ArticleThe international journal of behavioral nutrition and physical activity2019
Imputing accelerometer nonwear time in children influences estimates of sedentary time and its associations with cardiometabolic risk.
Article in The international journal of behavioral nutrition and physical activity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Active School-Based Interventions to Interrupt Prolonged Sitting Improve Daily Physical Activity: A Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2022Pooled it
- Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study.JMIR mHealth and uHealth · 2020Article
- Longitudinal Changes in Children's Accelerometer-derived Activity Pattern Metrics.Medicine and science in sports and exercise · 2020Article
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4 authors.
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Abstract
backgroundA limitation of measuring sedentary time with an accelerometer is device removal. The resulting nonwear time is typically deleted from the data prior to calculating sedentary time. This could impact estimates of sedentary time and its associations with health indicators. We evaluated whether using multiple imputation to replace nonwear accelerometer epochs influences such estimates in children.
methods452 children (50% male) aged 10-13 were tasked with wearing an accelerometer (15 s epochs) for 7 days. On average, 8% of waking time was classified as nonwear time. Sedentary time was derived from a "nonimputed" dataset using the typical approach of deleting epochs that occurred during nonwear time, as well as from an "imputed" dataset. In the imputed dataset, each nonwear epoch was re-classified as being as sedentary or not using multiple imputation (5 iterations) which was informed by the likelihood of a wear time epoch being classified as sedentary or not using parameter estimates from a logistic regression model. Estimates of sedentary time and associations between sedentary time and health indicators (cardiometabolic risk factor and internalizing mental health symptoms Z-scores) were compared between the nonimputed and imputed datasets.
resultsOn average, sedentary time was 33 min/day higher in the imputed dataset than in the nonimputed dataset (632 vs. 599 min/day). The association between sedentary time and the cardiometabolic risk factor Z-score was stronger in the imputed vs. the nonimputed dataset (β = 0.137 vs. β = 0.092 per 60 min/day change, respectively). These findings were more pronounced among children who had < 7 days with ≥10 h of wear time.
conclusionResearchers should consider using multiple imputation to address accelerometer nonwear time, rather than deleting it, in order to derive more unbiased estimates of sedentary time and its associations with health indicators.
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