Correction: Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase

Rosel, Jesús; Maldonado, E.; Jara, Pilar; Machancoses, Francisco Herrero; Pallarés, Jacinto; Torrente, Pedro; Puchol, Sara; Canales, Juan J.

doi:10.1371/journal.pone.0225745

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“…To measure Distress memory, first the configuration of the data has been changed from individual per line to data-day per line ( Singer and Willett, 2003 ), and then the stabilized Distress variable has been delayed up to 14 days in each participant, with the precautions to be taken in the pooled time series 256 models ( Sayrs, 1989 ; Andreß et al, 2013 ; Rosel et al, 2019 ). Furthermore, when time series data are used, it is assumed that serial correlation exists, which means that a person’s mean Distress level of that day will affect their mean Distress level of the next day/s.…”

Section: Methodsmentioning

confidence: 99%

“…Then, if longitudinal data were analyzed with cross-sectional models, Kmenta (1971 , p. 283) demonstrated that the residuals will be autocorrelated, the parameters ( b 0 , b 1 ,…) are not biased, but the variances of the errors are underestimated. Therefore the variances and the standard errors of the parameters (that are in the denominator) also tend to be underestimated and, likewise, the values of the t , z , F , R 2 , and b 0 , b 1 … statistics are overestimated and not efficient, leading to type I errors (the assumption that a statistical effect exists, when in fact it does not) ( Gujarati and Porter, 2013 ; Rosel et al, 2019 ). In addition, if we omit the values of the lagged variable, and this variable is part of the explanatory model of behavior, the coefficients obtained are biased and inconsistent, so the inferences drawn no longer have a substantive meaning ( Gujarati and Porter, 2013 ; Draper and Smith, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal Effects of Distress and Its Management During COVID-19 Lockdown in Spain

et al. 2021

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Introduction: The COVID-19 pandemic that hit Spain during March 2020 forced the strict confinement of the population for 2 months. The objectives of this study were (a) to assess the magnitude and duration of the influence of confinement on people’s Distress, (b) to study the temporal sequence of stress, and (c) to show how different day-to-day activities and personal variables influence perceived Distress levels.Method: A daily registration was completed by 123 people, with ages ranging from 21 to 75 years old (X¯ = 43, SD = 10 years), of which there were 40 men (32%) and 83 females (68%). During 45 days of lockdown, from March 19th to May 3rd, participants were asked to respond to a socio-demographic survey and make daily records comprising the MASQ-D30 and some day-to-day behaviors. Pooled time series was applied to establish what effect time had on the dependent variable.Results: Distress has a 14-day autoregressive function and gender, physical activity, sexual activity, listening to music, and teleworking also influence Distress. It has been hypothesized that the intercept presents variability at level 2 (individual), but it has not been significant. Interactions between Gender—Telecommuting, and Gender—Physical Activity were observed. Approximately 66% of the variance of Distress was explained (R2 = 0.663).Discussion: At the beginning of the lockdown, the average levels of Distress were well above the levels of the end (z = 3.301). The individuals in the sample have followed a very similar process in the development of Distress. During the lockdown, the “memory” of Distress was 2 weeks. Our results indicate that levels of Distress depend on activities during lockdown. Interactions exist between gender and some behavioral variables that barely influence Distress in men but decrease Distress in women. The importance of routine maintenance and gender differences must be considered to propose future interventions during confinement.

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