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37 results for “Walking speed”
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for independent validation study)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the independent validation study: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
Does the preferred walk-run transition speed on steep inclines minimize energetic cost, heart rate or neither?
Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.
Data for "Does the Preferred Walk-Run Transition Speed on Steep Inclines Minimize Energetic Cost, Heart Rate or Neither?"
<p>Abstract</p> <p>Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.</p> <p>Methods</p> <p>Subjects walked and ran on a classic Quinton 18-60 motorized treadmill with a rigid steel deck (Quinton Instrument Company, Bothell, WA).</p> <p><strong>Determination of PTS: </strong>The average of the walk-to-run transition speed (WRTS) and run-to-walk transition speed (RWTS) defined the PTS as per Hreljac et. al. (2007). We first determined the WRTS in the walk-first group and then their RWTS and <em>vice versa</em> for the run-first group. Based on pilot experiments, we selected starting speeds such that there was no doubt which gait would be preferred at the initial speed. Once the speed of the treadmill was correctly set, subjects mounted the treadmill and chose their gait <em>ad libitum</em>. After we determined the preferred gait at the particular speed, the subject straddled the treadmill belt while we changed the speed by 0.1 m/s (increased during WRTS trials, decreased during RWTS trials). The process repeated until a gait transition occurred and was sustained for 30 seconds.</p> <p><strong>Determination of EOTS and HROTS: </strong>For the energetics and heart rate trials, we set the initial speed based on pilot experiments that indicated it would be near the EOTS. Subjects in the walk-first group walked at the incline-specific initial speed for 5 min, rested for ∼5 min and then ran at that speed for 5 min. Subjects in the run-first group did the opposite. During the rest periods, we re-weighed the subject and they drank just enough water to compensate for the weight loss due mostly to sweating. Thus, each subject maintained a nearly constant weight throughout all the trials.</p> <p>To measure metabolic rate during walking and running, we used an open-circuit, expired gas analysis system (TrueOne 2400; ParvoMedics, Sandy, UT). Subjects wore a mouthpiece with a one-way breathing valve and a nose clip allowing us to collect their expired air. The ParvoMedics software calculated the STPD rates of oxygen consumption (V□O<sub>2</sub>) and carbon dioxide production (V□CO<sub>2</sub>) and we averaged the last 2 minutes of each 5-minute trial. We then calculated metabolic power using the equation of Péronnet and Massicotte (1991) equation, as clarified by Kipp et al. (2018). We only included trials with respiratory exchange ratios (RER) <1.0 to ensure that metabolic energy was predominantly being provided from oxidative pathways. We used an R7 Polar iWL (Polar Electro Oy, Kempele, Finland) to measure heart rate in beats per minute (bpm) and averaged the values for the last 2 min of each trial.</p> <p>Immediately after both gait trials were completed for the initial speed, we calculated and compared the metabolic power required for walking and running. If walking was the more economical gait, we increased the treadmill speed by 0.1 m/s, and the process repeated. If running was the more economical gait, we decreased the treadmill speed by 0.1 m/s, and the process repeated. Each subject performed three speeds, both walking and running at each incline. However, some subjects needed to complete walking and running trials at a fourth speed so that we could obtain energetics data for one speed faster and one speed slower than their EOTS.</p> <p>For the three speeds at which the differences between metabolic rates between walking and running were least, we calculated linear regression equations for both metabolic power and heart rate as functions of speed for both walking and running for each subject and incline. The speeds at which the two equations intersected defined the EOTS and HROTS for each subject.</p> <p>Overall, we analyzed ten subjects at four different inclines, i.e. 40 determinations of EOTS and HROTS. Of those 80 linear regression analyses, the walking vs. running regressions intersected at a speed < 3 m/sec for all but two subjects (one subject for EOTS at 15° and a different subject for HROTS at 10°). Essentially, those individuals’ regression lines were nearly parallel. We chose to exclude those two conditions from further statistical analysis and aggregate data compilation.</p> <p>Usage Notes</p> <p>There are two missing values, as noted in the methods: HROTS for subject 5 at 10 degrees and EOTS for subject 4 at 15 degrees.</p>
Data from: Kinematic trajectories in response to speed perturbations in walking suggest modular task-level control of leg angle and length
Abstract Navigating complex terrains requires dynamic interactions between the substrate, musculoskeletal and sensorimotor systems. Current perturbation studies have mostly used visible terrain height perturbations, which do not allow us to distinguish among the neuromechanical contributions of feedforward control, feedback-mediated and mechanical perturbation responses. Here, we use treadmill belt speed perturbations to induce a targeted perturbation to foot speed only, and without terrain-induced changes in joint posture and leg loading at stance onset. Based on previous studies suggesting a proximo-distal gradient in neuromechanical control, we hypothesized that distal joints would exhibit larger changes in joint kinematics, compared to proximal joints. Additionally, we expected birds to use feedforward strategies to increase the intrinsic stability of gait. To test these hypotheses, seven adult guinea fowl were video recorded while walking on a motorized treadmill, during both steady and perturbed trials. Perturbations consisted of repeated exposures to a deceleration and acceleration of the treadmill belt speed. Surprisingly, we found that joint angular trajectories and center of mass fluctuations remain very similar, despite substantial perturbation of foot velocity by the treadmill belt. Hip joint angular trajectories exhibit the largest changes, with the birds adopting a slightly more flexed position across all perturbed strides. Additionally, we observed increased stride duration across all strides, consistent with feedforward changes in the control strategy. The speed perturbations mainly influenced the timing of stance and swing, with the largest kinematic changes in the strides directly following a deceleration. Our findings do not support the general hypothesis of a proximo-distal gradient in joint control, as distal joint kinematics remain largely unchanged. Instead, we find that leg angular trajectory and the timing of stance and swing are most sensitive to this specific perturbation, and leg length actuation remains largely unchanged. Our results are consistent with modular task-level control of leg length and leg angle actuation, with different neuromechanical control and perturbation sensitivity in each actuation mode. Distal joints appear to be sensitive to changes in vertical loading but not foot fore-aft velocity. Future directions should include in vivo studies of muscle activation and force-length dynamics to provide more direct evidence of the sensorimotor control strategies for stability in response to belt speed perturbations.
Dataset for article: Gait Speed Assessment in the 10-meter Walk Test for Older Adults Using a Computer Vision-based System: A Cross-sectional Study on Validity, Reliability, and Usability
<p>This dataset provides the Validity, Reliability, and Usability for an assessment of gait speed detection system in the 10-meter Walk Test for Older Adults.</p> <p>The dataset is formatted for easy import into microsoft excel software consist of:<br>Supplementary1.xlsx - Validity <br>Supplementary2.xlsx - Reliability<br>Supplementary3.xlsx - Usability test</p>
Energetics of human locomotion near the walk-run transition speed.
<p><strong>Energetics of human locomotion near the walk-run transition speed.</strong></p> <p> </p> <p>This dataset includes the raw metabolic and mechanical data of 28 young subjects during locomotion at variable speed, walking and running on a treadmill at different speeds and gaits.</p> <p>Characteristics of the experimental group:</p> <p>- Sex: 28 Males - Age: 32. 53 (10.99SD)- height: 175.0 cm (0.008 SD)- weight: 72.96 kg (9.51 SD)</p> <p>Equipment:</p> <p>- Cosmed K5 portable metabolic analyzer- Cosmed Omnia Software v.1.6.5</p> <p>- Vicon Nexus 2.14 (Vicon Motion Systems Ltd, Oxford, UK)</p> <p>Experimental Design: The walking stroke transition speed (W-R Ts) was determined experimentally. Each subject was asked to perform 3 trials on a treadmill (GE T2100, General Electric, USA), with an escalating speed ladder protocol. The ramp was designed to start with a comfortable ride (3.0 km h-1), and to increase speed by 0.5 km.h-1 every 15 s. When the subject began to run, the ramp stopped and the speed was marked on a worksheet. The mean or modal transition speed was taken as the T of the subject. All treadmill tests were performed at the Biomechanics and Motion Analysis Research Laboratory (LIBiAM) of the University of the Republic in Paysandú (Uruguay), at a controlled temperature of 25ºC.</p> <p>The theoretical transition velocity tTs was calculated according to the Froude number equation (Alexander. 1976): v = (nFr g LL) 0.5, where v is the theoretical velocity, g is gravity, LL is the leg length, and nFr the Froude number, which was set to the constant value of 0.5, corresponding to the W-R transition (Alexander & Jayes, 1983; Alejandro, 2003; Bona et al., 2019).</p> <p>Experimental speed ramp:</p> <p>-A custom ascending and descending speed ramp was designed, focused on the transition speed and varied from (Ts = Transition Speed) Ts-20% to Ts+20%, each step with a duration of 5 s. Each ramp cycle lasted 50 s, and was repeated 5 times, for a total test time of 250 s. The trial was repeated twice.</p> <p> </p> <p><em>Mechanical Work (Mechanical cost of transport)</em></p> <p>The time course of the trajectory by <em>BcoM</em> was used to infer changes in the mechanical energies (potential and kinetics) involved. The horizontal work (<em>W<sub>h</sub></em>) was defined as the sum of the increments of the kinetic energy of the <em>BcoM</em> along the forward and mediolateral axes; the vertical work (<em>W<sub>v</sub></em>) was determined by the sum of the increments of gravitational potential energy and kinetic energy along the vertical axis; the external work (<em>W</em><sub>ext</sub> ), the mechanical work done to lift and accelerate the <em>BcoM</em>, was computed as the sum of the increments of the total mechanical energy of the <em>BcoM</em> (potential plus kinetic) (Cavagna et al., 1976; Willems et al., 1995). The internal work (<em>W</em><sub>int</sub>), the work necessary to accelerate the body segments with respects to the <em>BcoM</em>, was estimated with the methodology proposed by Cavagna & Kaneko (1977). <em>W</em><sub>int</sub> and <em>W</em><sub>ext</sub> were summed to give the total mechanical work (<em>W<sub>tot</sub></em>) (Cavagna & Kaneko, 1977; Willems et al., 1995). </p> <p>During locomotion cycles, especially in W, part of the potential energy of the <em>BcoM</em> is converted into kinetic energy, and vice versa, so that the sum of <em>W<sub>h</sub></em> and <em>W<sub>v</sub></em> is greater than the actual work done (<em>W<sub>ext</sub></em>). The difference, expressed as percentage, corresponds to the energy recovery R% (Cavagna et al., 1976), which formula is:</p> <p>R% = (<em>W</em><sub>h</sub> +<em> Wv</em> -<em> W</em><sub>ext</sub>) (<em>W</em><sub>h</sub> + <em>Wv</em>)</p> <p><em> Cost of transport (Metabolic transport cost)</em></p> <p>Oxygen uptake and respiratory quotient were measured breath-by-breath by a portable metabolimeter (K5, Cosmed, Italy). Reference resting values were measured during 5 min in orthostatic quiet position. Each trial was started when the metabolic parameters were near the reference resting values.</p> <p>The <sub>2</sub> (mlO<sub>2</sub>.kg<sup>-1</sup>.min<sup>-1</sup>) and RQ of the last 50 s of each recorded trial, corresponding to the last complete ramp, were averaged. The reference resting <sub>2</sub> was subtracted to the measured one to obtain the net oxygen uptake. VO<sub>2NET</sub> was then converted to mass-specific metabolic rate (W kg<sup>-1</sup>) using a RQ based energetic equivalent(P. E. Di Prampero et al., 2015). The C (J kg<sup>-1</sup> m<sup>-1</sup>) was finally obtained by dividing the metabolic rate for the average speed: </p> <p> </p> <p> (2)</p> <p> </p> <p><em>Apparent Mechanical Efficiency (AE)</em></p> <p>The AE was calculated as proposed by Cavagna and Kaneko, ie,</p> <p>AE = <em>W</em><sub>to</sub> C</p> <p>where <em>W</em><sub>tot</sub> is the total mechanical work and C the cost of transport (G. A. Cavagna & Kaneko, 1977).</p> <p> </p> <p><em>Data processing and calculation</em></p> <p>Image preprocessing was performed in Vicon Nexus 2.14 (Vicon Motion Systems Ltd, Oxford, UK), kinematic variable calculation performed with Python 2.7 and ProCalc 1.6 (Vicon Motion Systems Ltd, Oxford, UK), the calculation of mechanical variables was implemented in MatLab (The MathWorks, Inc., California, USA). The calculation of C was performed in Microsoft Excel (Microsoft Office 365).</p> <p> </p> <p>Note: Not all subjects performed the entire protocol. In particular, some data lack follow-up.</p> <p>Analysis of the cost of transportation:</p> <p>All participants signed an informed consent. The protocol was approved by the University's Ethics Committee (#311170-000921-19).<br> </p> <p>The legend of the dataset.</p> <p>There are 3 excel sheets where each row is associated with subjects from 1 to 28.</p> <p>Energy sheet:</p> <p>Subject: Subject</p> <p>Age</p> <p>Weigth</p> <p>Heigth(m)</p> <p>IMC</p> <p>Km x week: kilometers per week</p> <p>Background: history of injuries</p> <p>INT1 km/h: attempt 1</p> <p>INT2 km/h: attempt 2</p> <p>INT3 km/h: Attempt 3</p> <p>Average transition (km/h): average walk-race transition speed</p> <p>Froude estimated PST(m/s)</p> <p>Froude Estimated PST(km/h)</p> <p>Basal Vo2: Basal oxygen consumption in orthostasis</p> <p>VO2/kg/min: Oxygen consumption in the test</p> <p>RQ: RQ in the test</p> <p>VO2 Net VO2kg/min): VO2 net in the test</p> <p>VO2/kg/s</p> <p>J/kg/s = W/kg</p> <p>C (J/kg/m): transport cost obtained in the test</p> <p> </p> <p>Test: test performed</p> <p>Gait: type of gait that has been evaluated</p> <p>ASC/DESC: place on the ramp (ascending or descending)</p> <p>Stride: stride identification for each type of gait</p> <p>Duty Factor_tr: duty factor</p> <p>Stride Frequency_tr: stride frequency</p> <p>Stride Time_tr: stride time</p> <p>Time: time in the stride</p> <p>Speed in treadmill : speed that occurs in treadmill</p> <p>Distance: distance traveled by the stride</p> <p>Step frequency: frequency of passage</p> <p>Wext: External Work</p> <p>Rec: recovery</p> <p>Wv: trabajo vertical</p> <p>Wh: horizontal work</p> <p>WintTOT: Total internal work</p> <p> </p> <p> </p>
Does the preferred walk-run transition speed on steep inclines minimize energetic cost, heart rate or neither?
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Data from: Kinematic trajectories in response to speed perturbations in walking suggest modular task-level control of leg angle and length
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Data for: Quantifying human adaptation to a novel split-belt walking condition after broad experience at different belt speeds
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Uneven substrates constrain walking speed in ants through modulation of stride frequency more than stride length
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Walking Speeds in Patients With Chronic Obstructive Pulmonary Disease
ClinicalTrials.gov study NCT01768754. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Does Pulmonary Rehabilitation Change Self-Selected And Maximum Sustainable Walking Speed In Patients With Lung Disease?
ClinicalTrials.gov study NCT00781183. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Multitasking Rehabilitation She Enhanced Walking Speed Compared to the Simple Post Stroke Rehabilitation Task (AVC)?
ClinicalTrials.gov study NCT03009773. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Biofeedback on Walking Speed Post-stroke
ClinicalTrials.gov study NCT05420857. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors
The ostrich (Struthio camelus) is widely appreciated as a fast and agile bipedal athlete, and is a useful comparative bipedal model for human locomotion. Here, we used GPS-IMU sensors to measure naturally selected gait dynamics of ostriches roaming freely over a wide range of speeds in an open field and developed a quantitative method for distinguishing walking and running using accelerometry. We compared freely selected gait–speed distributions with previous laboratory measures of gait dynamics and energetics. We also measured the walk–run and run–walk transition speeds and compared them with those reported for humans. We found that ostriches prefer to walk remarkably slowly, with a narrow walking speed distribution consistent with minimizing cost of transport (CoT) according to a rigid-legged walking model. The dimensionless speeds of the walk–run and run–walk transitions are slower than those observed in humans. Unlike humans, ostriches transition to a run well below the mechanical limit necessitating an aerial phase, as predicted by a compass-gait walking model. When running, ostriches use a broad speed distribution, consistent with previous observations that ostriches are relatively economical runners and have a flat curve for CoT against speed. In contrast, horses exhibit U-shaped curves for CoT against speed, with a narrow speed range within each gait for minimizing CoT. Overall, the gait dynamics of ostriches moving freely over natural terrain are consistent with previous lab-based measures of locomotion. Nonetheless, ostriches, like humans, exhibit a gait-transition hysteresis that is not explained by steady-state locomotor dynamics and energetics. Further study is required to understand the dynamics of gait transitions.
Data from: The nature of functional variability in plantar pressure during a range of controlled walking speeds
During walking, variability in step parameters allows the body to adapt to changes in substrate or unexpected perturbations that may occur as the feet interface with the environment. Despite a rich literature describing biomechanical variability in step parameters, there are as yet no studies that consider variability at the body–environment interface. Here, we used pedobarographic statistical parametric mapping (pSPM) and two standard measures of variability, mean square error (m.s.e.) and the coefficient of variation (CV), to assess the magnitude and spatial variability in plantar pressure across a range of controlled walking speeds. Results by reduced major axis, and pSPM regression, revealed no consistent linear relationship between m.s.e. and speed or m.s.e. and Froude number. A positive linear relationship, however, was found between CV and walking speed and CV and Froude number. The spatial distribution of variability was highly disparate when assessed by m.s.e. and CV: relatively high variability was consistently confined to the medial and lateral forefoot when measured by m.s.e., while the forefoot and heel show high variability when measured by CV. In absolute terms, variability by CV was universally low (less than 2.5%). From these results, we determined that variability as assessed by m.s.e. is independent of speed, but dependent on speed when assessed by CV.
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for interventional clinical trial)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the interventional clinical trial: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
Data from: The nature of functional variability in plantar pressure during a range of controlled walking speeds
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Data from: The metabolic cost of changing walking speeds is significant, implies lower optimal speeds for shorter distances, and increases daily energy estimates
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Data from: The combined effects of body weight support and gait speed on gait related muscle activity: a comparison between walking in the Lokomat exoskeleton and regular treadmill walking
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