directly in traditional kinematic calculation algorithms. However, prior to widespread application of IMUs in the real world, the sensors and collection protocols need to be validated. Validation studies of IMUs used to capture upper and lower body kinematics are increasing. 14 – 30 To add to the body of
Melissa M.B. Morrow, Bethany Lowndes, Emma Fortune, Kenton R. Kaufman and M. Susan Hallbeck
J. Jimenez-Pardo, J.D. Holmes, M.E. Jenkins and A.M. Johnson
Physical activity is generally thought to be beneficial to individuals with Parkinson’s disease (PD). There is, however, limited information regarding current rates of physical activity among individuals with PD, possibly due to a lack of well-validated measurement tools. In the current study we sampled 63 individuals (31 women) living with PD between the ages of 52 and 87 (M = 70.97 years, SD = 7.53), and evaluated the amount of physical activity in which they engaged over a 7-day period using a modified form of the Physical Activity Scale for Individuals with Physical Disabilities (PASIPD). The PASIPD was demonstrated to be a reliable measure within this population, with three theoretically defensible factors: (1) housework and home-based outdoor activities; (2) recreational and fitness activities; and (3) occupational activities. These results suggest that the PASIPD may be useful for monitoring physical activity involvement among individuals with PD, particularly within large-scale questionnaire-based studies.
Oren Tirosh and W.A. Sparrow
Analysis of human gait requires accurate measurement of foot-ground contact, often determined using either foot-ground reaction force thresholds or kinematic data. This study examined the differences in calculating event times across five vertical force thresholds and validated a vertical acceleration-based algorithm as a measure of heel contact and toe-off. The experiment also revealed the accuracy in determining heel contact and toe-off when raw displacement/time data were smoothed using a range of digital filter cutoff frequencies. Four healthy young participants completed 10 walking trials: 5 at normal speed (1.2 m/s) and 5 at fast speed (1.8 m/s). A 3D optoelectric system was synchronized with a forceplate to measure the times when vertical force exceeded (heel contact) or fell below (toe-off) 10, 20, 30, 40, and 50 N. These were then compared and subsequently used to validate an acceleration-based method for calculating heel contact and toe-off with the displacement/time data filtered across a range of four cutoff frequencies. Linear regression analyses showed that during both normal and fast walking, any force threshold within 0 to 50 N could be used to predict heel-contact time. For estimating toe-off low force thresholds, 10 N or less should be used. When raw data were filtered with the optimal cutoff frequency, the absolute value (AbsDt) of the difference between the forceplate event times obtained using a 10-N threshold and the event times of heel contact and toe-off using the acceleration-based algorithms revealed average AbsDt of 10.0 and 16.5 ms for normal walking, and 7.4 and 13.5 ms for fast walking. Data smoothing with the non-optimal cutoff frequencies influenced the event times computed by the algorithms and produced greater AbsDt values. Optimal data filtering procedures are, therefore, essential for ensuring accurate measures of heel contact and toe-off when using the acceleration-based algorithms.
Nathalie Alexander and Hermann Schwameder
While inclined walking is a frequent daily activity, muscle forces during this activity have rarely been examined. Musculoskeletal models are commonly used to estimate internal forces in healthy populations, but these require a priori validation. The aim of this study was to compare estimated muscle activity using a musculoskeletal model with measured EMG data during inclined walking. Ten healthy male participants walked at different inclinations of 0°, ± 6°, ± 12°, and ± 18° on a ramp equipped with 2 force plates. Kinematics, kinetics, and muscle activity of the musculus (m.) biceps femoris, m. rectus femoris, m. vastus lateralis, m. tibialis anterior, and m. gastrocnemius lateralis were recorded. Agreement between estimated and measured muscle activity was determined via correlation coefficients, mean absolute errors, and trend analysis. Correlation coefficients between estimated and measured muscle activity for approximately 69% of the conditions were above 0.7. Mean absolute errors were rather high with only approximately 38% being ≤ 30%. Trend analysis revealed similar estimated and measured muscle activities for all muscles and tasks (uphill and downhill walking), except m. tibialis anterior during uphill walking. This model can be used for further analysis in similar groups of participants.
Michelle Renee Umstattd, Rob Motl, Sara Wilcox, Ruth Saunders and Melissa Watford
Theoretically, self-regulatory strategies (eg, goal setting, self-monitoring) are an important influence of behavior change, but very little research has examined the relationship between self-regulation and physical activity (PA) behavior. Petosa’s (1993) 43-item PA self-regulation scale (PASR-43) affords the opportunity for studying this construct in the context of PA; however the PASR-43 has not been tested for structural aspects of validity. Therefore, this study examines the structural validity of the PASR-43 in older adults.
The structural validity of the PASR-43 was tested in a large sample of older adults from North and South Carolina and Ohio (N = 460) using maximum likelihood estimation and confirmatory factor analysis in AMOS 5.0.
The original 6-factor model for the PASR-43 scale did not represent an acceptable fit to the data (x2 = 4732.25, df = 845, P < .0001, RMSEA = 0.10, NNFI = 0.67, CFI = 0.71). Based on a post hoc specification search, iterative model modifications resulted in a 12-item PA self-regulation scale (PASR-12) that represented an excellent fit to the data (x2 = 70.75, df = 39, P = .001, RMSEA = 0.04, NNFI = 0.98, CFI = 0.99).
The PASR-12 provides a concise and valid measure of PA self-regulation for use with older adults. Future studies should cross-validate the PASR-12 and examine invariance across time and between age, ethnic, gender, and geographical groups.
Eleanor Quested, Nikos Ntoumanis, Andreas Stenling, Cecilie Thogersen-Ntoumani and Jennie E. Hancox
.1080/17509840903235330 Bartholomew , K. , Ntoumanis , N. , & Thøgersen-Ntoumani , C. ( 2010 ). The controlling interpersonal style in a coaching context: Development and initial validation of a psychometric scale . Journal of Sport & Exercise Psychology, 32 ( 2 ), 193 – 216 . PubMed ID: 20479478 doi:10.1123/jsep.32
Anantha Narayanan, Farzanah Desai, Tom Stewart, Scott Duncan and Lisa Mackay
, device name, number of devices, number of device axes, device sampling frequency, device placement position, ground truth method, features generated from raw accelerometer data, epoch length, machine-learning algorithm, validation method (eg, cross-validation), and the model performance results. In cases
Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham and Søren Brage
bias if the studies that are included with optimal data have specific characteristics. Another approach to harmonization is to use validation studies which report the statistical (e.g., regression) models of relationships between values from the less precise methods and the latent true level of
Greg Petrucci Jr., Patty Freedson, Brittany Masteller, Melanna Cox, John Staudenmayer and John Sirard
validating research-grade accelerometers to objectively quantify the relationships among PA, SB, and health ( Matthews et al., 2008 ; Matthews et al., 2016 ; Troiano, McClain, Brychta, & Chen, 2014 ). Recently, similar technology and analytics initially used in research-grade accelerometers have been
Melanna F. Cox, Greg J. Petrucci Jr., Robert T. Marcotte, Brittany R. Masteller, John Staudenmayer, Patty S. Freedson and John R. Sirard
.A. , & Krapfl , J.R. ( 2011 ). Validation of the SOPLAY direct observation tool with an accelerometry-based physical activity monitor . Journal of Physical Activity and Health, 8 ( 8 ), 1108 – 1116 . PubMed ID: 22039129 doi:10.1123/jpah.8.8.1108 10.1123/jpah.8.8.1108 Sasaki , J.E. , Hickey , A