kinematics during each therapy session. Linear mixed regression models were used to examine relations between functional gait recovery (speed) and activity features including total joint displacements, defined as amount of motion (AoM), step number, change in heart rate (∆HR), and types of tasks performed.

Results

All activity features including AoM, step number, types of tasks performed (all p < 0.01), and ∆HR (p < 0.05) showed strong associations with gait speed. However, AoM (R² = 32.1%) revealed the greatest explained variance followed by step number (R² = 14.1%), types of tasks performed (R² = 8.0%) and ∆HR (R² = 5.8%). These relations included both gait and non-gait tasks. Contrary to our expectations, we did not observe a greater relation of functional recovery to motion in the impaired limb (R² = 27.8%) compared to the unimpaired limb (R² = 32.9%).

Conclusions

This proof-of-concept study shows that recording joint kinematics during gait therapy longitudinally after stroke is feasible and yields important information for the recovery process. These initial results suggest that compared to step number, more holistic outcome measures such as joint motions may be more informative and help elucidate the dosage of therapy.

Activity Tracker-Based Metrics as Digital Markers of Cardiometabolic Health in Working Adults: Cross-Sectional Study

Background

Greater adoption of wearable devices with multiple sensors may enhance personalized health monitoring, facilitate early detection of some diseases, and further scale up population health screening. However, few studies have explored the utility of data from wearable fitness trackers in cardiovascular and metabolic disease risk prediction.

Objective

This study aimed to investigate the associations between a range of activity metrics derived from a wearable consumer-grade fitness tracker and major modifiable biomarkers of cardiometabolic disease in a working-age population.

Methods

This was a cross-sectional study of 83 working adults. Participants wore Fitbit Charge 2 for 21 consecutive days and went through a health assessment, including fasting blood tests. The following clinical biomarkers were collected: BMI, waist circumference, waist-to-hip ratio, blood pressure, triglycerides (TGs), high-density lipoprotein (HDL) and low-density lipoprotein cholesterol, and blood glucose. We used a range of wearable-derived metrics based on steps, heart rate (HR), and energy expenditure, including measures of stability of circadian activity rhythms, sedentary time, and time spent at various intensities of physical activity. Spearman rank correlation was used for preliminary analysis. Multiple linear regression adjusted for potential confounders was used to determine the extent to which each metric of activity was associated with continuous clinical biomarkers. In addition, pairwise multiple regression was used to investigate the significance and mutual dependence of activity metrics when two or more of them had significant association with the same outcome from the previous step of the analysis.

Results

The participants were predominantly middle aged (mean age 44.3 years, SD 12), Chinese (62/83, 75%), and male (64/83, 77%). Blood biomarkers of cardiometabolic disease (HDL cholesterol and TGs) were significantly associated with steps-based activity metrics independent of age, gender, ethnicity, education, and shift work, whereas body composition biomarkers (BMI, waist circumference, and waist-to-hip ratio) were significantly associated with energy expenditure–based and HR-based metrics when adjusted for the same confounders. Steps-based interdaily stability of circadian activity rhythm was strongly associated with HDL (beta=5.4 per 10% change; 95% CI 1.8 to 9.0; P=.005) and TG (beta=−27.7 per 10% change; 95% CI −48.4 to −7.0; P=.01). Average daily steps were negatively associated with TG (beta=−6.8 per 1000 steps; 95% CI −13.0 to −0.6; P=.04). The difference between average HR and resting HR was significantly associated with BMI (beta=−.5; 95% CI −1.0 to −0.1; P=.01) and waist circumference (beta=−1.3; 95% CI −2.4 to −0.2; P=.03).

Conclusions

Wearable consumer-grade fitness trackers can provide acceptably accurate and meaningful information, which might be used in the risk prediction of cardiometabolic disease. Our results showed the beneficial effects of stable daily patterns of locomotor activity for cardiometabolic health. Study findings should be further replicated with larger population studies.

An investigation into the validity and reliability of mHealth devices for counting steps in chronic stroke survivors

Objective:

To investigate the validity and test–retest reliability of mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit Ultra) to estimate the number of steps in individuals after chronic stroke and to compare whether the measurement of the number of steps is affected by their location on the body (paretic and non-paretic side).

Design:

Observational study with repeated measures.

Setting:

University laboratory.

Subjects:

Fifty-five community-dwelling individuals with chronic stroke.

Intervention:

Not applicable.

Main measures:

The number of steps was measured using mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit Ultra), and compared against criterion-standard measure during the Two-Minute Walk Test using habitual speed.

Results:

Our sample was 54.5% men, mean age of 62.5 years (SD 14.9) with a chronicity after stroke of 66.8 months (SD 55.9). There was a statistically significant association between the actual number of steps and those estimated by the Google Fit, STEPZ Iphone and Android applications, Pacer iphone and Android, and Fitbit Ultra (0.30 ⩽  r ⩾ 0.80). The Pacer iphone application demonstrated the highest reliability coefficient (ICC(2,1) = 0.80; P < 0.001). There were no statistically significant differences in device measurements that depended on body location.

Conclusions:

mHealth devices (Pacer–iphone, Fitbit Ultra, Google Fit, and Pacer–Android) are valid and reliable for step counting in chronic stroke survivors. Body location (paretic or non-paretic side) does not affect validity or reliability of the step count metric.

Use of Activity Trackers in Orthopaedics

Activity trackers are relatively inexpensive, easily available, and widely used by consumers. There has been increased interest in healthcare practice and research to use activity tracker data. Continuous collection of health variables including step count, heart rate, and calorimetry provides researchers and clinicians data to monitor patients after an intervention and/or encourage physical activity. The purpose of this article is to review the current use of activity trackers in orthopaedics and discuss potential applications in clinical research.

Two Days of Measurement Provides Reliable Estimates of Physical Activity Poststroke: An Observational Study

OBJECTIVE

The aim of this study was to determine the duration of physical activity (PA) monitoring required for reliable measurements following stroke.

DESIGN

Single-center, prospective, observational study.

SETTING

PA was measured in a community setting.

PARTICIPANTS

Adults (N=70) poststroke.

MAIN OUTCOME MEASURES

The SenseWear armband was used to monitor PA for 5 days (≥10 hours wear per day).

DATA ANALYSIS

Variance among 2, 3, 4, and 5 days of consecutive measurements for PA variables was examined using intraclass correlation coefficients (ICCs). The minimum number of days to achieve acceptable reliability (ICC ≥0.8) was calculated. Differences between weekdays and weekend days were investigated using paired t tests and Wilcoxon signed rank tests.

RESULTS

Two days of measurement was sufficient to achieve an ICC ≥0.8 for daily averages of total energy expenditure, step count, and time spent sedentary (≤1.5 metabolic equivalent tasks [METs]) and in light (1.5-3 METs) and moderate- to vigorous-intensity (>3 METs) PA. At least 3 days were required to achieve an ICC ≥0.8 when investigating the number of and time spent in bouts (≥10 minutes) of moderate to vigorous PA and sedentary behavior. Participants took significantly more steps (P=.03) and spent more time in light PA (P=.03) on weekdays than weekends.

CONCLUSION

Following stroke, 2 days of measurement appears sufficient to represent habitual PA for many simple variables. Three or more days may be necessary for reliable estimates of bouts of PA and sedentary behavior. Consistent inclusion or exclusion of a weekend day is recommended for measuring step count and light PA. Short periods of monitoring provide reliable PA information and may make PA measurement more feasible in the clinical setting.