Hye Won Suk

Objectives: Sleep disturbances are associated with both the onset and progression of depressive disorders. It is important to capture day-to-day variability in sleep patterns; irregular sleep is associated with depressive symptoms. We used sleep efficiency, measured with wearable devices, as an objective indicator of daily sleep variability.Materials and methods: The total sample consists of 100 undergraduate and graduate students, 60% of whom were female. All were divided into three groups (with major depressive disorder, mild depressive symptoms, and controls). Self-report questionnaires were completed at the beginning of the experiment, and sleep efficiency data were collected daily for 2 weeks using wearable devices. We explored whether the mean value of sleep efficiency, and its variability, predicted the severity of depression using dynamic structural equation modeling.Results: More marked daily variability in sleep efficiency significantly predicted levels of depression and anxiety, as did the average person-level covariates (longer time in bed, poorer quality of life, lower extraversion, and higher neuroticism).Conclusion: Large swings in day-to-day sleep efficiency and certain clinical characteristics might be associated with depression severity in young adults.

Day-of-week (DOW) effects such as "blue Monday," "Thank God it's Friday" (TGIF), and weekend effects have mostly been investigated using a cross-sectional approach with Western samples and focusing on hedonic aspects of well-being. Using large-scale data (N = 859,749) containing multiple observations per person collected from Koreans, we examined various patterns of DOW effects on comprehensive measures of well-being. Unlike previous studies, we examined DOW effects at the within-person level. Blue Monday and TGIF effects were evident across affective and evaluative/cognitive aspects of well-being, and the Thursday nadir phenomenon was also found. The DOW effects were consistent for the affective well-being, whereas no weekend effects were observed for the cognitive aspects, including life satisfaction and meaning in life. The DOW effects were moderated by gender and age groups, with younger people tending to experience more salient DOW effects and women identifying fewer benefits from DOW effects. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

This didactic article aims to provide a gentle introduction to penalized splines as a way of estimating nonlinear growth curves in which many observations are collected over time on a single or multiple individuals. We begin by presenting piecewise linear models in which the time domain of the data is divided into consecutive phases and a separate linear regression line is fitted in each phase. Linear splines add the feature that the regression lines fitted in adjacent phases are always joined at the boundary so there is no discontinuity in level between phases. Splines are highly flexible raising the fundamental tradeoff between model fit and smoothness of the curve. Penalized spline models address this tradeoff by introducing a penalty term to achieve balance between fit and smoothness. The linear mixed-effects model, familiar from multilevel analysis, is introduced as a method for estimating penalized spline models. Higher order spline models using quadratic or cubic functions which further enhance a smooth fit are introduced. Technical issues in estimation, hypothesis testing, and constructing confidence intervals for higher order penalized spline models are considered. We then use data from the Early Childhood Longitudinal Study to illustrate each step in fitting a higher order penalized spline model, and to illustrate hypothesis testing, the construction of confidence intervals, and the comparison of the functions in 2 groups (boys and girls). Extensive graphical illustrations are provided throughout. Annotated computer scripts using the R package nlme are provided in online supplemental materials. Translational Abstract Methods such as daily diaries and technological advances such as wearable monitoring devices make it possible to repeatedly measure people during their daily lives. These advances produce rich longitudinal data sets that offer the possibility of providing a close representation of the trajectory of change over time. Traditional approaches such as regression with polynomial terms (e.g., X-2, X-3) may not be flexible enough to represent a complex pattern of change over time. Alternatives such as spline models that divide the data into adjacent time segments and fit a different polynomial function in each segment may be too flexible, producing a highly fluctuating trajectory that is too rough. In this didactic presentation we introduce penalized spline models that produce nonlinear trajectories that can balance the 2 goals of having a trajectory that fits complex patterns of change yet maintains a relatively smooth form. We show step-by-step the procedure of fitting penalized splines to represent the growth trajectory of a single individual or the mean trajectory of change in a group of individuals. We show how to perform hypothesis tests, construct confidence intervals, and compare the growth trajectories in 2 groups. We provide extensive graphical illustrations of each step of the procedure using data on the development of reading ability in children from kindergarten through 8th grade. We provide extensive annotated R computer scripts in online supplemental materials permitting researchers to apply these approaches to their own longitudinal data sets.

Park JE, et al. J Korean Med Sci. 2019 Oct;34(44):e293. https://doi.org/10.3346/jkms.2019.34.e293

The purpose of this study was to utilize thermal imaging and the Concealed Information Test to detect deception in participants who committed a mock crime. A functional analysis using a functional ANOVA and a functional discriminant analysis was conducted to decrease the variation in the physiological data collected through the thermal imaging camera. Participants chose between a non-crime mission (Innocent Condition: IC), or a mock crime (Guilty Condition: GC) of stealing a wallet in a computer lab. Temperature in the periorbital region of the face was measured while questioning participants regarding mock crime details. Results revealed that the GC showed significantly higher temperatures when responding to crime relevant items compared to irrelevant items, while the IC did not. The functional ANOVA supported the initial results that facial temperatures of the GC elevated when responding to crime relevant items, demonstrating an interaction between group (guilty/innocent) and relevance (relevant/irrelevant). The functional discriminant analysis revealed that answering crime relevant items can be used to discriminate guilty from innocent participants. These results suggest that measuring facial temperatures in the periorbital region while conducting the Concealed Information Test is able to differentiate the GC from the IC.