-living physical activity patterns. 13 Although DLW and accelerometers have been used extensively, these techniques pose some disadvantages, such as cost, participant burden, within-participant measurement error, and short time frame of measurement. 10 , 12 , 14 , 15 Physical activity questionnaires (PAQs) have
David R. Paul, Ryan McGrath, Chantal A. Vella, Matthew Kramer, David J. Baer, and Alanna J. Moshfegh
Stefan Altmann, Steffen Ringhof, Benedikt Becker, Alexander Woll, and Rainer Neumann
. This causes measurement errors (MEs), as researchers and coaches usually seek to capture the athlete’s torso during sprint testing. 2 To improve measurement accuracy, systems employing error correction processing (ECP) algorithms—termed “postprocessing timing” in the previous research 1 —have been
Sarah M. Nusser, Nicholas K. Beyler, Gregory J. Welk, Alicia L. Carriquiry, Wayne A. Fuller, and Benjamin M.N. King
Physical activity recall instruments provide an inexpensive method of collecting physical activity patterns on a sample of individuals, but they are subject to systematic and random measurement error. Statistical models can be used to estimate measurement error in activity recalls and provide more accurate estimates of usual activity parameters for a population.
We develop a measurement error model for a short-term activity recall that describes the relationship between the recall and an individual’s usual activity over a long period of time. The model includes terms for systematic and random measurement errors. To estimate model parameters, the design should include replicate observations of a concurrent activity recall and an objective monitor measurement on a subsample of respondents.
We illustrate the approach with preliminary data from the Iowa Physical Activity Measurement Study. In this dataset, recalls tend to overestimate actual activity, and measurement errors greatly increase the variance of recalls relative to the person-to-person variation in usual activity. Statistical adjustments are used to remove bias and extraneous variation in estimating the usual activity distribution.
Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.
Allan Munro, Lee Herrington, and Michael Carolan
Two-dimensional (2D) video analysis of frontal-plane dynamic knee valgus during common athletic screening tasks has been purported to identify individuals who may be at high risk of suffering knee injuries such as anterior cruciate ligament tear or patellofemoral pain syndrome. Although the validity of 2D video analysis has been studied, the associated reliability and measurement error have not.
To assess the reliability and associated measurement error of a 2D video analysis of lower limb dynamic valgus.
20 recreationally active university students (10 women age 21.5 ± 2.3 y, height 170.1 ± 6.1 cm, weight 66.2 ± 10.2 kg, and 10 men age 22.6 ± 3.1 y, height 177.9 ± 6.0 cm, weight 75.8 ± 7.9 kg).
Main Outcome Measurement:
Within-day and between-days reliability and measurement-error values of 2D frontal-plane projection angle (FPPA) during common screening tasks.
Participants performed single-leg squat and drop jump and single-leg landings from a standard 28-cm step with standard 2D digital video camera assessment.
Women demonstrated significantly higher FPPA in all tests except the left single-leg squat. Within-day ICCs showed good reliability and ranged from .59 to .88, and between-days ICCs were good to excellent, ranging from .72 to .91. Standard error of measurement and smallest detectable difference values ranged from 2.72° to 3.01° and 7.54° to 8.93°, respectively.
2D FPPA has previously been shown to be valid and has now also been shown to be a reliable measure of lower extremity dynamic knee valgus. Using the measurement error values presented along with previously published normative data, clinicians can now make informed judgments about individual performance and changes in performance resulting from interventions.
Barbara E. Ainsworth, Carl J. Caspersen, Charles E. Matthews, Louise C. Mâsse, Tom Baranowski, and Weimo Zhu
Assessment of physical activity using self-report has the potential for measurement error that can lead to incorrect inferences about physical activity behaviors and bias study results.
To provide recommendations to improve the accuracy of physical activity derived from self report.
We provide an overview of presentations and a compilation of perspectives shared by the authors of this paper and workgroup members.
We identified a conceptual framework for reducing errors using physical activity self-report questionnaires. The framework identifies 6 steps to reduce error: 1) identifying the need to measure physical activity, 2) selecting an instrument, 3) collecting data, 4) analyzing data, 5) developing a summary score, and 6) interpreting data. Underlying the first 4 steps are behavioral parameters of type, intensity, frequency, and duration of physical activities performed, activity domains, and the location where activities are performed. We identified ways to reduce measurement error at each step and made recommendations for practitioners, researchers, and organizational units to reduce error in questionnaire assessment of physical activity.
Self-report measures of physical activity have a prominent role in research and practice settings. Measurement error may be reduced by applying the framework discussed in this paper.
Gregory J. Welk
measurement error in physical activity. The studies use different approaches and target different populations but provide examples of how relatively simple calibration procedures can be used to promote harmonization of physical activity outcomes. It is important to acknowledge that the examples and
Zachary Zenko and Panteleimon Ekkekakis
susceptibility to random measurement error (i.e., reliability, in some cases, below commonly acceptable standards, such as less than 50% true variance); (b) typically low, or even near-zero, intercorrelations with each other; (c) generally low correlations with measures of explicit attitudes and behavior; (d
Paula Louise Hooper, Nicholas Middleton, Matthew Knuiman, and Billie Giles-Corti
There is increasing focus on the influence of neighborhood destinations on a variety of health behaviors. Commercial databases, integrated with Geographic Information Systems (GIS), are popular sources of destination information for public health researchers. However, the suitability and accuracy of these data for public health research purposes has been generally unexplored.
This study validated the presence and number of a broad range of destination types listed within an Australian-based commercial database (Yellow Pages), thought to be important for encouraging health behaviors, against those identified via field audit. The study was conducted in and around 5 housing developments within the RESIDential Environments project across metropolitan Perth, Western Australia.
Overall agreement of the count of destinations listed within the Yellow Pages ranged from 0.29–0.76, depending on the study area, the timing of the data extract and the geocoding methods used. Results also indicated considerable variation between different extracts from the same commercial dataset, and appreciable over- and under-counting of different destination types compared with field audit findings.
The choice of database and extraction time and methods, have important implications in the quantification of neighborhood destination mix and robustness of associations with public health behaviors.
Nick Dobbin, Richard Hunwicks, Ben Jones, Kevin Till, Jamie Highton, and Craig Twist
Purpose: To examine the criterion and construct validity of an isometric midthigh-pull dynamometer to assess whole-body strength in professional rugby league players. Methods: Fifty-six male rugby league players (33 senior and 23 youth players) performed 4 isometric midthigh-pull efforts (ie, 2 on the dynamometer and 2 on the force platform) in a randomized and counterbalanced order. Results: Isometric peak force was underestimated (P < .05) using the dynamometer compared with the force platform (95% LoA: −213.5 ± 342.6 N). Linear regression showed that peak force derived from the dynamometer explained 85% (adjusted R 2 = .85, SEE = 173 N) of the variance in the dependent variable, with the following prediction equation derived: predicted peak force = [1.046 × dynamometer peak force] + 117.594. Cross-validation revealed a nonsignificant bias (P > .05) between the predicted and peak force from the force platform and an adjusted R 2 (79.6%) that represented shrinkage of 0.4% relative to the cross-validation model (80%). Peak force was greater for the senior than the youth professionals using the dynamometer (2261.2 ± 222 cf 1725.1 ± 298.0 N, respectively; P < .05). Conclusion: The isometric midthigh pull assessed using a dynamometer underestimates criterion peak force but is capable of distinguishing muscle-function characteristics between professional rugby league players of different standards.
Arthur H. Bossi, Wouter P. Timmerman, and James G. Hopker
Purpose: There are several published equations to calculate energy expenditure (EE) from gas exchanges. The authors assessed whether using different EE equations would affect gross efficiency (GE) estimates and their reliability. Methods: Eleven male and 3 female cyclists (age 33  y; height: 178  cm; body mass: 76.0 [15.1] kg; maximal oxygen uptake: 51.4 [5.1] mL·kg−1·min−1; peak power output: 4.69 [0.45] W·kg−1) completed 5 visits to the laboratory on separate occasions. In the first visit, participants completed a maximal ramp test to characterize their physiological profile. In visits 2 to 5, participants performed 4 identical submaximal exercise trials to assess GE and its reliability. Each trial included three 7-minute bouts at 60%, 70%, and 80% of the gas exchange threshold. EE was calculated with 4 equations by Péronnet and Massicotte, Lusk, Brouwer, and Garby and Astrup. Results: All 4 EE equations produced GE estimates that differed from each other (all P < .001). Reliability parameters were only affected when the typical error was expressed in absolute GE units, suggesting a negligible effect—related to the magnitude of GE produced by each EE equation. The mean coefficient of variation for GE across different exercise intensities and calculation methods was 4.2%. Conclusions: Although changing the EE equation does not affect GE reliability, exercise scientists and coaches should be aware that different EE equations produce different GE estimates. Researchers are advised to share their raw data to allow for GE recalculation, enabling comparison between previous and future studies.