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.

Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery-A Narrative Review

The use of wearable technology is steadily increasing. In orthopedic trauma surgery, where the musculoskeletal system is directly affected, focus has been directed towards assessing aspects of physical functioning, activity behavior, and mobility/disability. This includes sensors and algorithms to monitor real-world walking speed, daily step counts, ground reaction forces, or range of motion. Several specific reviews have focused on this domain. In other medical fields, wearable sensors and algorithms to monitor digital biometrics have been used with a focus on domain-specific health aspects such as heart rate, sleep, blood oxygen saturation, or fall risk. This review explores the most common clinical and research use cases of wearable sensors in other medical domains and, from it, derives suggestions for the meaningful transfer and application in an orthopedic trauma context.

Behavior Change Approaches in Digital Technology-Based Physical Rehabilitation Interventions Following Stroke: Scoping Review

BACKGROUND

Digital health technologies (DHTs) are increasingly used in physical stroke rehabilitation to support individuals in successfully engaging with the frequent, intensive, and lengthy activities required to optimize recovery. Despite this, little is known about behavior change within these interventions.

OBJECTIVE

This scoping review aimed to identify if and how behavior change approaches (ie, theories, models, frameworks, and techniques to influence behavior) are incorporated within physical stroke rehabilitation interventions that include a DHT.

METHODS

Databases (Embase, MEDLINE, PsycINFO, CINAHL, Cochrane Library, and AMED) were searched using keywords relating to behavior change, DHT, physical rehabilitation, and stroke. The results were independently screened by 2 reviewers. Sources were included if they reported a completed primary research study in which a behavior change approach could be identified within a physical stroke rehabilitation intervention that included a DHT. Data, including the study design, DHT used, and behavior change approaches, were charted. Specific behavior change techniques were coded to the behavior change technique taxonomy version 1 (BCTTv1).

RESULTS

From a total of 1973 identified sources, 103 (5%) studies were included for data charting. The most common reason for exclusion at full-text screening was the absence of an explicit approach to behavior change (165/245, 67%). Almost half (45/103, 44%) of the included studies were described as pilot or feasibility studies. Virtual reality was the most frequently identified DHT type (58/103, 56%), and almost two-thirds (65/103, 63%) of studies focused on upper limb rehabilitation. Only a limited number of studies (18/103, 17%) included a theory, model, or framework for behavior change. The most frequently used BCTTv1 clusters were feedback and monitoring (88/103, 85%), reward and threat (56/103, 54%), goals and planning (33/103, 32%), and shaping knowledge (33/103, 32%). Relationships between feedback and monitoring and reward and threat were identified using a relationship map, with prominent use of both of these clusters in interventions that included virtual reality.

CONCLUSIONS

Despite an assumption that DHTs can promote engagement in rehabilitation, this scoping review demonstrates that very few studies of physical stroke rehabilitation that include a DHT overtly used any form of behavior change approach. From those studies that did consider behavior change, most did not report a robust underpinning theory. Future development and research need to explicitly articulate how including DHTs within an intervention may support the behavior change required for optimal engagement in physical rehabilitation following stroke, as well as establish their effectiveness. This understanding is likely to support the realization of the transformative potential of DHTs in stroke rehabilitation.

Effects of self-monitoring using an accelerometer on physical activity of older people with long-term care insurance in Japan: a randomized controlled trial

PURPOSE

This study aimed to investigate the effects of a self-monitoring intervention to promote an increase in physical activity, as measured by step count, and reduce sedentary behavior in older people covered by the long-term care insurance system (LTCI) in Japan.

METHODS

This was a randomized controlled trial conducted at a daycare center from October 2022 to January 2023. Fifty-two older adults with LTCI who were able to walk with or without aids were assigned to an intervention (n = 26) group and control (n = 26) group. During the 5-week follow-up period, the intervention group received education on physical activity and self-monitoring such as goal setting, self-management and feedback. The primary outcome was step count, and the secondary outcome was sedentary behavior.

RESULTS

Participants who completed the study to the end of the 5-week follow-up and drop-out participants for whom outcome data were available were included in the final analysis of 57 participants, n = 24 (79.8 ± 8.8 years, male 25.5%) in the intervention group and n = 23 (82.5 ± 8.5 years, male 39.1%) in the control group. Comparisons between the two groups at baseline showed no significant differences. In the results of a two-way mixed analysis of variance (ANOVA) including 2 (group: control, intervention) × 2 (term: baseline, 5-week follow-up) factors, an interaction was observed in the number of steps, sedentary behavior, and light physical activity (p < 0.05).

CONCLUSION

Self-monitoring of physical activity using an accelerometer may be effective in increasing the number of steps and light physical activity and in reducing sedentary behavior in older people with LTCI.

CLINICAL TRIAL REGISTRATION

UMIN000052044, registered on 2023/08/29.

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.

Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data

Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices. Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies. Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman's rank correlation coefficient (R) of 0.56 and -0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics. Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.

Effects of Acute Phase Intensive Physical Activity (ACTIVE-PA) Monitoring and Education for Cardiac Patients: Pilot Study of a Randomized Controlled Trial

BACKGROUND

Although physical activity (PA) decreases dramatically during hospitalization, an effective intervention method has not yet been established for this issue. We recently developed a multiperson PA monitoring system using information and communication technology (ICT) that can provide appropriate management and feedback about PA at the bedside or during rehabilitation. This ICT-based PA monitoring system can store accelerometer data on a tablet device within a few seconds and automatically display a graphical representation of activity trends during hospitalization.

OBJECTIVE

This randomized pilot study aims to estimate the feasibility and effect size of an educational PA intervention using our ICT monitoring system for in-hospital patients undergoing cardiac rehabilitation.

METHODS

A total of 41 patients (median age 70 years; 24 men) undergoing inpatient cardiac rehabilitation were randomly assigned to 2 groups as follows: wearing an accelerometer only (control) and using both an accelerometer and an ICT-based PA monitoring system. Patients assigned to the ICT group were instructed to gradually increase their step counts according to their conditions. Adherence to wearing the accelerometer was defined as having enough wear records for at least 2 days to allow for adequate analysis during the lending period. An analysis of covariance was performed to compare the change in average step count during hospitalization as a primary outcome and the 6-minute walking distance at discharge.

RESULTS

The median duration of wearing the accelerometer was 4 days in the ICT group and 6 days in the control group. Adherence was 100% (n=22) in the ICT group but 83% (n=20) in the control group. The ICT group was more active (mean difference=1370 steps, 95% CI 437-2303) and had longer 6-minute walking distances (mean difference=81.6 m, 95% CI 18.1-145.2) than the control group.

CONCLUSIONS

Through this study, the possibility of introducing a multiperson PA monitoring system in a hospital and promoting PA during hospitalization was demonstrated. These findings support the rationale and feasibility of a future clinical trial to test the efficacy of this educational intervention in improving the PA and physical function of in-hospital patients.

TRIAL REGISTRATION

University Hospital Medical Information Network UMIN000043312; http://tinyurl.com/m2bw8vkz.

Haptic Nudging Using a Wearable Device to Promote Upper Limb Activity during Stroke Rehabilitation: Exploring Diurnal Variation, Repetition, and Duration of Effect

Haptic nudging via wearable devices promotes physical activity and may increase upper limb movement in stroke rehabilitation. This study investigated the optimal approach to haptic nudging by examining diurnal variation, duration of effect, and repeated nudging. The study analysed data from a multiple-period randomised crossover study. A 12 h inpatient rehabilitation day was divided into 72 intervals in which participants with stroke (n = 20) randomly received either a 'nudge' or 'no nudge'. Upper limb movement was observed, classified, and analysed using longitudinal mixed models. The odds of affected upper limb movement following a nudge compared with no nudge were significantly higher during active periods such as breakfast, lunch, and morning and afternoon activities (odds ratios (ORs) 2.01-4.63, 95% CIs [1.27-2.67, 3.17-8.01]), but not dinner (OR 1.36, 95% CI [0.86, 2.16]). The effect of nudging was no longer statistically significant at 50-60 s post-nudge. Consecutive delays in nudging significantly decreased the odds of moving when a nudge was eventually delivered. Contrary to expectations, people with stroke appear more responsive to haptic nudging during active periods rather than periods of inactivity. By understanding the optimal timing and frequency of haptic nudging, the design of wearable devices can be optimised to maximise their therapeutic benefits.

Non-therapeutic strategies to promote physical activity of stroke survivors in hospital settings: A systematic review

OBJECTIVE

To systematically summarize the evidence of strategies other than therapy to promote physical activity in hospital settings.

METHODS

Studies testing the various strategies to promote the physical activity of stroke survivors in different hospital settings, including stroke units, hospitals and rehabilitation centres were included. Two independent reviewers screened, extracted data, and assessed the study quality. Quality assessments were performed using standardized checklists. Data synthesis was done from the selected articles and results were reported.

RESULTS

Of the 3396 records retrieved from database searches, 12 studies (n = 529 participants) were included. All the studies were of moderate to good quality. The strategies were grouped into five categories: i) physical environment, ii) device-based feedback, iii) self-management approaches, iv) family presence, and v) education. Physical environmental and device-based feedback were the most common strategies to promote physical activity after a stroke in a hospital setting. Strategies such as family presence and education improved physical activity levels, whereas device-based feedback showed mixed results.

CONCLUSION

Despite the importance of physical activity in early stroke, there is limited literature present to enhance activity levels. Physical environment and device-based feedback were the two most common strategies used in acute stroke survivors. The impact of these strategies remain suboptimal to be considered as effective intervention methods to enhance physical activity.

The effectiveness of an accelerometer-based physical activity enhancement intervention for patients undergoing lung resection - A pilot randomized controlled trial

OBJECTIVES

Increasing physical activity after lung resection is important for maintaining quality of life. It is unclear whether accelerometer-based exercise instruction contributes to increasing daily physical activity after lung resection. We examine whether accelerometer-based exercise instruction will lead to increased physical activity in patients undergoing lung resection.

MATERIALS AND METHODS

Forty-six patients undergoing lung resection were randomly assigned to either the intervention group (n = 22) or the control group (n = 24). Twelve participants dropped out. Ultimately, 16 participants in the intervention group and 18 participants in the control group were eligible for analysis. Each group allocation was only known to the person in charge of allocation. The physiotherapists and assessors were not blinded in this study. The intervention group participated in a postoperative rehabilitation program and received physical activity instruction preoperatively and at discharge. The control group participated in a postoperative rehabilitation program only. The primary outcomes was physical activity such as the number of daily steps, light intensity physical activity (LPA) and moderate-vigorous intensity physical activity (MVPA) at the two month postoperative follow-up.

RESULTS

Thirty-four participants were enrolled in this study. Sixteen participants in the intervention group and 18 participants in the control group were included for analysis. Although there was no significant difference in physical activity at baseline, the number of daily steps in the intervention group at the two month postoperative follow-up was significantly higher than that in the control group (8039.2 ± 3480.8 vs. 4887.0 ± 2376.5 steps/day, p = 0.004). Compared to the control group, the intervention group also had greater increases in LPA (63.8 ± 25.1 vs. 44.5 ± 24.5 min/day, p = 0.030) and MVPA (20.2 ± 19.6 vs. 9.6 ± 8.6 min/day, p = 0.022).

CONCLUSIONS

This study showed that accelerometer-based exercise instruction led to an increase in physical activity after lung resection in an unsupervised setting.

CLINICAL TRIAL REGISTRATION

The University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN trial No. UMIN000039369).

Movement Reminders to Encourage Arm Use During Daily Life in Stroke Patients

Stroke is a leading cause of long-term disability, such as loss of upper limb function. Active arm movement and frequent practice are essential to regain such function. Wearable sensors that trigger individualized movement reminders can promote awareness of the affected limb during periods of inactivity. This study investigated the immediate effect of vibrotactile reminders based on activity counts on affected arm use, the evolution of the effect throughout a 6-week intervention at home, and whether the time of the day influences the response to the reminder. Thirteen participants who experienced a unilateral ischemic stroke were included in the analysis. Activity counts were found to increase significantly after receiving a reminder. The immediate effect of receiving a reminder was maintained throughout the day as well as during the study duration of 6 weeks. In conclusion, wearable activity trackers with a feature to trigger individualized vibrotactile reminders could be a promising rehabilitation tool to increase arm activity of the affected side in stroke patients in their home environment.

Impact of wearable device-based interventions with feedback for increasing daily walking activity and physical capacities in cardiovascular patients: a systematic review and meta-analysis of randomised controlled trials

OBJECTIVE

To systematically review randomised controlled trials (RCTs) using a wearable physical activity monitoring device as an intervention to increase daily walking activity and improve physical capacities in patients with cardiovascular disease (CVD).

DESIGN

Systematic review and meta-analysis of RCTs.

DATA SOURCES

PubMed, Embase and Web of Science from inception to June 2022.

ELIGIBILITY CRITERIA

Randomised controlled studies including patients with CVD over 18 years of age at the end of a cardiac rehabilitation programme comparing an intervention group using a wearable physical activity monitoring device with feedback with usual care or with a control group receiving no feedback on their physical activity and reporting a change in the daily number of steps and/or a change in the distance covered in the 6-minute walk test (6-MWT) or a change in peak oxygen uptake (V̇O2_peak) as endpoints.

RESULTS

Sixteen RCTs were included. The intervention of wearing a physical activity monitoring device with feedback significantly improved daily number of steps compared with controls (standardised mean difference (SMD) 0.85; 95% CI (0.42; 1.27); p<0.01). The effect was greater when the duration of the intervention was less than 3 months (SMD 1.0; 95% CI (0.18; 1.82); p<0.01) than when the duration of the intervention was 3 months or longer (SMD 0.71; 95% CI (0.27; 1.16); p<0.01), but no significant interaction was found between subgroups (p=0.55). 6-MWT distance and V̇O2_peak showed only small effects (SMD 0.34; 95% CI (-0.11; 0.80); p=0.02 and SMD 0.54; 95% CI (0.03; 1.03); p=0.07, respectively).

CONCLUSION

The use of wearable physical activity monitoring devices appears to help patients with CVD to increase their daily walking activity and thus their physical activity, particularly in the short term.

PROSPERO REGISTRATION NUMBER

CRD42022300423.

Interventions Using Wearable Activity Trackers to Improve Patient Physical Activity and Other Outcomes in Adults Who Are Hospitalized: A Systematic Review and Meta-analysis

Importance

Low levels of physical activity during hospitalization are thought to contribute to a range of poor outcomes for patients. Using wearable activity trackers during hospitalization may help improve patient activity, sedentary behavior, and other outcomes.

Objective

To evaluate the association of interventions that use wearable activity trackers during hospitalization with patient physical activity, sedentary behavior, clinical outcomes, and hospital efficiency outcomes.

Data Sources

OVID MEDLINE, CINAHL, Embase, EmCare, PEDro, SportDiscuss, and Scopus databases were searched from inception to March 2022. The Cochrane Central Register for Controlled trials, ClinicalTrials.gov, and World Health Organization Clinical Trials Registry were also searched for registered protocols. No language restrictions were imposed.

Study Selection

Randomized clinical trials and nonrandomized clinical trials of interventions that used wearable activity trackers to increase physical activity or reduce sedentary behavior in adults (aged 18 years or older) who were hospitalized were included.

Data Extraction and Synthesis

Study selection, data extraction, and critical appraisal were conducted in duplicate. Data were pooled for meta-analysis using random-effects models. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was followed.

Main Outcomes and Measures

The primary outcomes were objectively measured physical activity or sedentary behavior. Secondary outcomes included clinical outcomes (eg, physical function, pain, mental health), and hospital efficiency outcomes (eg, length of stay, readmission).

Results

Fifteen studies with a total of 1911 participants were included, representing various surgical cohorts (4 studies), stroke rehabilitation (3 studies), orthopedic rehabilitation (3 studies), mixed rehabilitation (3 studies), and mixed medical (2 studies). All studies were included in meta-analyses. There was a significant association between wearable activity tracker interventions with higher overall physical activity (standardized mean difference, 0.35; 95% CI, 0.15 to 0.54; I2 = 72%; P < .002) and less sedentary behavior (mean difference, -35.46 min/d; 95% CI, -57.43 to -13.48 min/d; I2 = 0; P = .002), and a significant association between wearable activity tracker interventions with improvements in physical function (standardized mean difference, 0.27; 95% CI, 0.08 to 0.46; I2 = 0; P = .006) compared with usual care. There was no significant association between wearable activity tracker interventions with pain, mental health, length of stay, or readmission risk.

Conclusions and Relevance

In this systematic review and meta-analysis, interventions that used wearable activity trackers with patients who are hospitalized were associated with higher physical activity levels, less sedentary behavior, and better physical functioning compared with usual care.

Digital Health Behavioural Interventions to Support Physical Activity and Sedentary Behaviour in Adults after Stroke: A Systematic Literature Review with Meta-Analysis of Controlled Trials

Background: As the global prevalence of stroke continues to rise, it becomes increasingly pressing to investigate digital health behaviour change interventions that promote physical activity and reduce sedentary behaviour for stroke patients to support active lifestyles. Purpose: The primary aim of this study is to investigate the effectiveness of digital health interventions in promoting physical activity and reducing sedentary behaviour for stroke patients. The secondary aim is to investigate the intervention components that explain intervention effectiveness to further inform intervention development and policy making. Methods: A systematic search of the literature was conducted in four databases (Scopus, MEDLINE (PubMed), Web of Science, and PsychINFO) to identify the most robust evidence in the form of randomised controlled trials of digital interventions for patients with stroke. A random-effects meta-analysis were utilized to quantify the intervention effects on behaviour change, and subgroup analyses to characterise intervention effective components. Results: In total, 16 RCTs were deemed eligible and included in the systematic review. Meta-analyses suggested significant improvements in physical activity (SMD = 0.39, 95% CI 0.17, 0.61, N = 326, p < 0.001, I2 = 0%), and reductions in time of sedentary behaviour (SMD= −0.45, 95% CI −0.76, -0.14, N = 167, p = 0.00, I2 = 0%) after stroke. The 10 m walk test for physical activity, and the timed up and go test for sedentary behaviour, were the objective outcome measures in the most effective behavioural change interventions. Subgroup analyses found that most effective interventions were underpinned by theories of self-regulation and utilised interactive functions to engage patients with the processes of behaviour change. Conclusions: Digital self-monitoring behavioural interventions are effective in promoting physical activity for stroke patients in adjunct to usual care clinical practice and rehabilitation programmes. Rigorous studies are required to provide evidence to disentangle the most effective intervention components for preventative practices and rehabilitation programs and to inform policymaking for stroke treatment.

Enhancing patient self-management after a first stroke: An application of the wearable devices and the health management platform

BACKGROUND

Post-stroke disability restricts a patient's physical activity, affects the patient's quality of life, and leads to higher medical costs. Therefore, it is essential to promote patients' continuous exercise during this period of recovery.

OBJECTIVE

This study aimed to verify the effectiveness of applying a health management platform combined with wearable devices to enhance stroke patients' self-management of recovery and to allow comparisons with active care intervention management.

METHOD

This quasi-experimental study aimed at examining those participants who had sustained a stroke for the first time. A 90-day experiment was implemented with the intervention of monitoring and active care from the researchers who also interviewed the selected participants at the end of the study. A total of 26 participants were examined (14 in the experimental group and 12 in the control group).

RESULT

The participants in the experimental group made significant progress between the pre- and post-tests. Firstly, their six-minute walking distance improved by 89.5 m (p < 0.001). Secondly, their sit-to-stand transfers in 60 s improved 2.85 times (p = 0.017), and their Berg balance test improved by 6.36 points (p = 0.003). Finally, the Partners in Health scale (PIH) scores also improved. According to the data collected in the interviews, the researchers' intervention improved the patients' self-management ability.

CONCLUSION

The short-term physical performance in the experimental group after the intervention was better than that in the control group. In clinical practice, it is suggested that continuous interaction between medical staff and patients be sustained while applying wearable devices to promote the patient's self-management ability.

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.

Effects of a multidisciplinary intervention to promote physical activity in patients with stroke undergoing rehabilitation: study protocol for the ActivePAS pilot randomised controlled trial

Introduction

Physical activity after stroke is related to functional recovery and outcomes. To optimise physical activity adapted to a patient's walking ability and characteristics, multidisciplinary support and interventions are required. The Activate Physical Activity for Stroke pilot randomised controlled trial aims to assess the safety and feasibility of a multidisciplinary intervention that promotes physical activity in patients who had a stroke undergoing rehabilitation.

Methods and analysis

This single-centre, randomised controlled trial will enrol 32 patients who had a stroke undergoing rehabilitation. Patients who had a stroke with the ability to walk 50 m with at least hand assistance, regardless of the use of braces or walking aids, and aged≥20 years will be randomly allocated to a multidisciplinary intervention group or control group. Patients in the intervention group will receive instructions for the self-monitoring of hospitalised physical activity and support to promote physical activity by multidisciplinary staff. The primary outcome of the present study is the safety (adverse events) and feasibility (retention and completion rates) of the multidisciplinary intervention. We assess physical activity using a triaxial accelerometer (UW-204NFC, A&D Company) as one of the secondary outcomes.

Ethics and dissemination

The present study has been approved by the Research Ethics Committee of Konan Women's University and the Ethics Committee of Nishi-Kinen Port Island Rehabilitation Hospital. We will disseminate the results of the present study through a peer-reviewed manuscript and presentations at international conferences.

Trial registration number

UMIN000046731.

The effectiveness of physical activity interventions using activity trackers during or after inpatient care: a systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Promoting physical activity (PA) in patients during and/or after an inpatient stay appears important but challenging. Interventions using activity trackers seem promising to increase PA and enhance recovery of physical functioning.

OBJECTIVE

To review the effectiveness of physical activity interventions using activity trackers on improving PA and physical functioning, compared to usual care in patients during and/or after inpatient care. In addition, it was determined whether the following intervention characteristics increase the effectiveness of these interventions: the number of behaviour change techniques (BCTs) used, the use of a theoretical model or the addition of coaching by a health professional.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

PubMed, EMBASE, Cinahl, SportDiscus and Web of Science databases were searched in March 2020 and updated in March 2021.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Randomized controlled trials (RCTs) including interventions using activity trackers and feedback on PA in adult patients during, or less than 3 months after, hospitalization or inpatient rehabilitation.

METHODS

Following database search and title and abstract screening, articles were screened on full text for eligibility and then assessed for risk of bias by using the Physiotherapy Evidence Database (PEDro) scale. Meta-analyses, including subgroup analysis on intervention characteristics, were conducted for the outcomes PA and physical functioning.

RESULTS

Overall, 21 RCTs totalling 2355 patients were included. The trials covered a variety of clinical areas. There was considerable heterogeneity between studies. For the 13 studies that measured PA as an outcome variable(N = 1435), a significant small positive effect in favour of the intervention was found (standardized mean difference (SMD) = 0.34; 95%CI 0.12-0.56). For the 13 studies that measured physical functioning as an outcome variable (N = 1415) no significant effect was found (SMD = 0.09; 95%CI -0.02 - 0.19). Effectiveness on PA seems to improve by providing the intervention both during and after the inpatient period and by using a theoretical model, multiple BCTs and coaching by a health professional.

CONCLUSION

Interventions using activity trackers during and/or after inpatient care can be effective in increasing the level of PA. However, these improvements did not necessarily translate into improvements in physical functioning. Several intervention characteristics were found to increase the effectiveness of PA interventions.

TRIAL REGISTRATION

Registered in PROSPERO ( CRD42020175977 ) on March 23th, 2020.

Factors related to daily step counts of stroke patients during hospitalization in a convalescent rehabilitation ward

OBJECTIVES

Clarifying the factors related to decreased physical activity in post-stroke patients is essential for effective disease management. This study aimed to examine the factors influencing the amount of daily steps taken by post-stroke patients in a convalescent rehabilitation ward during activities other than rehabilitation (non-rehabilitation steps).

MATERIALS AND METHODS

Eighty-nine post-stroke patients (60.8±14.4 years; 55 men) were enrolled. The inclusion criteria were walking independently within the ward and having a walking speed of ≥24 m/min. Data on patient clinical characteristics including age, sex, body mass index, stroke type, hemiparetic side, and time from stroke onset were collected. Stroke impairment and motor and cognitive functional disabilities were assessed using the Stroke Impairment Assessment Set and the Functional Independence Measure, respectively. The non-rehabilitation steps were calculated by subtracting the steps during the rehabilitation activities from the total steps using Fitbit Flex2.

RESULTS

The average number of non-rehabilitation steps was 4,523±2,339 steps/day. The hierarchical multiple regression analysis revealed that sex, motor disability, and the interaction term of stroke impairment with cognitive disability were significantly related to non-rehabilitation steps. Simple slope analysis demonstrated that the stroke impairment slope was steeper at lower levels than at higher levels of cognitive disability for non-rehabilitation steps.

CONCLUSIONS

In addition to independent effects of sex and motor disability, this study found that stroke impairment and cognitive disability were interactively related to non-rehabilitation steps in post-stroke patients in a convalescent rehabilitation ward. These findings may provide useful information for managing physical activity in post-stroke patients after hospital discharge.

Behaviour change interventions to increase physical activity in hospitalised patients: a systematic review, meta-analysis and meta-regression

BACKGROUND

Low physical activity levels are a major problem for people in hospital and are associated with adverse outcomes.

OBJECTIVE

This systematic review, meta-analysis and meta-regression aimed to determine the effect of behaviour change interventions on physical activity levels in hospitalised patients.

METHODS

Randomised controlled trials of behaviour change interventions to increase physical activity in hospitalised patients were selected from a database search, supplemented by reference list checking and citation tracking. Data were synthesised with random-effects meta-analyses and meta-regression analyses, applying Grades of Recommendation, Assessment, Development and Evaluation criteria. The primary outcome was objectively measured physical activity. Secondary measures were patient-related outcomes (e.g. mobility), service level outcomes (e.g. length of stay), adverse events and patient satisfaction.

RESULTS

Twenty randomised controlled trials of behaviour change interventions involving 2,568 participants (weighted mean age 67 years) included six trials with a high risk of bias. There was moderate-certainty evidence that behaviour change interventions increased physical activity levels (SMD 0.34, 95% CI 0.14-0.55). Findings in relation to mobility and length of stay were inconclusive. Adverse events were poorly reported. Meta-regression found behaviour change techniques of goal setting (SMD 0.29, 95% CI 0.05-0.53) and feedback (excluding high risk of bias trials) (SMD 0.35, 95% CI 0.11-0.60) were independently associated with increased physical activity.

CONCLUSIONS

Targeted behaviour change interventions were associated with increases in physical activity in hospitalised patients. The trials in this review were inconclusive in relation to the patient-related or health service benefits of increasing physical activity in hospital.

Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury

Our goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, we recorded a median 24.1 (IQR: 22.8-25.1) hours of high-frequency accelerometry data per patient from a prospective cohort (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at discharge, measured with the Glasgow Outcome Scale-Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53-0.85]) and consistent (observation windows: 12 min-9 h) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated patients of upper moderate disability or better (GOSE > 5) with 2-6 h of observation (AUC: 0.82 [95% CI: 0.75-0.90]). Results suggest that time series analysis of motor activity yields clinically relevant insights on underlying functional states and short-term outcomes in patients with SBI.

Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review

Background

Extremity weakness, fatigue, and postural instability often contribute to mobility deficits in persons after stroke. Wearable technologies are increasingly being utilized to track many health-related parameters across different patient populations. The purpose of this systematic review was to identify how wearable technologies have been used over the past decade to assess gait and mobility in persons with stroke.

Methods

We performed a systematic search of Ovid MEDLINE, CINAHL, and Cochrane databases using select keywords. We identified a total of 354 articles, and 13 met inclusion/exclusion criteria. Included studies were quality assessed and data extracted included participant demographics, type of wearable technology utilized, gait parameters assessed, and reliability and validity metrics.

Results

The majority of studies were performed in either hospital-based or inpatient settings. Accelerometers, activity monitors, and pressure sensors were the most commonly used wearable technologies to assess gait and mobility post-stroke. Among these devices, spatiotemporal parameters of gait that were most widely assessed were gait speed and cadence, and the most common mobility measures included step count and duration of activity. Only 4 studies reported on wearable technology validity and reliability metrics, with mixed results.

Conclusion

The use of various wearable technologies has enabled researchers and clinicians to monitor patients’ activity in a multitude of settings post-stroke. Using data from wearables may provide clinicians with insights into their patients’ lived-experiences and enrich their evaluations and plans of care. However, more studies are needed to examine the impact of stroke on community mobility and to improve the accuracy of these devices for gait and mobility assessments amongst persons with altered gait post-stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00863-x.

Concurrent validity and reliability of an activity monitoring for rehabilitation (AMoR) platform for step counting and sitting/lying time in post-stroke individuals

BACKGROUND

Objective and reliable measurements to investigate daily behavior patterns in people with stroke could help therapeutic interventions after a stroke.

OBJECTIVE

To evaluate whether the Activity Monitoring for Rehabilitation (AMoR) platform has adequate concurrent validity and reliability for step counting and time spent sitting/lying in people post-stroke and to investigate its percentage accuracy for step counting at different walking speeds.

METHODS

Cross-sectional observational study. Fifty chronic post-stroke subjects used the AMoR platform and SAM simultaneously while a Video camera recorded the same activities during clinical trials. Spearman's correlation coefficient, the mean absolute percentage error, the intraclass correlation coefficient and Bland-Altman plot analyses were used to estimate the validity and reliability of the AMoR platform and StepWatch^TM Activity Monitor (SAM). The accuracy percentage was calculated for each device and plotted as a function of the walking speed during the 10-meter walk test (10MWT).

RESULTS

There was a very high correlation for step counting in all tests and a high correlation for time spent sitting/lying. The mean absolute percentage error values remained below 4% for step counting and time sitting/lying. The AMoR platform also showed excellent reliability for step counting and sitting/lying time, with values within the limit of agreement in the Bland-Altman plots. A high percentage of accuracy for step counting in the AMoR platform was observed during the 10MWT.

CONCLUSION

The AMoR platform is valid and reliable for step counting and time spent sitting/lying, with a high percentage of accuracy at different walking speeds in the post-stroke population.

Haptic Nudges Increase Affected Upper Limb Movement During Inpatient Stroke Rehabilitation: Multiple-Period Randomized Crossover Study

BACKGROUND

As many as 80% of stroke survivors experience upper limb (UL) disability. The strong relationships between disability, lost productivity, and ongoing health care costs mean reducing disability after stroke is critical at both individual and society levels. Unfortunately, the amount of UL-focused rehabilitation received by people with stroke is extremely low. Activity monitoring and promotion using wearable devices offer a potential technology-based solution to address this gap. Commonly, wearable devices are used to deliver a haptic nudge to the wearer with the aim of promoting a particular behavior. However, little is known about the effectiveness of haptic nudging in promoting behaviors in patient populations.

OBJECTIVE

This study aimed to estimate the effect of haptic nudging delivered via a wrist-worn wearable device on UL movement in people with UL disability following stroke undertaking inpatient rehabilitation.

METHODS

A multiple-period randomized crossover design was used to measure the association of UL movement with the occurrence of haptic nudge reminders to move the affected UL in 20 people with stroke undertaking inpatient rehabilitation. UL movement was observed and classified using movement taxonomy across 72 one-minute observation periods from 7:00 AM to 7:00 PM on a single weekday. On 36 occasions, a haptic nudge to move the affected UL was provided just before the observation period. On the other 36 occasions, no haptic nudge was given. The timing of the haptic nudge was randomized across the observation period for each participant. Statistical analysis was performed using mixed logistic regression. The effect of a haptic nudge was evaluated from the intention-to-treat dataset as the ratio of the odds of affected UL movement during the observation period following a "Planned Nudge" to the odds of affected limb movement during the observation period following "No Nudge."

RESULTS

The primary intention-to-treat analysis showed the odds ratio (OR) of affected UL movement following a haptic nudge was 1.44 (95% CI 1.28-1.63, P<.001). The secondary analysis revealed an increased odds of affected UL movement following a Planned Nudge was predominantly due to increased odds of spontaneous affected UL movement (OR 2.03, 95% CI 1.65-2.51, P<.001) rather than affected UL movement in conjunction with unaffected UL movement (OR 1.13, 95% CI 0.99-1.29, P=.07).

CONCLUSIONS

Haptic nudging delivered via a wrist-worn wearable device increases affected UL movement in people with UL disability following stroke undertaking inpatient rehabilitation. The promoted movement appears to be specific to the instructions given.

TRIAL REGISTRATION

Australia New Zealand Clinical Trials Registry 12616000654459; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370687&isReview=true.

A scoping review of interventions using accelerometers to measure physical activity or sedentary behaviour during hospitalization

Objective:

To identify interventions using wearable accelerometers to measure physical activity and/or sedentary behaviour in adults during hospitalization for an acute medical/surgical condition.

Data sources:

Four databases were searched in August 2019 (MEDLINE, CINAHL, Scopus, EMBASE).

Review methods:

Studies were selected if they described an intervention in adults with a medical/surgical condition, and concurrently reported an accelerometer-derived measure of physical activity and/or sedentary behaviour while participants were admitted. Items were screened for eligibility in duplicate. Included studies were synthesized to describe intervention types, feasibility and potential effectiveness.

Results:

Twenty-two studies were included, reporting on 3357 participants (2040 with accelerometer data). Identified types of interventions were: pre-habilitation ( n = 2) exercise ( n = 3), patient behaviour change with self-monitoring ( n = 6), models of care ( n = 5), implementing system change ( n = 2), surgical technique ( n = 2) patients wearing day clothes ( n = 1) and education about activity in hospital ( n = 1). Of 16 studies that reported intervention effects on physical activity, 11 reported a favourable impact including studies of: pre-habilitation, self-monitoring (accelerometry or an activity whiteboard), physiotherapy, an early mobility bundle, minimally invasive surgery, an education booklet and by implementing system change. Of the six studies that reported intervention effects on sedentary behaviour, there was a favourable impact with an activity whiteboard, models of care and an education booklet.

Conclusion:

Accelerometer-derived measures of physical activity and/or sedentary behaviour have been used to describe sample characteristics and intervention effects in studies of hospitalized adults. Interventions may involve a range of health professionals, but less is known about sedentary behaviour in this setting.

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.

Data management and wearables in older adults: A systematic review

BACKGROUND

Wearable trackers as research or clinical tools are increasingly used to support the care of older adults, due to their practicality in self-monitoring and potential to promote healthy lifestyle behaviours. However, there is limited understanding of appropriate data collection and analysis methods in different contexts.

AIM

To summarise evidence on wearable data generation and management in older adults, focusing on physical activity (PA), electrocardiogram (ECG), and vital signs monitoring. In addition to examine the accuracy and utility of wearable trackers in the care of older people.

METHODS

A systematic search of CINAHL, MEDLINE, PubMed and a manual search were conducted. Twenty studies on the use of wearable trackers by older adults met the inclusion criteria.

RESULTS

Methodological designs for data collection and analysis were heterogeneous, with diverse definitions of wear and no-wear time, the number and type of valid days, and proprietary algorithms. Wearable trackers had adequate accuracy for measuring step counts, moderate to vigorous physical activity (MVPA), ECG and heart rate (HR), but not for respiratory rate. Participants reported ease of use and had high-level adherence over daily long-term use. Moreover, wearable trackers encouraged users to increase their daily level of physical activity and decrease waist circumference, facilitating atrial fibrillation (AF) diagnoses and predicting length of stay.

CONCLUSION

Wearable trackers are multi-dimensional technologies offering a viable and promising approach for sustained and scaled monitoring of older people's health. Frameworks and/or guidelines, including standards for the design, data management and application of use specifically for older adults, are required to enhance validity and reliability.

Long-Term Effect of Promoting In-Hospital Physical Activity on Postdischarge Patients with Mild Ischemic Stroke

BACKGROUND

Although there are reports on the promotion of physical activity during hospitalization, there is no evidence that promoting in-hospital physical activity continues over time after discharge. The purpose of this study was to evaluate the long-term effect of promoting in-hospital physical activity on postdischarge physical activity and self-efficacy for physical activity in patients with mild ischemic stroke.

METHODS

This was a cross-sectional study of a post hoc analysis of a previous randomized controlled trial. Patients with mild ischemic stroke were divided into the intervention group (in which physical activity was promoted during hospitalization) and a control group. To promote in-hospital physical activity, patients in the intervention group were instructed in the self-monitoring approach. After discharge, we measured physical activity and self-efficacy for physical activity by mailing a questionnaire to the patients. The average number of steps taken was used the index of postdischarge physical activity.

RESULTS

The study sample comprised 30 patients, with 13 patients in the intervention group and 17 patients in the control group. There were no significant differences in physical activity values (6176.8 versus 6112.8 steps/day, P = .932) and self-efficacy for physical activity score (66.0 versus 76.0 points, P = .801) between the 2 groups.

CONCLUSIONS

This study showed that the promotion of in-hospital physical activity did not appear to increase physical activity and self-efficacy for physical activity in patients with mild ischemic stroke after discharge. Additional study is needed to establish a more specific approach to promote physical activity during hospitalization that will carry over during long-term follow-up.

Efficacy of interventions aimed at improving physical activity in individuals with stroke: a systematic review

Purpose: To identify interventions employed to increase post-stroke physical activity, evaluate their efficacy, and identify the gaps in literature.Materials and methods: Randomized controlled trials published until March 2018 were searched in MEDLINE, PEDro, EMBASE, LILACS, and SCIELO databases. The quality of each study and overall quality of evidence were assessed using the PEDro and the GRADE scales.Results: Eighteen studies were included (good PEDro and very low GRADE-scores). In seven, the experimental groups showed significant increases in physical activity (aerobics, resistance, and home-based training; counseling, aerobics, resistance, and home-based training; electrical stimulation during walking; functional-task training; robot-assisted arm therapy; accelerometer-based feedback, and physical activity encouragement). In seven, there were no significant between-group differences (physical activity plan; stretching, use of toe-spreaders, standard treatment; counseling; circuit video-game; functional-task; counseling and cognitive training). The combined experimental and control groups showed significant declines in physical activity in one study (aerobic training or stretching) and increases in three others (aerobic, resistance or sham resistance training; stroke-with advice or only stroke-counseling; aerobic training, educational sessions, standard treatment, and coaching, or mobilization and standard treatment). A meta-analysis could not be performed, due to heterogeneity.Conclusions: Some interventions improved physical activity after stroke. However, the interpretability is limited.Implications for rehabilitationIndividuals with stroke show low physical activity, which may compromise function and health.The use of interventions aimed at improving and maintaining physical activity of individuals with stroke are recommended.Some interventions, such as aerobic, resistance, and combined home-based training, electrical stimulation during walking, functional task training, and arm robot-assisted therapy, could improve the physical activity after stroke.

Wearable Sensors to Monitor, Enable Feedback, and Measure Outcomes of Activity and Practice

PURPOSE OF REVIEW

Measurements obtained during real-world activity by wearable motion sensors may contribute more naturalistic accounts of clinically meaningful changes in impairment, activity, and participation during neurologic rehabilitation, but obstacles persist. Here we review the basics of wearable sensors, the use of existing systems for neurological and rehabilitation applications and their limitations, and strategies for future use.

RECENT FINDINGS

Commercial activity-recognition software and wearable motion sensors for community monitoring primarily calculate steps and sedentary time. Accuracy declines as walking speed slows below 0.8 m/s, less so if worn on the foot or ankle. Upper-extremity sensing is mostly limited to simple inertial activity counts. Research software and activity-recognition algorithms are beginning to provide ground truth about gait cycle variables and reveal purposeful arm actions. Increasingly, clinicians can incorporate inertial and other motion signals to monitor exercise, activities of daily living, and the practice of specific skills, as well as provide tailored feedback to encourage self-management of rehabilitation. Efforts are growing to create a compatible collection of clinically relevant sensor applications that capture the type, quantity, and quality of everyday activity and practice in known contexts. Such data would offer more ecologically sound measurement tools, while enabling clinicians to monitor and support remote physical therapies and behavioral modification when combined with telemedicine outreach.

Activity monitors for increasing physical activity in adult stroke survivors

BACKGROUND

Stroke is the third leading cause of disability worldwide. Physical activity is important for secondary stroke prevention and for promoting functional recovery. However, people with stroke are more inactive than healthy age-matched controls. Therefore, interventions to increase activity after stroke are vital to reduce stroke-related disability.

OBJECTIVES

To summarise the available evidence regarding the effectiveness of commercially available, wearable activity monitors and smartphone applications for increasing physical activity levels in people with stroke.

SEARCH METHODS

We searched the Cochrane Stroke Group Trials Register, CENTRAL, MEDLINE, Embase, CINAHL, SPORTDiscus, and the following clinical trial registers: WHO International Clinical Trials Registry Platform, Clinical Trials, EU Clinical Trial Register, ISRCTN Registry, Australian and New Zealand Clinical Trial Registry, and Stroke Trials Registry to 3 March 2018. We also searched reference lists, Web of Science forward tracking, and Google Scholar, and contacted trial authors to obtain further data if required. We did not restrict the search on language or publication status.

SELECTION CRITERIA

We included all randomised controlled trials (RCTs) and randomised cross-over trials that included use of activity monitors versus no intervention, another type of intervention, or other activity monitor. Participants were aged 18 years or older with a diagnosis of stroke, in hospital or living in the community. Primary outcome measures were steps per day and time in moderate-to-vigorous intensity activity. Secondary outcomes were sedentary time, time spent in light intensity physical activity, walking duration, fatigue, mood, quality of life, community participation and adverse events. We excluded upper limb monitors that only measured upper limb activity.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methodology to analyse and interpret the data. At least two authors independently screened titles and abstracts for inclusion. We resolved disagreements by consulting a third review author. We extracted the following data from included studies into a standardised template: type of study, participant population, study setting, intervention and co-interventions, time-frame, and outcomes. We graded levels of bias as high, low, or unclear, and assessed the quality of evidence for each outcome using the GRADE approach.

MAIN RESULTS

We retrieved 28,098 references, from which we identified 29 potential articles. Four RCTs (in 11 reports) met the inclusion criteria.The sample sizes ranged from 27 to 135 (total 245 participants). Time poststroke varied from less than one week (n = 1), to one to three months (n = 2), or a median of 51 months (n = 1). Stroke severity ranged from a median of one to six on the National Institutes of Health Stroke Scale (NIHSS). Three studies were conducted in inpatient rehabilitation, and one was in a university laboratory. All studies compared use of activity monitor plus another intervention (e.g. a walking retraining programme or an inpatient rehabilitation programme) versus the other intervention alone. Three studies reported on the primary outcome of daily step counts.There was no clear effect for the use of activity monitors in conjunction with other interventions on step count in a community setting (mean difference (MD) -1930 steps, 95% confidence interval (CI) -4410 to 550; 1 RCT, 27 participants; very low-quality evidence), or in an inpatient rehabilitation setting (MD 1400 steps, 95% CI -40 to 2840; 2 RCTs, 83 participants; very low-quality evidence). No studies reported the primary outcome moderate-to-vigorous physical activity, but one did report time spent in moderate and vigorous intensity activity separately: this study reported that an activity monitor in addition to usual inpatient rehabilitation increased the time spent on moderate intensity physical activity by 4.4 minutes per day (95% CI 0.28 to 8.52; 1 RCT, 48 participants; low-quality evidence) compared with usual rehabilitation alone, but there was no clear effect for the use of an activity monitor plus usual rehabilitation for increasing time spent in vigorous intensity physical activity compared to usual rehabilitation (MD 2.6 minutes per day, 95% CI -0.8 to 6; 1 RCT, 48 participants; low-quality evidence). The overall risk of bias was low, apart from high-risk for blinding of participants and study personnel. None of the included studies reported any information relating to adverse effects.

AUTHORS' CONCLUSIONS

Only four small RCTs with 274 participants (three in inpatient rehabilitation and one in the community) have examined the efficacy of activity monitors for increasing physical activity after stroke. Although these studies showed activity monitors could be incorporated into practice, there is currently not enough evidence to support the use of activity monitors to increase physical activity after stroke.

Relation between health utility score and physical activity in community-dwelling ambulatory patients with stroke: a preliminary cross-sectional study

Background The health utility score in patients with stroke relates to physical, psychological, and various other factors. However, the relationship between the health utility score in patients with stroke and objective physical activity has not been clarified. Objective To clarify the relation between the health utility score and objective physical activity in community-dwelling ambulatory patients with stroke. Design A cross-sectional study. Method Patients who received outpatient consultation from a stroke certified nurse after discharge were recruited. We assessed health-related quality of life with the EuroQoL 5-Dimension 3-Level questionnaire and calculated the health utility score. We measured the daily number of steps taken as the index of objective physical activity using an accelerometer. Results Twenty-two patients (72.7% men, 69.5 years old) were included. The health utility score was 0.78 ± 0.14. The physical activity value as indicated by the number of steps taken was 6276.3 ± 4640.7 steps. The health utility score showed a significant positive correlation with the number of steps taken (r = 0.466, p = 0.029). Conclusions The present study showed that the health utility score correlated significantly with objective physical activity in community-dwelling ambulatory patients with stroke. The more the patients with stroke walked, the higher their health utility score was. Further studies should assess other domains of health-related quality of life to comprehensively verify this relationship.