Acute mountain sickness.

Acute mountain sickness (AMS) is a clinical syndrome occurring in otherwise healthy normal individuals who ascend rapidly to high altitude. Symptoms develop over a period ofa few hours or days. The usual symptoms include headache, anorexia, nausea, vomiting, lethargy, unsteadiness of gait, undue dyspnoea on moderate exertion and interrupted sleep. AMS is unrelated to physical fitness, sex or age except that young children over two years of age are unduly susceptible. One of the striking features ofAMS is the wide variation in individual susceptibility which is to some extent consistent. Some subjects never experience symptoms at any altitude while others have repeated attacks on ascending to quite modest altitudes. Rapid ascent to altitudes of 2500 to 3000m will produce symptoms in some subjects while after ascent over 23 days to 5000m most subjects will be affected, some to a marked degree. In general, the more rapid the ascent, the higher the altitude reached and the greater the physical exertion involved, the more severe AMS will be. Ifthe subjects stay at the altitude reached there is a tendency for acclimatization to occur and symptoms to remit over 1-7 days

Bayesian Estimation and Comparison of Idiographic Network Models

Idiographic network models are estimated on time-series data of a single individual and allow researchers to investigate person-specific associations between multiple variables over time. The most common approach for fitting graphical vector autoregressive (GVAR) models uses LASSO regularization to estimate a contemporaneous and a temporal network. However, estimation of idiographic networks can be unstable in relatively small data sets typical for psychological research. This bears the risk of misinterpreting differences in estimated networks as spurious heterogeneity between individuals. As a remedy, we evaluate the performance of a Bayesian alternative for fitting GVAR models that allows for regularization of parameters while accounting for estimation uncertainty. We also develop a novel test, implemented in the tsnet package in R, which assesses whether differences between estimated networks are reliable based on matrix norms. We first compare Bayesian and LASSO approaches across a range of conditions in a simulation study. Overall, LASSO estimation performs well, while a Bayesian GVAR without edge selection may perform better when the true network is dense. In an additional simulation study, the novel test is conservative and shows good false-positive rates. Finally, we apply Bayesian estimation and testing in an empirical example using daily data on clinical symptoms for 40 individuals. We additionally provide functionality to estimate Bayesian GVAR models in Stan within tsnet. Overall, Bayesian GVAR modelling facilitates the assessment of estimation uncertainty which is important for studying inter-individual differences of intra-individual dynamics. In doing so, the novel test serves as a safeguard against premature conclusions of heterogeneity

Bayesian Estimation and Comparison of Idiographic Network Models

Contains supplementary materials and code for the preprint "Bayesian Estimation and Comparison of Idiographic Network Models" (Siepe & Heck, 2023). Both Preprint and Supplement can be found in the folder "manuscript", where "v2" contains the updated version of the preprint and supplement after a first round of revisions. During the revision, Matthias Kloft was added as a co-author

Simulation Studies for Methodological Research in Psychology

Our overall goal is to assess the design and reporting standards of simulation studies in methodological psychology journals. Following a previous study by Morris et al. (2019), we will review aims, data-generating processes, estimands, methods, and performance measures (“ADEMP”) as well as the reporting of results. Building on the literature review, we will design a pre registration template for simulation studies. We will then showcase this template in a small simulation study of our own

Associations between ecological momentary assessment and passive sensor data in a large student sample

Ecological momentary assessment (EMA) increases ecological validity but can be burdensome. To reduce this burden and to better understand psychological constructs in daily life, a growing chorus of voices has called for augmenting or replacing EMA data with data passively collected from wearable devices. It is thus critical to investigate the quality of wearable data and its overlap with typical self-report measures. Here we compared results from passive sensing and EMA data from the WARN-D project in a large sample of 781 students. For 3 months, participants wore a Garmin VivoSmart 4 watch and answered EMA surveys (up to 352 measurement points). We investigated whether and to what extent passive sensor metrics were concurrently associated with different self-report measures purportedly measuring the same constructs. We focused on stress, tiredness, and sleep, all of which are relevant to mental health and can arguably be assessed with self-report and physiological measures. We used longitudinal mixed-effects models to estimate average momentary associations and their inter-individual heterogeneity. Self-report and wearable measures of sleep-related variables showed the strongest associations, whereas measures of stress showed a lack of overlap for most individuals. These findings suggest that wearable data and their corresponding self-report measures may not necessarily measure similar constructs. We provide several explanations for this result, including semantic differences and measurement issues, and offer insights and ways forward for research designs combining wearable and self-report data

03_Item Performance Paper

Contains supplementary materials for the preprint Understanding EMA Data: A Tutorial on Exploring Item Performance in Ecological Momentary Assessment Data (Siepe et al, 2024), https://doi.org/10.31234/osf.io/dvj8g

Simulation Studies for Methodological Research in Psychology: A Standardized Template for Planning, Preregistration, and Reporting

Simulation studies are widely used for evaluating the performance of statistical methods in psychology. However, the quality of simulation studies can vary widely in terms of their design, execution, and reporting. In order to assess the quality of typical simulation studies in psychology, we reviewed 321 articles published in Psychological Methods, Behavioral Research Methods, and Multivariate Behavioral Research in 2021 and 2022, among which 100/321 = 31.2% report a simulation study. We find that many articles do not provide complete and transparent information about key aspects of the study, such as justifications for the number of simulation repetitions, Monte Carlo uncertainty estimates, or code and data to reproduce the simulation studies. To address this problem, we provide a summary of the ADEMP (Aims, Data-generating mechanism, Estimands and other targets, Methods, Performance measures) design and reporting framework from Morris, White, and Crowther (2019) adapted to simulation studies in psychology. Based on this framework, we provide ADEMP-PreReg, a step-by-step template for researchers to use when designing, potentially preregistering, and reporting their simulation studies. We give formulae for estimating common performance measures, their Monte Carlo standard errors, and for calculating the number of simulation repetitions to achieve a desired Monte Carlo standard error. Finally, we give a detailed tutorial on how to apply the ADEMP framework in practice using an example simulation study on the evaluation of methods for the analysis of pre–post measurement experiments

Stop Being So Rigid: The Interplay of Psychological Flexibility and Cognitive-Affective Processes in the Daily Lives of Young Adults

https://doi.org/10.21203/rs.3.rs-3742788/v

Exploring the predictive value of different affect dynamics for psychological treatment outcome

In psychotherapy research, many studies have focussed on generating expected treatment response (ETR, Howard et al., 1996) curves at the onset of treatment to identify patients at risk of treatment failure (e.g., Delgadillo et al., 2016; Lutz et al., 2006; Lutz et al., 2019). This line of research resulted in establishing predictive models for ETR based on cross-sectional data, including patient characteristics like problem chronicity, previous treatment, treatment expectations, and global assessment of functioning (Lutz et al., 1999). Notably, initial impairment has consistently emerged as a robust and reliable predictor of treatment outcomes (Beutler et al., 2018; Lutz et al., 2018; Zimmerman et al., 2017). In recent years, the field has sought to advance the accuracy of data assessment and analysis to refine prediction models. To improve data quality, researchers have explored the implementation of Ecological Momentary Assessment (EMA; Stone & Shiffman, 1994). This method aims to mitigate retrospective bias, enhance ecological validity, and capture dynamic aspects of patient experiences (e.g., Hamaker & Wichers, 2017; Shiffman et al., 2008; Trull & Ebner-Priemer, 2013). Affect dynamics and their interplay, in particular, have been suggested as valuable predictors for treatment outcomes, with recent pilot studies demonstrating an incremental explanation of variance (Hehlmann et al., 2024; Lutz et al., 2018). However, the methodological approaches employed to capture affect dynamics demonstrate heterogeneity across studies, with each emphasizing distinct dynamic aspects. A prior study, Dejonckheere and colleagues (2019) investigated multiple approaches for the analysis of affect dynamics,and concluded that most of the indicators derived from these approaches had limited added value over the mean level of affect for predicting psychological well-being. Nonetheless, it remains uncertain whether any of these analytic indicators, including the mean level, have the potential to enhance predictive value beyond the initial impairment. To this end, our study aims to contribute to the existing literature by adopting an approach similar to that of Dejonckheere's study. We plan to apply a broad spectrum of analytic approaches on intensive longitudinal affect data, to examine the additional value the derived indicators may offer in predicting treatment outcomes. Through this examination, we aim to refine and advance our understanding of predictive indicators for psychotherapeutic outcomes

Open Science Days Marburg 2023

Materials Open Science Days Marburg 202