For better or for worse? Visualizing previous intensity levels improves emotion (dynamic) measurement in experience sampling.

It is a long known reality that humans have difficulty to accurately rate the absolute intensity of internal experiences, yet the predominant way experience sampling (ESM) researchers assess participants' momentary emotion levels is by means of absolute measurement scales. In a daily-life experiment (n = 178), we evaluate the efficacy of two alternative assessment methods that should solicit a simpler, relative emotional evaluation: (a) visualizing a relative anchor point on the absolute rating scale that depicts people's previous emotion rating and (b) phrasing emotion items in a relative way by asking for a comparison with earlier emotion levels, using a relative rating scale. Determining five quality criteria relevant for ESM, we conclude that a visual "Last" anchor significantly improves emotion measurement in daily life: (a) Theoretically, this method has the best perceived user experience, as people, for example, find it the easiest and most accurate way to rate their momentary emotions. Methodologically, this type of measurement generates ESM time series that (b) exhibit less measurement error, produce person-level emotion dynamic measures that are (c) often more stable, and in a few cases show stronger (d) univariate and (e) incremental relations with external criteria like neuroticism and borderline personality (e.g., emotional variability). In sum, we see value in the addition of a relative "Last" anchor to absolute measurement scales of future ESM studies on emotions, as it structures the ambiguous rating space and introduces more standardization within and between individuals. In contrast, using relatively phrased emotion items is not recommended. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

m-Path: an easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice.

In this paper, we present m-Path (www.m-Path.io), an online platform that provides an easy-to-use and highly tailorable framework for implementing smartphone-based ecological momentary assessment (EMA) and intervention (EMI) in both research and clinical practice in the context of blended care. Because real-time monitoring and intervention in people's everyday lives have unparalleled benefits compared to traditional data collection techniques (e.g., retrospective surveys or lab-based experiments), EMA and EMI have become popular in recent years. Although a surge in the use of these methods has led to a myriad of EMA and EMI applications, many existing platforms only focus on a single aspect of daily life data collection (e.g., assessment vs. intervention, active self-report vs. passive mobile sensing, research-dedicated vs. clinically-oriented tools). With m-Path, we aim to integrate all of these facets into a single platform, as it is exactly this all-in-one approach that fosters the clinical utility of accumulated scientific knowledge. To this end, we offer a comprehensive platform to set up complex and highly adjustable EMA and EMI designs with advanced functionalities, using an intuitive point-and click web interface that is accessible for researchers and clinicians with limited programming skills. We discuss the strengths of daily life data collection and intervention in general and m-Path in particular. We describe the regular workflow to set up an EMA or EMI design within the m-Path framework, and summarize both the basic functionalities and more advanced features of our software.

Psychological Resilience Factors and Their Association With Weekly Stressor Reactivity During the COVID-19 Outbreak in Europe: Prospective Longitudinal Study.

BACKGROUND: Cross-sectional relationships between psychosocial resilience factors (RFs) and resilience, operationalized as the outcome of low mental health reactivity to stressor exposure (low "stressor reactivity" [SR]), were reported during the first wave of the COVID-19 pandemic in 2020. OBJECTIVE: Extending these findings, we here examined prospective relationships and weekly dynamics between the same RFs and SR in a longitudinal sample during the aftermath of the first wave in several European countries. METHODS: Over 5 weeks of app-based assessments, participants reported weekly stressor exposure, mental health problems, RFs, and demographic data in 1 of 6 different languages. As (partly) preregistered, hypotheses were tested cross-sectionally at baseline (N=558), and longitudinally (n=200), using mixed effects models and mediation analyses. RESULTS: RFs at baseline, including positive appraisal style (PAS), optimism (OPT), general self-efficacy (GSE), perceived good stress recovery (REC), and perceived social support (PSS), were negatively associated with SR scores, not only cross-sectionally (baseline SR scores; all P<.001) but also prospectively (average SR scores across subsequent weeks; positive appraisal (PA), P=.008; OPT, P<.001; GSE, P=.01; REC, P<.001; and PSS, P=.002). In both associations, PAS mediated the effects of PSS on SR (cross-sectionally: 95% CI -0.064 to -0.013; prospectively: 95% CI -0.074 to -0.0008). In the analyses of weekly RF-SR dynamics, the RFs PA of stressors generally and specifically related to the COVID-19 pandemic, and GSE were negatively associated with SR in a contemporaneous fashion (PA, P<.001; PAC,P=.03; and GSE, P<.001), but not in a lagged fashion (PA, P=.36; PAC, P=.52; and GSE, P=.06). CONCLUSIONS: We identified psychological RFs that prospectively predict resilience and cofluctuate with weekly SR within individuals. These prospective results endorse that the previously reported RF-SR associations do not exclusively reflect mood congruency or other temporal bias effects. We further confirm the important role of PA in resilience.

Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach.

Contemporary emotion theories predict that how partners' emotions are coupled together across an interaction can inform on how well the relationship functions. However, few studies have compared how individual (i.e., mean, variability) and dyadic aspects of emotions (i.e., coupling) during interactions predict future relationship separation. In this exploratory study, we utilized machine learning methods to evaluate whether emotions during a positive and a negative interaction from 101 couples (N = 202 participants) predict relationship stability two years later (17 breakups). Although the negative interaction was not predictive, the positive was: Intra-individual variability of emotions as well as the coupling between partners' emotions predicted relationship separation. The present findings demonstrate that utilizing machine learning methods enables us to improve our theoretical understanding of complex patterns.

Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study.

BACKGROUND: The experience sampling methodology (ESM) has long been considered as the gold standard for gathering data in everyday life. In contrast, current smartphone technology enables us to acquire data that are much richer, more continuous, and unobtrusive than is possible via ESM. Although data obtained from smartphones, known as mobile sensing, can provide useful information, its stand-alone usefulness is limited when not combined with other sources of information such as data from ESM studies. Currently, there are few mobile apps available that allow researchers to combine the simultaneous collection of ESM and mobile sensing data. Furthermore, such apps focus mostly on passive data collection with only limited functionality for ESM data collection. OBJECTIVE: In this paper, we presented and evaluated the performance of m-Path Sense, a novel, full-fledged, and secure ESM platform with background mobile sensing capabilities. METHODS: To create an app with both ESM and mobile sensing capabilities, we combined m-Path, a versatile and user-friendly platform for ESM, with the Copenhagen Research Platform Mobile Sensing framework, a reactive cross-platform framework for digital phenotyping. We also developed an R package, named mpathsenser, which extracts raw data to an SQLite database and allows the user to link and inspect data from both sources. We conducted a 3-week pilot study in which we delivered ESM questionnaires while collecting mobile sensing data to evaluate the app's sampling reliability and perceived user experience. As m-Path is already widely used, the ease of use of the ESM system was not investigated. RESULTS: Data from m-Path Sense were submitted by 104 participants, totaling 69.51 GB (430.43 GB after decompression) or approximately 37.50 files or 31.10 MB per participant per day. After binning accelerometer and gyroscope data to 1 value per second using summary statistics, the entire SQLite database contained 84,299,462 observations and was 18.30 GB in size. The reliability of sampling frequency in the pilot study was satisfactory for most sensors, based on the absolute number of collected observations. However, the relative coverage rate-the ratio between the actual and expected number of measurements-was below its target value. This could mostly be ascribed to gaps in the data caused by the operating system pushing away apps running in the background, which is a well-known issue in mobile sensing. Finally, some participants reported mild battery drain, which was not considered problematic for the assessed participants' perceived user experience. CONCLUSIONS: To better study behavior in everyday life, we developed m-Path Sense, a fusion of both m-Path for ESM and Copenhagen Research Platform Mobile Sensing. Although reliable passive data collection with mobile phones remains challenging, it is a promising approach toward digital phenotyping when combined with ESM.

Ready for the worst? Negative affect in anticipation of a stressor does not protect against affective reactivity.

INTRODUCTION: Lay wisdom suggests feeling negative while awaiting an upcoming stressor-anticipatory negative affect-shields against the blow of the subsequent stressor. However, evidence is mixed, with different lines of research and theory indirectly suggesting that anticipatory negative affect is helpful, harmful, or has no effect on emotional outcomes. In two studies, we aimed to reconcile these competing views by examining the affective trajectory across hours, days, and months, separating affective reactivity and recovery. METHODS: In Study 1, first-year students (N = 101) completed 9 days of experience sampling (10 surveys/day) as they received their first-semester exam grades, and a follow-up survey 5 months later. In Study 2, participants (N = 73) completed 2 days of experience sampling (60 surveys/day) before and after a Trier Social Stress Test. We investigated the association between anticipatory negative affect and the subsequent affective trajectory, investigating (1) reactivity immediately after the stressor, (2) recovery across hours (Study 2) and days (Study 1), and (3) recovery after 5 months (Study 1). RESULTS: Across the two studies, feeling more negative in anticipation of a stressor was either associated with increased negative affective reactivity, or unassociated with affective outcomes. CONCLUSION: These results run counter to the idea that being affectively ready for the worst has psychological benefits, suggesting that instead, anticipatory negative affect can come with affective costs.

Practitioner and researcher perspectives on the utility of ecological momentary assessment in mental health care: A survey study.

Background: Ecological momentary assessment (EMA) is a scientific self-monitoring method to capture individuals' daily life experiences. Early on, EMA has been suggested to have the potential to improve mental health care. However, it remains unclear if and how EMA should be implemented. This requires an in-depth investigation of how practitioners and researchers view the implementation of EMA. Objective: Explore the perspectives of mental health practitioners and EMA researchers on the utility of EMA for mental health care. Methods: Practitioners (n = 89; psychiatrists, psychologists, psychiatric nurses) and EMA researchers (n = 62) completed a survey about EMA in clinical practice. This survey addressed EMA goals for practitioner and patient, requirements regarding clinical use of EMA, and (dis)advantages of EMA compared to treatment-as-usual. t-Tests were used to determine agreement with each statement and whether practitioners' and researchers' views differed significantly. Linear regression was used to explore predictors of goals and preferences (e.g., EMA experience). Results: Practitioners and researchers considered EMA to be a useful clinical tool for diverse stages of care. They indicated EMA to be most useful for gaining insight into the context specificity of symptoms (55.0 %), whereas receiving alerts when symptoms increase was rated the least useful (11.3 %, alerts is in 95 % of bootstrap iterations between rank 8 and 10). Compared to treatment-as-usual, EMA was considered easier to use (M = 4.87, t = 5.30, p < .001) and interpret (M = 4.52, t = 3.61, p < .001), but also more burdensome for the patient (M = 4.48, t = 3.17, p < .001). Although participants preferred personalization of the EMA diary, they also suggested that EMA should cost practitioners and patients limited time. The preference for creating personalized EMA was related to the level of experience with EMA. Finally, they highlighted the need for practitioner training and patient full-time access to the EMA feedback. Conclusions: This survey study demonstrated that practitioners and researchers expect EMA to have added value for mental health care. Concrete recommendations for implementation of EMA are formulated. This may inform the development of specific clinical applications and user-friendly EMA software.

Assessing the reliability of single-item momentary affective measurements in experience sampling.

Emotion researchers that use experience sampling methods (ESM) study how emotions fluctuate in everyday life. To reach valid conclusions, confirming the reliability of momentary emotion measurements is essential. However, to minimize participant burden, ESM researchers often use single-item measures, preventing a reliability assessment of people's emotion ratings. Furthermore, because emotions constantly change, checking reliability via conventional test-retest procedures is impractical, for it is impossible to separate measurement error from meaningful emotional variability. Here, drawing from classical test theory (CTT), we propose two time-varying test-retest adaptations to evaluate the reliability of single-item (emotion) measures in ESM. Following Method 1, we randomly repeat one emotion item within the same momentary survey and evaluate the discrepancy between test and retest ratings to determine reliability. Following Method 2, we introduce a subsequent, shortly delayed retest survey and extrapolate the size of test-retest discrepancies to the hypothetical instance where no time between assessments would exist. First, in an analytical study, we establish the mathematical relation between observed test-retest discrepancies and measurement error variance for both methods, based on common assumptions in the CTT literature. Second, in two empirical studies, we apply both methods to real-life emotion time series and find that the size of error in people's emotion ratings corresponds with almost a 10th of the scale, comprising around 27% of the total variability in participants' affective responses. Consequently, disregarding measurement error in ESM is problematic, and we encourage researchers to include a test-retest procedure in their future studies when relying on single-item measures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study.

BACKGROUND: Sleep influences moods and mood disorders. Existing methods for tracking the quality of people's sleep are laborious and obtrusive. If a method were available that would allow effortless and unobtrusive tracking of sleep quality, it would mark a significant step toward obtaining sleep data for research and clinical applications. OBJECTIVE: Our goal was to evaluate the potential of mobile sensing data to obtain information about a person's sleep quality. For this purpose, we investigated to what extent various automatically gathered mobile sensing features are capable of predicting (1) subjective sleep quality (SSQ), (2) negative affect (NA), and (3) depression; these variables are associated with objective sleep quality. Through a multiverse analysis, we examined how the predictive quality varied as a function of the selected sensor, the extracted feature, various preprocessing options, and the statistical prediction model. METHODS: We used data from a 2-week trial where we collected mobile sensing and experience sampling data from an initial sample of 60 participants. After data cleaning and removing participants with poor compliance, we retained 50 participants. Mobile sensing data involved the accelerometer, charging status, light sensor, physical activity, screen activity, and Wi-Fi status. Instructions were given to participants to keep their smartphone charged and connected to Wi-Fi at night. We constructed 1 model for every combination of multiverse parameters to evaluate their effects on each of the outcome variables. We evaluated the statistical models by applying them to training, validation, and test sets to prevent overfitting. RESULTS: Most models (on either of the outcome variables) were not informative on the validation set (ie, predicted R2≤0). However, our best models achieved R2 values of 0.658, 0.779, and 0.074 for SSQ, NA, and depression, respectively on the training set and R2 values of 0.348, 0.103, and 0.025, respectively on the test set. CONCLUSIONS: The approach demonstrated in this paper has shown that different choices (eg, preprocessing choices, various statistical models, different features) lead to vastly different results that are bad and relatively good as well. Nevertheless, there were some promising results, particularly for SSQ, which warrant further research on this topic.

In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study.

BACKGROUND: Emotions and mood are important for overall well-being. Therefore, the search for continuous, effortless emotion prediction methods is an important field of study. Mobile sensing provides a promising tool and can capture one of the most telling signs of emotion: language. OBJECTIVE: The aim of this study is to examine the separate and combined predictive value of mobile-sensed language data sources for detecting both momentary emotional experience as well as global individual differences in emotional traits and depression. METHODS: In a 2-week experience sampling method study, we collected self-reported emotion ratings and voice recordings 10 times a day, continuous keyboard activity, and trait depression severity. We correlated state and trait emotions and depression and language, distinguishing between speech content (spoken words), speech form (voice acoustics), writing content (written words), and writing form (typing dynamics). We also investigated how well these features predicted state and trait emotions using cross-validation to select features and a hold-out set for validation. RESULTS: Overall, the reported emotions and mobile-sensed language demonstrated weak correlations. The most significant correlations were found between speech content and state emotions and between speech form and state emotions, ranging up to 0.25. Speech content provided the best predictions for state emotions. None of the trait emotion-language correlations remained significant after correction. Among the emotions studied, valence and happiness displayed the most significant correlations and the highest predictive performance. CONCLUSIONS: Although using mobile-sensed language as an emotion marker shows some promise, correlations and predictive R2 values are low.

The Statistical Specificity of Emotion Dynamics in Borderline Personality Disorder.

Persons with borderline personality disorder (BPD) experience heightened emotional instability. Different components underlie instability, and the relation between instability and well-being could be confounded by average emotionality and within-person standard deviation across emotional states, reflecting variability. Therefore, the goal was to examine which pattern of emotion dynamics parsimoniously captures the emotional trajectories of persons with BPD. Forty persons with BPD, 38 clinical controls in a major depressive episode, and 40 healthy controls rated the intensity of their emotions 10 times a day for 1 week. After correction for differences in average emotionality, persons with BPD showed heightened emotional instability compared to both control groups. When additionally correcting for emotional variability, the authors found that instability indices did not differ between groups anymore. This shows that persons with BPD differ from control groups in the magnitude of emotional deviations from the emotional baseline, and not necessarily in the degree of abruptness of these deviations.

Ambulatory assessment in psychopathology research: Current achievements and future ambitions.

Ambulatory assessment (AA) - a collection of methods that aim to track individuals in the realm of everyday life via repeated self-reports or passive mobile sensing - is well established in contemporary psychopathology research. Unravelling the dynamic signature of patients' symptoms and emotions over time and in their own personal ecology, AA methodology has improved our understanding of the real-time pathogenic processes that underlie mental ill-being. In this article, we evaluate the current strengths and shortcomings of AA in psychopathology research and spell out important ambitions for next-generation AA studies to consider. Regarding AA's current achievements, a selective review of recent AA studies underscores the ecological qualities of this method, its ability to bypass retrospective biases in self-report and the introduction of a within-person perspective. Regarding AA's future ambitions, we advocate for a stronger idiosyncratic focus, the incorporation of contextual information and more psychometric scrutiny.

The relation between positive and negative affect becomes more negative in response to personally relevant events.

Can we experience positive (PA) and negative affect (NA) separately (i.e., affective independence), or do these emotional states represent the mutually exclusive ends of a single bipolar continuum (i.e., affective bipolarity)? Building on previous emotion theories, we propose that the relation between PA and NA is not invariable, but rather fluctuates in response to changing situational demands. Specifically, we argue that our affective system shifts from relative independence to stronger bipolarity when we encounter events or situations that activate personally relevant concerns. We test this idea in an experience sampling study, in which we tracked the positive and negative emotional trajectories of 101 first-year university students who received their exam results, an event that potentially triggers a personally significant concern. Using multilevel piecewise regression, we show that running PA-NA correlations become increasingly more negative in the anticipation of results release, indicating stronger affective bipolarity, and ease back toward greater independence as time after this event passes. Furthermore, we show that this dynamic trajectory is particularly apparent for event-related PA and NA, and not affect in general, and that such shifts are partly a function of the importance people attribute to that event. We suggest that such flexible changes in the affect relation may function as an emotional compass by signaling personally relevant information, and create a motivational push to respond to these meaningful events in an appropriate manner. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Affective Ising Model: A computational account of human affect dynamics.

The human affect system is responsible for producing the positive and negative feelings that color and guide our lives. At the same time, when disrupted, its workings lie at the basis of the occurrence of mood disorder. Understanding the functioning and dynamics of the affect system is therefore crucial to understand the feelings that people experience on a daily basis, their dynamics across time, and how they can become dysregulated in mood disorder. In this paper, a nonlinear stochastic model for the dynamics of positive and negative affect is proposed called the Affective Ising Model (AIM). It incorporates principles of statistical mechanics, is inspired by neurophysiological and behavioral evidence about auto-excitation and mutual inhibition of the positive and negative affect dimensions, and is intended to better explain empirical phenomena such as skewness, multimodality, and non-linear relations of positive and negative affect. The AIM is applied to two large experience sampling studies on the occurrence of positive and negative affect in daily life in both normality and mood disorder. It is examined to what extent the model is able to reproduce the aforementioned non-Gaussian features observed in the data, using two sightly different continuous-time vector autoregressive (VAR) models as benchmarks. The predictive performance of the models is also compared by means of leave-one-out cross-validation. The results indicate that the AIM is better at reproducing non-Gaussian features while their performance is comparable for strictly Gaussian features. The predictive performance of the AIM is also shown to be better for the majority of the affect time series. The potential and limitations of the AIM as a computational model approximating the workings of the human affect system are discussed.

Prepaid parameter estimation without likelihoods.

In various fields, statistical models of interest are analytically intractable and inference is usually performed using a simulation-based method. However elegant these methods are, they are often painstakingly slow and convergence is difficult to assess. As a result, statistical inference is greatly hampered by computational constraints. However, for a given statistical model, different users, even with different data, are likely to perform similar computations. Computations done by one user are potentially useful for other users with different data sets. We propose a pooling of resources across researchers to capitalize on this. More specifically, we preemptively chart out the entire space of possible model outcomes in a prepaid database. Using advanced interpolation techniques, any individual estimation problem can now be solved on the spot. The prepaid method can easily accommodate different priors as well as constraints on the parameters. We created prepaid databases for three challenging models and demonstrate how they can be distributed through an online parameter estimation service. Our method outperforms state-of-the-art estimation techniques in both speed (with a 23,000 to 100,000-fold speed up) and accuracy, and is able to handle previously quasi inestimable models.

Complex affect dynamics add limited information to the prediction of psychological well-being.

Over the years, many studies have demonstrated a relation between emotion dynamics and psychological well-being1. Because our emotional life is inherently time-dynamic2-6, affective scientists argue that, next to how positive or negative we feel on average, patterns of emotional change are informative for mental health7-10. This growing interest initiated a surge in new affect dynamic measures, each claiming to capture a unique dynamical aspect of our emotional life, crucial for understanding well-being. Although this accumulation suggests scientific progress, researchers have not always evaluated (a) how different affect dynamic measures empirically interrelate and (b) what their added value is in the prediction of psychological well-being. Here, we address these questions by analysing affective time series data from 15 studies (n = 1,777). We show that (a) considerable interdependencies between measures exist, suggesting that single dynamics often do not convey unique information, and (b) dynamic measures have little added value over mean levels of positive and negative affect (and variance in these affective states) when predicting individual differences in three indicators of well-being (life satisfaction, depressive symptoms and borderline symptoms). Our findings indicate that conventional emotion research is currently unable to demonstrate independent relations between affect dynamics and psychological well-being.

VAR(1) based models do not always outpredict AR(1) models in typical psychological applications.

In psychology, modeling multivariate dynamical processes within a person is gaining ground. A popular model is the lag-one vector autoregressive or VAR(1) model and its variants, in which each variable is regressed on all variables (including itself) at the previous time point. Many parameters have to be estimated in the VAR(1) model, however. The question thus rises whether the VAR(1) model is not too complex and overfits the data. If the latter is the case, the estimated model will not properly predict new unseen data. As a consequence, it cannot be trusted that the estimated parameters adequately characterize the individual from which the data at hand were sampled. In this article, we evaluate for current psychological applications whether the VAR(1) model outpredicts simpler models, using cross-validation (CV) techniques to determine the predictive accuracy. As it is unclear whether one should use standard CV techniques (leave-one-out CV or K-fold CV) or variants that take time dependence into account (blocked CV, hv-block CV, or accumulated prediction errors), we first compare the relative performance of these five CV techniques in a simulation study. The simulation settings mimic the data characteristics of current psychological VAR(1) applications and show that blocked CV has the best performance in general. Subsequently, we use blocked CV to assess to what extent the VAR(1) models predict unseen data for three recent psychological applications. We show that the VAR(1) based models do not outperform the AR(1) based ones for the three presented psychological applications. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Sidelining the mean: The relative variability index as a generic mean-corrected variability measure for bounded variables.

Variability indices are a key measure of interest across diverse fields, in and outside psychology. A crucial problem for any research relying on variability measures however is that variability is severely confounded with the mean, especially when measurements are bounded, which is often the case in psychology (e.g., participants are asked "rate how happy you feel now between 0 and 100?"). While a number of solutions to this problem have been proposed, none of these are sufficient or generic. As a result, conclusions on the basis of research relying on variability measures may be unjustified. Here, we introduce a generic solution to this problem by proposing a relative variability index that is not confounded with the mean by taking into account the maximum possible variance given an observed mean. The proposed index is studied theoretically and we offer an analytical solution for the proposed index. Associated software tools (in R and MATLAB) have been developed to compute the relative index for measures of standard deviation, relative range, relative interquartile distance and relative root mean squared successive difference. In five data examples, we show how the relative variability index solves the problem of confound with the mean, and document how the use of the relative variability measure can lead to different conclusions, compared with when conventional variability measures are used. Among others, we show that the variability of negative emotions, a core feature of patients with borderline disorder, may be an effect solely driven by the mean of these negative emotions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).