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Is questionnaire-based sitting time inaccurate and can it be improved? A cross-sectional investigation using accelerometer-based sitting time

OBJECTIVES: To investigate the differences between a questionnaire-based and accelerometer-based sitting time, and develop a model for improving the accuracy of questionnaire-based sitting time for predicting accelerometer-based sitting time. METHODS: 183 workers in a cross-sectional study reported...

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Detalles Bibliográficos
Autores principales: Gupta, Nidhi, Christiansen, Caroline Stordal, Hanisch, Christiana, Bay, Hans, Burr, Hermann, Holtermann, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5253534/
https://www.ncbi.nlm.nih.gov/pubmed/28093433
http://dx.doi.org/10.1136/bmjopen-2016-013251
Descripción
Sumario:OBJECTIVES: To investigate the differences between a questionnaire-based and accelerometer-based sitting time, and develop a model for improving the accuracy of questionnaire-based sitting time for predicting accelerometer-based sitting time. METHODS: 183 workers in a cross-sectional study reported sitting time per day using a single question during the measurement period, and wore 2 Actigraph GT3X+ accelerometers on the thigh and trunk for 1–4 working days to determine their actual sitting time per day using the validated Acti4 software. Least squares regression models were fitted with questionnaire-based siting time and other self-reported predictors to predict accelerometer-based sitting time. RESULTS: Questionnaire-based and accelerometer-based average sitting times were ≈272 and ≈476 min/day, respectively. A low Pearson correlation (r=0.32), high mean bias (204.1 min) and wide limits of agreement (549.8 to −139.7 min) between questionnaire-based and accelerometer-based sitting time were found. The prediction model based on questionnaire-based sitting explained 10% of the variance in accelerometer-based sitting time. Inclusion of 9 self-reported predictors in the model increased the explained variance to 41%, with 10% optimism using a resampling bootstrap validation. Based on a split validation analysis, the developed prediction model on ≈75% of the workers (n=132) reduced the mean and the SD of the difference between questionnaire-based and accelerometer-based sitting time by 64% and 42%, respectively, in the remaining 25% of the workers. CONCLUSIONS: This study indicates that questionnaire-based sitting time has low validity and that a prediction model can be one solution to materially improve the precision of questionnaire-based sitting time.