Mobility assessment in people with Alzheimer disease using smartphone sensors

Validity of an android device for assessing mobility in people with chronic stroke and hemiparesis: a cross-sectional study

BACKGROUND

Incorporating instrument measurements into clinical assessments can improve the accuracy of results when assessing mobility related to activities of daily living. This can assist clinicians in making evidence-based decisions. In this context, kinematic measures are considered essential for the assessment of sensorimotor recovery after stroke. The aim of this study was to assess the validity of using an Android device to evaluate kinematic data during the performance of a standardized mobility test in people with chronic stroke and hemiparesis.

METHODS

This is a cross-sectional study including 36 individuals with chronic stroke and hemiparesis and 33 age-matched healthy subjects. A simple smartphone attached to the lumbar spine with an elastic band was used to measure participants' kinematics during a standardized mobility test by using the inertial sensor embedded in it. This test includes postural control, walking, turning and sitting down, and standing up. Differences between stroke and non-stroke participants in the kinematic parameters obtained after data sensor processing were studied, as well as in the total execution and reaction times. Also, the relationship between the kinematic parameters and the community ambulation ability, degree of disability and functional mobility of individuals with stroke was studied.

RESULTS

Compared to controls, participants with chronic stroke showed a larger medial-lateral displacement (p = 0.022) in bipedal stance, a higher medial-lateral range (p < 0.001) and a lower cranio-caudal range (p = 0.024) when walking, and lower turn-to-sit power (p = 0.001), turn-to-sit jerk (p = 0.026) and sit-to-stand jerk (p = 0.001) when assessing turn-to-sit-to-stand. Medial-lateral range and total execution time significantly correlated with all the clinical tests (p < 0.005), and resulted significantly different between independent and limited community ambulation patients (p = 0.042 and p = 0.006, respectively) as well as stroke participants with significant disability or slight/moderate disability (p = 0.024 and p = 0.041, respectively).

CONCLUSION

This study reports a valid, single, quick and easy-to-use test for assessing kinematic parameters in chronic stroke survivors by using a standardized mobility test with a smartphone. This measurement could provide valid clinical information on reaction time and kinematic parameters of postural control and gait, which can help in planning better intervention approaches.

Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research

Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.

Smartphone-based activity tracking for spine patients: Current technology and future opportunities

Activity trackers and wearables allow accurate determination of physical activity, basic vital parameters, and tracking of complex medical conditions. This review attempts to provide a roadmap for the development of these applications, outlining the basic tools available, how they can be combined, and what currently exists in the marketplace for spine patients. Various types of sensors currently exist to measure distinct aspects of user movement. These include the accelerometer, gyroscope, magnetometer, barometer, global positioning system (GPS), Bluetooth and Wi-Fi, and microphone. Integration of data from these sensors allows detailed tracking of location and vectors of motion, resulting in accurate mobility assessments. These assessments can have great value for a variety of healthcare specialties, but perhaps none more so than spine surgery. Patient-reported outcomes (PROMs) are subject to bias and are difficult to track frequently - a problem that is ripe for disruption with the continued development of mobility technology. Currently, multiple mobile applications exist as an extension of clinical care. These include Manage My Surgery (MMS), SOVINITY-e-Healthcare Services, eHealth System, Beiwe Smartphone Application, QS Access, 6WT, and the TUG app. These applications utilize sensor data to assess patient activity at baseline and postoperatively. The results are evaluated in conjunction with PROMs. However, these applications have not yet exploited the full potential of available sensors. There is a need to develop smartphone applications that can accurately track the functional status and activity of spine patients, allowing a more quantitative assessment of outcomes, in contrast to legacy PROMs.

The Reliability and Accuracy of a Fall Risk Assessment Procedure Using Mobile Smartphone Sensors Compared with a Physiological Profile Assessment

Falls in older people are a major health concern as the leading cause of disability and the second most common cause of accidental death. We developed a rapid fall risk assessment based on a combination of physical performance measurements made with an inertial sensor embedded in a smartphone. This study aimed to evaluate and validate the reliability and accuracy of an easy-to-use smartphone fall risk assessment by comparing it with the Physiological Profile Assessment (PPA) results. Sixty-five participants older than 55 performed a variation of the Timed Up and Go test using smartphone sensors. Balance and gait parameters were calculated, and their reliability was assessed by the (ICC) and compared with the PPAs. Since the PPA allows classification into six levels of fall risk, the data obtained from the smartphone assessment were categorised into six equivalent levels using different parametric and nonparametric classifier models with neural networks. The F1 score and geometric mean of each model were also calculated. All selected parameters showed ICCs around 0.9. The best classifier, in terms of accuracy, was the nonparametric mixed input data model with a 100% success rate in the classification category. In conclusion, fall risk can be reliably assessed using a simple, fast smartphone protocol that allows accurate fall risk classification among older people and can be a useful screening tool in clinical settings.

A Systematic Review of the Effects of Capsaicin on Alzheimer's Disease

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterised by cognitive impairment, and amyloid-β plaques and neurofibrillary tau tangles at neuropathology. Capsaicin is a spicy-tasting compound found in chili peppers, with anti-inflammatory, antioxidant, and possible neuroprotective properties. Capsaicin intake has been associated with greater cognitive function in humans, and attenuating aberrant tau hyperphosphorylation in a rat model of AD. This systematic review discusses the potential of capsaicin in improving AD pathology and symptoms. A systematic analysis was conducted on the effect of capsaicin on AD-associated molecular changes, cognitive and behaviour resulting in 11 studies employing rodents and/or cell cultures, which were appraised with the Cochrane Risk of Bias tool. Ten studies showed capsaicin attenuated tau deposition, apoptosis, and synaptic dysfunction; was only weakly effective on oxidative stress; and had conflicting effects on amyloid processing. Eight studies demonstrated improved spatial and working memory, learning, and emotional behaviours in rodents following capsaicin treatment. Overall, capsaicin showed promise in improving AD-associated molecular, cognitive, and behavioural changes in cellular and animal models, and further investigations are recommended to test the readily available bioactive, capsaicin, to treat AD.

Classification of Parkinson's disease stages with a two-stage deep neural network

Introduction

Parkinson's disease is one of the most prevalent neurodegenerative diseases. In the most advanced stages, PD produces motor dysfunction that impairs basic activities of daily living such as balance, gait, sitting, or standing. Early identification allows healthcare personnel to intervene more effectively in rehabilitation. Understanding the altered aspects and impact on the progression of the disease is important for improving the quality of life. This study proposes a two-stage neural network model for the classifying the initial stages of PD using data recorded with smartphone sensors during a modified Timed Up & Go test.

Methods

The proposed model consists on two stages: in the first stage, a semantic segmentation of the raw sensor signals classifies the activities included in the test and obtains biomechanical variables that are considered clinically relevant parameters for functional assessment. The second stage is a neural network with three input branches: one with the biomechanical variables, one with the spectrogram image of the sensor signals, and the third with the raw sensor signals.

Results

This stage employs convolutional layers and long short-term memory. The results show a mean accuracy of 99.64% for the stratified k-fold training/validation process and 100% success rate of participants in the test phase.

Discussion

The proposed model is capable of identifying the three initial stages of Parkinson's disease using a 2-min functional test. The test easy instrumentation requirements and short duration make it feasible for use feasible in the clinical context.

An update on mobile applications collecting data among subjects with or at risk of Alzheimer's disease

Smart mobile phone use is increasing worldwide, as is the ability of mobile devices to monitor daily routines, behaviors, and even cognitive changes. There is a growing opportunity for users to share the data collected with their medical providers which may serve as an accessible cognitive impairment screening tool. Data logged or tracked in an app and analyzed with machine learning (ML) could identify subtle cognitive changes and lead to more timely diagnoses on an individual and population level. This review comments on existing evidence of mobile device applications designed to passively and/or actively collect data on cognition relevant for early detection and diagnosis of Alzheimer's disease (AD). The PubMed database was searched to identify existing literature on apps related to dementia and cognitive health data collection. The initial search deadline was December 1, 2022. Additional literature published in 2023 was accounted for with a follow-up search prior to publication. Criteria for inclusion was limited to articles in English which referenced data collection via mobile app from adults 50+ concerned, at risk of, or diagnosed with AD dementia. We identified relevant literature (n = 25) which fit our criteria. Many publications were excluded because they focused on apps which fail to collect data and simply provide users with cognitive health information. We found that although data collecting cognition-related apps have existed for years, the use of these apps as screening tools remains underdeveloped; however, it may serve as proof of concept and feasibility as there is much supporting evidence on their predictive utility. Concerns about the validity of mobile apps for cognitive screening and privacy issues remain prevalent. Mobile applications and use of ML is widely considered a financially and socially viable method of compiling symptomatic data but currently this large potential dataset, screening tool, and research resource is still largely untapped.

Effect of virtual running with exercise on functionality in pre-frail and frail elderly people: randomized clinical trial

BACKGROUND

Virtual mirror therapies could increase the results of exercise, since the mirror neuron system produces an activation of motor execution cortical areas by observing actions performed by others. In this way, pre-frail and frail people could use this system to reach an exercise capacity threshold and obtain health benefits.

AIM

The aim of this study is to evaluate the effects of a virtual running (VR) treatment combined with specific physical gait exercise (PE) compared to placebo VR treatment combined with PE on functionality, pain, and muscular tone in pre-frail and frail older persons.

METHODS

A single blinded, two-arm, randomised controlled trial design was employed. Thirty-eight participants were divided into two intervention arms: Experimental Intervention (EI) group, in which VR and gait-specific physical exercises were administered and Control Intervention (CI) group, in which a placebo virtual gait and the same exercise programme was administered. Functionality, pain, and tone were assessed.

RESULTS

EI group improved in aerobic capacity, functional lower-limb strength, reaction time, and pain, while CI group remained the same. Regarding static balance and muscle tone, no differences were found for either group. Further analysis is needed to asses VR effectiveness for improving gait, stand-up and sit-down performance and velocity.

CONCLUSIONS

Virtual running therapy appears to enhance capacities related with voluntary movements (i.e., aerobic capacity, functional lower-limb strength, and reaction time) and reduce pain.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Impact of Parkinson’s Disease on Functional Mobility at Different Stages

Introduction

Specific functional assessments to determine the progression of Parkinson’s Disease (PD) are important to slow down such progression and better plan rehabilitation. This study aimed to explore possible differences in the performance of different functional tasks included in a mobility test using sensors embedded in an Android device, in people at different PD stages.

Materials and Methods

Eighty-seven participants with PD agreed to participate in this cross-sectional study. They were assessed once using an inertial sensor and variables related to functional status were recorded (i.e., MLDisp, APDisp, DispA, Vrange, MLRange, PTurnSit, PStand, TTime, and RTime).

Results

There was significant impairment of the vertical range during gait between stages I and II. Further, when stages II and III were compared, the sit-to-stand power was significantly impaired, and the total time required to complete the test increased significantly (p < 0.05). Even more significant differences were obtained when stages I and III were compared, in particular, dysfunction in postural control, vertical range, sit to stand power and total time. Finally, there were no significant differences between stages in the medial-lateral displacements and reaction time (p > 0.05).

Conclusion

Functional mobility becomes more significantly impaired in the PD population as the PD stages progress. This implies impaired postural control, decreased ability to sit down or stand up from a chair, increased metabolic cost during walking, and overall slowing-down of motor function.

Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients

Impairment of navigation is one of the earliest symptoms of Alzheimer’s disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behaviour in AD. The aim was to use data-driven machine learning approaches to explore spatial metrics within real life navigational traces that discriminate AD patients from controls. 15 AD patients and 18 controls underwent tracking of their outdoor navigation over two weeks. Three kinds of spatiotemporal features of segments were extracted, characterising the mobility domain (entropy, segment similarity, distance from home), spatial shape (total turning angle, segment complexity), and temporal characteristics (stop duration). Patients significantly differed from controls on entropy (p-value 0.008), segment similarity (p-value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${10}^{-7}$$\end{document}10-7), and distance from home (p-value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${10}^{-14}$$\end{document}10-14). Graph-based analyses yielded preliminary data indicating that topological features assessing the connectivity of visited locations may also differentiate patients from controls. In conclusion, our results show that specific outdoor navigation features discriminate AD patients from controls, which has significant implication for future AD diagnostics, outcome measures and interventions. Furthermore, this work illustrates how wearables-based sensing of everyday behaviour may be used to deliver ecologically-valid digital biomarkers of AD pathophysiology.

JTrack: A Digital Biomarker Platform for Remote Monitoring of Daily-Life Behaviour in Health and Disease

Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. Despite recent contributions, the trade-off between privacy, optimization, stability and research-grade data quality is not well met by existing platforms. Here we introduce the JTrack platform as a secure, reliable and extendable open-source solution for remote monitoring in daily-life and digital-phenotyping. JTrack is an open-source (released under open-source Apache 2.0 licenses) platform for remote assessment of digital biomarkers (DB) in neurological, psychiatric and other indications. JTrack is developed and maintained to comply with security, privacy and the General Data Protection Regulation (GDPR) requirements. A wide range of anonymized measurements from motion-sensors, social and physical activities and geolocation information can be collected in either active or passive modes by using JTrack Android-based smartphone application. JTrack also provides an online study management dashboard to monitor data collection across studies. To facilitate scaling, reproducibility, data management and sharing we integrated DataLad as a data management infrastructure. Smartphone-based Digital Biomarker data may provide valuable insight into daily-life behaviour in health and disease. As illustrated using sample data, JTrack provides as an easy and reliable open-source solution for collection of such information.

FallSkip device is a useful tool for fall risk assessment in sarcopenic older community people

PURPOSE

Fall prevention is a major health concern for the ageing population. Sarcopenia is considered a risk factor for falls. Some instruments, such as Time Up and Go (TUG), are used for screening risk. The use of sensors has also been shown to be a viable tool that can provide accurate, cost-effective, and easy to manage assessment of fall risk. One novel sensor for assessing fall risk in older people is the Fallskip device. The present study evaluates the performance of the FallSkip device against the TUG method in fall risk screening and assesses its measurement properties in sarcopenic older people.

METHODS

A cross-sectional study was made in a sample of community-dwelling sarcopenic and non-sarcopenic older people aged 70 years or over.

RESULTS

The study sample consisted of 34 older people with a mean age of 77.03 (6.58) years, of which 79.4% (n = 27) were females, and 41.2% (n = 14) were sarcopenic. The Pearson correlation coefficient between TUG time and FallSkip time was 0.70 (p < 0.001). The sarcopenic individuals took longer in performing both TUG and FallSkip. They also presented poorer reaction time, gait and sit-to-stand - though no statistically significant differences were observed. The results in terms of feasibility, acceptability, reliability and validity in sarcopenic older people with FallSkip were acceptable.

CONCLUSIONS

The FallSkip device has suitable metric properties for the assessment of fall risk in sarcopenic community-dwelling older people. FallSkip analyses more parameters than TUG in assessing fall risk and has greater discriminatory power in evaluating the risk of falls.

The Use of Mobile Applications as Communication Aids for People with Dementia: Opportunities and Limitations

BACKGROUND

Communication difficulties are one of the primary symptoms associated with dementia, and mobile applications have shown promise as tools for facilitating communication in patients with dementia (PwD). The literature regarding mobile health (mHealth) applications, especially communications-based mHealth applications, is limited.

OBJECTIVE

This review aims to compile the existing literature on communications-based mobile applications regarding dementia and assess their opportunities and limitations. A PICO framework was applied with a Population consisting of PwD, Interventions consisting of communication technology, focusing primarily on mobile applications, Comparisons between patient well-being with and without technological intervention, and Outcomes that vary but can include usability of technology, quality of communication, and user acceptance.

METHODS

Searches of PubMed, IEEE XPLORE, and ACM Digital Library databases were conducted to establish a comprehensive understanding of the current literature on dementia care as related to 1) mobile applications, 2) communication technology, and 3) communications-based mobile applications. Applying certain inclusion and exclusion criteria, yielded a set of articles (n = 11).

RESULTS

The literature suggests that mobile applications as tools for facilitating communication in PwD are promising. Mobile applications are not only feasible socially, logistically, and financially, but also produce meaningful communication improvements in PwD and their caregivers. However, the number of satisfactory communications-based mobile applications in the mHealth marketplace and their usability is still insufficient.

CONCLUSION

Despite favorable outcomes, more research involving PwD using these applications are imperative to shed further light on their communication needs and on the role of mHealth.

Innovative motor and cognitive dual-task approaches combining upper and lower limbs may improve dementia early detection

Motor and Cognitive Dual-Task (MCDT) represents an innovative chance to assess Mild Cognitive Impairment (MCI). We compare two novel MCDTs, fore-finger tapping (FTAP), toe-tapping (TTHP), to gold standards for cognitive screening (Mini-Mental State Examination-MMSE), and to a well-established MCDT (GAIT). We administered the aforementioned MCDTs to 44 subjects (MCIs and controls). Motor parameters were extracted, and correlations with MMSE investigated. Logistic regression models were built, and AUC areas computed. Spearman's correlation demonstrated that FTAP and TTHP significantly correlate with MMSE, at each cognitive load. AUC areas computed report similar (FTAP, 0.87), and even higher (TTHP, 0.97) capability to identify MCIs, if compared to GAIT (0.92). We investigated the use of novel MCDT approaches to assess MCI, aiming to enrich the clinical repertoire with objective and non-invasive tools. Our protocol shows good correlations with MMSE, and reaches high performances in identifying MCI, adopting simpler exercises.

Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study

The most frequent form of dementia is Alzheimer's Disease (AD), a severe progressive neurological pathology in which the main cognitive functions of an individual are compromised. Recent studies have found that loneliness and living in isolation are likely to cause an acceleration in the cognitive decline associated with AD. Therefore, understanding social behaviours of AD patients is crucial to promote sociability, thus delaying cognitive decline, preserving independence, and providing a good quality of life. In this work, we analyze the localization data of AD patients living in assisted care homes to gather insights about the social dynamics among them. We use localization data collected by a system based on iBeacon technology comprising two components: a network of antennas scattered throughout the facility and a Bluetooth bracelet worn by the patients. We redefine the Relational Index to capture wandering and casual encounters, these being common phenomena among AD patients, and use the notions of Relational and Popularity Indexes to model, visualize and understand the social behaviour of AD patients. We leverage the data analyses to build predictive tools and applications to enhance social activities scheduling and sociability monitoring and promotion, with the ultimate aim of providing patients with a better quality of life. Predictions and visualizations act as a support for caregivers in activity planning to maximize treatment effects and, hence, slow down the progression of Alzheimer's disease. We present the Community Behaviour Prediction Table (CBPT), a tool to visualize the estimated values of sociability among patients and popularity of places within a facility. Finally, we show the potential of the system by analyzing the Coronavirus Disease 2019 (COVID-19) lockdown time-frame between February and June 2020 in a specific facility. Through the use of the indexes, we evaluate the effects of the pandemic on the behaviour of the residents, observing no particular impact on sociability even though social distancing was put in place.

The use of Motor and Cognitive Dual-Task quantitative assessment on subjects with mild cognitive impairment: A systematic review

Dementia and Alzheimer's Disease (AD) represent a health emergency. The identification of valid and noninvasive markers to identify people with Mild Cognitive Impairment (MCI) is profoundly advocated. This review outlines the use of quantitative Motor and Cognitive Dual-Task (MCDT) on MCI, by technologies aid. We describe the framework and the most valuable researches, displaying the adopted protocols, and the available technologies. PubMed Central, Web of Science, and Scopus were inspected between January 2010 and May 2020. 1939 articles were found in the initial quest. Exclusion criteria allowed the selection of the most relevant papers; 38 papers were included. The articles, regarding four technological solutions "wearable sensors", "personal devices", "optokinetic systems", and "electronic walkways", are organized into three categories: "Quantitative MCDT", "MCDT Inspired by Neuropsychological Test", and "MCDT for MCI Stimulation". MCDT might furnish clinical landmarks, supplying aid for disease stratication, risk prediction, and intervention optimization. Such protocols could foster the use of data mining and machine learning techniques. Notwithstanding, there is still a need to standardize and harmonize such protocols.

Assessment of Functional Activities in Individuals with Parkinson's Disease Using a Simple and Reliable Smartphone-Based Procedure

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to functional impairment. In order to monitor the progression of the disease and to implement individualized therapeutic approaches, functional assessments are paramount. The aim of this study was to determine the impact of PD on balance, gait, turn-to-sit and sit-to-stand by means of a single short-duration reliable test using a single inertial measurement unit embedded in a smartphone device. Study participants included 29 individuals with mild-to moderate PD (PG) and 31 age-matched healthy counterparts (CG). Functional assessment with FallSkip^® included postural control (i.e., Medial-Lateral (ML) and Anterior-Posterior (AP) displacements), gait (Vertical (V) and Medial-Lateral (ML) ranges), turn-to-sit (time) and sit-to-stand (power) tests, total time and gait reaction time. Our results disclosed a reliable procedure (intra-class correlation coefficient (ICC) = 0.58–0.92). PG displayed significantly larger ML and AP displacements during the postural test, a decrease in ML range while walking and a longer time needed to perform the turn-to-sit task than CG (p < 0.05). No differences between groups were found for V range, sit-to-stand test, total time and reaction time (p > 0.05). In conclusion, people with mild-to-moderate PD exhibit impaired postural control, altered gait strategy and slower turn-to-sit performance than age-matched healthy people.