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.     

Comparison of Common Amplitude Metrics in Event-Related Potential Analysis

Abstract

Waveform data resulting from time-intensive longitudinal designs require careful treatment. In particular, the statistical properties of summary metrics in this area are crucial. We draw on event-related potential (ERP) studies, a field with a relatively long history of collecting and analyzing such data, to illustrate our points. In particular, three summary measures for a component in the average ERP waveform feature prominently in the literature: the maximum (or peak amplitude), the average (or mean amplitude) and a combination (or adaptive mean). We discuss the methodological divide associated with these summary measures. Through both analytic work and simulation study, we explore the properties (e.g., Type I and Type II errors) of these competing metrics for assessing the amplitude of an ERP component across experimental conditions. The theoretical and simulation-based arguments in this article illustrate how design (e.g., number of trials per condition) and analytic (e.g., window location) choices affect the behavior of these amplitude summary measures in statistical tests and highlight the need for transparency in reporting the analytic steps taken. There is an increased need for analytic tools for waveform data. As new analytic methods are developed to address these time-intensive longitudinal data, careful treatment of the statistical properties of summary metrics used for null hypothesis testing is crucial.