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Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase

Citation

Rosel, JF and Jara, P and Machancoses, FH and Pallares, J and Torrente, P and Puchol, S and Canales, J, Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase, PLoS ONE, 14, (1) Article e0209475. ISSN 1932-6203 (2019) [Refereed Article]


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Copyright Statement

Copyright 2019 Rosel et al. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1371/journal.pone.0209475

Abstract

Salivary alpha-amylase (sAA) activity has been widely used in psychological and medical research as a surrogate marker of sympathetic nervous system activation, though its utility remains controversial. The aim of this work was to compare alternative intensive longitudinal models of sAA data: (a) a traditional model, where sAA is a function of hour (hr) and hr squared (sAAj,t = f(hr, hr2 ), and (b) an autoregressive model, where values of sAA are a function 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 were obtained 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 a process whereby, to forecast its value at any given time, sAA values one and two hr prior to that time (sAA j,t = f(SAAj,t-1, SAAj,t-2) are most predictive, thus indicating that sAA has its own 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 considerable implications for physiological and biobehavioural research involving sAA measurements and other stress-related biomarkers.

Item Details

Item Type:Refereed Article
Research Division:Psychology and Cognitive Sciences
Research Group:Psychology
Research Field:Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology)
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Psychology and Cognitive Sciences
UTAS Author:Canales, J (Professor Juan Canales)
ID Code:134172
Year Published:2019
Deposited By:Psychology
Deposited On:2019-08-01
Last Modified:2019-09-05
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