Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase
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Other documents of the author: Rosel, Jesús F.; Jara Jiménez, Pilar; Machancoses, Francisco H.; Pallarés, Jacinto; Torrente, Pedro; Puchol, Sara; Canales, Juan J.
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comunitat-uji-handle2:10234/8034
comunitat-uji-handle3:10234/8637
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Title
Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylaseAuthor (s)
Date
2019Publisher
Public Library of ScienceISSN
1932-6203Bibliographic citation
Rosel JF, Jara P, Machancoses FH, Pallare´s J, Torrente P, Puchol S, et al. (2019) Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase. PLoS ONE 14(1): e0209475. https://doi.org/10.1371/journal. pone.0209475Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionAbstract
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 ... [+]
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. [-]
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