Differences in trunk accelerometry between frail and non-frail elderly persons in functional tasks

Accelerometry applications and methods to assess standing balance in older adults and mobility-limited patient populations: a narrative review

Accelerometers provide an opportunity to expand standing balance assessments outside of the laboratory. The purpose of this narrative review is to show that accelerometers are accurate, objective, and accessible tools for balance assessment. Accelerometry has been validated against current gold standard technology, such as optical motion capture systems and force plates. Many studies have been conducted to show how accelerometers can be useful for clinical examinations. Recent studies have begun to apply classification algorithms to accelerometry balance measures to discriminate populations at risk for falls. In addition to healthy older adults, accelerometry can monitor balance in patient populations such as Parkinson's disease, multiple sclerosis, and traumatic brain injury. The lack of software packages or easy-to-use applications have hindered the shift into the clinical space. Lack of consensus on outcome metrics has also slowed the clinical adoption of accelerometer-based balance assessments. Future studies should focus on metrics that are most helpful to evaluate balance in specific populations and protocols that are clinically efficacious.

Instrumented Timed Up and Go Test (iTUG)-More Than Assessing Time to Predict Falls: A Systematic Review

The Timed Up and Go (TUG) test is a widely used tool for assessing the risk of falls in older adults. However, to increase the test's predictive value, the instrumented Timed Up and Go (iTUG) test has been developed, incorporating different technological approaches. This systematic review aims to explore the evidence of the technological proposal for the segmentation and analysis of iTUG in elderlies with or without pathologies. A search was conducted in five major databases, following PRISMA guidelines. The review included 40 studies that met the eligibility criteria. The most used technology was inertial sensors (75% of the studies), with healthy elderlies (35%) and elderlies with Parkinson's disease (32.5%) being the most analyzed participants. In total, 97.5% of the studies applied automatic segmentation using rule-based algorithms. The iTUG test offers an economical and accessible alternative to increase the predictive value of TUG, identifying different variables, and can be used in clinical, community, and home settings.

Kinematic Parameters That Can Discriminate in Levels of Functionality in the Six-Minute Walk Test in Patients with Heart Failure with a Preserved Ejection Fraction

It is a challenge to manage and assess heart failure with preserved left ventricular ejection fraction (HFpEF) patients. Six-Minute Walk Test (6MWT) is used in this clinical population as a functional test. The objective of the study was to assess gait and kinematic parameters in HFpEF patients during the 6MWT with an inertial sensor and to discriminate patients according to their performance in the 6MWT: (1) walk more or less than 300 m, (2) finish or stop the test, (3) women or men and (4) fallen or did not fall in the last year. A cross-sectional study was performed in patients with HFpEF older than 70 years. 6MWT was carried out in a closed corridor larger than 30 m. Two Shimmer3 inertial sensors were used in the chest and lumbar region. Pure kinematic parameters analysed were angular velocity and linear acceleration in the three axes. Using these data, an algorithm calculated gait kinematic parameters: total distance, lap time, gait speed and step and stride variables. Two analyses were done according to the performance. Student’s t-test measured differences between groups and receiver operating characteristic assessed discriminant ability. Seventy patients performed the 6MWT. Step time, step symmetry, stride time and stride symmetry in both analyses showed high AUC values (>0.75). More significant differences in velocity and acceleration in the maximum Y axis or vertical movements. Three pure kinematic parameters obtained good discriminant capacity (AUC > 0.75). The new methodology proved differences in gait and pure kinematic parameters that can distinguish two groups according to the performance in the 6MWT and they had discriminant capacity.

Muscle Strength, Physical Fitness, Balance, and Walking Ability at Risk of Fall for Prefrail Older People

AIM

This study was to explore the relationship of older adults' demographic information, physiological indices, and stages of frailty with their risk of falling.

METHODS

In the cross-sectional study, a total of 221 older adults with the mean age 74.9 (SD = 6.8) years old were surveyed by senior fitness test.

RESULTS

Results were observed in terms of participants' physical fitness, with significant differences being observed in the correlations of left-hand grip strength (t = 5.05, p < .000), right-hand grip strength (t = 6.03, p < .000), and total grip strength (t = 5.70, p < .000), time up and go test (t = -6.25, p < .000), and 30-sec chair stand test (t = 7.19, p < .000) with the risk of falling. According to the logistic regression analysis results, long-term medication (OR = 0.12, 95% CI =0.02-0.62, p < .01) and right-hand grip strength (OR = 0.86, 95% CI =0.76-0.97, p < .01) are the main predictors of older adults' risk of falling.

CONCLUSIONS

Older females with low education, history of falls, weaker grip strengths; taking longer to finish the TUG test; and standing fewer times during the 30-second chair stand test were at risk of fall. In prediction, older people using long-term medication were at lower risk of falling, and the greater the hand grip strength was, the lower the fall risk was. According to the research results, nursing personnel must develop care programs and improve older adults' risk of falls.

A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors

Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson's disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations. We propose a three-segment body model for estimating sit-to-stand kinematics using only two wearable inertial sensors, placed on the shank and back. Reducing the number of sensors to two instead of one per body segment facilitates monitoring and classifying movements over extended periods, making it more comfortable to wear while reducing the power requirements of sensors. We applied this model on 10 younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with Parkinson's disease (PwP). We have achieved this by incorporating unique sit-to-stand classification technique using unsupervised learning in the model based reconstruction of angular kinematics using extended Kalman filter. Our proposed model showed that it was possible to successfully estimate thigh kinematics despite not measuring the thigh motion with inertial sensor. We classified sit-to-stand transitions, sitting and standing states with the accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We have proposed a novel integrated approach of modelling and classification for estimating the body kinematics during sit-to-stand motion and successfully applied it on YH, OH and PwP groups.

Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection

This work analyses different concepts for frailty diagnosis based on affordable standard technology such as smartphones or wearable devices. The goal is to provide ideas that go beyond classical diagnostic tools such as magnetic resonance imaging or tomography, thus changing the paradigm; enabling the detection of frailty without expensive facilities, in an ecological way for both patients and medical staff and even with continuous monitoring. Fried's five-point phenotype model of frailty along with a model based on trials and several classical physical tests were used for device classification. This work provides a starting point for future researchers who will have to try to bridge the gap separating elderly people from technology and medical tests in order to provide feasible, accurate and affordable tools for frailty monitoring for a wide range of users.

ICT technologies as new promising tools for the managing of frailty: a systematic review

OBJECTIVES

Frailty is a major health issue as it encompasses functional decline, physical dependence, and increased mortality risk. Recent studies explored Information and Communication Technology (ICT) interventions as alternatives to manage frailty in older persons. The aim of the present systematic review was to synthesize current evidence on ICT application within the complex models of frailty care in older people.

METHODS

Data sources included PubMed, PsycINFO, EMBASE and Web of Science, considering eligible those reviews on ICT application in samples of older persons formally assessed as frail. Records were screened by two independent researchers, who extracted data and appraised methodological quality of reviews and studies.

RESULTS

Among the 764 retrieved papers, two systematic reviews were included. Most of the studies analyzed defined frailty considering only few components of the phenotype and used ICT to stratify different levels of frailty or to support traditional screening strategies. Assessment of frailty was the context in which ICT has been mostly tested as compared to intervention. Cost effectiveness evaluations of the ICT technologies were not reported.

CONCLUSIONS

The research investigating the use of ICT in the context of frailty is still at the very beginning. Few studies strictly focused on the assessment of frailty, while intervention on frailty using ICT was rarely reported. The lack of a proper characterization of the frail condition along with the methodological limitations prevented the investigation of ICT within complex care models. Future studies are needed to effectively integrate ICT in the care of frailty in orders.

Turning assessment for discrimination of frailty syndrome among community-dwelling older adults

BACKGROUND

Frailty is a common geriatric syndrome and is characterized by decreased physiological reserve and increased vulnerability towards adverse health outcomes including falls. Turning is a challenging task and is reported to be one of the daily activities that leads to falling in older populations.

RESEARCH QUESTION

Does 180° walking turns and 360° turning on the spot differ among frail, pre-frail, and non-frail older adults? Can 180° walking turns and 360° turning on the spot cutoffs discriminate older adults with frailty from those without?

METHODS

A cross-sectional study was conducted on community-dwelling older adults aged over 65 years. Frailty was assessed using Fried's phenotype method, and turning tasks were measured by inertial sensors. The turn duration (s) and angular velocity (°/s) were recorded for analysis.

RESULTS

In total, 109 participants were enrolled including 50 pre-frail and 12 frail individuals. Frail older adults took significantly longer and had slower angular velocities to complete the 180° and 360° turning than did either pre-frail (p = 0.002 and p < 0.001, respectively) or non-frail (p = 0.03 and p < 0.001, respectively) older adults. Cutoff times of 2.45 and 3.46 s were found to best discriminate frail people from those without frailty in both the 180° (sensitivity 83.3 %, specificity 71.1 %, area under the receiver operating characteristic curve (AUC) 0.796) and 360° (sensitivity 91.7 %, specificity 74.2 %, AUC 0.857) turn tasks.

SIGNIFICANCE

Older individuals with frailty syndrome had difficulty turning as evidenced by a longer turning duration and a slower angular velocity. The turn duration could be a potential biomarker of frailty in older populations. Assessing the turning performance can facilitate early detection of the onset of frailty and inform early prevention and rehabilitation interventions in clinical practice.

Unobtrusive Sensors for the Assessment of Older Adult's Frailty: A Scoping Review

Ubiquity (devices becoming part of the context) and transparency (devices not interfering with daily activities) are very significant in healthcare monitoring applications for elders. The present study undertakes a scoping review to map the literature on sensor-based unobtrusive monitoring of older adults’ frailty. We aim to determine what types of devices comply with unobtrusiveness requirements, which frailty markers have been unobtrusively assessed, which unsupervised devices have been tested, the relationships between sensor outcomes and frailty markers, and which devices can assess multiple markers. SCOPUS, PUBMED, and Web of Science were used to identify papers published 2010–2020. We selected 67 documents involving non-hospitalized older adults (65+ y.o.) and assessing frailty level or some specific frailty-marker with some sensor. Among the nine types of body worn sensors, only inertial measurement units (IMUs) on the waist and wrist-worn sensors comply with ubiquity. The former can transparently assess all variables but weight loss. Wrist-worn devices have not been tested in unsupervised conditions. Unsupervised presence detectors can predict frailty, slowness, performance, and physical activity. Waist IMUs and presence detectors are the most promising candidates for unobtrusive and unsupervised monitoring of frailty. Further research is necessary to give specific predictions of frailty level with unsupervised waist IMUs.

Development of a Novel Computational Model for Evaluating Fall Risk in Patient Room Design

OBJECTIVES

This study proposes a computational model to evaluate patient room design layout and features that contribute to patient stability and mitigate the risk of fall.

BACKGROUND

While common fall risk assessment tools in nursing have an acceptable level of sensitivity and specificity, they focus on intrinsic factors and medications, making risk assessment limited in terms of how the physical environment contributes to fall risk.

METHODS

We use literature to inform a computational model (algorithm) to define the relationship between these factors and the risk of fall. We use a trajectory optimization approach for patient motion prediction.

RESULTS

Based on available data, the algorithm includes static factors of lighting, flooring, supportive objects, and bathroom doors and dynamic factors of patient movement. This preliminary model was tested using four room designs as examples of typical room configurations. Results show the capabilities of the proposed model to identify the risk associated with different room layouts and features.

CONCLUSIONS

This innovative approach to room design evaluation and resulting estimation of patient fall risk show promise as a proactive evidence-based tool to evaluate the relationship of potential fall risk and room design. The development of the model highlights the challenge of heterogeneity in factors and reporting found in the studies of patient falls, which hinder our understanding of the role of the built environment in mitigating risk. A more comprehensive investigation comparing the model with actual patient falls data is needed to further refine model development.

The timed up & go test sit-to-stand transition: Which signals measured by inertial sensors are a viable route for continuous analysis?

BACKGROUND

The Timed Up and Go test is a well-known clinical test for assessing of mobility and fall risk. It has been shown that the IMU which use an accelerometer and gyroscope are capable of analysing the quantitative parameters of the sit-to-stand transition.

RESEARCH QUESTION

Which signals obtained by the inertial sensors are suitable for continuous Timed Up & Go test sit-to-stand transition analysis?

METHODS

In the study we included 29 older adult volunteers and 31 de-novo Parkinson disease (PD) patients. All subjects performed an instrumented extended TUG wearing a gyro-accelerometer. The sit-to-stand transition was detected from an angular velocity signal. The sit-to-stand signal pattern within the subject group was analyzed via an intra-class correlation between curves. Inter-subjects' variability was visualized using prediction bands.

RESULTS

The angular velocity about the pitch axis exhibited the best signal match across subjects in both groups (0.50 < ICC < 0.75). When analysing acceleration, the acceleration along the antero-posterior axis showed moderate inter-subjects signal pattern match (0.50 < ICC < 0.75) in the reference group. The analysis of other signals revealed a poor signal pattern in both subject groups.

SIGNIFICANCE

For optimal interpretation of the analysis of continuous curves, the signal pattern must be considered. Also, the inter-subject variability along this pattern can be informative and useful.

Sensors are Capable to Help in the Measurement of the Results of the Timed-Up and Go Test? A Systematic Review

The analysis of movements used in physiotherapy areas related to the elderly is becoming increasingly important due to factors such as the increase in the average life expectancy and the rate of elderly people over the whole population. In this systematic review, we try to determine how the inertial sensors embedded in mobile devices are exploited for the measurement of the different parameters involved in the Timed-Up and Go test. The results show the mobile devices equipped with onboard motion sensors can be exploited for these types of studies: the most commonly used sensors are the magnetometer, accelerometer and gyroscope available in consumer off-the-shelf smartphones. Other features typically used to evaluate the Timed-Up and Go test are the time duration, the angular velocity and the number of steps, allowing for the recognition of some diseases as well as the measurement of the subject's performance during the test execution.

Performance of Different Timed Up and Go Subtasks in Frailty Syndrome

BACKGROUND AND PURPOSE

Gait speed, mobility, and postural transitions should be taken into account in older adults with frailty syndrome and can be assessed by the Timed Up and Go (TUG) Test. However, it is unclear which TUG subtasks have greater influence in identifying frail people and whether prefrail individuals present with any reduced subtask performance. The objective of this study was to investigate the differences in performance of TUG subtasks between frail, prefrail, and nonfrail older adults.

METHODS

A cross-sectional study was performed with community-dwelling older adults, including 43 nonfrail, 30 prefrail, and 7 frail individuals. The TUG subtasks (sit-to-stand, walking forward, turning, walking back, and turn-to-sit) were assessed using a Qualisys motion system. Data were captured by Qualisys Track Manager software and processed by Visual 3D software. The Matlab program was used to detect, separate, and analyze the TUG subtasks. Statistical significance was set at α= .05 and SigmaPlot software (11.0) was used.

RESULTS AND DISCUSSION

The total time to complete the TUG was significantly longer among frail participants than among those who were prefrail and nonfrail. Statistically significant differences in temporal parameters in the turning, walking forward, and walking back subtasks between nonfrail/prefrail and frail older people were found. In addition, the transition TUG subtasks (average and peak velocities of the trunk) distinguished the frail group from the other groups, demonstrating altered quality of movement.

CONCLUSIONS

The findings support the value of analyzing the TUG subtasks to improve understanding of mobility deficits in frailty syndrome.

FrailSafe: An ICT Platform for Unobtrusive Sensing of Multi-Domain Frailty for Personalized Interventions

The implications of frailty in older adults' health status and autonomy necessitates the understanding and effective management of this widespread condition as a priority for modern societies. Despite its importance, we still stand far from early detection, effective management and prevention of frailty. One of the most important reasons for this is the lack of sensitive instruments able to early identify frailty and pre-frailty conditions. The FrailSafe system provides a novel approach to this complex, medical, social and public health problem. It aspires to identify the most important components of frailty, construct cumulative metrics serving as biomarkers, and apply this knowledge and expertise for self-management and prevention. This paper presents a high-level overview of the FrailSafe system architecture providing details on the monitoring sensors and devices, the software front-ends for the interaction of the users with the system, as well as the back-end part including the data analysis and decision support modules. Data storage, remote processing and security issues are also discussed. The evaluation of the system by older individuals from 3 different countries highlighted the potential of frailty prediction strategies based on information and communication technology (ICT).

Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution.

Differences in Timed Up and Go Subtasks Between Older People With Mild Cognitive Impairment and Mild Alzheimer's Disease

This study compared performances of timed up and go test subtasks between 40 older people with preserved cognition, 40 with mild cognitive impairment, and 38 with mild Alzheimer's disease. The assessment consisted of anamneses and timed up and go test subtasks (sit-to-stand, walking forward, turn, walking back, and turn-to-sit). Data were captured by Qualisys Track Manager software and processed by Visual3D software. The MATLAB program was applied to detect and analyze timed up and go test subtasks. All subtasks differentiated people with Alzheimer's disease and preserved cognition, except the sit-to-stand subtask, which did not distinguish any group. The walking forward subtask differed older people with preserved cognition from mild cognitive impairment, specifically on minimum peak of knee, average value of knee, and hip (pitch axis) during stance phase. The walking back, turn, and turn-to-sit subtasks distinguished subjects with Alzheimer's disease from mild cognitive impairment. The separated analysis of transition and walking subtasks is important in identifying mobility patterns among cognitive profiles.

Effectiveness of a muticomponent workout program integrated in an evidence based multimodal program in hyperfrail elderly patients: POWERAGING randomized clinical trial protocol

BACKGROUND

Short-term and mid-term comparison of the efficacy of a multimodal program that incorporates a therapeutic workout program, medication review, diet adjustment and health education, in comparison to the standard medical practice in the improvement of the neuromuscular and physiological condition. Furthermore, it is intended to analyse the maintenance of these effects in a long-term follow-up (12 months) from the onset of the intervention.

METHODS

A randomized clinical trial of elderly frail patients drawn from the Clinical Management Unit "Tiro de Pichón", Health District of Malaga, will be included in the study (after meeting the inclusion / exclusion criteria) will be randomized in two groups: a control group that will undergo an intervention consistent of medication review + diet adjustment + health education (regular workout recommendations within a complete advice on healthy lifestyles) and an experimental group whose intervention will consist of a multimodal treatment: therapeutic workout program+ medication review+ diet adjustment + health education. The sociodemographic, clinical and tracing variables will be reflected at the beginning of the study. In addition, the follow-up variables will be gathered at the second and sixth months after the beginning of the treatment and at the third and sixth months after the treatment (follow-up). The follow-up variables that will be measured are: body mass index, general health condition, fatigue, frailty, motor control, attention- concentration- memory, motor memory, spatial orientation, grip strength, balance (static, semi-dynamic), gait speed and metabolomics. A descriptive analysis of the sociodemographic variables of the participants will be conducted. One-Factor ANOVA will be used for the Within-Subject analysis and as for the Between-Subject analysis, the outcome variables between both the groups in each moment of the data collection will be compared.

DISCUSSION

A multimodal program that incorporates a therapeutic workout program, medication review, diet adjustment and health education may be effective treatment to reduce the functional decline in elderly. The results of the study will provide information on the possible strengths and benefits in multimodal program in elderly.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02772952 registered May 2017.

Assessment of frailty: a survey of quantitative and clinical methods

BACKGROUND

Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty.

RESULTS

The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty.

CONCLUSION

Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

A randomised controlled trial comparing the effectiveness of tai chi alongside usual care with usual care alone on the postural balance of community-dwelling people with dementia: protocol for the TACIT trial (TAi ChI for people with demenTia)

Background

Falls are a public health issue for the older adult population and more so for people with dementia (PWD). Compared with their cognitively intact peers, PWD are at higher risk of falls and injurious falls. This randomised controlled trial aims to test the clinical and cost effectiveness of Tai Chi to improve postural balance among community-dwelling PWD and to assess the feasibility of conducting a larger definitive trial to reduce the incidence of falls among PWD.

Methods

A 3-centre parallel group randomised controlled trial with embedded process evaluation. One hundred and fifty community-dwelling dyads of a person with dementia and their informal carer will be recruited and assessed at baseline and at six-month follow-up. Dyads will be randomised in a 1:1 ratio to either usual care or usual care plus a Tai Chi intervention for 20 weeks. The Tai Chi intervention will consist of weekly classes (45 min’ Tai Chi plus up to 45 min for informal discussion, with up to 10 dyads per class) and home-based exercises (20 min per day to be facilitated by the carer). Home practice of Tai Chi will be supported by the use of behaviour change techniques with the Tai Chi instructor at a home visit in week 3–4 of the intervention (action planning, coping planning, self-monitoring, and alarm clock reminder) and at the end of each class (feedback on home practice). The primary outcome is dynamic balance measured using the Timed Up and Go test, coinciding with the end of the 20-week intervention phase for participants in the Tai Chi arm. Secondary outcomes for PWD include functional balance, static balance, fear of falling, global cognitive functioning, visual-spatial cognitive functioning, quality of life, and falls. Secondary outcomes for carers include dynamic balance, static balance, quality of life, costs, and carer burden.

Discussion

This trial is the first in the UK to test the effectiveness of Tai Chi to improve balance among PWD. The trial will inform a future study that will be the first in the world to use Tai Chi in a trial to prevent falls among PWD.

Trial registration

NCT02864056.

Automated Measurement and Subtask Analysis of the Timed Up-and-Go Test in the Field of Geriatrics

Multimorbidity and age-physiological functional restrictions can lead to frailty and a loss of a self-determined life in elderly patients. The Timed Up-and-Go test (TUG) is a sensitive and specific measure of frailty and has also many other areas of application, for example in chronic diseases. Besides the measurement of the complete TUG time, the analysis of subtasks may also reveal important information about particular aspects of the health status of test subjects. We developed an ultrasonic-based device for performing the TUG automatically, which can be attached to the backrest of a chair. This device provides the total TUG time as well as the displacement-time data for all included subtasks. To prepare for its use in clinical studies, we performed a field test at a geriatric center. The goal was to confirm feasibility, i.e., to assess its application in real patients. Despite some improvement potential revealed by the field test, the concept turned out to be an appropriate method for monitoring the TUG time and its subtasks.

Performances on the Timed Up and Go Test and subtasks between fallers and non-fallers in older adults with cognitive impairment

ABSTRACT This work aimed to compare performances on the Timed Up and Go (TUG) test and its subtasks between faller and non-faller older adults with mild cognitive impairment (MCI) and mild Alzheimer’s disease (AD). A prospective study was conducted, with 38 older adults with MCI and 37 with mild AD. Participants underwent an assessment at baseline (the TUG and its subtasks using the Qualisys ProReflex system) and the monitoring of falls at the six-month follow up. After six months, 52.6% participants with MCI and 51.3% with AD fell. In accordance with specific subtasks, total performance on the TUG distinguished fallers from non-fallers with AD, fallers from non-fallers with MCI and non-fallers with MCI from non-fallers with AD. Although no other difference was found in total performances, non-fallers with MCI and fallers with AD differed on the walking forward, turn and turn-to-sit subtasks; and fallers with MCI and non-fallers with AD differed on the turn-to-sit subtask.

Toward Smart Footwear to Track Frailty Phenotypes-Using Propulsion Performance to Determine Frailty

Frailty assessment is dependent on the availability of trained personnel and it is currently limited to clinic and supervised setting. The growing aging population has made it necessary to find phenotypes of frailty that can be measured in an unsupervised setting for translational application in continuous, remote, and in-place monitoring during daily living activity, such as walking. We analyzed gait performance of 161 older adults using a shin-worn inertial sensor to investigate the feasibility of developing a foot-worn sensor to assess frailty. Sensor-derived gait parameters were extracted and modeled to distinguish different frailty stages, including non-frail, pre-frail, and frail, as determined by Fried Criteria. An artificial neural network model was implemented to evaluate the accuracy of an algorithm using a proposed set of gait parameters in predicting frailty stages. Changes in discriminating power was compared between sensor data extracted from the left and right shin sensor. The aim was to investigate the feasibility of developing a foot-worn sensor to assess frailty. The results yielded a highly accurate model in predicting frailty stages, irrespective of sensor location. The independent predictors of frailty stages were propulsion duration and acceleration, heel-off and toe-off speed, mid stance and mid swing speed, and speed norm. The proposed model enables discriminating different frailty stages with area under curve ranging between 83.2–95.8%. Furthermore, results from the neural network suggest the potential of developing a single-shin worn sensor that would be ideal for unsupervised application and footwear integration for continuous monitoring during walking.

IMPLEMENTATION OF A SMARTPHONE AS A WIRELESS ACCELEROMETER PLATFORM FOR QUANTIFYING HEMIPLEGIC GAIT DISPARITY IN A FUNCTIONALLY AUTONOMOUS CONTEXT

The utility of the smartphone, such as the iPhone, constitutes considerable potential for the advancement of the biomedical and healthcare industry. A notable feature of the iPhone is the capacity to combine the internal accelerometer sensor with a software application to enable the functionality of a wireless accelerometer platform. Preliminary research has demonstrated the iPhone’s ability to quantify features of healthy gait. The research applies a single iPhone mounted proximal to the lateral malleolus of the affected leg and subsequently the unaffected leg to ascertain quantified disparity of hemiplegic gait from an engineering proof of concept perspective. In order to maintain a consistent gait velocity, a constant velocity treadmill is incorporated into the research endeavor. Post-processing of the gait acceleration waveform is greatly facilitated through the use of a software automation program using Matlab that emphasizes on the rhythmicity of gait. Two gait parameters were obtained: stance-to-stance temporal disparity and stance-to-stance time-averaged acceleration, and demonstrated considerable accuracy, consistency, and reliability. As noted per the constant treadmill velocity, stance-to-stance temporal disparity for the affected and unaffected legs was established as not statistically significant. A statistical significance was determined for the stance-to-stance time-averaged acceleration regarding the affected and unaffected legs. The iPhone application represents a wireless accelerometer platform capable of identifying statistically significant and quantified disparity of hemiplegic gait features through automated post-processing in a functionally autonomous environment.

Dual Task Gait Performance in Frail Individuals with and without Mild Cognitive Impairment

BACKGROUND

Several studies have stated that frailty is associated with cognitive impairment. Based on various studies, cognition impairment has been considered as a component of frailty. Other authors have shown that physical frailty is associated with low cognitive performance. Dual task gait tests are used as a strong predictor of falls in either dementia or frailty. Consequently, it is important to investigate dual task walking tests in elderly populations including control robust oldest old, frail oldest old with mild cognitive impairment (MCI) and frail oldest old without MCI.

METHODS

Dual task walking tests were carried out to examine the association between frailty and cognitive impairment in a population with advanced age. Forty-one elderly men and women participated in this study. The subjects from control, frail with MCI and frail without MCI groups, completed the 5-meter walk test at their own gait velocity. Arithmetic and verbal dual task walking performance was also assessed. Kinematic data were acquired from a unique tri-axial inertial sensor.

RESULTS

The spatiotemporal and frequency parameters related to gait disorders did not show any significant differences between frail with and without MCI groups.

CONCLUSIONS

The evaluation of these parameters extracted from the acceleration signals led us to conclude that these results expand the knowledge regarding the common conditions in frailty and MCI and may highlight the idea that the impairment in walking performance does not depend of frailty and cognitive status.

Capturing the Cranio-Caudal Signature of a Turn with Inertial Measurement Systems: Methods, Parameters Robustness and Reliability

Background

Turning is a challenging mobility task requiring coordination and postural stability. Optimal turning involves a cranio-caudal sequence (i.e., the head initiates the motion, followed by the trunk and the pelvis), which has been shown to be altered in patients with neurodegenerative diseases, such as Parkinson’s disease as well as in fallers and frails. Previous studies have suggested that the cranio-caudal sequence exhibits a specific signature corresponding to the adopted turn strategy. Currently, the assessment of cranio-caudal sequence is limited to biomechanical labs which use camera-based systems; however, there is a growing trend to assess human kinematics with wearable sensors, such as attitude and heading reference systems (AHRS), which enable recording of raw inertial signals (acceleration and angular velocity) from which the orientation of the platform is estimated. In order to enhance the comprehension of complex processes, such as turning, signal modeling can be performed.

Aim

The current study investigates the use of a kinematic-based model, the sigma-lognormal model, to characterize the turn cranio-caudal signature as assessed with AHRS.

Methods

Sixteen asymptomatic adults (mean age = 69.1 ± 7.5 years old) performed repeated 10-m Timed-Up-and-Go (TUG) with 180° turns, at varying speed. Head and trunk kinematics were assessed with AHRS positioned on each segments. Relative orientation of the head to the trunk was then computed for each trial and relative angular velocity profile was derived for the turn phase. Peak relative angle (variable) and relative velocity profiles modeled using a sigma-lognormal approach (variables: Neuromuscular command amplitudes and timing parameters) were used to extract and characterize the cranio-caudal signature of each individual during the turn phase.

Results

The methodology has shown good ability to reconstruct the cranio-caudal signature (signal-to-noise median of 17.7). All variables were robust to speed variations (p > 0.124). Peak relative angle and commanded amplitudes demonstrated moderate to strong reliability (ICC between 0.640 and 0.808).

Conclusion

The cranio-caudal signature assessed with the sigma-lognormal model appears to be a promising avenue to assess the efficiency of turns.

Convergent Validity of a Wearable Sensor System for Measuring Sub-Task Performance during the Timed Up-and-Go Test

Background: The timed-up-and-go test (TUG) is one of the most commonly used tests of physical function in clinical practice and for research outcomes. Inertial sensors have been used to parse the TUG test into its composite phases (rising, walking, turning, etc.), but have not validated this approach against an optoelectronic gold-standard, and to our knowledge no studies have published the minimal detectable change of these measurements. Methods: Eleven adults performed the TUG three times each under normal and slow walking conditions, and 3 m and 5 m walking distances, in a 12-camera motion analysis laboratory. An inertial measurement unit (IMU) with tri-axial accelerometers and gyroscopes was worn on the upper-torso. Motion analysis marker data and IMU signals were analyzed separately to identify the six main TUG phases: sit-to-stand, 1st walk, 1st turn, 2nd walk, 2nd turn, and stand-to-sit, and the absolute agreement between two systems analyzed using intra-class correlation (ICC, model 2) analysis. The minimal detectable change (MDC) within subjects was also calculated for each TUG phase. Results: The overall difference between TUG sub-tasks determined using 3D motion capture data and the IMU sensor data was <0.5 s. For all TUG distances and speeds, the absolute agreement was high for total TUG time and walk times (ICC > 0.90), but less for chair activity (ICC range 0.5–0.9) and typically poor for the turn time (ICC < 0.4). MDC values for total TUG time ranged between 2–4 s or 12–22% of the TUG time measurement. MDC of the sub-task times were higher proportionally, being 20–60% of the sub-task duration. Conclusions: We conclude that a commercial IMU can be used for quantifying the TUG phases with accuracy sufficient for clinical applications; however, the MDC when using inertial sensors is not necessarily improved over less sophisticated measurement tools.

Is Technology Present in Frailty? Technology a Back-up Tool for Dealing with Frailty in the Elderly: A Systematic Review

This study analyzes the technologies used in dealing with frailty within the following areas: prevention, care, diagnosis and treatment. The aim of this paper is, on the one hand, to analyze the extent to which technology is present in terms of its relationship with frailty and what technological resources are used to treat it. Its other purpose is to define new challenges and contributions made by physiotherapy using technology. Eighty documents related to research, validation and/or the ascertaining of different types of hardware, software or both were reviewed in prominent areas. The authors used the following scales: in the area of diagnosis, Fried's phenotype model of frailty and a model based on trials for the design of devices. The technologies developed that are based on these models accounted for 55% and 45% of cases respectively. In the area of prevention, the results proved similar regarding the use of wireless sensors with cameras (35.71%), and Kinect™ sensors (28.57%) to analyze movements and postures that indicate a risk of falling. In the area of care, results were found referring to the use of different motion, physiological and environmental wireless sensors (46,15%), i.e. so-called smart homes. In the area of treatment, the results show with a percentage of 37.5% that the Nintendo® Wii™ console is the most used tool for treating frailty in elderly persons. Further work needs to be carried out to reduce the gap existing between technology, frail elderly persons, healthcare professionals and carers to bring together the different views about technology. This need raises the challenge of developing and implementing technology in physiotherapy via serious games that may via play and connectivity help to improve the functional capacity, general health and quality of life of frail individuals.

Evaluating physical function and activity in the elderly patient using wearable motion sensors

Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand.

Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures.

In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts.

In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the ‘micro-level’ of activity such as step frequency or sit-stand duration.

Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability remain difficult, calling for consensus or standardisation.

Cite this article: Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016;1:112–120. DOI: 10.1302/2058-5241.1.160022.

Postural Transitions during Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty during Unsupervised Condition

BACKGROUND

Impairment of physical function is a major indicator of frailty. Functional performance tests have been shown to be useful for identification of frailty in older adults. However, these tests are often not translatable into unsupervised and remote monitoring of frailty status at home and/or community settings.

OBJECTIVE

In this study, we explored daily postural transition quantified using a chest-worn wearable technology to identify frailty in community-dwelling older adults.

METHODS

Spontaneous daily physical activity was monitored over 24 h in 120 community-dwelling elderly (age: 78 ± 8 years) using an unobtrusive wearable sensor (PAMSys™, BioSensics LLC, Watertown, MA, USA). Participants were classified as non-frail and pre-frail/frail using Fried's criteria. A validated software package was used to identify body postures and postural transition between each independent postural activity such as sit-to-stand, stand-to-sit, stand-to-walk, and walk-to-stand. The transition from walking to sitting was further classified as quick sitting and cautious sitting based on presence/absence of a standing posture pause between sitting and walking. A general linear model univariate test was used for between-group comparison. Pearson's correlation was used to determine the association between sensor-derived parameters and age. Logistic regression model was used to identify independent predictors of frailty.

RESULTS

According to Fried's criteria, 63% of participants were pre-frail/frail. The total number of postural transitions, stand-to-walk, and walk-to-stand were, respectively, 25.2, 30.2, and 30.6% lower in the pre-frail/frail group when compared to the non-frail group (p < 0.05, Cohen's d = 0.73-0.79). Furthermore, the ratio of cautious sitting was significantly higher by 6.2% in pre-frail/frail compared to non-frail (p = 0.025, Cohen's d = 0.22). Total number of postural transitions and the ratio of cautious sitting also showed significant negative and positive correlations with age, respectively (r = -0.51 and 0.29, p < 0.05). After applying a logistic regression model, among tested parameters, walk-to-stand (odds ratio [OR] = 0.997 p = 0.013), quick sitting (OR = 1.036, p = 0.05), and age (OR = 1.073, p = 0.016) were recognized as independent variables to identify frailty status.

CONCLUSIONS

This study demonstrated that daily number of specific postural transitions such as walk-to-stand and quick sitting could be used for monitoring frailty status by unsupervised monitoring of daily physical activity. Further study is warranted to explore whether tracking the daily number of specific postural transitions is also sensitive to track change in the status of frailty over time.

Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18-75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45) and older age group (age 46-75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice.

A validation study of a smartphone application for functional mobility assessment of the elderly

Background

To minimize the reaction time and position judgment error using stopwatch-timed measures, we developed a smartphone application to measure performance in the five-time sit-to-stand (FTSTS) and timed up-and-go (TUG) tests.

Objective

This study aimed to validate this smartphone application by comparing its measurement with a laboratory-based reference condition.

Methods

Thirty-two healthy elderly people were asked to perform the FTSTS and TUG tests in a randomized sequence. During the tests, their performance was concurrently measured by the smartphone application and a force sensor installed in the backrest of a chair. The intraclass correlation coefficient [ICC_(2,1)] and Bland-Altman analysis were used to calculate the measurement consistency and agreement, respectively, between these two methods.

Results

The smartphone application demonstrated excellent measurement consistency with the lab-based reference condition for the FTSTS test [ICC_(2,1) = 0.988] and TUG test [ICC_(2,1) = 0.946]. We observed a positive bias of 0.27 seconds (95% limits of agreement, -1.22 to 1.76 seconds) for the FTSTS test and 0.48 seconds (95% limits of agreement, -1.66 to 2.63 seconds) for the TUG test.

Conclusion

We cross-validated the newly developed smartphone application with the laboratory-based reference condition during the examination of FTSTS and TUG test performance in healthy elderly.

Lean Body Mass Associated with Upper Body Strength in Healthy Older Adults While Higher Body Fat Limits Lower Extremity Performance and Endurance

Impaired strength adversely influences an older person’s ability to perform activities of daily living. A cross-sectional study of 117 independently living men and women (age = 73.4 ± 9.4 year; body mass index (BMI) = 27.6 ± 4.8 kg/m²) aimed to assess the association between body composition and: (1) upper body strength (handgrip strength, HGS); (2) lower extremity performance (timed up and go (TUG) and sit to stand test (STS)); and (3) endurance (6-minute walk (SMWT). Body composition (% fat; lean body mass (LBM)) was assessed using bioelectrical impedance. Habitual physical activity was measured using the Minnesota Leisure Time Physical Activity Questionnaire (MLTPA) and dietary macronutrient intake, assessed using 24 h recalls and 3-day food records. Regression analyses included the covariates, protein intake (g/kg), MLTPA, age and sex. For natural logarithm (Ln) of right HGS, LBM (p < 0.001) and % body fat (p < 0.005) were significant (r² = 46.5%; p < 0.000). For left LnHGS, LBM (p < 0.000), age (p = 0.036), protein intake (p = 0.015) and LnMLTPA (p = 0.015) were significant (r² = 0.535; p < 0.000). For SMW, % body fat, age and LnMLTPA were significant (r² = 0.346; p < 0.000). For STS, % body fat and age were significant (r² = 0.251; p < 0.000). LBM is a strong predictor of upper body strength while higher % body fat and lower physical activity are associated with poorer outcomes on tests of lower extremity performance.

Role of body-worn movement monitor technology for balance and gait rehabilitation

This perspective article will discuss the potential role of body-worn movement monitors for balance and gait assessment and treatment in rehabilitation. Recent advances in inexpensive, wireless sensor technology and smart devices are resulting in an explosion of miniature, portable sensors that can quickly and accurately quantify body motion. Practical and useful movement monitoring systems are now becoming available. It is critical that therapists understand the potential advantages and limitations of such emerging technology. One important advantage of obtaining objective measures of balance and gait from body-worn sensors is impairment-level metrics characterizing how and why functional performance of balance and gait activities are impaired. Therapy can then be focused on the specific physiological reasons for difficulty in walking or balancing during specific tasks. A second advantage of using technology to measure balance and gait behavior is the increased sensitivity of the balance and gait measures to document mild disability and change with rehabilitation. A third advantage of measuring movement, such as postural sway and gait characteristics, with body-worn sensors is the opportunity for immediate biofeedback provided to patients that can focus attention and enhance performance. In the future, body-worn sensors may allow therapists to perform telerehabilitation to monitor compliance with home exercise programs and the quality of their natural mobility in the community. Therapists need technological systems that are quick to use and provide actionable information and useful reports for their patients and referring physicians. Therapists should look for systems that provide measures that have been validated with respect to gold standard accuracy and to clinically relevant outcomes such as fall risk and severity of disability.

Kinematic analysis of motor strategies in frail aged adults during the Timed Up and Go: how to spot the motor frailty?

Objective

The purpose of this work was to analyze and compare the movement kinematics of sit-to-stand (STS) and back-to-sit (BTS) transfers between frail aged adults and young subjects, as well as to determine the relationship between kinematic changes and functional capacities.

Methods

We analyzed the Timed Up and Go (TUG) movements by using a 3D movement analysis system for real-time balance assessment in frail elderly. Ten frail aged adults (frail group [FG]) and ten young subjects (young group [YG]) performed the TUG. Seven spatiotemporal parameters were extracted and compared between the two groups. Moreover, these parameters were plotted with TUG test duration.

Results

The experiments revealed that there were significant differences between FG and YG in trunk angle during both STS and BTS, and in TUG duration. The trunk angle of the young subjects was more than two times higher than that of the FG. As expected, the TUG duration was higher in the FG than in YG. Trunk angles during both transfers were the most different parameters between the groups. However, the BTS trunk angle and STS ratio were more linked to functional capacities.

Conclusion

There was a relationship between kinematic changes, representing the motor planning strategies, and physical frailty in these aged adults. These changes should be taken into account in clinical practice.

Reliability and criterion-related validity with a smartphone used in timed-up-and-go test

BACKGROUND

The capacity to diagnosys, quantify and evaluate movement beyond the general confines of a clinical environment under effectiveness conditions may alleviate rampant strain on limited, expensive and highly specialized medical resources. An iPhone 4® mounted a three dimensional accelerometer subsystem with highly robust software applications. The present study aimed to evaluate the reliability and concurrent criterion-related validity of the accelerations with an iPhone 4® in an Extended Timed Get Up and Go test. Extended Timed Get Up and Go is a clinical test with that the patient get up from the chair and walking ten meters, turn and coming back to the chair.

METHODS

A repeated measure, cross-sectional, analytical study. Test-retest reliability of the kinematic measurements of the iPhone 4® compared with a standard validated laboratory device. We calculated the Coefficient of Multiple Correlation between the two sensors acceleration signal of each subject, in each sub-stage, in each of the three Extended Timed Get Up and Go test trials. To investigate statistical agreement between the two sensors we used the Bland-Altman method.

RESULTS

With respect to the analysis of the correlation data in the present work, the Coefficient of Multiple Correlation of the five subjects in their triplicated trials were as follows: in sub-phase Sit to Stand the ranged between r = 0.991 to 0.842; in Gait Go, r = 0.967 to 0.852; in Turn, 0.979 to 0.798; in Gait Come, 0.964 to 0.887; and in Turn to Stand to Sit, 0.992 to 0.877. All the correlations between the sensors were significant (p < 0.001). The Bland-Altman plots obtained showed a solid tendency to stay at close to zero, especially on the y and x-axes, during the five phases of the Extended Timed Get Up and Go test.

CONCLUSIONS

The inertial sensor mounted in the iPhone 4® is sufficiently reliable and accurate to evaluate and identify the kinematic patterns in an Extended Timed Get and Go test. While analysis and interpretation of 3D kinematics data continue to be dauntingly complex, the iPhone 4® makes the task of acquiring the data relatively inexpensive and easy to use.

Mobile Romberg test assessment (mRomberg)

Background

The diagnosis of frailty is based on physical impairments and clinicians have indicated that early detection is one of the most effective methods for reducing the severity of physical frailty. Maybe, an alternative to the classical diagnosis could be the instrumentalization of classical functional testing, as Romberg test or Timed Get Up and Go Test. The aim of this study was (I) to measure and describe the magnitude of accelerometry values in the Romberg test in two groups of frail and non-frail elderly people through instrumentation with the iPhone 4^®, (II) to analyse the performances and differences between the study groups, and (III) to analyse the performances and differences within study groups to characterise accelerometer responses to increasingly difficult challenges to balance.

Methods

This is a cross-sectional study of 18 subjects over 70 years old, 9 frail subjects and 9 non-frail subjects. The non-parametric Mann–Whitney U test was used for between-group comparisons in means values derived from different tasks. The Wilcoxon Signed-Rank test was used to analyse differences between different variants of the test in both independent study groups.

Results

The highest difference between groups was found in the accelerometer values with eyes closed and feet parallel: maximum peak acceleration in the lateral axis (p < 0.01), minimum peak acceleration in the lateral axis (p < 0.01) and minimum peak acceleration from the resultant vector (p < 0.01). Subjects with eyes open and feet parallel, greatest differences found between the groups were in the maximum peak acceleration in the lateral axis (p < 0.01), minimum peak acceleration in the lateral axis (p < 0.01) and minimum peak acceleration from the resultant vector (p < 0.001). With eyes closed and feet in tandem, the greatest differences found between the groups were in the minimum peak acceleration in the lateral axis (p < 0.01).

Conclusions

The accelerometer fitted in the iPhone 4^® is able to study and analyse the kinematics of the Romberg test between frail and non-frail elderly people. In addition, the results indicate that the accelerometry values also were significantly different between the frail and non-frail groups, and that values from the accelerometer accelerometer increased as the test was made more complicated.

Parameterization and reliability of single-leg balance test assessed with inertial sensors in stroke survivors: a cross-sectional study

Background and purpose

There are no published studies on the parameterisation and reliability of the single-leg stance (SLS) test with inertial sensors in stroke patients. Purpose: to analyse the reliability (intra-observer/inter-observer) and sensitivity of inertial sensors used for the SLS test in stroke patients. Secondary objective: to compare the records of the two inertial sensors (trunk and lumbar) to detect any significant differences in the kinematic data obtained in the SLS test.

Methods

Design: cross-sectional study. While performing the SLS test, two inertial sensors were placed at lumbar (L₅-S₁) and trunk regions (T₇–T₈). Setting: Laboratory of Biomechanics (Health Science Faculty - University of Málaga). Participants: Four chronic stroke survivors (over 65 yrs old). Measurement: displacement and velocity, Rotation (X-axis), Flexion/Extension (Y-axis), Inclination (Z-axis); Resultant displacement and velocity (V):

Along with SLS kinematic variables, descriptive analyses, differences between sensors locations and intra-observer and inter-observer reliability were also calculated.

Results

Differences between the sensors were significant only for left inclination velocity (p = 0.036) and extension displacement in the non-affected leg with eyes open (p = 0.038). Intra-observer reliability of the trunk sensor ranged from 0.889-0.921 for the displacement and 0.849-0.892 for velocity. Intra-observer reliability of the lumbar sensor was between 0.896-0.949 for the displacement and 0.873-0.894 for velocity. Inter-observer reliability of the trunk sensor was between 0.878-0.917 for the displacement and 0.847-0.884 for velocity. Inter-observer reliability of the lumbar sensor ranged from 0.870-0.940 for the displacement and 0.863-0.884 for velocity.

Conclusion

There were no significant differences between the kinematic records made by an inertial sensor during the development of the SLS testing between two inertial sensors placed in the lumbar and thoracic regions. In addition, inertial sensors. Have the potential to be reliable, valid and sensitive instruments for kinematic measurements during SLS testing but further research is needed.