Search Results

You are looking at 1 - 10 of 425 items for :

  • "overestimate" x
Clear All
Restricted access

Lisa Price, Katrina Wyatt, Jenny Lloyd, Charles Abraham, Siobhan Creanor, Sarah Dean and Melvyn Hillsdon

overestimate of the true prevalence. 13 This study aims to expand the current understanding of PA prevalence by comparing 2 different methods for determining prevalence estimates in a large cohort of 9- to 10-year-old children and to report these estimates for the whole cohort and by gender. The prevalence

Restricted access

Yongming Li, Margot Niessen, Xiaoping Chen and Ulrich Hartmann

partly, an overestimate of the relative aerobic contribution (W AER %). 9 In terms of women’s Olympic kayaking, either of the aforementioned 2 methods (m-MAOD and PCr-La-O 2 ) was adopted. 4 – 8 However, so far there has been no study comparing the energy contributions in women’s Olympic kayaking with

Restricted access

Barbara B. Brown and Carol M. Werner

Background:

Accelerometer output feedback might enable assessment of recall biases for moderate bouts by obese and nonobese individuals; accelerometry might also help residents recall destinations for moderate-intensity walking bouts.

Methods:

Adult residents’ 1-week accelerometer-measured physical activity and obesity status were measured before and after a new rail stop opened (n = 51 Time 1; n = 47 Time 2). Participants recalled the week’s walking bouts, described them as brisk (moderate) or not, and reported a rail stop destination or not.

Results:

At the end of the week, we provided accelerometry output to residents as a prompt. Recall of activity intensity was accurate for about 60% of bouts. Nonobese participants had more moderate bouts and more “stealth exercise” —moderate bouts recalled as not brisk—than did obese individuals. Obese participants had more overestimates—recalling light bouts as brisk walks—than did nonobese individuals. Compared with unprompted recall, accelerometry-prompted recalls allowed residents to describe where significantly more moderate bouts of activity occurred.

Conclusion:

Coupling accelerometry feedback with self-report improves research by measuring the duration, intensity, and destination of walking bouts. Recall errors and different patterns of errors by obese and nonobese individuals underscore the importance of validation by accelerometry.

Restricted access

Andrea Nicolò, Ilenia Bazzucchi and Massimo Sacchetti

Purpose:

To verify the accuracy of predicting performance in the severe-intensity domain by means of end-test power output (EP) and the work performed above EP (WEP) obtained from a 3-min all-out test in competitive cyclists.

Methods:

Ten welltrained cyclists performed a ramp incremental test and a 3-min all-out familiarization test. Subsequently, they performed a 3-min all-out experimental test to obtain EP and WEP and a 10-min time trial (TT). The actual 10-min-TT mean power output was then compared with the power output predicted as P = WEP/T lim + EP, where T lim corresponds to 600 s. The ramp-test peak power output (PPO) was compared with PPO predicted as PPO=EP+2WEP S, where S represents the ramp slope (0.5 W/s).

Results:

The actual (347 ± 30 W) and predicted (376 ± 48 W) 10-min TT mean power output were correlated (r = .87, P = .001) but significantly different (P < .01). The coefficient of variation (CV) between the predicted and actual performance was 5.6% ± 4.4%. The error of prediction was positively correlated to EP (r = .80, P = .005) and negatively correlated to WEP (r = –.71, P = .021). No significant difference was found between the 10-min-TT mean power output and EP (351 ± 53 W). The actual (438 ± 30 W) and predicted (472 ± 41 W) ramp PPO were correlated (r = .88, P < .001) but significantly different (P < .001). The CV between the predicted and actual PPO was 5.2% ± 3%. The error of prediction was positively correlated to EP (r = .63, P = .051).

Conclusions:

EP and WEP obtained from a 3-min all-out test overestimate severe-intensity performance in competitive cyclists.

Full access

Courtney Coughenour and Timothy J. Bungum

Background:

Neighborhood walkability is being promoted as an important factor in public health efforts to decrease rates of physical inactivity. Single entry communities (SEC), communities with only 1 entrance/exit, may result in an over estimation of walkability. This design makes direct walking routes outside the community nearly impossible and results in increased trip distance. The purpose of this study was to determine if accounting for SECs resulted in a significant difference in street connectivity.

Methods:

Twenty geographically different Las Vegas neighborhoods were chosen and the number of true intersections measured in ArcGIS. Neighborhoods were then assessed for the presence of SECs using google maps, ArcGIS land imagery, and field observation. Intersections inside SECs were removed. A paired t test was used to assess the mean difference of intersection density before and after adjustment.

Results:

There was a statistically significant decrease in the number of true intersections after the adjustment (before mean = 57.8; after mean = 45.7). The eta squared statistic indicates a large effect size (0.3).

Conclusions:

Single entry communities result in an over estimation of street connectivity. If SECs are not accounted for, trip distances will be underestimated and public health efforts to promote walking through walkable neighborhoods may prove less effective.

Restricted access

Danilo R. Silva, Cláudia S. Minderico, Pedro B. Júdice, André O. Werneck, David Ohara, Edilson S. Cyrino and Luís B. Sardinha

(8 AM–5 PM), after-school time (5 PM–10 PM), and weekend time (10 AM–10 PM). Results Table  1 presents the comparisons between the alternative (GT3X) and the reference method (ActivPAL) for the estimates of school, after-school, and weekend sedentary time. The GT3X overestimated sedentary time in

Restricted access

Caterina Pesce, Ilaria Masci, Rosalba Marchetti, Giuseppe Vannozzi and Mirko Schmidt

in children ( Bardid, De Meester, et al., 2016 ) and adolescents ( De Meester, Maes, et al., 2016 ), since a higher perception of motor competence resulted associated with a higher motivation also when an actual low level of motor proficiency mismatched the high perception (i.e., overestimation). The

Restricted access

Athanasios Kolovelonis and Marios Goudas

regulation of study and increased learning ( Thiede, Anderson, & Therriault, 2003 ). Research Findings Extensive research across different educational levels has shown that an overconfidence effect exists. University students have been shown to overestimate their performance in three multiple-choice tests in

Restricted access

Kathryn J. DeShaw, Laura Ellingson, Yang Bai, Jeni Lansing, Maria Perez and Greg Welk

averaging the individual percent errors (i.e., [reference-estimation])/reference) to capture the degree of overall overestimation or underestimation for FBC against the reference measures. Mean absolute percent error (MAPE) was calculated by averaging the individual absolute percent errors (i

Restricted access

Kayla J. Nuss, Joseph L. Sanford, Lucas J. Archambault, Ethan J. Schlemer, Sophie Blake, Jimikaye Beck Courtney, Nicholas A. Hulett and Kaigang Li

possible that inaccurate EE estimations by the wearable device could have led participants to overestimate their EE, negatively impacting their weight loss. Numerous studies have examined the accuracy of step count, distance traveled, HR, and EE in wearable devices; however, HR and EE are the two newest