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In the past three decades, methods that go by the generic name of everyday-experience methods have matured from the status of promising innovations to standard, widely used tools. This term refers to a paradigm that examines social psychological theories and phenomena in the ebb and flow of everyday activity, as it is displayed in its natural context. This technique, which includes daily diary studies, experience sampling, and ecological momentary assessment, has become remarkably popular in the past two decades, so much so that all researchers must be familiar with its advantages and limitations. The current chapter aims to help budding researchers become familiar with this tool and its potential for expanding the validity, relevance, and usefulness of our research.
Ecological momentary assessment (EMA) involves repeated collection of real-time self-report data, often multiple times per day, nearly always delivered electronically by smartphone. While EMA has shown promise for researching internal states, behaviors, and experiences in multiple populations, concerns remain regarding its feasibility in samples with cognitive impairments, like those associated with chronic moderate-to-severe traumatic brain injury (TBI).
Methods:
This study examines adherence to a 7-week high-frequency (5x daily) EMA protocol in individuals with moderate-to-severe TBI, considering changes in response rate over time, as well as individual participant characteristics (memory function, education, injury severity, and age).
Results:
In the sample of 39 participants, the average overall response rate was 65% (range: 5%–100%). Linear mixed-effects modeling revealed a small but statistically significant linear decay in response rate over 7 weeks of participation. Individual trajectories were variable, as evidenced by the significant effect of random slope. A better response rate was positively associated with greater educational attainment and better episodic memory function (statistical trend), whereas the effects of age and injury severity were not significant.
Conclusions:
These findings shed light on the potential of EMA in TBI studies but underscore the need for tailored strategies to address individual barriers to adherence.
Individuals in a depressive episode and healthy controls exhibit robust differences on affect dynamics captured with ecological momentary assessment (EMA). However, few studies have explored affect dynamics in individuals in remission from depression, and results have been mixed.
Methods
A community sample of 18-year-olds (N = 345) completed diagnostic interviews and EMA probing emotions and low interest/motivation 5× daily for 2 weeks. Affect home base, variability, and inertia were compared across currently depressed, remitted, and never-depressed groups.
Results
Both depression groups had a higher negative affect (NA) and low interest/motivation home base, lower positive affect (PA) home base, greater variability of NA, PA, and low interest/motivation, and greater NA and low interest/motivation inertia than never-depressed participants. Additionally, the currently depressed group had a higher sad home base specifically, greater variability across most negative emotions and low interest/motivation, and greater low interest/motivation inertia than the remitted group. The currently depressed and remitted groups did not differ in anxious, upset, or PA home base, anxious or PA variability, and inertia of all negative emotions and PA.
Conclusions
Findings suggest that a number of abnormalities in emotion and reward functioning persist after a depressive episode resolves, however, the tendency to experience higher levels of sadness, greater range of a variety of negative emotions, and more variable and persistent low interest/motivation are exacerbated during depressive episodes. Conversely, greater intensity and persistence of some negative emotions (anxiety, upset) and blunted positive emotions appear to equally characterize depression in both the symptomatic and remitted state.
Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown.
Methods
Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events – the reveal of midterm exam grades – to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs.
Results
Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes – that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding.
Conclusions
Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.
This chapter begins an in-depth discussion of the proposed STRIVE-4 Model. It focuses on the S (scalar) and T (trait) of the model. To demonstrate that virtues can be captured in a scalar manner, It highlights studies that support the reliability and validity of constructs. Virtues such as courage, gratitude, and compassion have all been established as scalar constructs and have been related to a variety of expected well-being outcomes. The chapter further highlights empirical evidence showing that virtues cannot be subsumed by personality research or social desirability, and that informant reports further confirm researchers’ ability to capture scalar virtues. To highlight empirical work on virtues as traits, it discusses the value of intensive longitudinal studies. These studies demonstrate between-person variability and within-person consistency to support the hypothesis that virtues are traits. Finally, the chapter closes by discussing some challenges of virtue assessment, including Aristotle’s assertion of the golden mean and how to understand vice traits. Altogether, the evidence favors assessing virtues as scalar traits. It suggests it is time for researchers to advance virtue science with more sophisticated methods.
Childhood trauma (CT) may increase vulnerability to psychopathology through affective dysregulation (greater variability, autocorrelation, and instability of emotional symptoms). However, CT associations with dynamic affect fluctuations while considering differences in mean affect levels across CT status have been understudied.
Methods
346 adults (age = 49.25 ± 12.55, 67.0% female) from the Netherlands Study of Depression and Anxiety participated in ecological momentary assessment. Positive and negative affect (PA, NA) were measured five times per day for two weeks by electronic diaries. Retrospectively-reported CT included emotional neglect and emotional/physical/sexual abuse. Linear regressions determined associations between CT and affect fluctuations, controlling for age, sex, education, and mean affect levels.
Results
Compared to those without CT, individuals with CT reported significantly lower mean PA levels (Cohen's d = −0.620) and higher mean NA levels (d = 0.556) throughout the two weeks. CT was linked to significantly greater PA variability (d = 0.336), NA variability (d = 0.353), and NA autocorrelation (d = 0.308), with strongest effects for individuals reporting higher CT scores. However, these effects were entirely explained by differences in mean affect levels between the CT groups. Findings suggested consistency of results in adults with and without lifetime depressive/anxiety disorders and across CT types, with sexual abuse showing the smallest effects.
Conclusions
Individuals with CT show greater affective dysregulation during the two-week monitoring of emotional symptoms, likely due to their consistently lower PA and higher NA levels. It is essential to consider mean affect level when interpreting the impact of CT on affect dynamics.
Affective disturbances in schizophrenia and bipolar disorder may represent a transdiagnostic etiological process as well as a target of intervention. Hypotheses on similarities and differences in various parameters of affective dynamics (intensity, successive/acute changes, variability, and reactivity to stress) between the two disorders were tested.
Methods
Experience sampling method was used to assess dynamics of positive and negative affect, 10 times a day over 6 consecutive days. Patients with schizophrenia (n = 46) and patients with bipolar disorder (n = 46) were compared against age-matched healthy controls (n = 46).
Results
Compared to controls, the schizophrenia group had significantly more intense momentary negative affect, a lower likelihood of acute changes in positive affect, and reduced within-person variability of positive affect. The bipolar disorder group was not significantly different from either the schizophrenia group or the healthy control group on any affect indexes. Within the schizophrenia group, level of depression was associated with weaker reactivity to stress for negative affect. Within the bipolar disorder group, level of depression was associated with lower positive affect.
Conclusions
Patients with schizophrenia endured a more stable and negative affective state than healthy individuals, and were less likely to be uplifted in response to happenings in daily life. There is little evidence that these affective constructs characterize the psychopathology of bipolar disorder; such investigation may have been limited by the heterogeneity within group. Our findings supported the clinical importance of assessing multiple facets of affective dynamics beyond the mean levels of intensity.
Smartphones and wearables have made in-vivo assessment of stress, coping, and emotion via intensive longitudinal designs (ILDs) especially appealing. In this chapter, we briefly address the usefulness of adding an ILD framework to the coping researcher’s toolbox in the quest to gain a comprehensive and developmentally informed understanding of adolescent coping. Their importance rests on the ability of ILDs to capture coping microprocesses. Next, we draw on data to answer a pertinent question related to popular approaches to assessing coping via ILD: whether delivering ILD surveys via phone calls or text messages to adolescents reveals differences in compliance and data quality. We follow this with a discussion of several challenges associated with implementing ILDs, including types of coping questions these methods are less well-suited to address. We highlight the need to match theory to methods, and the need for a priori consideration of analytic approaches. This section further points to useful published resources for making optimal use of ILDs in developmental coping research, as well as describes novel passive sensing methods and physiological measurement approaches available via smartphones and wearables. We conclude the chapter with a brief discussion of how ILDs complement traditional longitudinal examinations of coping development.
In the present chapter we investigate how reward-rich environments can lead to the persistence of (initial) biases. More specifically, we argue that frequent rewards invite the exploitation of a supposedly best option which in turn will reinforce the biased preference. Because feedback is often contingent on the choices made, exploitation will result mostly in the aggregation of information about the exploited option. This, in turn, restricts the extent to which beliefs can be updated, with downstream consequences for further decisions. This dynamic might be responsible for why false beliefs about the outcomes of behavioral options can be maintained even when decision makers are motivated to choose the best choice alternative. We present data from simulations and empirical work to support this argument and conclude that the exploration–exploitation tradeoff serves as a particularly vivid example of the interplay between one’s cognition (goal-directed) behavior, and the sample that is aggregated.
Social interactions provide a large proportion of the information that people gather on a daily basis. The fundamental question guiding this chapter is whether and how social motivations influence the samples people gather, and how this drives downstream evaluative biases. We begin by highlighting how group-based motivations may influence three different stages of information processing: (1) where and how much information people gather, (2) how people interpret sampled information, and (3) how sampling strategies change recursively over time based on the congeniality of the environment. We then review recent empirical work that tests these possibilities using different social identities and contexts. Across seven studies we found that most participants began sampling from their own group, and that they sampled overall more information from their own group, giving rise to more variable ingroup (relative to outgroup) experiences. We also found that participants asymmetrically integrated their initial experiences into their evaluations based on congeniality: initial positive experiences were integrated into evaluations, whereas initial negative experiences were not. Lastly, we demonstrated that participants adopted different sampling strategies over time when the ingroup was de facto worse, obfuscating real-group differences. Together, we demonstrate that group-based motivations permeate each stage of information sampling, collectively giving rise to biased evaluations. These results unite extant research on sampling and interpretive sources of bias and provide a springboard for future research on sampling behavior across social motivations and contexts.
Creativity can be seen in many facets of our lives, from experimenting with your dinner recipes or using ingenuity to resolve problems to inventing a new technology or working as a professional artist. Researchers are increasingly moving beyond investigating creativity in the lab to examine how and when creativity occurs in people’s everyday lives and environments. This chapter provides an overview of ecological momentary assessment techniques (i.e., daily dairy and experience sampling methods) commonly used in creativity research. To illustrate how ecological momentary assessment methods have featured in creativity research, I review several exemplars of research focused on creativity in daily life.
Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them.
Methods
Literature and expert opinions were integrated to formulate relevant guiding principles.
Results
Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it.
Conclusions
The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field.
This chapter reviews how ubiquitous mobile technology can be used to better understand and improve recovery from alcohol use disorder. Distinct applications of both active and passive technology-assisted data collection (i.e., ecological momentary assessment, ambulatory assessment) to assess alcohol use and broader recovery outcomes are described. Previous studies of and future opportunities to use these methods to examine recovery-related processes and mechanisms of behavior change are highlighted. Promising mobile-based interventions or recovery support services examined to date are described, ranging from classic telehealth approaches to sophisticated interventions relying on both self-reported and sensor-based inputs to tailor the timing and content of intervention (i.e., ecological momentary interventions, Just-In-Time Adaptive Interventions). The chapter concludes with discussion of the potential for these interventions to achieve individualized intervention optimization (i.e., personalized treatment, precision medicine).
Sleep disruption is a common precursor to deterioration and relapse in people living with psychotic disorders. Understanding the temporal relationship between sleep and psychopathology is important for identifying and developing interventions which target key variables that contribute to relapse.
Methods
We used a purpose-built digital platform to sample self-reported sleep and psychopathology variables over 1 year, in 36 individuals with schizophrenia. Once-daily measures of sleep duration and sleep quality, and fluctuations in psychopathology (positive and negative affect, cognition and psychotic symptoms) were captured. We examined the temporal relationship between these variables using the Differential Time-Varying Effect (DTVEM) hybrid exploratory-confirmatory model.
Results
Poorer sleep quality and shorter sleep duration maximally predicted deterioration in psychosis symptoms over the subsequent 1–8 and 1–12 days, respectively. These relationships were also mediated by negative affect and cognitive symptoms. Psychopathology variables also predicted sleep quality, but not sleep duration, and the effect sizes were smaller and of shorter lag duration.
Conclusions
Reduced sleep duration and poorer sleep quality anticipate the exacerbation of psychotic symptoms by approximately 1–2 weeks, and negative affect and cognitive symptoms mediate this relationship. We also observed a reciprocal relationship that was of shorter duration and smaller magnitude. Sleep disturbance may play a causal role in symptom exacerbation and relapse, and represents an important and tractable target for intervention. It warrants greater attention as an early warning sign of deterioration, and low-burden, user-friendly digital tools may play a role in its early detection.
Depression treatment might be enhanced by ecological momentary interventions (EMI) based on self-monitoring and person-specific feedback. This study is the first to examine the efficacy of two different EMI modules for depression in routine clinical practice.
Methods
Outpatients starting depression treatment at secondary mental health services (N = 161; MIDS−DEPRESSION = 35.9, s.d. = 10.7; MAGE = 32.8, s.d. = 12.1; 46% male) participated in a pragmatic randomized controlled trial with three arms. Two experimental groups engaged in 28 days of systematic self-monitoring (5 times per day), and received weekly feedback on either positive affect and activities (Do-module) or negative affect and thinking patterns (Think-module). The control group received no additional intervention. Participants completed questionnaires on depressive symptoms (primary outcome), social functioning, and empowerment before and after the intervention period, and at four measurements during a 6-month follow-up period.
Results
Of the 90 (out of 110) participants who completed the intervention, 86% would recommend it. However, the experimental groups did not show significantly more or faster changes over time than the control group in terms of depressive symptoms, social functioning, and empowerment. Furthermore, the trajectories of the two EMI modules were very similar.
Conclusions
We did not find statistical evidence that this type of EMI augments the efficacy of regular depression treatment, regardless of module content. We cannot rule out that EMIs have a positive impact on other domains or provide a more efficient way of delivering care. Nonetheless, EMI's promise of effectiveness has not materialized yet.
Standard depression rating scales like the Hamilton Depression Rating Scale and the Montgomery–Åsberg Depression Rating Scale were developed more than 40 years ago. They are mandatory in clinical trials but are for a variety of reasons seldom used in clinical practice. Moreover, most clinicians are less familiar with more recent trends or with some dilemmas in assessment tools for major depression.
Methods
Narrative review.
Results
Asssessment tools can be observer-rating or self-rating scales, disease-specific or non–disease-specific scales, subjective scales or objective lab assessments, standard questionnaires or experience sampling methods. An overarching question is to what degree current assessment methods really address the individual patient’s needs and treatment expectations.
Conclusions
The present paper aims to offer a framework for understanding the current trends in assessment tools that can orientate and guide the clinician.
Cognitive models propose that behavioural responses to voices maintain distress by preventing disconfirmation of negative beliefs about voices. We used Experience Sampling Methodology (ESM) to examine the hypothesized maintenance role of behavioural responses during daily life.
Method
Thirty-one outpatients with frequent voices completed a smartphone-based ESM questionnaire 10 times a day over 9 days, assessing voice-related distress; resistance and compliance responses to voices; voice characteristics (intensity and negative content); appraisals of voice dominance, uncontrollability and intrusiveness.
Results
In line with predictions, behavioural responses were associated with voice appraisals (dominance and uncontrollability), but not voice characteristics. Greater resistance and compliance were reported in moments of increased voice distress, but these associations did not persist after controlling for concurrent voice appraisals and characteristics. Voice distress was predicted by appraisals, and, unexpectedly, also by voice characteristics. As predicted, compliance and resistance were related to increases in distress at subsequent timepoints, whilst antecedent voice appraisals and characteristics had no such effect. Compliance, but not resistance, additionally predicted subsequent increases in voice uncontrollability. In both cases, the reverse models showed no association, indicating directional effects of responses on subsequent distress, and of compliance on uncontrollability appraisals.
Conclusions
These results provide support for the cognitive model by suggesting that momentary behavioural and emotional responses to voices are associated with concurrent negative voice appraisals. Findings suggest that behavioural responses may be driven by voice appraisals, rather than directly by distress, and may in turn maintain voice appraisals and associated distress during the course of daily life.
Negative affect (NA) has been suggested to be both an antecedent and a consequence of auditory verbal hallucinations (AVH). Furthermore, negative appraisals of voices have been theorized to contribute to the maintenance of AVH. Using the experience sampling method (ESM), this study examined the bi-directional relationship between NA and AVH, and the moderating effect of negative beliefs about voices.
Methods
Forty-seven patients diagnosed with schizophrenia spectrum disorders with frequent AVH completed a clinical interview, followed by ESM for 10 times a day over 6 days on an electronic device. Time-lagged analyses were conducted using multilevel regression modeling. Beliefs about voices were assessed at baseline.
Results
A total of 1654 data points were obtained. NA predicted an increase in AVH in the subsequent moment, and AVH predicted an increase in NA in the subsequent moment. Baseline beliefs about voices as malevolent and omnipotent significantly strengthened the association between NA and AVH within the same moment. In addition, the belief of omnipotence was associated with more hallucinatory experiences in the moment following NA. However, beliefs about voices were not associated directly with momentary levels of NA or AVH.
Conclusions
Experiences of NA and AVH drove each other, forming a feedback loop that maintained the voices. The associations between NA and AVH, either within the same moment or across moments, were exacerbated by negative beliefs about voices. Our results suggest that affect-improving interventions may stop the feedback loop and reduce AVH frequency.
Individuals with bipolar disorder respond to affective symptoms with a range of coping behaviours, which may further maintain the symptoms.
Aims
To examine moment-to-moment dynamics between affective states and coping behaviours, and to evaluate the role of cognitive appraisals of internal states as moderators.
Method
Forty-six individuals with bipolar disorder completed a clinical interview and an experience sampling assessment over 6 days. Time-lagged analyses were conducted by multilevel regression modelling.
Results
A total of 1807 momentary entries were analysed. Negative affect predicted an increase in rumination at the subsequent time point (β = 0.21, s.e. = 0.08, P = 0.009, 95% CI 0.05–0.36), and vice versa (β = 0.03, s.e. = 0.01, P = 0.009, 95% CI 0.01–0.05). Positive affect predicted an increase in adaptive coping (β = 0.26, s.e. = 0.11, P = 0.018, 95% CI 0.04–0.47), and vice versa (β = 0.02, s.e. = 0.01, P = 0.019, 95% CI 0.00–0.03). Positive affect also predicted a decrease in rumination (β = −0.15, s.e. = 0.06, P = 0.014, 95% CI −0.26 to −0.03), and vice versa (β = −0.03, s.e. = 0.01, P = 0.016, 95% CI −0.06 to −0.01). Extreme cognitive appraisals predicted stronger associations between affective states and coping behaviours.
Conclusions
Feedback loops between affective states and coping behaviours were revealed in the daily life of individuals with bipolar disorder, which were moderated by extreme cognitive appraisals.
Prior studies using self-report questionnaires and laboratory-based methods suggest that schizophrenia is characterized by abnormalities in emotion regulation (i.e. using strategies to increase or decrease the frequency, duration, or intensity of negative emotion). However, it is unclear whether these abnormalities reflect poor emotion regulation effort or adequate effort, but limited effectiveness. It is also unclear whether dysfunction results primarily from one of the three stages of the emotion regulation process: identification, selection, or implementation.
Method
The current study used ecological momentary assessment (EMA) to address these questions in the context of everyday activities. Participants included 28 outpatients diagnosed with schizophrenia (SZ) and 28 demographically matched healthy controls (CN) who completed 6 days of EMA reports of in-the-moment emotional experience, emotion regulation strategy use, and context.
Results
Results indicated that SZ demonstrated adequate emotion regulation effort, but poor effectiveness. Abnormalities were observed at each of the three stages of the emotion regulation process. At the identification stage, SZ initiated emotion regulation efforts at a lower threshold of negative emotion intensity. At the selection stage, SZ selected more strategies than CN and strategies attempted were less contextually appropriate. At the implementation stage, moderate to high levels of effort were ineffective at decreasing negative emotion.
Conclusions
Findings suggest that although SZ attempt to control their emotions using various strategies, often applying more effort than CN, these efforts are unsuccessful; emotion regulation abnormalities may result from difficulties at the identification, selection, and implementation stages.