2014
DOI: 10.1007/s12603-014-0022-6
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Body mass index is related to autonomic nervous system activity as measured by heart rate variability — A replication using short term measurements

Abstract: The present data supports previous findings, that sympatho-vagal balance is related to BMI in non-obese, healthy individuals, providing evidence for a prominent role of the vagus nerve in the modulation of the energy expenditure of the human organism. Furthermore, this relation can be observed in short term recordings of HRV of 5 minutes in length.

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Cited by 141 publications
(92 citation statements)
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“…A final limitation is that extrinsic influences on infants' vagal regulation were not examined. Measures of autonomic functioning have been linked to body mass index in adults (Koenig et al, 2014) and time of day in children (Massin, Maeyns, Withofs, Ravet, & Gérard, 2000), and could have impacted RSA values in this study. Prior exposure to screens in particular could have influenced infants' vagal regulation during the video task.…”
Section: Discussionmentioning
confidence: 97%
“…A final limitation is that extrinsic influences on infants' vagal regulation were not examined. Measures of autonomic functioning have been linked to body mass index in adults (Koenig et al, 2014) and time of day in children (Massin, Maeyns, Withofs, Ravet, & Gérard, 2000), and could have impacted RSA values in this study. Prior exposure to screens in particular could have influenced infants' vagal regulation during the video task.…”
Section: Discussionmentioning
confidence: 97%
“…It is also important to acknowledge some limitations of our study. There are potential confounding factors that we did not control for here including physical activity (Rennie et al, 2003; Soares-Miranda et al, 2014), smoking status (Sjoberg and Saint, 2011; Harte and Meston, 2014), alcohol use (Quintana et al, 2013a,b), body mass index (Britton et al, 2007; Koenig et al, 2014), and biomarkers including fasting glucose (Stein et al, 2007) and cholesterol (Britton et al, 2007; Thayer and Fischer, 2013), all of which may impact on heart rate parameters. We refer interested readers to a recent review of the various issues that researchers should consider when collecting measures of HRV (Quintana and Heathers, 2014).…”
Section: Discussionmentioning
confidence: 99%
“…This model was programmed in two blocks/steps: (a) the first block included and set the covariates: patient’s age, BMI (since it has been reported that HRV is related to BMI; Koenig et al, 2014, 2015), SCL-90R depression and the ED subtype; and (b) the second block added anxiety and HRV measures. Goodness-of-fit was measured with the Hosmer–Lemeshow test ( p > 0.05 was considered adequate fitting), the global predictive capacity with the Nagelkerke’s pseudo- R 2 coefficient (the adjusted contribution of the predictors was calculated as the increase between blocks 1 and 2, Δ R 2 ) and the global discriminative capacity with the area under the ROC curve (AUC).…”
Section: Methodsmentioning
confidence: 99%