Validity of the Walked Distance Estimated by Wearable Devices in Stroke Individuals

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Validity of Estimated Results from a Wearable Device for the Tests Time Up and Go and Sit to Stand in Young Adults and in People with Chronic Diseases

BACKGROUND

Health care professionals need a valid tool to assess the physical ability of patients with chronic diseases. We aimed to assess the validity of the results of physical fitness tests estimated by a wrist wearable device in young adults and chronic disease people.

METHODS

Participants wore a sensor placed on their wrist and performed two physical fitness tests (sit to stand (STS) and time up and go (TUG)). We checked the concordance of sensor-estimated results using Bland-Altman analysis, root-mean-square error, and intraclass coefficient of correlation (ICC).

RESULTS

In total, 31 young adults (groups A; median age = 25 ± 5 years) and 14 people with chronic diseases (groups B; median age = 70 ± 15 years) were included. Concordance was high for both STS (ICC_A = 0.95, and ICC_B = 0.90), and TUG (ICC_A = 0.75, ICC_B = 0.98). The best estimations were given by the sensor during STS tests in young adults (mean bias = 0.19 ± 2.69; p = 0.12) and chronic disease people (mean bias = -0.14 ± 3.09 s; p = 0.24). The sensor provided the largest estimation errors over 2 s during the TUG test in young adults.

CONCLUSION

This study showed that the results provided by the sensor are consistent with those of the gold standard during STS and TUG in both healthy youth and people with chronic diseases.

A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment

The purpose of the review is to evaluate wearable sensor placement, their impact and validation of wearable sensors on analyzing gait, primarily the postural instability in people with stroke. Databases, namely PubMed, Cochrane, SpringerLink, and IEEE Xplore were searched to identify related articles published since January 2005. The authors have selected the articles by considering patient characteristics, intervention details, and outcome measurements by following the priorly set inclusion and exclusion criteria. From a total of 1077 articles, 142 were included in this study and classified into functional fields, namely postural stability (PS) assessments, physical activity monitoring (PA), gait pattern classification (GPC), and foot drop correction (FDC). The review covers the types of wearable sensors, their placement, and their performance in terms of reliability and validity. When employing a single wearable sensor, the pelvis and foot were the most used locations for detecting gait asymmetry and kinetic parameters, respectively. Multiple Inertial Measurement Units placed at different body parts were effectively used to estimate postural stability and gait pattern. This review article has compared results of placement of sensors at different locations helping researchers and clinicians to identify the best possible placement for sensors to measure specific kinematic and kinetic parameters in persons with stroke.

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.

Criterion validity of ActiGraph monitoring devices for step counting and distance measurement in adults and older adults: a systematic review

BACKGROUND

Wearable activity monitors such as ActiGraph monitoring devices are widely used, especially in research settings. Various research studies have assessed the criterion validity of ActiGraph devices for step counting and distance estimation in adults and older adults. Although several studies have used the ActiGraph devices as a reference system for activity monitoring, there is no summarized evidence of the psychometric properties. The main objective of this systematic review was to summarize evidence related to the criterion validity of ActiGraph monitoring devices for step counting and distance estimation in adults and/or older adults.

METHODS

Literature searches were conducted in six databases (Medline (OVID), Embase, IEEExplore, CINAHL, Engineering Village and Web of Science). Two reviewers independently conducted selection, a quality analysis of articles (using COSMIN and MacDermid's grids) and data extraction.

RESULTS

This review included 21 studies involving 637 participants (age 30.3 ± 7.5 years (for adults) and 82.7 ± 3.3 years (for older adults)). Five ActiGraph devices (7164, GT1M, wGTX +, GT3X +/wGT3X + and wGT3X - BT) were used to collect data at the hip, wrist and ankle to assess various walking and running speeds (ranging from 0.2 m/s to 4.44 m/s) over durations of 2 min to 3 days (13 h 30 mins per day) for step counting and distance estimation. The ActiGraph GT3X +/wGT3X + and wGT3X - BT had better criterion validity than the ActiGraph 7164, wGTX + and GT1M according to walking and running speeds for step counting. Validity of ActiGraph wGT3X + was good for distance estimation.

CONCLUSION

The ActiGraph wGT3X - BT and GT3X +/wGT3X + have good criterion validity for step counting, under certain conditions related to walking speeds, positioning and data processing.

Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population.

Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations.

Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN.

Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.

The Best Choice of Oxygen Cost Prediction Equation for Computing Post-Stroke Walking Energy Expenditure Using an Accelerometer

BACKGROUND

The integration of oxygen cost into the accelerometer's algorithms improves accuracy of total energy expenditure (TEE) values as post-stroke individuals walk. Recent work has shown that oxygen cost can be estimated from specific prediction equations for stroke patients.

OBJECTIVE

The objective is to the validity of the different oxygen cost estimation equations available in the literature for calculating TEE using ActigraphGT3x as individuals with stroke sequelae walk.

METHOD

Individuals with stroke sequelae who were able to walk without human assistance were included. The TEE was calculated by multiplying the walking distance provided by an ActigraphGT3x worn on the healthy ankle and the patient's oxygen cost estimated from the selected prediction equations. The TEE values from each equation were compared to the TEE values measured by indirect calorimetry. The validity of the prediction methods was evaluated by Bland-Altman analysis (mean bias (MB) and limits of agreement (LoA) values).

RESULTS

We included 26 stroke patients (63.5 years). Among the selected equations, those of Compagnat and Polese obtained the best validity parameters for the ActigraphGT3x: MB_Compagnat = 1.2 kcal, 95% LoA_Compagnat = [-12.0; 14.3] kcal and MB_Polese = 3.5 kcal, 95% LoA_Polese = [-9.2; 16.1] kcal. For comparison, the estimated TEE value according to the manufacturer's algorithm reported MB_Manufacturer = -15 kcal, 95% LoA_Manufacturer = [-52.9; 22.8] kcal.

CONCLUSION

The Polese and Compagnat equations offer the best validity parameters in comparison with the criterion method. Using oxygen cost prediction equations is a promising approach to improving assessment of TEE by accelerometers in post-stroke individuals.

Wearable devices for tracking physical activity in the community after an acquired brain injury: A systematic review

OBJECTIVE

The application of wearable devices in individuals with acquired brain injury (ABI) resulting from stroke or traumatic brain injury (TBI) for monitoring physical activity (PA) has been relatively recent. The current systematic review aims to provide insights into the adaption of these devices, outcome metrics, and their transition from the laboratory to the community for PA monitoring of individuals with ABI.

LITERATURE SURVEY

The PubMed and Google Scholar databases were systematically reviewed using appropriate search terms. A total of 20 articles were reviewed from the past 15 years.

METHODOLOGY

Articles were classified into three categories - PA measurement studies, PA classification studies, and validation studies. The quality of studies was assessed using a quality appraisal checklist.

SYNTHESIS

It was found that the transition of wearable devices from in-lab to community-based studies in individuals with stroke has started but is not widespread. The transition of wearable devices in the community has not yet started for individuals with TBI. Accelerometer-based devices were more frequently chosen than pedometers and inertial measurement units. No consensus on a preferred wearable device (make or model) or wear location could be identified, though step count was the most common outcome metric. The accuracy and validity of most outcome metrics used in the community were not reported for many studies.

CONCLUSIONS

To facilitate future studies use wearable devices for PA measurement in the community, we recommend that researchers provide details on the accuracy and validity of the outcome metrics specific to the study environment. Once the accuracy and validity are established for a specific population, wearable devices and their derived outcomes can provide objective information on mobility impairment as well as the effect of rehabilitation in the community. This article is protected by copyright. All rights reserved.

Cognitive Function is a Predictor of the Daily Step Count in Patients With Subacute Stroke With Independent Walking Ability: A Prospective Cohort Study

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Cognition at admission may predict daily step count.

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Cognitive impairment may increase risk of poor ambulation after subacute stroke.

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Ambulation poststroke is influenced by both physical and cognitive factors.

Objectives

To investigate the physical, cognitive, and psychological factors related to daily step count in patients with subacute stroke.

Design

Prospective cohort study.

Setting

A subacute rehabilitation ward with 160 beds.

Participants

Patients with subacute stroke who could walk independently (N=101). Among the 101 participants enrolled in this study (mean age, 64.5±13.5y), 64.4% (n=65) were men and 69.3% (n=70) were patients with cerebral infarction.

Interventions

We assessed ambulatory activity using a pedometer placed in the pants pocket on the nonparalyzed side continuously for 7 consecutive days. We also obtained demographic and clinical information and recorded the following measurement scores: Stroke Impairment Assessment Set, FIM, Mini-Mental State Examination (MMSE), Self-Rating Depression Scale, and Apathy Scale. All measurements were collected at admission and discharge.

Main Outcome Measures

The outcomes assessed were ambulatory activity, motor and sensory functions, functional disability, cognitive function, depressive symptoms, and motivation.

Results

The median daily steps ambulated at admission and discharge were 5584 steps (interquartile range, 3763-7096 steps) and 5991 steps (interquartile range, 4329-8204 steps), respectively. In the univariate regression analysis, age, sex, serum albumin level, affected side of the brain, and MMSE score at admission were significantly associated with the daily step count at discharge. Multiple regression analysis using these 5 items as independent variables revealed that the MMSE score at admission (reference, 28-30 points; B, −2.07; 95% confidence interval, −3.89 to −0.35; β, −0.22; P=.027) was significantly associated with the daily step count at discharge.

Conclusions

Cognitive function at admission had a significant association with the daily step count at discharge in patients with subacute stroke who could walk independently.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

Validity of the International Physical Activity Questionnaire Long Form for Assessing Physical Activity and Sedentary Behavior in Subjects with Chronic Stroke

Validation studies of questionnaires used to assess physical activity (PA) and sedentary behavior (SB) in stroke survivors are scarce. This cross-sectional study aimed to examine the validity of the International Physical Activity Questionnaire long-form (IPAQ-LF) in community living adults with post-stroke sequelae (≥6 months) and preserved ambulation. Participants’ functional mobility, lower limb strength, ambulatory level, stroke severity, and disability were assessed. An accelerometer (ActiGraph GT3X+) was worn for ≥7 consecutive days. Subsequently, the IPAQ-LF was interview-administered. Fifty-six participants (58.1 ± 11.1 years, 66.1% male) were included. A strong correlation between the two methods was found for total PA time (ρ = 0.55, p < 0.001). According to the Bland-Altman analyses, over-reporting moderate-to-vigorous PA and under-reporting total PA in the IPAQ-LF were found in those participants with higher PA levels. Both methods measured sedentary time similarly, though random error was observed between them. Moderate-strong correlations were found between the IPAQ-LF and physical function (ρ = 0.29–0.60, p < 0.05). In conclusion, in people with chronic stroke, the IPAQ-LF presented acceptable levels of validity for estimating total PA time in those who are insufficiently active. Therefore, it could be a useful tool to screen for inactive individuals with chronic stroke who can benefit from PA interventions addressed to implement healthier lifestyles.

Equations for estimating the oxygen cost of walking in stroke patients: a systematic review

OBJECTIVE

To report all equations that can potentially be used to estimate the oxygen cost of walking (Cw) without using a respiratory gas exchange analyzer and to provide the level of reliability of each equation.

DATA SOURCES

Webline, Medline, Scopus, ScienceDirect, Bielefeld Academic Search Engine (BASE), and Wiley Online Library databases from 1950 to August 2019 with search terms related to stroke and oxygen cost of walking.

METHODS

This systematic review was reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the methodological quality of included studies was determined with the Critical Appraisal Skills Programme (CASP).

RESULTS

We screened 2065 articles, and 33 were included for full-text analysis. Four articles were included in the data synthesis (stroke individuals=184). Analysis reported 4 equations estimating Cw that were developed from logistic regression equations between Cw and self-selected walking speed. The equations differed in several methodological aspects (characteristics of individuals, type of equation, Cw reference measurement methods). The Compagnat et al. study had the highest quality (CASP score=9/9).

CONCLUSIONS

This literature review highlighted 4 equations for estimating Cw from self-selected walking speed. Compagnat et al. presented the best quality parameters, but this work involved a population restricted to individuals with hemispheric stroke sequelae.

FeetMe® Monitor-connected insoles are a valid and reliable alternative for the evaluation of gait speed after stroke

BACKGROUND

One of the main challenges after stroke is gait recovery. To provide patients with an individualized rehabilitation program, it is helpful to have real-life objective evaluations at baseline and at regular follow-ups to adjust the program and verify potential improvements.

OBJECTIVES

To evaluate the accuracy and reliability of a fully stand-alone system of connected insoles (FeetMe® Monitor) against a widely used clinical walkway system (GAITRite®).

METHODS

Twenty-nine subjects with a stroke that occurred >6 months prior participated in the study. Their comfortable gait over three 8-m trials was evaluated by four raters, on Day 1 and Day 7, using simultaneously FeetMe® Monitor and GAITRite®. Velocity, stride length, cadence, stance, and swing duration were calculated on both sides over three sequences of gait: one single stride, 8 m, and three 8-m trials pooled. The Intra-class Correlation Coefficient (ICC) and the Bland-Altman plot evaluated the construct validity (inter-device) and the reliability (test-retest and inter-rater) of FeetMe® Monitor.

RESULTS

Through all gait analysis sequences, the inter-device ICCs were >0.95 for velocity, stride length, and cadence. Ranges of inter-device ICCs were [0.77-0.94] for stance duration for both limbs, and for swing duration [0.32-0.57] on the non-paretic side and [0.75-0.90] on the paretic side. Test-retest and inter-rater ICCs for all parameters were >0.73 for one single stride, >0.88 for 8-m trials and >0.94 for three 8-m trials.

CONCLUSION

FeetMe® Monitor is an accurate and reliable system for measurement of gait velocity, stride length, cadence, and stance duration in chronic hemiparesis.

Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health

Objectives

Recently, wearable device technology has gained more popularity in supporting a healthy lifestyle. Hence, researchers have begun to put significant efforts into studying the direct and indirect benefits of wearable devices for health and wellbeing. This paper summarizes recent studies on the use of consumer wearable devices to improve physical activity, mental health, and health consciousness.

Methods

A thorough literature search was performed from several reputable databases, such as PubMed, Scopus, ScienceDirect, arXiv, and bioRxiv mainly using “wearable device research” as a keyword, no earlier than 2018. As a result, 25 of the most recent and relevant papers included in this review cover several topics, such as previous literature reviews (9 papers), wearable device accuracy (3 papers), self-reported data collection tools (3 papers), and wearable device intervention (10 papers).

Results

All the chosen studies are discussed based on the wearable device used, complementary data, study design, and data processing method. All these previous studies indicate that wearable devices are used either to validate their benefits for general wellbeing or for more serious medical contexts, such as cardiovascular disorders and post-stroke treatment.

Conclusions

Despite their huge potential for adoption in clinical settings, wearable device accuracy and validity remain the key challenge to be met. Some lessons learned and future projections, such as combining traditional study design with statistical and machine learning methods, are highlighted in this paper to provide a useful overview for other researchers carrying out similar research.