scholarly journals Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT

2021 ◽  
Vol 3 ◽  
Author(s):  
Tim Kaiser ◽  
Björn Butter ◽  
Samuel Arzt ◽  
Björn Pannicke ◽  
Julia Reichenberger ◽  
...  
Keyword(s):  
Sensor Data ◽  
Food Craving ◽  
Passive Sensing ◽  
Temporal Features ◽  
Psychometric Data ◽  
Time Lagged ◽  
Feature Dimension ◽  
Ecological Momentary ◽  
Prediction Approach ◽  
Momentary Assessment

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from 2 weeks of ecological momentary assessment (EMA) questionnaires on stress and emotions (active EMA, aEMA) alongside temporal features and smartphone sensor data (passive EMA, pEMA) are able to predict FCs ~2.5 h into the future in N = 46 individuals. A logistic prediction approach with feature dimension reduction via Best Item Scale that is Cross-Validated, Weighted, Informative and Transparent (BISCWIT) was performed. While overall prediction accuracy was acceptable, passive sensing data alone was equally predictive to psychometric data. The frequency of which single predictors were considered for a model was rather balanced, indicating that aEMA and pEMA models were fully idiosyncratic.

scholarly journals Time-Lagged Prediction of Food Craving with Elastic Net Regression and BISCWIT: Prediction Performance of Qualitative Distinct Predictor Types

2020 ◽  
Author(s):  
Tim Kaiser ◽  
Björn Butter
Keyword(s):  
Binary Classification ◽  
Elastic Net ◽  
Sensor Data ◽  
Food Craving ◽  
Food Cravings ◽  
Passive Sensing ◽  
Psychometric Data ◽  
Time Lagged ◽  
Ecological Momentary

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from two weeks of Ecological Momentary Assessment questionnaires on stress and emotions alongside smartphone sensor data would be able to predict food cravings approximately 2.5 hours into the future in N = 46 individuals. We compared two prediction approaches (logistic BISCWIT and Elastic Net Regression (ENR). While overall prediction accuracy was low, passive sensing data alone was superior to predictions based on psychometric data and BISCWIT was superior to ENR for binary classification of FC peaks.

scholarly journals Food craving in daily life: comparison of overweight and normal‐weight participants with ecological momentary assessment

2019 ◽  
Vol 32 (6) ◽  
pp. 765-774 ◽  
Author(s):  
A. Roefs ◽  
B. Boh ◽  
G. Spanakis ◽  
C. Nederkoorn ◽  
L. H. J. M. Lemmens ◽  
...  
Keyword(s):  
Ecological Momentary Assessment ◽  
Daily Life ◽  
Normal Weight ◽  
Food Craving ◽  
Ecological Momentary ◽  
Momentary Assessment

Perceived momentary willpower fluctuates with affect and predicts subsequent distress intolerance

2020 ◽  
Author(s):  
Jennifer Veilleux ◽  
Kayla D Skinner ◽  
Danielle Baker ◽  
Kaitlyn Chamberlain
Keyword(s):  
Negative Affect ◽  
Positive Affect ◽  
Externalizing Problems ◽  
Self Control ◽  
Distress Intolerance ◽  
Time Lagged ◽  
Personality Features ◽  
Ecological Momentary ◽  
Borderline Personality Features ◽  
Momentary Assessment

Although willpower—a common synonym for self-control—is typically construed as an individual difference, people may experience subjective shifts in their momentary willpower over time and based on context, where context includes affect. People who struggle with self-control (i.e., those with externalizing problems such as substance use, difficulties with eating, and those with borderline personality features) may be particularly vulnerable to willpower fluctuations. In three studies with four different sample groups (college students without borderline features: n = 49; borderline features group: n = 50; current smokers: n = 61; chronic dieters: n = 92), participants completed one week of ecological momentary assessment (EMA) where momentary willpower, positive affect, negative affect, tiredness, and distress intolerance were assessed randomly 7 times per day. Smokers and dieters provided additional reports about states prior to smoking and eating. Results revealed that higher momentary willpower was associated with lower negative affect and greater positive affect. Smokers and dieters reported lower willpower immediately before smoking and eating, respectively, and reported lower willpower alongside heightened craving uncontrollability. Lower momentary willpower also was associated with higher tiredness and concurrent distress intolerance. Finally, lower willpower predicted subsequent distress intolerance via time-lagged analyses for the psychopathology groups (borderline features, smokers, and dieters) only. Overall, this study suggests that perceptions of willpower are indicators of self-control self-efficacy, particularly for those who struggle with externalizing problems.

scholarly journals Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3572 ◽  
Author(s):  
Nicholas C. Jacobson ◽  
Yeon Joo Chung
Keyword(s):  
Depressed Mood ◽  
Wearable Sensors ◽  
Smartphone Application ◽  
Sensor Data ◽  
Just In Time ◽  
Modeling Framework ◽  
Daily Lives ◽  
Passive Sensing ◽  
Out Of Sample ◽  
Ecological Momentary

Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life.

Initially Negative Affect Predicts Lower Satisfaction With Future Social Contact: A Time-Lagged Analysis Using Ecological Momentary Assessment

2020 ◽  
Author(s):  
Huiying Liu ◽  
Boye Fang ◽  
Yuekang Li ◽  
Vivian W Q Lou
Keyword(s):  
Negative Affect ◽  
Ecological Momentary Assessment ◽  
Social Contact ◽  
Well Being ◽  
Just In Time ◽  
Social Situations ◽  
High Arousal ◽  
Time Lagged ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract Objectives Prior research has linked subjective features of social situations with short-term changes in affect (e.g., across days, hours), but little is known about the directionality of such links. Our study examined the concurrent and lead–lag relationships between social contact satisfaction and affect in the flow of daily life. Method Using ecological momentary assessment (EMA), wherein 78 late-middle-aged and older adults reported on 2,739 social contacts (average 5.02 per day, SD = 2.95) across seven consecutive days, we examined how the level of social contact satisfaction was concurrently and prospectively associated with affect (high-arousal and low-arousal positive affect [PA], high-arousal and low-arousal negative affect [NA]). Results Higher contact satisfaction was concurrently associated with more high- and low-arousal PA and less high- and low-arousal NA. The influence of contact satisfaction remains for predicting greater low-arousal PA (quietness, calmness) during the next social contact. NA (either high- or low-arousal) predicted lower satisfaction during the next social contact, but such sustainable influence was not observed for PA. Discussion The study reveals a cycle in which elevated NA may trigger unsatisfactory social contact, which subsequently predicted less low-arousal PA such as quietness and calmness. Our study provided a more nuanced and differentiated picture of the temporal sequencing of everyday social contact and momentary affect. Practitioners may gain insights from our study into the development of just-in-time adaptive interventions that aim for the betterment of affective well-being in old age.

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.

Modeling Intraindividual Variability in Three-Level Multilevel Models

Methodology ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 95-108 ◽  
Author(s):  
Steffen Nestler ◽  
Katharina Geukes ◽  
Mitja D. Back
Keyword(s):  
Multilevel Model ◽  
Multilevel Models ◽  
Mixed Effects ◽  
Repeated Measurements ◽  
Scale Model ◽  
Suggested Approach ◽  
Psychological Study ◽  
Level Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-level data. Furthermore, we show how the suggested model can be estimated with Bayesian software, and we present the results of a small simulation study that was conducted to investigate the statistical properties of the suggested approach. Finally, we illustrate the approach by presenting an example from a psychological study that employed ecological momentary assessment.

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