The mediating role of mood in the relationship between perseverative cognition,sleep and subjective health complaints in music students

Objective: Subjective health complaints (SHC) are frequent in musicians. These complaints may be particularly distressing in this population because they are performance relevant. This paper aims at testing a model positing that (a) perseverative cognition (PC) predicts sleep duration/quality, (b) sleep duration/quality predicts SHC and (c) mood is a mediator of these associations.

Design: Participants were 72 music students (mean age (SD): 22.7 (3.0) years), and the assessment period consisted of seven consecutive days, with a solo performance on the fifth day.

Main outcome measures: Self-reported total sleep time (TST) and sleep quality were assessed 30?min after wake-up, and objective TST/sleep quality were assessed with an actigraphy watch. PC and mood were measured five times a day. Daily SHC were assessed at 9 p.m.

Results: PC did not significantly predict sleep duration/quality. Self-reported and objective TST and sleep quality were all significantly associated with SHC. Mood played a mediating role in each of these relationships with the exception of objective sleep quality.

Conclusion: The tested model on the association among PC, sleep and SHC and the mediating role of mood received partial support, highlighting the importance of sleep and mood in the emergence of SHC among university music students.     

Empirical Bayes Derivative Estimates

Abstract

A dynamic system is a set of interacting elements characterized by changes occurring over time. The estimation of derivatives is a mainstay for exploring dynamics of constructs, particularly when the dynamics are complicated or unknown. The presence of measurement error in many social science constructs frequently results in poor estimates of derivatives, as even modest proportions of measurement error can compound when estimating derivatives. Given the overlap in the specification of latent differential equation models and latent growth curve models, and the equivalence of latent growth curve models and mixed models under some conditions, derivatives could be estimated from estimates of random effects. This article proposes a new method for estimating derivatives based on calculating the Empirical Bayes estimates of derivatives from a mixed model. Two simulations compare four derivative estimation methods: Generalized Local Linear Approximation, Generalized Orthogonal Derivative Estimates, Functional Data Analysis, and the proposed Empirical Bayes Derivative Estimates. The simulations consider two data collection scenarios: short time series (≤10 observations) from many individuals or occasions, and long individual time series (25–500 observations). A substantive example visualizing the dynamics of intraindividual positive affect time series is also presented.