Real-life Walking Research Articles

Optimizing rehabilitation strategies in Parkinson’s disease: a comparison of dual cognitive-walking treadmill training and single treadmill training

Dual cognitive-walking treadmill training (DTT), designed to replicate real-life walking conditions, has shown promise effect in individuals with Parkinson’s disease (PD). This study aims to compare the effects of DTT versus single treadmill training (STT) on cognitive and walking performance under both single and dual task conditions, as well as on fall, patients’ subjective feeling, and quality of life. Sixteen individuals with PD were randomly assigned to DTT or STT group and underwent 8 weeks of training. The DTT group received treadmill training with cognitive loads, while the STT group received treadmill training without cognitive load. Outcome measures included gait parameters (speed, step length) and cognitive performance (reaction time, accuracy, composite score) under both single and dual task conditions. Unified Parkinson’s Disease Rating Scale-part III (UPDRS-III), Falls Efficacy Scale (FES), Patient Global Impression of Change (PGIC), and Parkinson’s Disease Questionnaire (PDQ-39) were also measured. Both DTT and STT groups showed increased comfortable walking speed and step length. Only the DTT group demonstrated significant improvements in cognitive composite score under both single and dual task conditions, as well as UPDRS-III, FES, and PDQ-39(p < 0.05). DTT can enhance cognitive function without compromising walking ability and also have real-world transferability.

Navigating the Challenges Encountered While Dual-task Walking in Real-life Environment by Elderly with Type 2 Diabetes Mellitus

Abstract Background and Objectives: Real-life walking environment comprises various attentionally demanding activities that require intact dual-tasking ability. Dual-task cost is higher in older adults with type 2 diabetes mellitus (T2DM) while walking due to the alterations in cognition and gait. However, contextual variations in the familial, social, and environmental conditions also influence walking in real-life environments. There is a scarcity of literature exploring the challenges linked with contextual variations while dual-task walking in real-life environments for older adults with T2DM. Hence, this study aims to identify barriers and facilitators to dual-task walking in real-life environments for T2DM elderly. Methods: The study used a phenomenological approach and interviewed older adults with and without T2DM using a semi-structured interview guide. It comprised open-ended questions on walking difficulties in a real-life environment. Results: Four themes were identified which comprised individual, social, environmental, and policy-level factors influencing walking. Role transitions within the family, fear, infrastructural impediments, and overprotectiveness of family members were identified as barriers whereas health literacy, positive outlook, occupational demands, and devout engagement in religious participation were identified as facilitators to walking in the elderly. The participants expressed several policy-level challenges, and emphasis was made on outdoor modifications and the need for community programs. Conclusion: The findings of this study provide insights into various constructs close to real-life walking environment that needs to be included in dual-task cost evaluation tools and several measures that can be taken at the familial and policy levels to increase the community mobility of older adults with T2DM.

USABILITY AND PSYCHOMETRIC PROPERTIES OF THE MOBITEC-GP APP FOR REAL-LIFE MOBILITY ASSESSMENT

Abstract Modern technologies such as the Global Navigation Satellite System and accelerometers which are integrated in every smartphone enable tracking mobility patterns over time, which in turn enables early detection of mobility impairments. Aim of the MOBITEC-GP project was to develop a mobile app which measures walking speed and spatial mobility under everyday conditions. Reliability and validity of the application was tested first (Study 1). The applicability in everyday life and the acceptance by older adults (Study 2) was also investigated. In study 1, participants underwent several supervised walking speed assessments as well as a 1-week life-space assessment during two assessment sessions 9 days apart. Fifty-seven older adults (47.4% male, mean age= 75.3 years) were included in the study. The app showed moderate to excellent test-retest reliability and validity of walking speed measurements of 50 meters and above and of most 1-week life-space parameters, including life-space area, time spent out-of-home, and action range. Study 2 revealed a good usability of the app (mean System Usability Scale score=77.18) among older adults (N=60 with ≥2 chronic conditions) for a 1-week usage of the app including both, a 1-week life space assessment and at least one gait speed measurement in a 30-minute stroll. The MOBITEC-GP app is a reliable, valid and usable tool for the assessment of real-life walking speed (at distances of 50 metres and above) and life-space parameters of older adults. Future studies should look into technical issues more systematically in order to avoid invalid measurements.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases.

The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

A Novel Low-Cost ZMP Estimation Method for Humanoid Gait using Inertial Measurement Devices: Concept and Experiments

Estimation and control of zero-moment point (ZMP) is a widely used concept for planning the locomotion of bipedal robots and is commonly measured using integrated joint angle encoders and foot force sensors. Contemporary methods for ZMP measurement involve built-in contact sensors such as joint encoders or instrumented foot force sensors. This paper presents a novel approach for computing ZMP for a humanoid robot using inertial sensor-based wireless foot sensor modules (WFSMs). The developed WFSMs, strapped at different limb segments of a bipedal robot, measure lower limb joint angles in real time. The joint angle trajectories, further transformed into Cartesian position coordinates, are used for estimating the ZMP positions of humanoid robots using the planar biped model. The whole framework is presented through experimental studies for different real-life walking scenarios. Since the modules work based on the limb motion and inclination, any ground unevenness would be automatically reflected in the module output. Hence, this measurement process can be a convenient method for applications requiring humanoid control on uneven surfaces/outdoor terrains. To compare the performance of the proposed model, ZMP is simultaneously measured from inbuilt foot force sensors and joint encoders of the robot. Statistical tests exhibit a high linear correlation between the proposed method with integrated encoders and foot force sensors (Pearson’s coefficient, [Formula: see text]). Results indicate that ZMP estimated by WFSM is a viable method to monitor the dynamic gait balance of a humanoid robot and has potential application in outdoor and uneven terrains.

Psychometric properties of the MOBITEC-GP mobile application for real-life mobility assessment in older adults

Aim of this study was to test the reliability and validity of the life-space measures and walking speed delivered by the MOBITEC-GP app. Participants underwent several supervised walking speed assessments as well as a 1-week life-space assessment during two assessment sessions 9 days apart. Fifty-seven older adults (47.4% male, mean age= 75.3 (±5.9) years) were included in the study. The MOBITEC-GP app showed moderate to excellent test-retest reliability (ICCs between 0.584 and 0.920) and validity (ICCs between 0.468 and 0.950) of walking speed measurements of 50 meters and above and of most 1-week life-space parameters, including life-space area, time spent out-of-home, and action range. The MOBITEC-GP app for Android is a reliable and valid tool for the assessment of real-life walking speed (at distances of 50 metres and above) and life-space parameters of older adults. Future studies should look into technical issues more systematically in order to avoid invalid measurements.

Real-Life Social-Skills Training and Motor-Skills Training in Adults With Autism Spectrum Disorder: The Con-Tatto Project Walking Down the Francigena Route.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with an early onset and a genetic and epigenetic component. ASD is characterized by deficits in socio-emotional reciprocity, impaired verbal and non-verbal communication skills, and specific difficulties in developing and maintaining adequate social relationships with peers. Indeed, restricted, repetitive patterns of behavior, interests, or activities are required by DSM-5 diagnostic criteria. Autistic people usually need an unchanging environment (or in any case predictable and stable) and may have hypo- or hyper-sensitivity to sensory inputs. The onset of clinical symptoms occurs during the early years of life. Social skills competence is a significant therapeutic aim to be pursued when addressing ASD core symptoms. Several considerable motor difficulties (87%) in people with autism spectrum disorder in adulthood have been found. The Con-tatto project developed a project addressing social, physical, and mental health difficulties in real-life walking down the Francigena route for 9 days with 12 autistic people, by (1) Implementing daily sessions of social skills training program whose abilities were addressed to be immediately generalized and used throughout the day. (2) Educational movement and walking activity programs were led by a fitness coach. (3) The creation of walking peers’ social community with a strong and relevant impact on adults with ASD social life respecting every person’s individuality. (4) Provision of social reinforcers to reduce the stigma of people with autism and the experienced perception of low self-esteem, especially when they are bullied.

Feasibility of VR Technology in Eliciting State Anxiety Changes While Walking in Older Women.

Virtual reality (VR) technology offers an exciting way to emulate real-life walking conditions that may better elicit changes in emotional state. We aimed to determine whether VR technology is a feasible way to elicit changes in state anxiety during walking. Electrocardiogram data were collected for 18 older adult women while they navigated a baseline walking task, a dual walking task, and four walking VR environments. Using heart rate variability (HRV) analysis, we found that all four of the VR environments successfully elicited a significantly higher level of state anxiety as compared to the walking baseline, with 84% of participants eliciting a significantly lower HRV in each of the four VR conditions as compared to baseline walking. VR was also found to be a more reliable tool for increasing state anxiety as compared to a dual task, where only 47% of participants demonstrated a significantly lower HRV as compared to baseline walking. VR, therefore, could be promising as a tool to elicit changes in state anxiety and less limited in its ability to elicit changes as compared to a traditional dual task condition.

Magnetometer-Free, IMU-Based Foot Progression Angle Estimation for Real-Life Walking Conditions.

Foot progression angle (FPA) is vital in many disease assessment and rehabilitation applications, however previous magneto-IMU-based FPA estimation algorithms can be prone to magnetic distortion and inaccuracies after walking starts and turns. This paper presents a foot-worn IMU-based FPA estimation algorithm comprised of three key components: orientation estimation, acceleration transformation, and FPA estimation via peak foot deceleration. Twelve healthy subjects performed two walking experiments to evaluation IMU algorithm performance. The first experiment aimed to validate the proposed algorithm in continuous straight walking tasks across seven FPA gait patterns (large toe-in, medium toe-in, small toe-in, normal, small toe-out, medium toe-out, and large toe-out). The second experiment was performed to evaluate the proposed FPA algorithm for steps after walking starts and turns. Results showed that FPA estimations from the IMU-based algorithm closely followed marker-based system measurements with an overall mean absolute error of 3.1±1.3 deg, and the estimation results were valid for all steps immediately after walking starts and turns. This work could enable FPA assessment in environments where magnetic distortion is present due to ferrous metal structures and electrical equipment, or in real-life walking conditions when walking starts, stops, and turns commonly occur.

Real-life gait assessment in degenerative cerebellar ataxia: Toward ecologically valid biomarkers.

With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid motor biomarkers are highly warranted. In this observational study, we aimed to unravel and validate markers of ataxic gait in real life by using wearable sensors. We assessed gait characteristics of 43 patients with degenerative cerebellar disease (Scale for the Assessment and Rating of Ataxia [SARA] 9.4 ± 3.9) compared with 35 controls by 3 body-worn inertial sensors in 3 conditions: (1) laboratory-based walking; (2) supervised free walking; (3) real-life walking during everyday living (subgroup n = 21). Movement analysis focused on measures of spatiotemporal step variability and movement smoothness. A set of gait variability measures was identified that allowed us to consistently identify ataxic gait changes in all 3 conditions. Lateral step deviation and a compound measure of spatial step variability categorized patients vs controls with a discrimination accuracy of 0.86 in real life. Both were highly correlated with clinical ataxia severity (effect size ρ = 0.76). These measures allowed detecting group differences even for patients who differed only 1 point in the clinical SARAposture&gait subscore, with highest effect sizes for real-life walking (d = 0.67). We identified measures of ataxic gait that allowed us not only to capture the gait variability inherent in ataxic gait in real life, but also to demonstrate high sensitivity to small differences in disease severity, with the highest effect sizes in real-life walking. They thus represent promising candidates for motor markers for natural history and treatment trials in ecologically valid contexts. This study provides Class I evidence that a set of gait variability measures, even if accessed in real life, correlated with the clinical severity of ataxia in patients with degenerative cerebellar disease.

The immediate influence of implicit motor learning strategies on spatiotemporal gait parameters in stroke patients: a randomized within-subjects design

People with PD (PWP) have an increased risk of becoming inactive. Wearable sensors can provide insights into daily physical activity and walking patterns.(1) Is the severity of motor fluctuations associated with sensor-derived average daily walking quantity? (2) Is the severity of motor fluctuations associated with the amount of change in sensor-derived walking quantity after levodopa intake?304 Dutch PWP from the [email protected] study were included. At baseline, all participants received a clinical examination. During the follow-up period (median: 97 days; 25-Interquartile range-IQR: 91 days, 75-IQR: 188 days), participants used the Fox Wearable Companion app and streamed smartwatch accelerometer data to a cloud platform. The first research question was assessed by linear regression on the sensor-derived mean time spent walking/day with the severity of fluctuations (MDS-UPDRS item 4.4) as independent variable, controlled for age and MDS-UPDRS part-III score. The second research question was assessed by linear regression on the sensor-derived mean post-levodopa walking quantity, with the sensor-derived mean pre-levodopa walking quantity and severity of fluctuations as independent variables, controlled for mean time spent walking per day, age and MDS-UPDRS part-III score.PWP spent most time walking between 8am and 1pm, summing up to 72 ± 39 (mean ± standard deviation) minutes of walking/day. The severity of motor fluctuations did not influence the mean time spent walking (B = 2.4 ± 1.9, p = 0.20), but higher age (B = −1.3 ± 0.3, p = < 0.001) and greater severity of motor symptoms (B = −0.6 ± 0.2, p < 0.001) was associated with less time spent walking (F(3216) = 14.6, p < .001, R2 = .17). The severity of fluctuations was not associated with the amount of change in time spent walking in relation to levodopa intake in any part of the day.Analysis of sensor-derived gait quantity suggests that the severity of motor fluctuations is not associated with changes in real-life walking patterns in mildly to moderate affected PWP.

Acting your avatar’s age: effects of virtual reality avatar embodiment on real life walking speed

ABSTRACTWhen immersive virtual reality users employ digital self-representations, i.e., avatars, they may be subject to the Proteus effect. This effect describes changes in attitudes and behaviors in accordance with identity cues derived from the employed avatar’s appearance, which can persist after leaving virtual reality. Individual reactions to the experience can affect the strength of observed Proteus effects. Especially the experienced illusions of body ownership of avatars and of being in the virtual environment (spatial presence) have been discussed in this context. This study investigated a Proteus effect of avatar age on post-embodiment walking speed, with special focus on how body ownership and spatial presence moderated this effect. Participants who had previously embodied older avatars took significantly longer to walk a set distance than either young avatar or control group participants. This was only apparent during the first half of the walking phase, which may indicate fast decay rates of the effect after embodiment ended. The reported body ownership could not be shown to impact the strength of the Proteus effect. Participants reporting more pronounced spatial presence were subject to stronger Proteus effects, with only the two-thirds of the sample with higher spatial presence showing evidence of the effect.

Gait Event Detection in Controlled and Real-Life Situations: Repeated Measures From Healthy Subjects

A benchmark and time-effective computational method is needed to assess human gait events in real-life walking situations using few sensors to be easily reproducible. This paper fosters a reliable gait event detection system that can operate at diverse gait speeds and on diverse real-life terrains by detecting several gait events in real time. This detection only relies on the foot angular velocity measured by a wearable gyroscope mounted in the foot to facilitate its integration for daily and repeated use. To operate as a benchmark tool, the proposed detection system endows an adaptive computational method by applying a finite-state machine based on heuristic decision rules dependent on adaptive thresholds. Repeated measurements from 11 healthy subjects (28.27 ± 4.17 years) were acquired in controlled situations through a treadmill at different speeds (from 1.5 to 4.5 km/h) and slopes (from 0% to 10%). This validation also includes heterogeneous gait patterns from nine healthy subjects (27 ± 7.35 years) monitored at three self-selected paces (from 1 ± 0.2 to 2 ± 0.18 m/s) during forward walking on flat, rough, and inclined surfaces and climbing staircases. The proposed method was significantly more accurate ( ) and time effective (< 30.53 ± 9.88 ms, ) in a benchmarking analysis with a state-of-the-art method during 5657 steps. Heel strike was the gait event most accurately detected under controlled (accuracy of 100%) and real-life situations (accuracy > 96.98%). Misdetection was more pronounced in middle mid swing (accuracy > 90.12%). The lower computational load, together with an improved performance, makes this detection system suitable for quantitative benchmarking in the locomotor rehabilitation field.

Impact of motor fluctuations on real-life gait in Parkinson’s patients

BackgroundPeople with PD (PWP) have an increased risk of becoming inactive. Wearable sensors can provide insights into daily physical activity and walking patterns. Research questions(1) Is the severity of motor fluctuations associated with sensor-derived average daily walking quantity? (2) Is the severity of motor fluctuations associated with the amount of change in sensor-derived walking quantity after levodopa intake? Methods304 Dutch PWP from the Parkinson@Home study were included. At baseline, all participants received a clinical examination. During the follow-up period (median: 97 days; 25-Interquartile range-IQR: 91 days, 75-IQR: 188 days), participants used the Fox Wearable Companion app and streamed smartwatch accelerometer data to a cloud platform. The first research question was assessed by linear regression on the sensor-derived mean time spent walking/day with the severity of fluctuations (MDS-UPDRS item 4.4) as independent variable, controlled for age and MDS-UPDRS part-III score. The second research question was assessed by linear regression on the sensor-derived mean post-levodopa walking quantity, with the sensor-derived mean pre-levodopa walking quantity and severity of fluctuations as independent variables, controlled for mean time spent walking per day, age and MDS-UPDRS part-III score. ResultsPWP spent most time walking between 8am and 1pm, summing up to 72 ± 39 (mean ± standard deviation) minutes of walking/day. The severity of motor fluctuations did not influence the mean time spent walking (B = 2.4 ± 1.9, p = 0.20), but higher age (B = −1.3 ± 0.3, p = < 0.001) and greater severity of motor symptoms (B = −0.6 ± 0.2, p < 0.001) was associated with less time spent walking (F(3216) = 14.6, p < .001, R2 = .17). The severity of fluctuations was not associated with the amount of change in time spent walking in relation to levodopa intake in any part of the day. SignificanceAnalysis of sensor-derived gait quantity suggests that the severity of motor fluctuations is not associated with changes in real-life walking patterns in mildly to moderate affected PWP.

Accuracy and precision of smartphone applications and commercially available motion sensors in multiple sclerosis.

BackgroundThere is increased interest in the application of smartphone applications and wearable motion sensors among multiple sclerosis (MS) patients.ObjectiveThis study examined the accuracy and precision of common smartphone applications and motion sensors for measuring steps taken by MS patients while walking on a treadmill.MethodsForty-five MS patients (Expanded Disability Status Scale (EDSS) = 1.0–5.0) underwent two 500-step walking trials at comfortable walking speed on a treadmill. Participants wore five motion sensors: the Digi-Walker SW-200 pedometer (Yamax), the UP2 and UP Move (Jawbone), and the Flex and One (Fitbit). The smartphone applications were Health (Apple), Health Mate (Withings), and Moves (ProtoGeo Oy).ResultsThe Fitbit One had the best absolute (mean = 490.6 steps, 95% confidence interval (CI) = 485.6–495.5 steps) and relative accuracy (1.9% error), and absolute (SD = 16.4) and relative precision (coefficient of variation (CV) = 0.0), for the first 500-step walking trial; this was repeated with the second trial. Relative accuracy was correlated with slower walking speed for the first (rs = −.53) and second (rs = −.53) trials.ConclusionThe results suggest that the waist-worn Fitbit One is the most precise and accurate sensor for measuring steps when walking on a treadmill, but future research is needed (testing the device across a broader range of disability, at different speeds, and in real-life walking conditions) before inclusion in clinical research and practice with MS patients.

Ecological validity of walking capacity tests in multiple sclerosis.

BackgroundEcological validity implicates in how far clinical assessments refer to real life. Short clinical gait tests up to ten meters and 2- or 6-Minutes Walking Tests (2MWT/6MWT) are used as performance-based outcomes in Multiple Sclerosis (MS) studies and considered as moderately associated with real life mobility.ObjectiveTo investigate the ecological validity of 10 Meter Walking Test (10mWT), 2MWT and 6MWT.MethodsPersons with MS performed 10mWT, 6MWT including 2MWT and 7 recorded days by accelerometry. Ecological validity was assumed if walking tests represented a typical walking sequence in real-life and correlations with accelerometry parameters were strong.ResultsIn this cohort (n=28, medians: age=45, EDSS=3.2, disease duration=9 years), uninterrupted walking of 2 or 6 minutes occurred not frequent in real life (2.61 and 0.35 sequences/day). 10mWT correlated only with slow walking speed quantiles in real life. 2MWT and 6MWT correlated moderately with most real life walking parameters.ConclusionClinical gait tests over a few meters have a poor ecological validity while validity is moderate for 2MWT and 6MWT. Mobile accelerometry offers the opportunity to control and improve the ecological validity of MS mobility outcomes.

Commentary on ‘Physical Activity Monitoring in Patients with Peripheral Arterial Occlusive Disease: Validation of an Activity Monitor’

Despite an enormous body of evidence supporting the positive effects of exercise, physical activity levels are lower in patients with peripheral arterial disease (PAD) than in age-matched controls. 1 It has been shown that walking speed is a good indicator for increased risk of cardiovascular death. In a study with over 4000 participants aged 65e85, it was found that those in the lowest third of walking speed had a 44% increased risk of death compared with those in the upper thirds. 2 Walking advice alone is ineffective, as patients are usually non-compliant because of lack of support and motivation. 3 With this sort of convincing data available, it is possible to see that increasing the activity of these patients is of paramount importance. Wearable activity monitors are increasingly being used to objectively monitor physical activity in research studies within the field of exercise science. Calibration and validation of these devices are vital to obtaining accurate data. Bassett and Rowlands (2012) 4 compared six different activity monitors in a study where each patient wore six different monitors at a time (one of these was the Dynaport min mov), and performed an hour’s worth of activities which were then compared for accuracy. Three were found to be more accurate (the Dynaport was found to be particularly accurate at walking speeds). Tri-axial activity monitors are getting more and more sophisticated.They can track how many steps taken, stairs climbed, distance travelled, calories burned, and even quality of sleep. Some measure heart rate, and have altimeters for greater accuracy. Most sync with mobile phones or computers, and have websites to enable performance to be studied. Some have red lights that turn green when patients have reached a daily target of activity, and an algorithm that increases the amount of work they have to do to obtain a green light. The author attempts to validate the Dynaport move monitor. 5 Patients were filmed during a routine hospital visit (wearing a Dynoport move monitor). Seven activities were identified and data collected. Analysis showed that the move monitor correlated best for walking activities, both real life walking and treadmill walking, and worst for ‘shuffling’ or changing position activities (low sensitivity 46.2%). The main drawback of the Dynaport move monitor is its cost. At 690 Euros it is one of the most expensive on the market, and yet still fails to distinguish accurately between lying and sitting, sitting and standing, and shuffling activity. Most of the other monitors available vary from 75 to 200 Euros, a much more affordable amount for use with patients. Walking on a treadmill (to record claudication distance maximum walking distance) is an internationally respected gold standard exercise test for these patients, but bears little similarity to the exercise that patients experience in their daily lives.The author suggests that activity monitoring may be a more effective way of assessing the exercise capacity of these patients. There is a need for more research in this area, and this paper highlights the problems of validating these new monitoring ‘tools’. Research comparing patient compliance, looking at the use of activity monitors instead of pedometers (which have already been shown to increase patient compliance with exercise programmes 6 )i s the next logical step. Activity monitors can give positive feedback to the patient immediately (by way of rewards using an app on mobile phones, or lights that change colour when targets are reached, and analysis of performance), and may therefore be useful as a way of motivating patients with peripheral arterial disease to take more exercise.

Gait and six-minute walk performance in persons with multiple sclerosis

The six-minute walk (6MW) has been established as a clinic-based, performance measure of walking endurance that reflects community ambulation in multiple sclerosis (MS). Consequently, identifying the contribution of variables to 6MW performance may provide targets for improving real-life walking in MS, and these variables may differ as a function of disability. This study examined cadence and stride length as gait variables that explain differences in 6MW performance between persons with MS and controls, and by level of disability. 256 community-residing persons with MS and 49 non-MS controls performed a standard 6MW test and completed 2 trials of comfortable walking on an electronic walkway for quantifying gait. Regression analyses indicated that cadence and stride length explain differences in 6MW performance between MS and controls, and by level of disability in MS. The contribution of cadence and stride length to walking endurance differed as a function of disability, such that cadence and to a greater extent stride length explained variance in 6MW performance in mild MS, whereas cadence and stride length explained approximately an equivalent amount of variance in 6MW performance in moderate-to-severe MS. We provide evidence for intervention strategies that are specific to disability level to improve walking endurance in MS.

Accelerometry is associated with walking mobility, not physical activity, in persons with multiple sclerosis

Accelerometers are seemingly a criterion standard of real-life walking mobility and this is supported by assumptions and empirical data. This application would be strengthened by including objective measures of walking mobility along with a matched control sample for verifying specificity versus generality in accelerometer output.We compared associations among accelerometer output, walking mobility, and physical activity between persons with multiple sclerosis (MS) and controls without a neurological disorder. Sixty-six persons (33 MS, 33 matched controls) completed a battery of questionnaires, performed the six-minute walk (6MW) and timed-up-and-go (TUG), and wore an accelerometer for a 7-day period. After this period, participants completed the Godin Leisure-Time Exercise Questionnaire (GLTEQ) and International Physical Activity Questionnaire (IPAQ). Accelerometer output was significantly correlated with only mobility measures (6MW, ρ=.78; TUG, ρ=−.68) in MS, whereas it correlated with both mobility (6MW, ρ=.58; TUG, ρ=−.49) and physical activity (GLTEQ, ρ=.56; IPAQ, ρ=.53) measures in controls. Regression analysis indicated that only 6MW explained variance in accelerometer output in MS (β=.65, R2=.43). These findings support the possibility that accelerometers primarily and specifically measure real-life walking mobility, not physical activity, in persons with MS.