Prediction of objectively measured physical activity and sedentariness among blue-collar work using survey questionnaires

Gupta, Nidhi; Heiden, Marina; Mathiassen, Svend Erik; Holtermann, Andreas

doi:10.5271/sjweh.3561

Cited by 25 publications

(31 citation statements)

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“…The use of objective technical measurements of physical activity is a clear strength of the current study as it allows precise exposure estimates while minimizing potential bias associated with self-reported measures of physical activity [33]. Since the use of activity classification from multiple accelerometers is a rather novel approach in observational studies, comparisons with current recommended guidelines for time and intensity of physical activity are limited.…”

Section: Discussionmentioning

confidence: 99%

“…Objective measurements of physical activity are crucial for obtaining precise exposure estimates and minimize potential bias associated with self-reported measures of physical activity [33]. Moreover, because OPA and LTPA are correlated with socio-economic position, effect modification and confounding by socio-economic factors are an issue in the investigation of LTPA, OPA and health.…”

Section: Introductionmentioning

confidence: 99%

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On the health paradox of occupational and leisure-time physical activity using objective measurements: Effects on autonomic imbalance

2017

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ObjectiveLeisure-time physical activity (LTPA) has considerable benefits for cardiovascular health and longevity, while occupational physical activity (OPA) is associated with an elevated cardiovascular risk. This “health paradox” may be explained by different effects on the autonomic nervous system from OPA and LTPA. Thus, we aimed to investigate whether objectively measured OPA and LTPA are differentially associated with autonomic regulation among workers.MethodsThe study comprised 514 blue-collar workers from the Danish cohort DPHACTO. Physical activity (i.e. walking, climbing stairs, running and cycling) was assessed objectively using accelerometers worn on the thigh, hip and trunk over multiple working days. During this period, a heart rate monitor was used to sample heart period intervals from the ECG signal. Heart rate and heart rate variability (HRV) indices were analyzed during nocturnal sleep as markers of autonomic regulation. Multiple regression analysis was used to determine the main effects of OPA and LTPA and their interaction on heart rate and HRV, adjusting for multiple confounders.ResultsStatistically significant interaction was found between OPA and LTPA on heart rate (adjusted p<0.0001) and HRV indices in time (rMSSD, adjusted p = 0.004) and frequency-domains (HF, adjusted p = 0.022; LF, adjusted p = 0.033). The beneficial effect of LTPA on nocturnal heart rate and HRV clearly diminished with higher levels of OPA, and high levels of both OPA and LTPA had a detrimental effect.ConclusionWe found contrasting associations for objectively measured OPA and LTPA with heart rate and HRV during sleep. Differential effects of OPA and LTPA on autonomic regulation may contribute to the physical activity health paradox.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the health paradox of occupational and leisure-time physical activity using objective measurements: Effects on autonomic imbalance

2017

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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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“…Self-reported total sitting time, work and leisure combined, has been shown to be underestimated by 15−37% (15,16), while underestimation ranged from 1.5−43% among workers selfreporting their occupational sitting time (17,18). Bias in self-reported sitting time may vary depending on the worker's body mass index (BMI) (17,19,20), gender (17,19,21), age (19,22), musculoskeletal pain (17,21), psychosocial work demands (17), and education level (22). Bias in self-reported sitting time will result in flawed relations with health outcomes, while nondifferential misclassification (eg, as a result of random variation in self-reported sitting time) can result in attenuation of associations with health outcomes (23).…”

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confidence: 99%

“…Other studies have developed models to improve self-reported sedentary behavior using objective measurements as an estimate of the 'truth' (13,14). Thus far, only few studies have, however, addressed self-reported sitting at work (17,19,21). All these occupational studies were based on blue-collar workers, while models developed for office workers are lacking.…”

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confidence: 99%

Correction of bias in self-reported sitting time among office workers – a study based on compositional data analysis

Coenen¹,

Mathiassen²,

Beek³

et al. 2019

Scand J Work Environ Health

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Correction of bias in self-reported sitting time among office workers -a study based on compositional data analysis by Coenen P, Mathiassen SE, van der Beek AJ, Hallman DM Being the first to develop a calibration model using compositional data analysis, we found that a model in which "true" occupational sitting was estimated from self-reported sitting, or even additional predictors, led to substantially more correct estimates than if only self-reports were used. Our models can be used for post-hoc improvement of self-reported sitting data and while designing future studies.Coenen P, Mathiassen SE, van der Beek AJ, Hallman DM. Correction of bias in self-reported sitting time among office workers -a study based on compositional data analysis.Objective Emerging evidence suggests that excessive sitting has negative health effects. However, this evidence largely relies on research using self-reported sitting time, which is known to be biased. To correct this bias, we aimed at developing a calibration model estimating "true" sitting from self-reported sitting.Methods Occupational sitting time was estimated by self-reports (the International Physical Activity Questionnaire) and objective measurements (thigh-worn accelerometer) among 99 Swedish office workers at a governmental agency, at baseline and 3 and 12 months afterwards. Following compositional data analysis procedures, both sitting estimates were transformed into isometric log-ratios (ILR). This effectively addresses that times spent in various activities are inherently dependent and can be presented as values of only 0−100%. Linear regression was used to develop a simple calibration model estimating objectively measured "true" sitting ILR (dependent variable) from self-reported sitting ILR (independent variable). Additional self-reported variables were then added to construct a full calibration model. Performance of the models was assessed by root-meansquare (RMS) differences between estimated and objectively measured values. Models developed on baseline data were validated using the follow-up datasets.Results Uncalibrated self-reported sitting ILR showed an RMS error of 0.767. Simple and full calibration models (incorporating body mass index, office type, and gender) reduced this error to 0.422 (55%) and 0.398 (52%), respectively. In the validations, model performance decreased to 57%/62% (simple models) and 57%/62% (full models) for the two follow-up data sets, respectively.Conclusion Calibration adjusting for errors in self-reported sitting led to substantially more correct estimates of "true" sitting than uncalibrated self-reports. Validation indicated that model performance would change somewhat in new datasets and that full models perform no better than simple models, but calibration remained effective.

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Prediction of objectively measured physical activity and sedentariness among blue-collar work using survey questionnaires

Abstract: Refers to the following text of the Journal: 2007;33(6):401-480The following article refers to this text: 2018;44(2):111-223

Cited by 25 publications

References 47 publications

On the health paradox of occupational and leisure-time physical activity using objective measurements: Effects on autonomic imbalance

On the health paradox of occupational and leisure-time physical activity using objective measurements: Effects on autonomic imbalance

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

Correction of bias in self-reported sitting time among office workers – a study based on compositional data analysis

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