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Christopher McCrum, Katrin Eysel-Gosepath, Gaspar Epro, Kenneth Meijer, Hans H.C.M. Savelberg, Gert-Peter Brüggemann and Kiros Karamanidis

Posturography is used to assess balance in clinical settings, but its relationship to gait stability is unclear. We assessed if dynamic gait stability is associated with standing balance in 12 patients with unilateral vestibulopathy. Participants were unexpectedly tripped during treadmill walking and the change in the margin of stability (MoSchange) and base of support (BoSchange) relative to nonperturbed walking was calculated for the perturbed and first recovery steps. The center of pressure (COP) path during 30-s stance with eyes open and closed, and the distance between the most anterior point of the COP and the anterior BoS boundary during forward leaning (ADist), were assessed using a force plate. Pearson correlations were conducted between the static and dynamic variables. The perturbation caused a large decrease in the BoS, leading to a decrease in MoS. One of 12 correlations was significant (MoSchange at the perturbed step and ADist; r = −.595, P = .041; nonsignificant correlations: .068 ≤ P ≤ .995). The results suggest that different control mechanisms may be involved in stance and gait stability, as a consistent relationship was not found. Therefore, posturography may be of limited use in predicting stability in dynamic situations.

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Daniel A. Jacobs and Daniel P. Ferris

Instrumented insoles could benefit locomotion research on healthy and clinical populations by providing data in natural settings outside of the laboratory. We designed a low-cost, instrumented insole with 8 pneumatic bladders to measure localized plantar pressure information. We collected gait data during treadmill walking at 1.0 m/s and 1.5 m/s and for sit-to-stand and stand-tosit tasks for 10 subjects. We estimated a common representation of ground kinetics (3-component force vector, 2-component center of pressure position vector, and a single-component torque vector) from the insole data. We trained an intertask neural network for each component of the kinetic data. For the walking tasks at 1.0 m/s and 1.5 m/s, the normalized root mean square error was between 3.1% and 12.9% and for the sit-to-stand and stand-to-sit tasks, the normalized root mean square error was between 3.3% and 21.3% Our findings suggest that the proposed low-cost, instrumented insoles could provide useful data about movement kinetics during real-world activities.

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James J. McClain, Teresa L. Hart, Renee S. Getz and Catrine Tudor-Locke

Background:

This study evaluated the utility of several lower cost physical activity (PA) assessment instruments for detecting PA volume (steps) and intensity (time in MVPA or activity time) using convergent methods of assessment.

Methods:

Participants included 26 adults (9 male) age 27.3 ± 7.1 years with a BMI of 23.8 ± 2.8 kg/m2. Instruments evaluated included the Omron HJ-151 (OM), New Lifestyles NL-1000 (NL), Walk4Life W4L Pro (W4L), and ActiGraph GT1M (AG). Participants wore all instruments during a laboratory phase, consisting of 10 single minute treadmill walking bouts ranging in speed from 40 to 112 m/min, and immediate following the laboratory phase and during the remainder of their free-living day (11.3 ± 1.5 hours). Previously validated AG MVPA cutpoints were used for comparison with OM, NL, and W4L MVPA or activity time outputs during the laboratory and free-living phase.

Results:

OM and NL produced similar MVPA estimates during free-living to commonly used AG walking cutpoints, and W4L activity time estimates were similar to one AG lifestyle cutpoint evaluated.

Conclusion:

Current findings indicate that the OM, NL, and W4L, ranging in price from $15 to $49, can provide reasonable estimates of free-living MVPA or activity time in comparison with a range of AG walking and lifestyle cutpoints.

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Yuri Feito, David R. Bassett, Dixie L. Thompson and Brian M. Tyo

Background:

Activity monitors are widely used in research, and are currently being used to study physical activity (PA) trends in the US and Canada. The purpose of this study was to determine if body mass index (BMI) affects the step count accuracy of commonly used accelerometer-based activity monitors during treadmill walking.

Methods:

Participants were classified into BMI categories and instructed to walk on a treadmill at 3 different speeds (40, 67, and 94 m·min−1) while wearing 4 accelerometer-based activity monitors (ActiGraph GT1M, ActiCal, NL-2000, and StepWatch).

Results:

There was no significant main effect of BMI on pedometer accuracy. At the slowest speed, all waist-mounted devices significantly underestimated actual steps (P < .001), with the NL-2000 recording the greatest percentage (72%). At the intermediate speed, the ActiGraph was the least accurate, recording only 80% of actual steps. At the fastest speed, all of the activity monitors demonstrated a high level of accuracy.

Conclusion:

Our data suggest that BMI does not greatly affect the step-counting accuracy of accelerometer-based activity monitors. However, the accuracy of the ActiGraph, ActiCal, and NL-2000 decreases at slower speeds. The ankle-mounted StepWatch was the most accurate device across a wide range of walking speeds.

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Ann F. Maliszewski, Patty S. Freedson, Chris J. Ebbeling, Jill Crussemeyer and Kari B. Kastango

The Caltrac accelerometer functions as either an activity monitor that provides activity counts based on vertical acceleration as the individual moves about, or as a calorie counter in which the acceleration units are used in conjunction with body size, age, and sex to estimate energy expenditure. This study compared VO2 based energy expenditure with Caltrac estimated energy expenditure during three speeds of treadmill walking in children and adults. It also tested the validity of the Caltrac to differentiate between high and low levels of walking activity (activity counts). Ten boys and 10 men completed three randomly assigned walks while oxygen consumption was monitored and Caltrac estimates were obtained. The results indicate that the Caltrac does not accurately predict energy expenditure for boys and men across the three speeds of walking. Although there were no significant differences between actual and predicted energy expenditure values, the standard errors of estimate were high (17-25%) and the only significant correlation was found for men at the fastest walking speed (r=.81). However, the 95% confidence intervals of the activity counts and energy expenditure estimates from the Caltrac support its use as an activity monitor during walking.

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Timo Rantalainen, Nicolas H. Hart, Sophia Nimphius and Daniel W. Wundersitz

Inertial measurement units (IMU) provide a convenient tool for gait stability assessment. However, it is unclear how various gait characteristics relate to each other and whether gait characteristics can be obtained from resultant acceleration. Therefore, step duration variability was measured in treadmill walking from 39 young ambulant volunteers (age 24.2 [± 2.5] y; height 1.79 [± 0.09] m; mass 71.6 [± 12.0] kg) using motion capture. Accelerations and gyrations were simultaneously recorded with an IMU. Harmonic ratio, maximum Lyapunov exponents, and multiscale sample entropy (MSE) were calculated. Step duration variability was positively associated with MSE with coarseness levels = 3–6 (r = –.33 to –.42, P ≤ .045). Harmonic ratio and MSE with all coarseness levels were negatively associated (r = –.45 to –.57, P ≤ .004). The MSE with coarseness level = 2 was negatively associated with short-term maximum Lyapunov exponents (r = –.32, P = .047). The agreement between resultant and vertical acceleration derived gait characteristics was excellent (ICC = 0.97–0.99). In conclusion, MSE with varying coarseness levels was associated with the other gait characteristics evaluated in the study. Resultant and vertical acceleration derived results had excellent agreement, which suggests that resultant acceleration is a viable alternative to considering the acceleration dimensions independently.

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Meaghan Nolan, J. Ross Mitchell and Patricia K. Doyle-Baker

Background:

The popularity of smartphones has led researchers to ask if they can replace traditional tools for assessing free-living physical activity. Our purpose was to establish proof-of-concept that a smartphone could record acceleration during physical activity, and those data could be modeled to predict activity type (walking or running), speed (km·h−1), and energy expenditure (METs).

Methods:

An application to record and e-mail accelerations was developed for the Apple iPhone®/iPod Touch®. Twentyfive healthy adults performed treadmill walking (4.0 km·h−1 to 7.2 km·h1) and running (8.1 km·h−1 to 11.3 km·h−1) wearing the device. Criterion energy expenditure measurements were collected via metabolic cart.

Results:

Activity type was classified with 99% accuracy. Speed was predicted with a bias of 0.02 km·h−1 (SEE: 0.57 km·h−1) for walking, –0.03 km·h−1 (SEE: 1.02 km·h−1) for running. Energy expenditure was predicted with a bias of 0.35 METs (SEE: 0.75 METs) for walking, –0.43 METs (SEE: 1.24 METs) for running.

Conclusion:

Our results suggest that an iPhone/iPod Touch can predict aspects of locomotion with accuracy similar to other accelerometer-based tools. Future studies may leverage this and the additional features of smartphones to improve data collection and compliance.

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Herman-J. Engels and Emily M. Haymes

This study examined the effects of a single dose of caffeine (5 mg:kg−1) on energy metabolism during 60-min treadmill walking at light (30% VO2max) and moderate (50% VO2max) aerobic intensities in eight sedentary (VO2max 39.6 ±t3.1 ml.kg−1.min−1) males. Caffeine intake 60 min prior to walking exercise increased pre- and postexercise FFA, glycerol, and lactate concentrations (p < 0.05). Blood glucose levels following walking trials were lower than preexercise values (p < 0.05). Gas exchange indicated that caffeine did not change exercise oxygen uptake, RER values, and carbon dioxide production (p0.05). In contrast, a small but statistically significant effect of caffeine on exercise minute ventilation was noted (p~0.01). It is concluded that ingestion of 5 mg.kg−1 caffeine increases the mobilization of energy substrate from fat sources; however, the present data do not provide evidence of a caffeineinduced shift in energy substrate usage. Caffeine is not an effective means for enhancing the energy cost of prolonged walking.

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Randall J. Bergman, Justin W. Spellman, Michael E. Hall and Shawn M. Bergman

Background:

This study examined the validity of a selected free pedometer application (iPedometer; IP) for the iPhone that could be used to assess physical activity.

Methods:

Twenty college students (10 men, 10 women; mean age: 21.85 ± 1.57 yrs) wore an iPhone at 3 locations (pocket, waist, arm) and a StepWatch 3 Step Activity Monitor (SW) on their right ankle while walking on a treadmill at 5 different speeds (54, 67, 80, 94, 107 m·min−1). A research assistant counted steps with a tally counter (TC).

Results:

Statistical significance between the TC, SW, and IP was found during every condition except IP in the pocket at 107 m·min−1 (F 2,38 = .64, P = .54). Correlations involving the IP revealed only 1 positive correlation (IP on arm at 54 m·min−1) for any of the conditions (r = .46, P = .05).

Conclusion:

The IP application was not accurate in counting steps and recorded significantly lower step counts than the SW and TC. Thus, the free pedometer application used is not a valid instrument for monitoring activity during treadmill walking.

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Amanda Hickey, Dinesh John, Jeffer E. Sasaki, Marianna Mavilia and Patty Freedson

Background:

There is a need to examine step-counting accuracy of activity monitors during different types of movements. The purpose of this study was to compare activity monitor and manually counted steps during treadmill and simulated free-living activities and to compare the activity monitor steps to the StepWatch (SW) in a natural setting.

Methods:

Fifteen participants performed laboratory-based treadmill (2.4, 4.8, 7.2 and 9.7 km/h) and simulated free-living activities (eg, cleaning room) while wearing an activPAL, Omron HJ720-ITC, Yamax Digi-Walker SW-200, 2 ActiGraph GT3Xs (1 in “low-frequency extension” [AGLFE] and 1 in “normal-frequency” mode), an ActiGraph 7164, and a SW. Participants also wore monitors for 1-day in their free-living environment. Linear mixed models identified differences between activity monitor steps and the criterion in the laboratory/free-living settings.

Results:

Most monitors performed poorly during treadmill walking at 2.4 km/h. Cleaning a room had the largest errors of all simulated free-living activities. The accuracy was highest for forward/rhythmic movements for all monitors. In the free-living environment, the AGLFE had the largest discrepancy with the SW.

Conclusion:

This study highlights the need to verify step-counting accuracy of activity monitors with activities that include different movement types/directions. This is important to understand the origin of errors in step-counting during free-living conditions.