Modeling Errors in Physical Activity Recall Data

Nusser, Sarah M.; Beyler, Nicholas; Welk, Gregory J.; Carriquiry, Alicia L.; Fuller, Wayne A.; King, Benjamin M.N.

doi:10.1123/jpah.9.s1.s56

Cited by 55 publications

(94 citation statements)

References 22 publications

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“…An attractive option is to correct for self-report bias in a calibration procedure and use the corrected exposure values for further analyses. Calibration, or exposure modeling, is an established method for correcting self-reported information in the field of nutritional and body composition (41,42) and in occupational hygiene (43)(44)(45), and it is becoming increasingly popular in the field of physical activity (46)(47)(48)(49), even in an occupational context (50)(51)(52). The present study contributes to the development of effective models for estimating "true" sedentary time and time in MVPA by identifying a number of factors that may, in addition to the self-reported exposure in its own right, influence the extent of self-report bias, and that may, therefore, be candidates for inclusion in calibration models.…”

Section: Gupta Et Almentioning

confidence: 99%

Is self-reported time spent sedentary and in physical activity differentially biased by age, gender, body mass index, and low-back pain?

Gupta¹,

Heiden²,

Mathiassen³

et al. 2017

Scand J Work Environ Health

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As one of the first of its kind, this study found that age and low-back pain, but not body mass index and gender, introduce differential bias in self-reported time spent sedentary and in physical activity. We therefore recommend that self-reported information on these behaviors should be used with due caution in clinical practice, in research, as well as when designing health promotion programs. Key terms: age; back pain; bias; blue-collar worker; body mass index; body mass index; exposure modelling; gender; low-back pain; measurement error; musculoskeletal pain; pain; physical activity; questionnaire; sedentary; self-report; self-reported time This article in PubMed: www.ncbi.nlm.nih.gov/pubmed/29184965 Additional materialPlease note that there is additional material available belonging to this article on the Scandinavian Journal of Work, Environment & Health -website. Scand J Work Environ Health 2018, vol 44, no 2 163Original article Scand J Work Environ Health. 2018;44(2):163-170. doi:10.5271/sjweh.3693 Is self-reported time spent sedentary and in physical activity differentially biased by age, gender, body mass index, and low-back pain?by Nidhi Gupta, PhD, 1 Marina Heiden, PhD, 2 Svend Erik Mathiassen, PhD, 2 Andreas Holtermann, PhD 1,3 Gupta N, Heiden M, Mathiassen SE, Holtermann A. Is self-reported time spent sedentary and in physical activity differentially biased by age, gender, body mass index and low-back pain? Scand J Work Environ Health. 2018;44(2):163-170. doi:10.5271/sjweh.3693 Objectives This study aimed to determine the extent to which age, gender, body mass index (BMI) and lowback pain (LBP) influence bias in self-reported sedentary behavior and moderate-to-vigorous physical activity (MVPA) among blue-collar workers.Methods For 2-4 consecutive working days, 147 workers wore an Actigraph accelerometer on the thigh. Proportional time spent sedentary and in MVPA was determined using the Acti4 software. The same variables were also self-reported in a questionnaire. The difference between self-reported and accelerometer-based sedentary time and MVPA was calculated and linearly regressed against age, gender, BMI, and self-reported LBP intensity as main effects, as well as interaction terms combining each of these factors with objectively measured exposure.Results Workers objectively spent 64% of their time sedentary and 9% in MVPA. On average, self-reports underestimated sedentary time by 1.5% and overestimated MVPA by 5.5%. Workers with mild/no LBP appeared to have the same size of self-report bias in MVPA regardless of how much MVPA they actually had, while workers with high LBP overestimated MVPA to an increasing extent with increasing exposure [interaction: B=0.29, 95% confidence interval (CI) 0.05-0.53]. Age was positively associated with self-report bias in sedentary time (B=0.31, 95% CI 0.09-0.54) regardless of actual sedentary time.Conclusions LBP and age, but not BMI and gender, introduced differential bias in self-reported information on sedentary behavior and MVPA among blue-collar w...

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Section: Gupta Et Almentioning

confidence: 99%

Is self-reported time spent sedentary and in physical activity differentially biased by age, gender, body mass index, and low-back pain?

Gupta¹,

Heiden²,

Mathiassen³

et al. 2017

Scand J Work Environ Health

View full text Add to dashboard Cite

show abstract

“…This is likely too optimistic an assumption and was used in our simulations to focus, specifically, on random measurement error. Other studies have shown evidence of systematic biases in self-reports of physical activity outcomes (Ferrari et al 2007;Nusser et al 2012;Tooze et al 2013), although they focused on data analyzed from convenience samples that are not representative of all adult Americans. To better understand measurement error properties of self-reported physical activity data in national surveys, multiple measurements from different points in time should be considered for at least a subsample of survey participants in order to distinguish measurement error and other forms of intra-individual variation in the data from actual variability in physical activity across individuals.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the terminology used in self-report questionnaires may be confusing or unclear, which can result in misreporting (Sallis and Saelens 2000). Self-reporting errors like these often result in much larger estimates of physical activity than those taken from more objective instruments like accelerometers or other monitoring devices and can fundamentally bias estimates of physical activity in adult populations (Ferrari et al 2007;Nusser et al 2012;Tooze et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Model (1) is often referred to as the classical measurement error model (Carroll et al 2006) and assumes that X ij is an unbiased measurement of T i . Such an assumption is considered for the purposes of model development for this simulation study but in practice is often not justifiable for self-report measures and more robust model assumptions and model equations should be considered (Ferrari et al 2007;Nusser et al 2012;Tooze et al 2013).…”

Section: Model Developmentmentioning

confidence: 99%

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Adjusting for Measurement Error and Nonresponse in Physical Activity Surveys: A Simulation Study

Beyler¹,

Beyler

2017

Journal of Official Statistics

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Adult Americans are encouraged to engage in at least 150 minutes of moderate to vigorous physical activity (MVPA) each week. National surveys that collect physical activity data to assess whether or not adults adhere to this guideline use self-report questionnaires that are prone to measurement error and nonresponse. Studies have examined the individual effects of each of these error sources on estimators of physical activity, but little is known about the consequences of not adjusting for both error sources. We conducted a simulation study to determine how estimators of adherence to the guideline for adults to engage in 150 minutes of MVPA each week respond to different magnitudes of measurement and nonresponse errors in self-reported physical activity survey data. Estimators that adjust for both measurement and nonresponse errors provide the least amount of bias regardless of the magnitudes of measurement error and nonresponse. In some scenarios, the naïve estimator, which does not adjust for either error source, results in less bias than estimators that adjust for only one error source. To avoid biased physical activity estimates using data collected from self-report questionnaires, researchers should adjust for both measurement error and nonresponse.

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“…33 While there are limitations to measurement error models, they are becoming more established and can be used to correct for measurement error associated with parenting measures. 33,37,41 Structural equation modeling can provide an alternative approach to account for measurement error. For example, this is achieved by treating all survey items measuring parenting as a latent construct, meaning that the items are aggregated as one factor.…”

Section: Correcting For Social Desirability and Measurement Errorsmentioning

confidence: 99%

Stimulating Innovations in the Measurement of Parenting Constructs

Mâsse

Watts

2013

Childhood Obesity

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Parents can play a crucial role in the development of children's behaviors associated with dietary habits, physical activity, and sedentary lifestyles. Many parenting practices and/or styles measures have been developed; however, there is little agreement as to how the influence of parenting should be measured. More importantly, our ability to relate parenting practices and/or styles to children's behaviors depends on its accurate assessment. While there is a need to standardize our assessment to further advance knowledge in this area, this article will discuss areas that may stimulate advances in the measurement of parenting constructs. Because self-report measures are important for the assessment of parenting, this article discusses whether solutions to improve selfreport measures may lie in: (1) Improving the questions asked; (2) improving the methods used to correct for social desirability or measurement errors; (3) changing our measurement paradigm to assess implicit parenting behaviors; (4) changing how self-report is collected by taking advantage of ecological momentary assessment methods; (5) using better psychometric methods to validate parenting measures or alternatively using advances in psychometric methods, such as item banking and computerized adaptive testing, to solve common administration issues (i.e., response burden and comparability of results across studies); and (6) employing novel technologies to collect data such as portable technologies, gaming, and virtual reality simulation. This article will briefly discuss the potential of technologies to measure parenting constructs.

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Modeling Errors in Physical Activity Recall Data

Abstract: Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.

Cited by 55 publications

References 22 publications

Is self-reported time spent sedentary and in physical activity differentially biased by age, gender, body mass index, and low-back pain?

Is self-reported time spent sedentary and in physical activity differentially biased by age, gender, body mass index, and low-back pain?

Adjusting for Measurement Error and Nonresponse in Physical Activity Surveys: A Simulation Study

Stimulating Innovations in the Measurement of Parenting Constructs

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