scholarly journals Using experience sampling methods/ecological momentary assessment (ESM/EMA) in clinical assessment and clinical research: Introduction to the special section.

2009 ◽  
Vol 21 (4) ◽  
pp. 457-462 ◽  
Author(s):  
Timothy J. Trull ◽  
Ulrich W. Ebner-Priemer
Keyword(s):  
Clinical Research ◽  
Clinical Assessment ◽  
Ecological Momentary Assessment ◽  
Sampling Methods ◽  
Special Section ◽  
Experience Sampling ◽  
Ecological Momentary ◽  
Momentary Assessment ◽  
Experience Sampling Methods

scholarly journals Making the black box transparent: A template and tutorial for (pre-)registration of studies using Experience Sampling Methods (ESM)

2019 ◽  
Author(s):  
Olivia J Kirtley ◽  
Ginette Lafit ◽  
Robin Achterhof ◽  
Anu Pauliina Hiekkaranta ◽  
Inez Myin-Germeys
Keyword(s):  
Degrees Of Freedom ◽  
Sampling Method ◽  
Experience Sampling ◽  
Open Science ◽  
Science Practices ◽  
Methodological Rigor ◽  
Assessment Techniques ◽  
Ecological Momentary ◽  
Momentary Assessment ◽  
Experience Sampling Methods

A growing interest in understanding complex and dynamic psychological processes as they occur in everyday life has led to an increase in studies using Ambulatory Assessment techniques, including the Experience Sampling Method (ESM) and Ecological Momentary Assessment (EMA). There are, however, numerous “forking paths” and researcher degrees of freedom, even beyond those typically encountered with other research methodologies. Whilst a number of researchers working with ESM techniques are actively engaged in efforts to increase the methodological rigor and transparency of such research, currently, there is little routine implementation of open science practices in ESM research. In the current paper, we discuss the ways in which ESM research is especially vulnerable to threats to transparency, reproducibility and replicability. We propose that greater use of (pre-)registration, a cornerstone of open science, may address some of these threats to the transparency of ESM research. (Pre-)registration of ESM research is not without challenges, including model selection, accounting for potential model convergence issues and the use of pre-existing datasets. As these may prove to be significant barriers to (pre-)registration for ESM researchers, we also discuss ways of overcoming these challenges and of documenting them in a (pre-)registration. A further challenge is that current general templates do not adequately capture the unique features of ESM. Here we present a (pre-)registration template for ESM research, adapted from the original Pre-Registration Challenge (Mellor et al., 2019) and pre-registration of pre-existing data (van den Akker et al., 2020) templates, and provide examples of how to complete this.

scholarly journals MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data

2020 ◽  
Vol 52 (4) ◽  
pp. 1403-1427 ◽  
Author(s):  
Eldin Dzubur ◽  
Aditya Ponnada ◽  
Rachel Nordgren ◽  
Chih-Hsiang Yang ◽  
Stephen Intille ◽  
...  
Keyword(s):  
Random Effects ◽  
Ecological Momentary Assessment ◽  
Sampling Methods ◽  
Time Varying ◽  
Intensive Longitudinal Data ◽  
Subject Specific ◽  
The Subject ◽  
Ecological Momentary ◽  
Intensive Sampling ◽  
Momentary Assessment

AbstractThe use of intensive sampling methods, such as ecological momentary assessment (EMA), is increasingly prominent in medical research. However, inferences from such data are often limited to the subject-specific mean of the outcome and between-subject variance (i.e., random intercept), despite the capability to examine within-subject variance (i.e., random scale) and associations between covariates and subject-specific mean (i.e., random slope). MixWILD (Mixed model analysis With Intensive Longitudinal Data) is statistical software that tests the effects of subject-level parameters (variance and slope) of time-varying variables, specifically in the context of studies using intensive sampling methods, such as ecological momentary assessment. MixWILD combines estimation of a stage 1 mixed-effects location-scale (MELS) model, including estimation of the subject-specific random effects, with a subsequent stage 2 linear or binary/ordinal logistic regression in which values sampled from each subject’s random effect distributions can be used as regressors (and then the results are aggregated across replications). Computations within MixWILD were written in FORTRAN and use maximum likelihood estimation, utilizing both the expectation-maximization (EM) algorithm and a Newton–Raphson solution. The mean and variance of each individual’s random effects used in the sampling are estimated using empirical Bayes equations. This manuscript details the underlying procedures and provides examples illustrating standalone usage and features of MixWILD and its GUI. MixWILD is generalizable to a variety of data collection strategies (i.e., EMA, sensors) as a robust and reproducible method to test predictors of variability in level 1 outcomes and the associations between subject-level parameters (variances and slopes) and level 2 outcomes.

scholarly journals Making the Black Box Transparent: A Template and Tutorial for Registration of Studies Using Experience-Sampling Methods

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592092468
Author(s):  
Olivia J. Kirtley ◽  
Ginette Lafit ◽  
Robin Achterhof ◽  
Anu P. Hiekkaranta ◽  
Inez Myin-Germeys
Keyword(s):  
Degrees Of Freedom ◽  
Experience Sampling ◽  
Open Science ◽  
Data Sets ◽  
Science Practices ◽  
Methodological Rigor ◽  
Assessment Techniques ◽  
Ecological Momentary ◽  
Momentary Assessment ◽  
Experience Sampling Methods

A growing interest in understanding complex and dynamic psychological processes as they occur in everyday life has led to an increase in studies using ambulatory assessment techniques, including the experience-sampling method (ESM) and ecological momentary assessment. These methods, however, tend to involve numerous forking paths and researcher degrees of freedom, even beyond those typically encountered with other research methodologies. Although a number of researchers working with ESM techniques are actively engaged in efforts to increase the methodological rigor and transparency of research that uses them, currently there is little routine implementation of open-science practices in ESM research. In this article, we discuss the ways in which ESM research is especially vulnerable to threats to transparency, reproducibility, and replicability. We propose that greater use of study registration, a cornerstone of open science, may address some of these threats to the transparency of ESM research. Registration of ESM research is not without challenges, including model selection, accounting for potential model-convergence issues, and the use of preexisting data sets. As these may prove to be significant barriers for ESM researchers, we also discuss ways of overcoming these challenges and of documenting them in a registration. A further challenge is that current general preregistration templates do not adequately capture the unique features of ESM. We present a registration template for ESM research and also discuss registration of studies using preexisting data.

Emotionsregulation bei Depression

2013 ◽  
Vol 64 (4) ◽  
pp. 235-243 ◽  
Author(s):  
Sven Barnow ◽  
Maren Aldinger ◽  
Ines Ulrich ◽  
Malte Stopsack
Keyword(s):  
Ecological Momentary Assessment ◽  
Ecological Momentary ◽  
Momentary Assessment

Die Anzahl der Studien, die sich mit dem Zusammenhang zwischen Emotionsregulation (ER) und depressiven Störungen befassen, steigt. In diesem Review werden Studien zusammengefasst und metaanalytisch ausgewertet, die den Zusammenhang zwischen ER und Depression mittels Fragebögen bzw. Ecological Momentary Assessment (EMA) erfassen. Dabei zeigt sich ein ER-Profil welches durch die vermehrte Nutzung von Rumination, Suppression und Vermeidung bei gleichzeitig seltenerem Einsatz von Neubewertung und Problemlösen gekennzeichnet ist. Mit mittleren bis großen Effekten, ist der Zusammenhang zwischen Depression und maladaptiven Strategien besser belegt als bei den adaptiven Formen, wo die Effekte eher moderat ausfielen. EMA-Messungen bestätigen dieses Profil. Da EMA-Studien neben der Häufigkeit des Strategieeinsatzes auch die Erfassung anderer ER-Parameter wie Effektivität und Flexibilität ermöglichen, sollten solche Designs in der ER-Forschung zukünftig vermehrt Einsatz finden.

Using Personal Cell Phones for Ecological Momentary Assessment

2013 ◽  
Vol 18 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Emmanuel Kuntsche ◽  
Florian Labhart
Keyword(s):  
Data Collection ◽  
Ecological Momentary Assessment ◽  
Communication Systems ◽  
Cell Phones ◽  
Personal Digital Assistants ◽  
Natural Environments ◽  
Mobility Patterns ◽  
Standard Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Ecological Momentary Assessment (EMA) is a way of collecting data in people’s natural environments in real time and has become very popular in social and health sciences. The emergence of personal digital assistants has led to more complex and sophisticated EMA protocols but has also highlighted some important drawbacks. Modern cell phones combine the functionalities of advanced communication systems with those of a handheld computer and offer various additional features to capture and record sound, pictures, locations, and movements. Moreover, most people own a cell phone, are familiar with the different functions, and always carry it with them. This paper describes ways in which cell phones have been used for data collection purposes in the field of social sciences. This includes automated data capture techniques, for example, geolocation for the study of mobility patterns and the use of external sensors for remote health-monitoring research. The paper also describes cell phones as efficient and user-friendly tools for prompt manual data collection, that is, by asking participants to produce or to provide data. This can either be done by means of dedicated applications or by simply using the web browser. We conclude that cell phones offer a variety of advantages and have a great deal of potential for innovative research designs, suggesting they will be among the standard data collection devices for EMA in the coming years.

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