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Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase


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Rosel, JF, Jara, P, Machancoses, FH, Pallares, J, Torrente, P, Puchol, S and Canales, J ORCID: 0000-0003-2884-5227 2019 , 'Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase' , PLoS ONE, vol. 14, no. 1 , pp. 1-17 , doi: 10.1371/journal.pone.0209475.

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Salivary alpha-amylase (sAA) activity has been widely used in psychological and medicalresearch as a surrogate marker of sympathetic nervous system activation, though its utilityremains controversial. The aim of this work was to compare alternative intensive longitudinalmodels of sAA data: (a) a traditional model, where sAA is a function of hour (hr) and hrsquared (sAAj,t = f(hr, hr2), and (b) an autoregressive model, where values of sAA are afunction of previous values (sAAj,t = f(sAA j,t-1, sAA j,t-2, . . ., sAA j,t-p). Nineteen normal subjects (9 males and 10 females) participated in the experiments and measurements were performed every hr between 9:00 and 21:00 hr. Thus, a total of 13 measurements wereobtained per participant. The Napierian logarithm of the enzymatic activity of sAA was analysed. Data showed that a second-order autoregressive (AR(2)) model was more parsimonious and fitted better than the traditional multilevel quadratic model. Therefore, sAA follows aprocess whereby, to forecast its value at any given time, sAA values one and two hr prior tothat time (sAA j,t = f(SAAj,t-1, SAAj,t-2) are most predictive, thus indicating that sAA has itsown inertia, with a “memory” of the two previous hr. These novel findings highlight the relevance of intensive longitudinal models in physiological data analysis and have considerableimplications for physiological and biobehavioural research involving sAA measurementsand other stress-related biomarkers.

Item Type: Article
Authors/Creators:Rosel, JF and Jara, P and Machancoses, FH and Pallares, J and Torrente, P and Puchol, S and Canales, J
Journal or Publication Title: PLoS ONE
Publisher: Public Library of Science
ISSN: 1932-6203
DOI / ID Number: 10.1371/journal.pone.0209475
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Copyright 2019 Rosel et al. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

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