Acceleration metrics are responsive to change in upper extremity function of stroke survivors

The feasibility of the adapted H-GRASP program for perceived and actual daily-life upper limb activity in the chronic phase post-stroke

(Purpose: Assessing feasibility and initial impact of the Home-Graded Repetitive Arm Supplementary Program combined with in-home accelerometer-based feedback (AH-GRASP) on perceived and actual daily-life upper limb (UL) activity in stroke survivors during the chronic phase with good UL motor function but low perceived daily-life activity. Material and methods: A 4-week intervention program (4 contact hours, 48 h self-practice) encompassing task-oriented training, behavioral techniques, phone-based support, monitoring, and weekly feedback sessions using wrist-worn accelerometery was implemented using a pre-post double baseline repeated measures design. Feasibility, clinical assessments, patient-reported outcomes, and accelerometer data were investigated. Results: Of the 34 individuals approached, nineteen were included (recruitment rate 56%). Two dropped out, one due to increased UL pain (retention rate 89%). Seven (41%) achieved the prescribed exercise target (120 min/day, six days/week). Positive patient experiences and improvements in UL capacity, self-efficacy, and contribution of the affected UL to overall activity (p < 0.05, small to large effect sizes) were observed. Additionally, seven participants (41%) surpassed the minimal clinically important difference in perceived UL activity.Conclusions: A home-based UL exercise program with accelerometer-based feedback holds promise for enhancing perceived and actual daily-life UL activity for our subgroup of chronic stroke survivors.

Wearable Technology to Capture Arm Use of People With Stroke in Home and Community Settings: Feasibility and Early Insights on Motor Performance

OBJECTIVE

The objectives of this study were to establish the short-term feasibility and usability of wrist-worn wearable sensors for capturing the arm and hand activity of people with stroke and to explore the association between factors related to the use of the paretic arm and hand.

METHODS

Thirty people with chronic stroke were monitored with wrist-worn wearable sensors for 12 hours per day for a 7-day period. Participants also completed standardized assessments to capture stroke severity, arm motor impairments, self-perceived arm use, and self-efficacy. The usability of the wearable sensors was assessed using the adapted System Usability Scale and an exit interview. Associations between motor performance and capacity (arm and hand impairments and activity limitations) were assessed using Spearman correlations.

RESULTS

Minimal technical issues or lack of adherence to the wearing schedule occurred, with 87.6% of days procuring valid data from both sensors. The average sensor wear time was 12.6 (standard deviation [SD] = 0.2) hours per day. Three participants experienced discomfort with 1 of the wristbands, and 3 other participants had unrelated adverse events. There were positive self-reported usability scores (mean = 85.4/100) and high user satisfaction. Significant correlations were observed for measures of motor capacity and self-efficacy with paretic arm use in the home and the community (Spearman correlation coefficients = 0.44-0.71).

CONCLUSIONS

This work demonstrates the feasibility and usability of a consumer-grade wearable sensor for capturing paretic arm activity outside the laboratory. It provides early insight into the everyday arm use of people with stroke and related factors, such as motor capacity and self-efficacy.

IMPACT

The integration of wearable technologies into clinical practice offers new possibilities to complement in-person clinical assessments and to better understand how each person is moving outside of therapy and throughout the recovery and reintegration phase. Insight gained from monitoring the arm and hand use of people with stroke in the home and community is the first step toward informing future research with an emphasis on causal mechanisms with clinical relevance.

Minimal Clinically Important Difference of Scales Reported in Stroke Trials: A Review

There is a growing awareness of the significance of using minimum clinically important differences (MCIDs) in stroke research. An MCID is the smallest change in an outcome measure that is considered clinically meaningful. This review is the first to provide a comprehensive summary of various scales and patient-reported outcome measures (PROMs) used in stroke research and their MCID values reported in the literature, including a concise overview of the concept of and methods for determining MCIDs in stroke research. Despite the controversies and limitations surrounding the estimation of MCIDs, their importance in modern clinical trials cannot be overstated. Anchor-based and distribution-based methods are recommended for estimating MCIDs, with patient self-evaluation being a crucial component in capturing the patient's perspective on their health. A combination of methods can provide a more comprehensive understanding of the clinical relevance of treatment effects, and incorporating the patient's perspective can enhance the care of stroke patients.

The relationship between Rating of Everyday Arm-use in the Community and Home (REACH) scale affected arm-use assessment, activity and participation after stroke

BACKGROUND AND OBJECTIVES

While arm function has been traditionally used as a primary goal for upper extremity rehabilitation post-stroke, we propose a simple measure of arm use, which may translate into better activities and participation. The aim was to determine the relationship between arm use and measures of activity and participation.

METHODS

This was a cross-sectional study with evaluative components involving community-dwelling individuals with chronic stroke. The Rating of Everyday Arm-Use in the Community and Home (REACH) Scale was used to assess affected arm use, Barthel Index and activity domain of the Stroke Impact Scale (SIS) for activities, and participation domain of the SIS for participation. The participants were also asked if they resumed driving after the stroke.

RESULTS

Forty-nine individuals (mean age = 70.3 ± 11.5 years, male sex = 51%) living with the effects of a stroke for at least 3 months participated in this study. There was a positive relationship between affected arm use and activities (Barthel Index score - rs  = 0.464; SIS activities - rs  = 0.686), participation (rs  = 0.479), and driving (rs  = 0.581). The Barthel Index scores were higher for individuals with dominant arm hemiparesis (p = 0.003) or left hemisphere lesions (p = 0.005). There was also greater arm use in left hemisphere lesions (p = 0.018).

CONCLUSIONS

Affected arm use in individuals with chronic stroke is related to activities and participation. Given the importance of arm use in activities and participation after stroke, rehabilitation therapists may consider utilizing the REACH Scale, a simple and quick outcome measure, as a means to assess arm use and implement effective interventions for improving arm use.

Wearable technology to capture arm use of stroke survivors in home and community settings: feasibility and early insights on motor performance

Objective

To establish short-term feasibility and usability of wrist-worn wearable sensors to capture arm/hand activity of stroke survivors and to explore the association between factors related to use of the paretic arm/hand.

Methods

30 chronic stroke survivors were monitored with wrist-worn wearable sensors during 12h/day for a 7-day period. Participants also completed standardized assessments to capture stroke severity, arm motor impairments, self-perceived arm use and self-efficacy. Usability of the wearable sensors was assessed using the adapted System Usability Scale and an exit interview. Associations between motor performance and capacity (arm/hand impairments and activity limitations) were assessed using Spearman's correlations.

Results

Minimal technical issues or lack of adherence to the wearing schedule occurred, with 87.6% of days procuring valid data from both sensors. Average sensor wear time was 12.6 (standard deviation: 0.2) h/day. Three participants experienced discomfort with one of the wristbands and three other participants had unrelated adverse events. There were positive self-reported usability scores (mean: 85.4/100) and high user satisfaction. Significant correlations were observed for measures of motor capacity and self-efficacy with paretic arm use in the home and the community (Spearman's correlation ρs: 0.44-0.71).

Conclusions

This work demonstrates the feasibility and usability of a consumer-grade wearable sensor to capture paretic arm activity outside the laboratory. It provides early insight into stroke survivors' everyday arm use and related factors such as motor capacity and self-efficacy.

Impact

The integration of wearable technologies into clinical practice offers new possibilities to complement in-person clinical assessments and to better understand how each person is moving outside of therapy and throughout the recovery and reintegration phase. Insights gained from monitoring stroke survivors arm/hand use in the home and community is the first step towards informing future research with an emphasis on causal mechanisms with clinical relevance.

Protocol for a remote home-based upper extremity self-training program for community-dwelling individuals after stroke

Background

Half of all stroke survivors experience hemiparesis on the contralateral side, resulting in chronic upper extremity (UE) impairment. Remote rehabilitation is a promising approach to optimize the gains made in the clinic to maximize function and promote UE use at home. This paper describes the study protocol for a remote home-based UE self-training program.

Design

This was a feasibility study that used a convergent mixed methods approach.

Methods

We collected data on 15 community-dwelling individuals with UE hemiparesis after stroke. The study used motivational interviewing (MI) and ecological momentary assessments (EMA) to maximize engagement in a 4-week personalized UE self-training program. The study consisted of three phases: 1) training in MI for the interventionists 2) creating customized treatment plans using shared decision making, and 3) four weeks of UE self-training.

Measures and analysis

To evaluate feasibility, we will summarize recruitment and retention rates, intervention delivery, acceptance, adherence, and safety. Quantitative UE outcomes will measure change in UE status after the intervention (Fugl-Meyer Assessment, Motor Activity Log, Canadian Occupational Performance Measure, and bilateral magnitude ratio). Qualitative data (1:1 semi-structured interviews) will capture participants' perceptions and experience with the intervention. Quantitative and qualitative data will be integrated to gain a deeper understanding of the facilitators and barriers for engagement and adherence to UE self-training.

Conclusion

The results of this study will advance the scientific knowledge for use of MI and EMA as methods for enhancing adherence and engagement in UE self-training in stroke rehabilitation. The ultimate impact of this research will be to improve UE recovery for individuals with stroke transitioning back into community.

Clinical trials registration

NCT05032638.

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

BACKGROUND

Accelerometers allow for direct measurement of upper limb (UL) activity. Recently, multi-dimensional categories of UL performance have been formed to provide a more complete measure of UL use in daily life. Prediction of motor outcomes after stroke have tremendous clinical utility and a next step is to explore what factors might predict someone's subsequent UL performance category.

PURPOSE

To explore how different machine learning techniques can be used to understand how clinical measures and participant demographics captured early after stroke are associated with the subsequent UL performance categories.

METHODS

This study analyzed data from two time points from a previous cohort (n = 54). Data used was participant characteristics and clinical measures from early after stroke and a previously established category of UL performance at a later post stroke time point. Different machine learning techniques (a single decision tree, bagged trees, and random forests) were used to build predictive models with different input variables. Model performance was quantified with the explanatory power (in-sample accuracy), predictive power (out-of-bag estimate of error), and variable importance.

RESULTS

A total of seven models were built, including one single decision tree, three bagged trees, and three random forests. Measures of UL impairment and capacity were the most important predictors of the subsequent UL performance category, regardless of the machine learning algorithm used. Other non-motor clinical measures emerged as key predictors, while participant demographics predictors (with the exception of age) were generally less important across the models. Models built with the bagging algorithms outperformed the single decision tree for in-sample accuracy (26-30% better classification) but had only modest cross-validation accuracy (48-55% out of bag classification).

CONCLUSIONS

UL clinical measures were the most important predictors of the subsequent UL performance category in this exploratory analysis regardless of the machine learning algorithm used. Interestingly, cognitive and affective measures emerged as important predictors when the number of input variables was expanded. These results reinforce that UL performance, in vivo, is not a simple product of body functions nor the capacity for movement, instead being a complex phenomenon dependent on many physiological and psychological factors. Utilizing machine learning, this exploratory analysis is a productive step toward the prediction of UL performance. Trial registration NA.

Validity of Novel Outcome Measures for Hand Function Performance After Stroke Using Egocentric Video

BACKGROUND

Evaluating upper limb (UL) interventions after stroke calls for outcome measures that describe impact on daily life in the community. UL use ratio has been used to quantify the performance domain of UL function, but generally focuses on arm use only. A hand use ratio could provide additional information about UL function after stroke. Additionally, a ratio based on the role of the more-affected hand in bilateral activities (stabilizer or manipulator) may also reflect hand function recovery. Egocentric video is a novel modality that can record both dynamic and static hand use and hand roles at home after stroke.

OBJECTIVE

To validate hand use and hand role ratios from egocentric video against standardized clinical UL assessments.

METHODS

Twenty-four stroke survivors recorded daily tasks in a home simulation laboratory and their daily routines at home using egocentric cameras. Spearman's correlation was used to compare the ratios with the Fugl-Meyer Assessment-Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), and Motor Activity Log-30 (MAL, Amount of Use (AoU), and Quality of Movement (QoM)).

RESULTS

Hand use ratio significantly correlated with the FMA-UE (0.60, 95% CI: 0.26, 0.81), ARAT (0.44, CI: 0.04, 0.72), MAL-AoU (0.80, CI: 0.59, 0.91), and MAL-QoM (0.79, CI: 0.57, 0.91). Hand role ratio had no significant correlations with the assessments.

CONCLUSION

Hand use ratio automatically extracted from egocentric video, but not hand role ratio, was found to be a valid measure of hand function performance in our sample. Further investigation is necessary to interpret hand role information.

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

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

Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors' and occupational therapists' perspectives

BACKGROUND

The key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients' functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients' arm use patterns in their naturalistic environments. However, these technologies have remained a proof of concept and have not been adopted as mainstream therapeutic products, and we lack understanding of how key stakeholders perceive the use of wearable technologies in their practice.

OBJECTIVE

We aim to understand how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how we could design a wearable-based performance monitoring system to better support the needs of the stakeholders.

METHODS

We conducted semi-structured interviews with four stroke survivors and 15 occupational therapists (OTs) based on real-world arm use data that we collected for contextualization. To situate our participants, we leveraged a pair of finger-worn accelerometers to collect stroke survivors' arm use data in real-world settings, which we used to create study probes for stroke survivors and OTs, respectively. The interview data was analyzed using the thematic approach.

RESULTS

Our study unveiled a detailed account of (1) the receptiveness of stroke survivors and OTs for using wearable sensors in clinical practice, (2) OTs' envisioned strategies to utilize patient-generated sensor data in the light of providing patients with personalized therapy programs, and (3) practical challenges and design considerations to address for the accelerated integration of wearable systems into their practice.

CONCLUSIONS

These findings offer promising directions for the design of a wearable solution that supports OTs to develop individually-tailored therapy programs for stroke survivors to improve their affected arm use.

Music-based intervention drives paretic limb acceleration into intentional movement frequencies in chronic stroke rehabilitation

This study presented a novel kinematic assessment of paretic limb function "online" during the actual therapeutic exercisers rooted within the acceleration domain. Twenty-eight patients at chronic stroke stages participated in an auditory-motor intervention mapping reaching movements of the paretic arm unto surfaces of large digital musical instruments and sound tablets that provided rhythmic entrainment cues and augmented auditory feedback. Patients also wore a tri-axial accelerometer on the paretic limb during the nine-session intervention. The resulting acceleration profiles were extracted and quantified within the frequency domain. Measures of peak power and peak width were leveraged to estimate volitional control and temporal consistency of paretic limb movements, respectively. Clinical assessments included the Wolf Motor Function Test and Fugl-Meyer - Upper Extremity subtest. The results showed that peak power increased significantly from Session 1 to Session 9 within oscillatory frequency ranges associated with intentional movement execution (i.e., 4.5 Hz). Decreases in peak width over time provided additional evidence for improved paretic arm control from a temporal perspective. In addition, Peak width values obtained in Session 1 was significantly correlated with pre-test Fugl-Meyer - Upper Extremity scores. These results highlighted improvements in paretic limb acceleration as an underlying mechanism in stroke motor recovery and shed further light on the utility of accelerometry-based measures of paretic limb control in stroke rehabilitation. The data reported here was obtained from a larger clinical trial: https://clinicaltrials.gov/ct2/show/NCT03246217 ClinicalTrials.gov Identifier: NCT03246217.

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke

Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75-82%) to conventional thresholds (58-66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors' real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use.

Suitability of accelerometry as an objective measure for upper extremity use in stroke patients

Background

Upper limb (UL) paresis is one of the most common stroke consequences and significantly restricts patients in everyday life. Instruments objectively measuring direct arm use in stroke patients are lacking, but might be helpful to understand patients’ impairment. Aiming to examine whether accelerometry is a suitable objective measure for everyday UL use in stroke patients, we conducted a systematic review on the association between accelerometer-derived measurements and clinical scales.

Methods

Articles were systematically searched in PubMed, Scopus, Cochrane Library, PeDro and LIVIVO through December 12^th, 2021, screened for inclusion by AH, and subsequently independently screened by CK and MK. Disagreements were discussed until consensus. We included English and German peer-reviewed articles dealing with the validity of accelerometers as a measurement of UL use in stroke patients and eligible systematic reviews. Studies exclusively using accelerometry as an outcome parameter, book contributions, conference abstracts and case studies were excluded. Data extraction was conducted by AH and confirmed by CK focussing on study type, objective, accelerometer device, sample size, stroke status, assessments conducted, measurement method, wearing time and key results. We analysed all eligible articles regarding the correlation between accelerometry and other clinical assessments and the validity in accordance with the type of accelerometer.

Results

Excluding duplicates, the initial search yielded 477 records. In the 34 eligible studies accelerometers was used with a predominance of tri-axial accelerometery (n = 12) and only few with two-axial application (n = 4). Regarding measures to examine association to accelerometer data different clinical scales were applied depending on the setting, the degree of impairment and/or the status of stroke. Cut-off values to determine correlations varied largely; most significant correlations are reported for the MAL [Range 0.31- 0.84] and the ARAT [Range 0.15–0.79].

Conclusions

Accelerometers can provide reliable data about daily arm use frequency but do not supply information about the movements´ quality and restrictions on everyday activities of stroke patients. Depending on the context, it is advisable to use both, accelerometry and other clinical measures. According to the literature there is currently no accelerometer device most suitable to measure UL activity. High correlations indicate that multi-dimensional accelerometers should be preferred.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02743-w.

Concomitant sensory stimulation during therapy to enhance hand functional recovery post stroke

Background

Post-stroke hand impairment is prevalent and persistent even after a full course of rehabilitation. Hand diminishes stroke survivors’ abilities for activities of daily living and independence. One way to improve treatment efficacy is to augment therapy with peripheral sensory stimulation. Recently, a novel sensory stimulation, TheraBracelet, has been developed in which imperceptible vibration is applied during task practice through a wrist-worn device. The objective of this trial is to determine if combining TheraBracelet with hand task practice is superior to hand task practice alone.

Methods

A double-blind randomized controlled trial will be used. Chronic stroke survivors will undergo a standardized hand task practice therapy program (3 days/week for 6 weeks) while wearing a device on the paretic wrist. The device will deliver TheraBracelet vibration for the treatment group and no vibration for the control group. The primary outcome is hand function measured by the Wolf Motor Function Test. Other outcomes include the Box and Block Test, Action Research Arm Test, upper extremity use in daily living, biomechanical measure of the sensorimotor grip control, and EEG-based neural communication.

Discussion

This research will determine clinical utility of TheraBracelet to guide future translation. The TheraBracelet stimulation is delivered via a wrist-worn device, does not interfere with hand motion, and can be easily integrated into clinical practice. Enhancing hand function should substantially increase stroke survivors' independence and quality of life and reduce caregiver burden.

Trial registration

NCT04569123. Registered on September 29, 2020

Determinants of Different Aspects of Upper-Limb Activity after Stroke

We examined factors associated with different aspects of upper-limb (UL) activity in chronic stroke to better understand and improve UL activity in daily life. Three different aspects of UL activity were represented by four sensor measures: (1) contribution to activity according to activity ratio and magnitude ratio, (2) intensity of activity according to bilateral magnitude, and (3) variability of activity according to variation ratio. We combined data from a Belgian and Danish patient cohort (n = 126) and developed four models to determine associated factors for each sensor measure. Results from standard multiple regression show that motor impairment (Fugl−Meyer assessment) accounted for the largest part of the explained variance in all sensor measures (18−61%), with less motor impairment resulting in higher UL activity values (p < 0.001). Higher activity ratio, magnitude ratio, and variation ratio were further explained by having the dominant hand affected (p < 0.007). Bilateral magnitude had the lowest explained variance (adjusted R2 = 0.376), and higher values were further associated with being young and female. As motor impairment and biological aspects accounted for only one- to two-thirds of the variance in UL activity, rehabilitation including behavioral strategies might be important to increase the different aspects of UL activity.

Digital Phenotyping in Clinical Neurology

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life.

Upper Limb Performance in Daily Life Approaches Plateau Around Three to Six Weeks Post-stroke

Background. Wearable sensors allow for direct measurement of upper limb (UL) performance in daily life. Objective. To map the trajectory of UL performance and its relationships to other factors post-stroke. Methods. Participants (n = 67) with first stroke and UL paresis were assessed at 2, 4, 6, 8, 12, 16, 20, and 24 weeks after stroke. Assessments captured UL impairment (Fugl-Meyer), capacity for activity (Action Research Arm Test), and performance of activity in daily life (accelerometer variables of use ratio and hours of paretic limb activity), along with other potential modifying factors. We modeled individual trajectories of change for each measurement level and the moderating effects on UL performance trajectories. Results. Individual trajectories were best fit with a 3-parameter logistic model, capturing the rapid growth early after stroke within the longer data collection period. Plateaus (90% of asymptote) in impairment (bootstrap mean ± SE: 32 ± 4 days post-stroke) preceded those in capacity (41 ± 4 days). Plateau in performance, as measured by the use ratio (24 ± 5 days), tended to precede plateaus in impairment and capacity. Plateau in performance, as measured by hours of paretic activity (41 ± 6 days), occurred at a similar time to that of capacity and slightly lagged impairment. Modifiers of performance trajectories were capacity, concordance, UL rehabilitation, depressive symptomatology, and cognition. Conclusions. Upper limb performance in daily life approached plateau 3 to 6 weeks post-stroke. Individuals with stroke started to achieve a stable pattern of UL use in daily life early, often before neurological impairments and functional capacity started to stabilize.

A Self-Empowered Upper Limb Repetitive Engagement Program to Improve Upper Limb Recovery Early Post-Stroke: Phase II Pilot Randomized Controlled Trial

Background. Time outside therapy provides an opportunity to increase upper limb (UL) use during post-stroke hospitalization. Objective. To determine if a self-directed UL program outside therapy (Self-Empowered UL Repetitive Engagement, SURE) was feasible and to explore the potential effect of the SURE program on UL use and recovery. Methods. Twenty-three patients from an inpatient rehabilitation center who were ≤21 days post-stroke and had a Fugl Meyer UL (FMUL) score ≤50 and a positive motor evoked potential (MEP+) response were randomized (stratified by impairment) to either experimental group (SURE: individualized, UL self-exercise and use outside therapy for 6 hours/week for 4 weeks) or control group (education booklet). Feasibility was evaluated by program adherence, dropout rate, adverse events, and satisfaction. Potential effect was measured by paretic UL use via accelerometry weekly during the intervention, FMUL and Action Research Arm Test (ARAT) at baseline (Week 0), post-intervention (Week 4), and follow-up (Week 8 and Week 16). Results. Adherence to SURE was high: 87% program completion (mean 313±75 repetitions/day). There were no dropouts, no adverse events related to SURE, and patient satisfaction averaged 7.8/10. Experimental participants achieved an additional hour of UL use daily (range: .3-1.2 hours/day) compared to control. Significant improvements in FMUL and ARAT were observed in both groups from Week 0 to Week 4 and to Week 8 (P ≤ .002), which were maintained to Week 16. There were no differences between groups (P ≥ .119). Conclusions. SURE was a feasible self-directed program that increased UL use in MEP+ individuals with moderate-severe impairment early post-stroke. Further studies with larger sample sizes and potentially higher dose are required to determine efficacy.

Arm impairment and walking speed explain real-life activity of the affected arm and leg after stroke

OBJECTIVE

To determine to what extent accelerometer-based arm, leg and trunk activity is associated with sensorimotor impairments, walking capacity and other factors in subacute stroke.

DESIGN

Cross-sectional study.

PATIENTS

Twenty-six individuals with stroke (mean age 55.4 years, severe to mild motor impairment).

METHODS

Data on daytime activity were collected over a period of 4 days from accelerometers placed on the wrists, ankles and trunk. A forward stepwise linear regression was used to determine associations between free-living activity, clinical and demographic variables.

RESULTS

Arm motor impairment (Fugl-Meyer Assessment) and walking speed explained more than 60% of the variance in daytime activity of the more-affected arm, while walking speed alone explained 60% of the more-affected leg activity. Activity of the less-affected arm and leg was associated with arm motor impairment (R2 = 0.40) and independence in walking (R2 = 0.59). Arm activity ratio was associated with arm impairment (R2 = 0.63) and leg activity ratio with leg impairment (R2 = 0.38) and walking speed (R2 = 0.27). Walking-related variables explained approximately 30% of the variance in trunk activity.

CONCLUSION

Accelerometer-based free-living activity is dependent on motor impairment and walking capacity. The most relevant activity data were obtained from more-affected limbs. Motor impairment and walking speed can provide some information about real-life daytime activity levels.

Identifying Hand Use and Hand Roles After Stroke Using Egocentric Video

Objective: Upper limb (UL) impairment impacts quality of life, but is common after stroke. UL function evaluated in the clinic may not reflect use in activities of daily living (ADLs) after stroke, and current approaches for assessment at home rely on self-report and lack details about hand function. Wrist-worn accelerometers have been applied to capture UL use, but also fail to reveal details of hand function. In response, a wearable system is proposed consisting of egocentric cameras combined with computer vision approaches, in order to identify hand use (hand-object interactions) and the role of the more-affected hand (as stabilizer or manipulator) in unconstrained environments. Methods: Nine stroke survivors recorded their performance of ADLs in a home simulation laboratory using an egocentric camera. Motion, hand shape, colour, and hand size change features were generated and fed into random forest classifiers to detect hand use and classify hand roles. Leave-one-subject-out cross-validation (LOSOCV) and leave-one-task-out cross-validation (LOTOCV) were used to evaluate the robustness of the algorithms. Results: LOSOCV and LOTOCV F1-scores for more-affected hand use were 0.64 ± 0.24 and 0.76 ± 0.23, respectively. For less-affected hands, LOSOCV and LOTOCV F1-scores were 0.72 ± 0.20 and 0.82 ± 0.22. F1-scores for hand role classification were 0.70 ± 0.19 and 0.68 ± 0.23 in the more-affected hand for LOSOCV and LOTOCV, respectively, and 0.59 ± 0.23 and 0.65 ± 0.28 in the less-affected hand. Conclusion: The results demonstrate the feasibility of predicting hand use and the hand roles of stroke survivors from egocentric videos.

Longitudinal analysis of the recovery of trunk control and upper extremity following stroke: An individual growth curve approach

Background and Purpose: Trunk control is thought to contribute to upper extremity function. It is unclear whether recovery of trunk control has an impact on the recovery of the upper extremity in people with stroke. This longitudinal study monitored the recovery of trunk control and upper extremity in the first 6 months following stroke.Methods: Forty-five participants with stroke were assessed monthly for 6 months following stroke. Trunk control was assessed using the Trunk Impairment Scale (TIS); upper extremity impairment and function were assessed with the Fugl-Meyer (FMA) and Streamlined Wolf Motor Function Test (SWMFT) respectively. The SWMFT included the performance time (SWMFT-Time) and functional ability scale (SWMFT-FAS). The individual growth curve modeling was used to analyze the longitudinal data.Results: The recovery curve of TIS, FMA, SWMFT-Time and SWMFT-FAS followed a quadratic trend, with the rate of recovery decreasing from the first to sixth month. As TIS score improved over time, FMA, SWMFT-Time and SWMFT-FAS improved in parallel with the TIS score. TIS at each time point was found to be a significant predictor of FMA, SWMFT-Time and SWMFT-FAS at 6 months post stroke.Conclusion: Our work has provided, for the first time, substantial evidence that the pattern of recovery of trunk control is similar to that of the recovery of upper extremity following stroke. In addition, this study provides evidence on which to design a prospective study to evaluate whether improvement in trunk control early post-stroke results in better long-term upper extremity function.

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

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

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

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

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

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

Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method

BACKGROUND

Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results.

OBJECTIVE

Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity.

METHODS

Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb.

RESULTS

The counts ratio was not significantly correlated with ground truth and had large errors (r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 (P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 (P = .005; average error = 5.2%) with ground truth.

CONCLUSIONS

In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.

Generalizability of Hand-Object Interaction Detection in Egocentric Video across Populations with Hand Impairment

Stroke survivors often experience unilateral sensorimotor impairment. The restoration of upper limb function is an important determinant of quality of life after stroke. Wearable technologies that can measure hand function at home are needed to assess the impact of new interventions. Egocentric cameras combined with computer vision algorithms have been proposed as a means to capture hand use in unconstrained environments, and have shown promising results in this application for individuals with cervical spinal cord injury (cSCI). The objective of this study was to examine the generalizability of this approach to individuals who have experienced a stroke. An egocentric camera was used to capture the hand use (hand-object interactions) of 6 stroke survivors performing daily tasks in a home simulation laboratory. The interaction detection classifier previously trained on 9 individuals with cSCI was applied to detect hand use in the stroke survivors. The processing pipeline consisted of hand detection, hand segmentation, feature extraction, and interaction detection. The resulting average F1 scores for affected and unaffected hands were 0.66 ± 0.25 and 0.80 ± 0.15, respectively, indicating that the approach is feasible and has the potential to generalize to stroke survivors. Using stroke-specific training data may further increase the accuracy obtained for the affected hand.

Relationships between accelerometry and general compensatory movements of the upper limb after stroke

Background

Standardized assessments are used in rehabilitation clinics after stroke to measure restoration versus compensatory movements of the upper limb. Accelerometry is an emerging tool that can bridge the gap between in- and out-of-clinic assessments of the upper limb, but is limited in that it currently does not capture the quality of a person’s movement, an important concept to assess compensation versus restoration. The purpose of this analysis was to characterize how accelerometer variables may reflect upper limb compensatory movement patterns after stroke.

Methods

This study was a secondary analysis of an existing data set from a Phase II, single-blind, randomized, parallel dose–response trial (NCT0114369). Sources of data utilized were: (1) a compensatory movement score derived from video analysis of the Action Research Arm Test (ARAT), and (2) calculated accelerometer variables quantifying time, magnitude and variability of upper limb movement from the same time point during study participation for both in-clinic and out-of-clinic recording periods.

Results

Participants had chronic upper limb paresis of mild to moderate severity. Compensatory movement scores varied across the sample, with a mean of 73.7 ± 33.6 and range from 11.5 to 188. Moderate correlations were observed between the compensatory movement score and each accelerometer variable. Accelerometer variables measured out-of-clinic had stronger relationships with compensatory movements, compared with accelerometer variables in-clinic. Variables quantifying time, magnitude, and variability of upper limb movement out-of-clinic had relationships to the compensatory movement score.

Conclusions

Accelerometry is a tool that, while measuring movement quantity, can also reflect the use of general compensatory movement patterns of the upper limb in persons with chronic stroke. Individuals who move their limbs more in daily life with respect to time and variability tend to move with less movement compensations and more typical movement patterns. Likewise, individuals who move their paretic limbs less and their non-paretic limb more in daily life tend to move with more movement compensations at all joints in the paretic limb and less typical movement patterns.

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

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

Bilateral capacity is related to bilateral upper limb use after stroke: a study by behavioral maps, accelerometers and perceived amount of use

PURPOSE

This study aimed to assess the following in individuals after stroke: (1) relationship between upper limb (UL) use by direct observation at home with use perceived and measured by accelerometers; (2) complementarity of these three measurements; and (3) relationship between UL bilateral capacity and bilateral use.

MATERIALS AND METHODS

Thirty-one individuals with chronic hemiparesis participated in this cross-sectional study. UL use was assessed using a behavioral map (BM), the Motor Activity Log-Amount of Use (MAL-AOU), and accelerometers, while UL capacity was assessed using the Test d'Evaluation des Membres Supérieurs des Personnes Âgées (TEMPA).

RESULTS

The BM was strongly correlated with perceived use (MAL-AOU, ρ = 0.76) and accelerometer (ρ = 0.70). Bilateral UL use (BM) was moderately (ρ = 0.65) correlated with bilateral MAL-AOU and bilateral use by accelerometers (ρ = 0.62). The BM aided our understanding of how the paretic UL was used. The correlation between bilateral capacity (TEMPA bilateral) and bilateral use (BM) was significant (ρ = 0.49), while that with bilateral MAL-AOU and accelerometer were ρ = 0.68 and ρ = 0.50, respectively.

CONCLUSION

A BM is a valid way to quantify UL use and can complement information assessed regarding perceived use and by accelerometers.Implications for rehabilitationBehavioral maps may be valuable to complement information assessed by perceived UL use and accelerometers.Quantifying bilateral capacity will reflect in a better understanding of actual paretic UL use after stroke.Accelerometers can underestimate the amount of paretic UL use in asymmetrical bilateral tasks.

Accelerometer Measurements Indicate That Arm Movements of Children With Cerebral Palsy Do Not Increase After Constraint-Induced Movement Therapy (CIMT)

IMPORTANCE

Constraint-induced movement therapy (CIMT) is a common treatment for children with unilateral cerebral palsy (CP). Although clinic-based assessments have demonstrated improvements in arm function after CIMT, whether these changes are translated and sustained outside of a clinic setting remains unclear.

OBJECTIVE

Accelerometers were used to quantify arm movement for children with CP 1 wk before, during, and 4 wk or more after CIMT; measurements were compared with those from typically developing (TD) peers.

DESIGN

Observational.

SETTING

Tertiary hospital and community.

PARTICIPANTS

Seven children with CP (5 boys, 2 girls; average [AVE] age ± standard deviation [SD] = 7.4 ± 1.2 yr) and 7 TD peers (2 boys, 5 girls; AVE age ± SD = 7.0 ± 2.3 yr).

INTERVENTION

30-hr CIMT protocol.

OUTCOMES AND MEASURES

Use ratio, magnitude ratio, and bilateral magnitude were calculated from the accelerometer data. Clinical measures were administered before and after CIMT, and parent surveys assessed parent and child perceptions of wearing accelerometers.

RESULTS

During CIMT, the frequency and magnitude of paretic arm use among children with CP increased in the clinic and in daily life. After CIMT, although clinical scores showed sustained improvement, the children's accelerometry data reverted to baseline values. Children and parents in both cohorts had positive perceptions of accelerometer use.

CONCLUSIONS AND RELEVANCE

The lack of sustained improvement in accelerometry metrics after CIMT suggests that therapy gains did not translate to increased movement outside the clinic. Additional therapy may be needed to help transfer gains outside the clinic.

WHAT THIS ARTICLE ADDS

Accelerometer measurements were effective at monitoring arm movement outside of the clinic during CIMT and suggested that additional interventions may be needed after CIMT to sustain benefits.

Characterizing upper extremity motor behavior in the first week after stroke

Background

Animal models of brain recovery identify the first days after lesioning as a time of great flux in sensorimotor function and physiology. After rodent motor system lesioning, daily skill training in the less affected forelimb reduces skill acquisition in the more affected forelimb. We asked whether spontaneous human motor behaviors of the less affected upper extremity (UE) early after stroke resemble the animal training model, with the potential to suppress clinical recovery.

Methods

This prospective observational study used a convenience sample of patients (n = 25, mean 4.5 ±1.8) days after stroke with a wide severity range; Controls were hospitalized for non-neurological conditions (n = 12). Outcome measures were Accelerometry, Upper-Extremity Fugl-Meyer (UEFM), Action Research Arm Test (ARAT), Shoulder Abduction/ Finger Extension Test (SAFE), NIH Stroke Scale (NIHSS).

Results

Accelerometry indicated total paretic UE movement was reduced compared to controls, primarily due to a 44% reduction of bilateral UE use. Unilateral paretic movement was unchanged. Thus, movement shifted early after stroke; bilateral use was reduced and unilateral use of the non-paretic UE was increased by 77%. Low correlations between movement time and motor performance prompted an exploratory factor analysis (EFA) revealing a 2-component solution; motor performance tests load on one component (motor performance) whereas accelerometry-derived variables load on a second orthogonal component (quantity of movement).

Conclusions

Early after stroke, spontaneous overall UE movement is reduced, and movement shifts to unilateral use of the non-paretic UE. Two mechanisms that could influence motor recovery may already be in place 4.5 ± 1.8 days post stroke: (1) the overuse of the less affected UE, which could set the stage for learned non-use and (2) skill acquisition in the non-paretic limb that could impede recovery. Accurate UE motor assessment requires two independent constructs: motor performance and quantity of movement. These findings provide opportunities and measurement methods for studies to develop new behaviorally-based stroke recovery treatments that begin early after onset.

Estimating Quality of Reaching Movement Using a Wrist-Worn Inertial Sensor

Stroke is a major cause of long-term disability. Because patients recovering from stroke often perform differently in clinical settings than in their naturalistic environments, remote monitoring of motor performance is needed to evaluate the true impact of prescribed therapies. Wearable sensors have been considered as a technical solution to this problem, but most existing systems focus on measuring the amount of movement without considering the quality of movement. We present a novel method to seamlessly and unobtrusively measure the quality of individual reaching movements by leveraging a motor control theory that describes how the central nervous system plans and executes movements. We trained and evaluated our system on 19 stroke survivors to estimate the Functional Ability Scale (FAS) of reaching movements. The analysis showed that we can estimate the FAS scores of reaching movements, with some confusion between adjacent scores. Furthermore, we estimated the average FAS scores of subjects with a normalized root mean square error (NRMSE) of 22.5%. Though our model's high error on two severe subjects influenced our overall estimation performance, we could accurately estimate scores in most of the mild-to-moderate subjects (NRMSE of 13.1% without the outliers). With further development and testing, we believe the proposed technique can be applied to monitor patient recovery in home and community settings.

Comparing Home Upper Extremity Activity with Clinical Evaluations of Arm Function in Chronic Stroke

OBJECTIVE

To determine if clinical evaluations of post-stroke arm function correspond to everyday motor performance indexed by arm accelerometers.

DESIGN

Cross-sectional study analyzing baseline data from a larger trial (NCT02665052).

SETTING

Outpatient research center.

PARTICIPANTS

Twenty community-dwelling adults with chronic arm motor deficits (stroke≥6mo).

INTERVENTION

72-hours of home wrist-worn accelerometry during normal routine.

MAIN OUTCOME MEASURES

Clinical evaluations included the Fugl-Meyer (FM), Action Research Arm Test (ARAT), Wolf Motor Function Test (WMFT), and two self-assessments: the Motor Activity Log (MAL) and hand motor subscale of the Stroke Impact Scale (SIS). Accelerometer-derived variables included quantifications of movement intensity (magnitude) and duration of arm use.

RESULTS

Participants had moderate arm impairment (FM 36.1 ± 9.4). The accelerometer-derived mean magnitude ratio correlated significantly with the FM (ρ = 0.60, p < 0.01), WMFT functional score (ρ = 0.59, p < 0.01), and ARAT (ρ = 0.50, p < 0.05). The hours of use ratio correlated with the MAL amount of use (ρ = 0.58, p < 0.01) and quality of movement (ρ = 0.61, p < 0.01). Total paretic hours did not correlate with the FM, WMFT or ARAT, and intensity variables did not correlate with the MAL or SIS.

CONCLUSIONS

Participants with higher baseline function had greater intensity of paretic arm movement at home; similarly, those who perceived they had less disability used their paretic arm more relative to their non-paretic arm. However, some participants with higher clinical scores did not exhibit greater arm use in everyday life, possibly due to neglect and learned non-use. Therefore, individualized home accelerometry profiles could provide valuable insight to better tailor post-stroke rehabilitation.

Feasibility of a Sensor-Based Technological Platform in Assessing Gait and Sleep of In-Hospital Stroke and Incomplete Spinal Cord Injury (iSCI) Patients

Recovery of the walking function is one of the most common rehabilitation goals of neurological patients. Sufficient and adequate sleep is a prerequisite for recovery or training. To objectively monitor patients’ progress, a combination of different sensors measuring continuously over time is needed. A sensor-based technological platform offers possibilities to monitor gait and sleep. Implementation in clinical practice is of utmost relevance and has scarcely been studied. Therefore, this study examined the feasibility of a sensor-based technological platform within the clinical setting. Participants (12 incomplete spinal cord injury (iSCI), 13 stroke) were asked to wear inertial measurement units (IMUs) around the ankles during daytime and the bed sensor was placed under their mattress for one week. Feasibility was established based on missing data, error cause, and user experience. Percentage of missing measurement days and nights was 14% and 4%, respectively. Main cause of lost measurement days was related to missing IMU sensor data. Participants were not impeded, did not experience any discomfort, and found the sensors easy to use. The sensor-based technological platform is feasible to use within the clinical rehabilitation setting for continuously monitoring gait and sleep of iSCI and stroke patients.

Feasibility of using acceleration-derived jerk to quantify bimanual arm use

BACKGROUND

Accelerometers have become common for evaluating the efficacy of rehabilitation for patients with neurologic disorders. For example, metrics like use ratio (UR) and magnitude ratio (MR) have been shown to differentiate movement patterns of children with cerebral palsy (CP) compared to typically-developing (TD) peers. However, these metrics are calculated from "activity counts" - a measure based on proprietary algorithms that approximate movement duration and intensity from raw accelerometer data. Algorithms used to calculate activity counts vary between devices, limiting comparisons of clinical and research results. The goal of this research was to develop complementary metrics based on raw accelerometer data to analyze arm movement after neurologic injury.

METHOD

We calculated jerk, the derivative of acceleration, to evaluate arm movement from accelerometer data. To complement current measures, we calculated jerk ratio (JR) as the relative jerk magnitude of the dominant (non-paretic) and non-dominant (paretic) arms. We evaluated the JR distribution between arms and calculated the 50th percentile of the JR distribution (JR50). To evaluate these metrics, we analyzed bimanual accelerometry data for five children with hemiplegic CP who underwent Constraint-Induced Movement Therapy (CIMT) and five typically developing (TD) children. We compared JR between the CP and TD cohorts, and to activity count metrics.

RESULTS

The JR50 differentiated between the CP and TD cohorts (CP = 0.578 ± 0.041 before CIMT, TD = 0.506 ± 0.026), demonstrating increased reliance on the dominant arm for the CP cohort. Jerk metrics also quantified changes in arm use during and after therapy (e.g., JR50 = 0.378 ± 0.125 during CIMT, 0.591 ± 0.057 after CIMT). The JR was strongly correlated with UR and MR (r = - 0.92, 0.89) for the CP cohort. For the TD cohort, JR50 was repeatable across three data collection periods with an average similarity of 0.945 ± 0.015.

CONCLUSIONS

Acceleration-derived jerk captured differences in motion between TD and CP cohorts and correlated with activity count metrics. The code for calculating and plotting JR is open-source and available for others to use and build upon. By identifying device-independent metrics that can quantify arm movement in daily life, we hope to facilitate collaboration for rehabilitation research using wearable technologies.

Effect of investigator observation on gait parameters in individuals with stroke

Improvements in gait speed following various training paradigms applied to patients post-stroke does not always lead to changes in walking performance, defined as gains in daily stepping activity. We hypothesized that testing conditions, specifically the presence of an observer, influences patient behaviors and resultant outcomes may overestimate their true walking capacity. This potential Hawthorne effect on spatiotemporal and biomechanical measures of locomotor function in individuals post-stroke has not been assessed previously. Fifteen ambulatory individuals with chronic stroke wore instrumented insoles and performed two separate normal-pace walking assessments, including unobserved conditions during which participants were unattended and unaware of data collection, and observed conditions with an investigator present. Gait analysis was conducted outside of a laboratory setting using instrumented insoles equipped with a 3D accelerometer and pressure sensors which captured the spatiotemporal kinematics, vertical ground reaction forces and foot acceleration. Data were compared using paired comparisons, with subsequent correlation and stepwise regression analyses to explore potential associations between Hawthorne-induced changes in walking strategies, gait speed and locomotor performance (daily stepping). Except for cadence, other measures of spatiotemporal parameters and swing kinematics (acceleration) were not significantly different between observed vs unobserved conditions. However, analyses of ground reaction forces revealed significantly greater paretic limb loading (Δ1^st peak = 1.5 ± 1.6 N/kg Δ2^nd peak = 1.4 ± 1.8 N/kg; p < 0.01) and increases in weight bearing symmetry (11-24%, p < 0.01) during observed vs unobserved conditions. This potential Hawthorne effect was greater in those with slower walking speeds and shorter stride lengths but was not related to daily stepping. The present findings suggest that biomechanical parameters of walking function may be related to the presence of an observer and highlight the need to separately measure locomotor capacity (gait speed) and performance (daily stepping).

Actigraphic measurement of the upper limbs movements in acute stroke patients

Background

Stroke units provide patients with a multiparametric monitoring of vital functions, while no instruments are actually available for a continuous monitoring of patients motor performance. Our aim was to develop an actigraphic index able both to identify the paretic limb and continuously monitor the motor performance of stroke patients in the stroke unit environment.

Methods

Twenty consecutive acute stroke patients (mean age 69.2 years SD 10.1, 8 males and 12 females) and 17 bed-restrained patients (mean age 70.5 years SD 7.3, 7 males and 10 females) hospitalized for orthopedic diseases of the lower limbs, but not experiencing neurological symptoms, were enrolled. This last group represented our control group. The motor activity of arms was recorded for 24 h using two programmable actigraphic systems showing off as wrist-worn watches. The firmware segmented the acquisition in epochs of 1 minute and for each epoch calculates two motor activity indices: MA_e1 (Epoch-related Motor Activity index) and MA_e2 (Epoch-related Motor Activity index 2). MA_e1 is defined as the standard deviation of the acceleration module and MA_e2 as the module of the standard deviation of acceleration components. To describe the 24 h motor performance of each limb, we calculated the mean value of MA_e1 and MA_e2 (respectively MA_{1_24h} and MA_{2_24h}). Then we obtained two Asymmetry Rate Indices: AR_{1_24h} and AR_{2_24h} to show the motor activity prevalence. AR_{1_24h} refers to the asymmetry index between the values of MA_e1 of both arms and AR_{2_24h} to MA_e2 values.

The stroke patients were clinically evaluated by NIHSS at the beginning (NIHSS_T0) and at the end (NIHSS_T1) of the 24 h actigraphic recordings.

Results

Both MA_{1_24h} and MA_{2_24h} indices were smaller in the paretic than in the unaffected arm (respectively p = 0.004 and p = 0.004). AR_{2_24h} showed a better capability (95% of paretic arms correctly identified, Phi Coefficient: 0.903) to discriminate the laterality of the clinical deficit than AR_{1_24h} (85% of paretic arms correctly identified, Phi Coefficient: 0,698). We also found that AR_{1_24h} did not differ between the two groups of patients while AR_{2_24h} was greater in stroke patients than in controls and positively correlated with NIHSS total scores (r: 0.714, p < 0.001 for NIHSS, IC95%: 0.42–0.90) and with the sub-score relative to the paretic upper limb (r: 0.812, p < 0.001, IC95%: 0.62–0.96).

Conclusions

Our data show that actigraphic monitoring of upper limbs can detect the laterality of the motor deficit and measure the clinical severity. These findings suggest that the above described actigraphic system could implement the existing multiparametric monitoring in stroke units.

Are changes in upper extremity use during sub-acute rehabilitation after stroke associated with physical, cognitive, and social activities? An observational cohort pilot study

OBJECTIVES

To investigate how changes in physical, social, and cognitive activity levels are associated with the use of the affected upper extremity at different time points during inpatient rehabilitation after stroke.

METHODS

In an observational longitudinal cohort pilot study activity of 14 patients with subacute stroke was registered 2-4 times during their inpatient rehabilitation from 2 p.m. to 8 p.m. At the same time, patients wore accelerometers on both wrists to register amount of use and use ratio of the affected to the unaffected upper extremity. Before and after the observation period, patients were assessed with action research arm test, Box and Blocks Test, and Functional Independence Measure. Linear regression models were used to examine the influence of different categories of activities and motor function levels on affected upper extremity use.

RESULTS

Increasing physical activity levels during rehabilitation and improvement in upper extremity motor function were associated with increased use of the affected upper extremity. Cognitive and social activity levels did not change and were not associated with affected upper extremity use.

CONCLUSION

Our findings suggest that the use of the affected upper extremity and a general increase in activity are associated. Facilitating both general physical activity and specific upper extremity use at a rehabilitation ward may benefit overall recovery.

Changes in reaching skill in patients with cervical spondylosis after cervical decompression surgery

[Purpose] The aim of this study was to evaluate the changes in reaching function during a reaching task in cervical spondylosis (CS) patients before and after surgery. [Participants and Methods] Nine patients participated in the study. Wrist acceleration peaks were monitored pre- and postoperatively using a tri-axial accelerometer, and the Japanese Orthopedic Association (JOA) score was recorded preoperatively. Additional upper extremity function tests were performed pre- and postoperatively. Multiple stepwise regression analysis was used to investigate the contribution of wrist acceleration peak to the severity of clinical symptoms. Moreover, we compared the acceleration peaks produced during the reaching task before and after surgery. [Results] Multiple regression analysis showed that wrist acceleration peak, grip strength and pinch strength were associated with the upper extremity function of the JOA score, explaining 61.0% of the variance. There was a significant improvement in x-axis acceleration peak after surgery. [Conclusion] Our results suggested that quantitative assessments of reaching function are useful to objectively evaluate the changes in reaching function in patients undergoing cervical decompression surgery.

Upper Limb Performance in Daily Life Improves Over the First 12 Weeks Poststroke

Background. Upper limb (UL) performance, or use, in daily life is complex and likely influenced by many factors. While the recovery trajectory of UL impairment poststroke is well documented, little is known about the recovery trajectory of sensor-measured UL performance in daily life early after stroke and the potential moderating role of psychosocial factors. Objective. To examine the recovery trajectory of UL performance within the first 12 weeks poststroke and characterize the potential moderating role of belief, confidence, and motivation on UL performance. Methods. This was a longitudinal, prospective cohort study quantifying UL performance and related psychosocial factors early after stroke. UL performance was quantified via bilateral, wrist-worn accelerometers over 5 assessment sessions for 24 hours. Belief, confidence, and motivation to use the paretic UL, and self-perceived barriers to UL recovery were quantified via survey. Change in 4 accelerometer variables and the moderating role of psychosocial factors was tested using hierarchical linear modeling. The relationship between self-perceived barriers and UL performance was tested via Spearman rank-order correlation analysis. Results. UL performance improved over the first 12 weeks after stroke. Belief, confidence, and motivation did not moderate UL performance over time. There was a negative relationship between UL performance and self-perceived barriers to UL recovery at week 2, which declined over time. Conclusions. Sensor-measured UL performance can improve early after stroke. Early after stroke, rehabilitation interventions may not need to directly target belief, confidence, and motivation but may instead focus on reducing self-perceived barriers to UL recovery.

Measuring Reliability of Movement With Accelerometry: Fitbit® Versus ActiGraph®

OBJECTIVE

We sought to determine the reliability of the Fitbit^® Flex™ compared with the ActiGraph^® Bluetooth^® Smart wGT3X-BT wireless activity monitor and to contribute to the clinical utility of accelerometry measurement of upper extremity (UE) movement.

METHOD

Two studies were conducted at different sites with healthy adult participants. In Study 1, participants wore both accelerometers on both wrists during everyday activities for a 24-hr period. In Study 2, participants wore both accelerometers on the dominant wrist for 4 hr during an active period of the day. All participants wore the accelerometers during regular daily activities.

RESULTS

Data recorded from the Fitbit and the ActiGraph showed a high positive correlation; however, the Fitbit recorded significantly fewer movements than the ActiGraph.

CONCLUSION

Although the Fitbit and the ActiGraph measure UE activity similarly, the Fitbit was not as sensitive as the ActiGraph. This study provides informative data on the clinical utility of the Fitbit compared with the ActiGraph.

An accelerometry and observational study to quantify upper limb use after stroke during inpatient rehabilitation

OBJECTIVES

The objectives of the study are to characterize paretic upper limb (UL) use in people with different levels of impairment 4 weeks poststroke and to compare accelerometry and direct observational approaches.

METHODS

Twelve stroke inpatients (five mild, three moderate, and four severe UL impairment) were recruited from a rehabilitation hospital. UL use was measured using accelerometry (24 hr) and direct observation (12 hr of behavioural mapping). Accelerometry variables included duration of use, use ratio, magnitude ratio, bilateral magnitude, and variation ratio. Direct observation recorded the duration of use and type of UL movement (e.g., functional vs. non-functional).

RESULTS

From accelerometry data, people with mild, moderate, and severe UL impairments used their paretic UL 59%, 45%, and 22% of a 24 hr-day, respectively. People with severe UL impairment had the lowest paretic UL use duration (median 1.49 hr/day), magnitude ratio, and variation ratio compared with people with mild and moderate UL impairment. From 12 hr of observational data, people with mild impairment were using their UL for 37.8% of the observed time, whereas the people with moderate and severe impairment were using their UL 15.8% and 4.9%, respectively. UL movements for the mild cohort were mainly functional, whereas UL movements of the moderate and severe cohorts were mainly non-functional. UL movements were predominantly active for the mild and moderate cohorts but passive for the severe cohort. Duration of paretic UL use from accelerometry and observation data were highly correlated (ICC > 0.8), but the absolute percentage error between methods ranged from 34.2% to 42.7%.

CONCLUSIONS

Paretic UL use within the first 4 weeks poststroke differs across levels of impairment in this exploratory study. Accelerometry and observation findings of paretic UL use were correlated and may be needed in different situations as they capture different information.

Changes in actual arm-hand use in stroke patients during and after clinical rehabilitation involving a well-defined arm-hand rehabilitation program: A prospective cohort study

Introduction

Improvement of arm-hand function and arm-hand skill performance in stroke patients is reported by many authors. However, therapy content often is poorly described, data on actual arm-hand use are scarce, and, as follow-up time often is very short, little information on patients’ mid- and long-term progression is available. Also, outcome data mainly stem from either a general patient group, unstratified for the severity of arm-hand impairment, or a very specific patient group.

Objectives

To investigate to what extent the rate of improvement or deterioration of actual arm-hand use differs between stroke patients with either a severely, moderately or mildly affected arm-hand, during and after rehabilitation involving a well-defined rehabilitation program.

Methods

Design: single–armed prospective cohort study. Outcome measure: affected arm-hand use during daily tasks (accelerometry), expressed as ‘Intensity-of arm-hand-use’ and ‘Duration-of-arm-hand-use’ during waking hours. Measurement dates: at admission, clinical discharge and 3, 6, 9, and 12 months post-discharge. Statistics: Two-way repeated measures ANOVAs.

Results

Seventy-six patients (63 males); mean age: 57.6 years (sd:10.6); post-stroke time: 29.8 days (sd:20.1) participated. Between baseline and 1-year follow-up, Intensity-of-arm-hand-use on the affected side increased by 51%, 114% and 14% (p < .000) in the mildly, moderately and severely affected patients, respectively. Similarly, Duration-of-arm-hand-use increased by 26%, 220% and 161% (p < .000). Regarding bimanual arm-hand use: Intensity-of-arm-hand-use increased by 44%, 74% and 30% (p < .000), whereas Duration-of-arm-hand-use increased by 10%, 22% and 16% (p < .000).

Conclusion

Stroke survivors with a severely, moderately or mildly affected arm-hand showed different, though (clinically) important, improvements in actual arm-hand use during the rehabilitation phase. Intensity-of-arm-hand-use and Duration-of-arm-hand-use significantly improved in both unimanual and bimanual tasks/skills. These improvements were maintained until at least 1 year post-discharge.

Sensor Measures of Symmetry Quantify Upper Limb Movement in the Natural Environment Across the Lifespan

Knowledge of upper limb activity in the natural environment is critical for evaluating the effectiveness of rehabilitation services. Wearable sensors allow efficient collection of these data and have the potential to be less burdensome than self-report measures of activity. Sensors can capture many different variables of activity and daily performance, many of which could be useful in identifying deviation from typical movement behavior or measuring outcomes from rehabilitation interventions. Although it has potential, sensor measurement is just emerging, and there is a lack of consensus regarding which variables of daily performance are valid, sensitive, specific, and useful. We propose that symmetry of full-day upper limb movement is a key variable. We describe here that symmetry is valid, robustly observed within a narrow range across the lifespan in typical development, and shows evidence of being different in populations with neuromotor impairment. Key next steps include the determination of sensitivity, specificity, minimal detectable change, and minimal clinically important change/difference. This information is needed to determine whether an individual belongs to the typical or atypical group, whether change has occurred, and whether that change is beneficial.

Consideration of Dose and Timing When Applying Interventions After Stroke and Spinal Cord Injury

BACKGROUND AND PURPOSE

Nearly 4 decades of investigation into the plasticity of the nervous system suggest that both timing and dose could matter. This article provides a synopsis of our lectures at the IV STEP meeting, which presented a perspective of current data on the issues of timing and dose for adult stroke and spinal cord injury motor rehabilitation.

SUMMARY OF KEY POINTS

For stroke, the prevailing evidence suggests that greater amounts of therapy do not result in better outcomes for upper extremity interventions, regardless of timing. Whether or not greater amounts of therapy result in better outcomes for lower extremity and mobility interventions needs to be explicitly tested. For spinal cord injury, there is a complex interaction of timing postinjury, task-specificity, and the microenvironment of the spinal cord. Inflammation appears to be a key determinant of whether or not an intervention will be beneficial or maladaptive, and specific retraining of eccentric control during gait may be necessary.

RECOMMENDATIONS FOR CLINICAL PRACTICE

To move beyond the limitations of our current interventions and to effectively reach nonresponders, greater precision in task-specific interventions that are well-timed to the cellular environment may hold the key. Neurorehabilitation that ameliorates persistent deficits, attains greater recovery, and reclaims nonresponders will decrease institutionalization, improve quality of life, and prevent multiple secondary complications common after stroke and spinal cord injury.

Towards the Ambulatory Assessment of Movement Quality in Stroke Survivors using a Wrist-worn Inertial Sensor

Stroke is a leading cause of long-term disability that may lead to significant functional motor impairments in the upper limb (UL). Wrist-worn inertial sensors have emerged as an objective, minimally-obtrusive tool to monitor UL motor function in the real-world setting, such that rehabilitation interventions can be individually tailored to maximize functional performance. However, current wearable solutions focus on capturing the quantity of movement without considering the quality of movement. This paper introduces a novel approach to unobtrusively estimate the quality of UL movements in stroke survivors using a single wrist-worn inertial sensor during any type of voluntary UL movements. The proposed method exploits kinematic characteristics of voluntary limb movements that are optimized by the central nervous system during motor control. This work demonstrates that the proposed method could extract clinically important information during random UL movements in 16 stroke survivors, showing a statistically significant correlation to the Functional Ability Scale - a clinically validated score for movement quality.

Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training

High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$c$ \end{document}-statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$F$ \end{document}-score of 84.3%, thus enabling the generation of appropriate feedback. In a survey to gather preliminary data concerning the clinical adequacy of the proposed approach, 91.7% of occupational therapists demonstrated willingness to use it in their practice, and 88.2% of stroke survivors indicated that they would use it if recommended by their therapist.

Influence of upper limb movement patterns on accelerometer measurements: a pediatric case series

OBJECTIVE

Previous studies showed success using wrist-worn accelerometers to monitor upper-limb activity in adults and children with hemiparesis. However, a knowledge gap exists regarding which specific joint movements are reflected in accelerometry readings. We conducted a case series intended to enrich data interpretation by characterizing the influence of different pediatric upper-limb movements on accelerometry data.

APPROACH

The study recruited six typically developing children and five children with hemiparetic cerebral palsy. The participants performed unilateral and bilateral activities, and their upper limb movements were measured with wrist-worn accelerometers and the Microsoft Kinect, a markerless motion-capture system that tracks skeletal data. The Kinect data were used to quantify specific upper limb movements through joint angle calculations (trunk, shoulder, elbow and wrist). Correlation coefficients (r) were calculated to quantify the influence of individual joint movements on accelerometry data. Regression analyses were performed to examine multi-joint patterns and explain variability across different activities and participants.

MAIN RESULTS

Single-joint correlation results suggest that pediatric wrist-worn accelerometry data are not biased to particular individual joint movements. Rather, the accelerometry data could best be explained by the movements of the joints with the most functional relevance to the performed activity.

SIGNIFICANCE

This case series provides deeper insight into the interpretation of wrist-worn accelerometry data, and supports the use of this tool in quantifying functional upper-limb movements in pediatric populations.

Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review

OBJECTIVES

Wearable technology is increasingly used to monitor neurological disorders. The purpose of this systematic review was to synthesize knowledge from quantitative and qualitative clinical researches using wearable sensors in epilepsy, Parkinson's disease (PD), and stroke.

METHODS

A systematic literature search was conducted in PubMed and Scopus spanning from 1995 to January 2017. A synthesis of the main findings, reported adherence to wearables and missing data from quantitative studies, is provided. Clinimetric properties of measures derived from wearables in laboratory, free activities in hospital, and free-living environment were also evaluated. Qualitative thematic synthesis was conducted to explore user experiences and acceptance of wearables.

RESULTS

In total, 56 studies (50 reporting quantitative and 6 reporting qualitative data) were included for data extraction and synthesis. Among studies reporting quantitative data, 5 were in epilepsy, 21 PD, and 24 studies in stroke. In epilepsy, wearables are used to detect and differentiate seizures in hospital settings. In PD, the focus is on quantification of cardinal motor symptoms and medication-evoked adverse symptoms in both laboratory and free-living environment. In stroke upper extremity activity, walking and physical activity have been studied in laboratory and during free activities. Three analytic themes emerged from thematic synthesis of studies reporting qualitative data: acceptable integration in daily life, lack of confidence in technology, and the need to consider individualization.

CONCLUSIONS

Wearables may provide information of clinical features of interest in epilepsy, PD and stroke, but knowledge regarding the clinical utility for supporting clinical decision making remains to be established.