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

Beyler, Nicholas; Beyler, Amy

doi:10.1515/jos-2017-0025

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Even though the probing questions were employed, variations in how the YAP questions were interpreted would have contributed to measurement and processing errors [54], which would have affected the resultant algorithms. The participants in our study were also relatively active and any expected over-estimations of their PA [55] may have been exacerbated when recalling their MVPA behaviours. YAP-predicted SB was also overestimated but the 15% equivalence was promising.…”

Section: Discussionmentioning

confidence: 99%

Calibration and Validation of the Youth Activity Profile as a Physical Activity and Sedentary Behaviour Surveillance Tool for English Youth

Fairclough

Christian

Saint‐Maurice

et al. 2019

IJERPH

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Self-reported youth physical activity (PA) is typically overestimated. We aimed to calibrate and validate a self-report tool among English youth. Four-hundred-and-two participants (aged 9–16 years; 212 boys) wore SenseWear Armband Mini devices (SWA) for eight days and completed the self-report Youth Activity Profile (YAP) on the eighth day. Calibration algorithms for temporally matched segments were generated from the YAP data using quantile regression. The algorithms were applied in an independent cross-validation sample, and student- and school-level agreement were assessed. The utility of the YAP algorithms to assess compliance to PA guidelines was also examined. The school-level bias for the YAP estimates of in-school, out-of-school, and weekend moderate-to-vigorous PA (MVPA) were 17.2 (34.4), 31.6 (14.0), and −4.9 (3.6) min·week−1, respectively. Out-of-school sedentary behaviour (SB) was over-predicted by 109.2 (11.8) min·week−1. Predicted YAP values were within 15%–20% equivalence of the SWA estimates. The classification accuracy of the YAP MVPA estimates for compliance to 60 min·day−1 and 30 min·school-day−1 MVPA recommendations were 91%/37% and 89%/57% sensitivity/specificity, respectively. The YAP generated robust school-level estimates of MVPA and SB and has potential for surveillance to monitor compliance with PA guidelines. The accuracy of the YAP may be further improved through research with more representative UK samples to enhance the calibration process and to refine the resultant algorithms.

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Section: Discussionmentioning

confidence: 99%

Calibration and Validation of the Youth Activity Profile as a Physical Activity and Sedentary Behaviour Surveillance Tool for English Youth

Fairclough

Christian

Saint‐Maurice

et al. 2019

IJERPH

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“…Not all identified surveillance systems performed nonresponse analyses and the surveillance systems that did a non-response bias analysis only looked at basic demographic variables such as gender, age, education level and sometimes body mass index. To avoid biased PA estimates in survey research it is preferable to adjust for non-response error [ 85 ]. We believe that non-response bias analysis should also be done on PA levels.…”

Section: Discussionmentioning

confidence: 99%

Comparing national device-based physical activity surveillance systems: a systematic review

de Wolf,

Elevelt,

van Nassau

et al. 2024

Int J Behav Nutr Phys Act

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Background Physical activity surveillance systems are important for public health monitoring but rely mostly on self-report measurement of physical activity. Integration of device-based measurements in such systems can improve population estimates, however this is still relatively uncommon in existing surveillance systems. This systematic review aims to create an overview of the methodology used in existing device-based national PA surveillance systems. Methods Four literature databases (PubMed, Embase.com, SPORTDiscus and Web of Science) were searched, supplemented with backward tracking. Articles were included if they reported on population-based (inter)national surveillance systems measuring PA, sedentary time and/or adherence to PA guidelines. When available and in English, the methodological reports of the identified surveillance studies were also included for data extraction. Results This systematic literature search followed the PRISMA guidelines and yielded 34 articles and an additional 18 methodological reports, reporting on 28 studies, which in turn reported on one or multiple waves of 15 different national and 1 international surveillance system. The included studies showed substantial variation between (waves of) systems in number of participants, response rates, population representativeness and recruitment. In contrast, the methods were similar on data reduction definitions (e.g. minimal number of valid days, non-wear time and necessary wear time for a valid day). Conclusions The results of this review indicate that few countries use device-based PA measurement in their surveillance system. The employed methodology is diverse, which hampers comparability between countries and calls for more standardized methods as well as standardized reporting on these methods. The results from this review can help inform the integration of device-based PA measurement in (inter)national surveillance systems.

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“…Unexpectedly, they also detected a correlation between social approval and the tendency to underreport physical activity, but the magnitude of this association was small. Several other studies have stressed the role of social desirability in the overreporting of physical activity, and have found different propensities to overreport for subgroups distinguished by sex, age, education, weight, and other characteristics (Beyler and Beyler 2017;Ferrari et al 2007;Hill et al 1997;Nusser et al 2012;Sallis and Saelens 2000;Troiano et al 2008); or by the data collection procedures used (Brenner andDeLamater 2016, 2014).…”

Section: Why Do Respondents Misreport?mentioning

confidence: 99%

ICT skills measurement in social surveys: Can we trust self-reports?

Palczyńska

Rynko

2020

Qual Quant

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Self-reports are the most common way of measuring information and communications technology (ICT) skills in social surveys. Studies comparing self-reported computer skills with objective assessments have found evidence of significant overreporting of skills, but were conducted only among non-representative groups of individuals. This paper fills an important gap by analysing the degree to which ICT skills are overreported in the workingage population of Poland, and the potential causes of this behaviour. We compare answers to Eurostat questions on ICT usage with direct assessments of the corresponding tasks. The results suggest that those individuals who are most likely to possess ICT skills are also most likely to overreport having these skills. The propensity to overreport decreases with age and increases with years of education and numeracy level; women are less likely than men to overestimate their skills. The positive relationship between the probability of a group to overreport their own skills and their expected levels of skills suggests that social desirability bias may explain this phenomenon.

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

Cited by 4 publications

References 27 publications

Calibration and Validation of the Youth Activity Profile as a Physical Activity and Sedentary Behaviour Surveillance Tool for English Youth

Calibration and Validation of the Youth Activity Profile as a Physical Activity and Sedentary Behaviour Surveillance Tool for English Youth

Comparing national device-based physical activity surveillance systems: a systematic review

ICT skills measurement in social surveys: Can we trust self-reports?

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