From Affective Experience to Motivated Action: Tracking Reward-Seeking and Punishment-Avoidant Behaviour in Real-Life

Temporal pathways from attachment insecurity to paranoia in a heterogeneous clinical sample: An experience sampling study

INTRODUCTION

There has been growing interest in the role of attachment mechanisms in the onset and maintenance of paranoia. The latest systematic reviews of mixed samples of healthy individuals and psychiatric patients have revealed that increased trait attachment anxiety and avoidance are associated with experiencing paranoia, with trait attachment anxiety showing a stronger association. Few studies have examined attachment and paranoia in naturalistic conditions via the Experience Sampling Method.

OBJECTIVES

The present study examined whether experiences of attachment anxiety and avoidance fluctuate in the flow of daily life, and whether a within-person change in both attachment states precedes the experience of momentary paranoia, and negative and positive emotions.

METHODS

Thirty-seven clinical participants and twenty-six healthy controls were studied over six consecutive days using the Experience Sampling Method (ESM). An experience-in-close-relationships questionnaire (ECR-R 16 SF) was used to capture trait attachment dimensions. Several ESM items were used to capture momentary negative and positive affect, paranoia and attachment insecurity states.

RESULTS

The findings revealed that fluctuations in both attachment insecurity states were significantly higher in the clinical group. A prior elevated attachment anxiety and avoidance was followed by an increase in negative affect in the next moment and elevated attachment avoidance was additionally followed by a decrease in positive affect and an increase in paranoia.

CONCLUSION

Our findings reveal the specific temporal associations between momentary attachment insecurity states as predictors of change in emotions/affects and paranoia, along with evidence that state attachment avoidance has a superior impact on momentary affect and paranoia compared to state attachment anxiety. These results contrast with those of recent cross-sectional studies.

Recovering Within-Person Dynamics from Psychological Time Series

Idiographic modeling is rapidly gaining popularity, promising to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: first, time series models will arguably always be misspecified, meaning it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest a modeling methodology that goes beyond fitting time series models alone and puts formal theories at the center of theory development.

mHealth technology for ecological momentary assessment in physical activity research: a systematic review

OBJECTIVE

To systematically review the publications on ecological momentary assessment (EMA) relating to physical activity (PA) behavior in order to classify the methodologies, and to identify the main mHealth technology-based tools and procedures that have been applied during the first 10 years since the emergence of smartphones. As a result of this review, we want to ask if there is enough evidence to propose the use of the term "mEMA" (mobile-based EMA).

DESIGN

A systematic review according to PRISMA Statement (PROSPERO registration: CRD42018088136).

METHOD

Four databases (PsycINFO, CINALH, Medline and Web of Science Core Collection) were searched electronically from 2008 to February 2018.

RESULTS

A total of 76 studies from 297 potential articles on the use of EMA and PA were included in this review. It was found that 71% of studies specifically used "EMA" for assessing PA behaviors but the rest used other terminology that also adjusted to the inclusion criteria. Just over half (51.3%) of studies (39) used mHealth technology, mainly smartphones, for collecting EMA data. The majority (79.5%) of these studies (31 out of 39) were published during the last 4 years. On the other hand, 58.8% of studies that only used paper-and-pencil were published during the first 3 years of the 10-year period analyzed. An accelerometer was the main built-in sensor used for collecting PA behavior by means of mHealth (69%). Most of the studies were carried out on young-adult samples, with only three studies in older adults. Women were included in 60% of studies, and healthy people in 82%. The studies lasted between 1 and 7 days in 57.9%, and between three and seven assessments per day were carried out in 37%. The most popular topics evaluated together with PA were psychological state and social and environmental context.

CONCLUSIONS

We have classified the EMA methodologies used for assessing PA behaviors. A total of 71% of studies used the term "EMA" and 51.3% used mHealth technology. Accelerometers have been the main built-in sensor used for collecting PA. The change of trend in the use of tools for EMA in PA coincides with the technological advances of the last decade due to the emergence of smartphones and mHealth technology. There is enough evidence to use the term mEMA when mHealth technology is being used for monitoring real-time lifestyle behaviors in natural situations. We define mEMA as the use of mobile computing and communication technologies for the EMA of health and lifestyle behaviors. It is clear that the use of mHealth is increasing, but there is still a lot to be gained from taking advantage of all the capabilities of this technology in order to apply EMA to PA behavior. Thus, mEMA methodology can help in the monitoring of healthy lifestyles under both subjective and objective perspectives. The tendency for future research should be the automatic recognition of the PA of the user without interrupting their behavior. The ecological information could be completed with voice messages, image captures or brief text selections on the touch screen made in real time, all managed through smartphone apps. This methodology could be extended when EMA combined with mHealth are used to evaluate other lifestyle behaviors.

Experience sampling methodology in mental health research: new insights and technical developments

In the mental health field, there is a growing awareness that the study of psychiatric symptoms in the context of everyday life, using experience sampling methodology (ESM), may provide a powerful and necessary addition to more conventional research approaches. ESM, a structured self-report diary technique, allows the investigation of experiences within, and in interaction with, the real-world context. This paper provides an overview of how zooming in on the micro-level of experience and behaviour using ESM adds new insights and additional perspectives to standard approaches. More specifically, it discusses how ESM: a) contributes to a deeper understanding of psychopathological phenomena, b) allows to capture variability over time, c) aids in identifying internal and situational determinants of variability in symptomatology, and d) enables a thorough investigation of the interaction between the person and his/her environment and of real-life social interactions. Next to improving assessment of psychopathology and its underlying mechanisms, ESM contributes to advancing and changing clinical practice by allowing a more fine-grained evaluation of treatment effects as well as by providing the opportunity for extending treatment beyond the clinical setting into real life with the development of ecological momentary interventions. Furthermore, this paper provides an overview of the technical details of setting up an ESM study in terms of design, questionnaire development and statistical approaches. Overall, although a number of considerations and challenges remain, ESM offers one of the best opportunities for personalized medicine in psychiatry, from both a research and a clinical perspective.

Constructing a Reward-Related Quality of Life Statistic in Daily Life-a Proof of Concept Study Using Positive Affect

Background: Mental healthcare needs person-tailored interventions. Experience Sampling Method (ESM) can provide daily life monitoring of personal experiences. This study aims to operationalize and test a measure of momentary reward-related Quality of Life (rQoL). Intuitively, quality of life improves by spending more time on rewarding experiences. ESM clinical interventions can use this information to coach patients to find a realistic, optimal balance of positive experiences (maximize reward) in daily life. rQoL combines the frequency of engaging in a relevant context (a 'behavior setting') with concurrent (positive) affect. High rQoL occurs when the most frequent behavior settings are combined with positive affect or infrequent behavior settings co-occur with low positive affect. Methods: Resampling procedures (Monte Carlo experiments) were applied to assess the reliability of rQoL using various behavior setting definitions under different sampling circumstances, for real or virtual subjects with low-, average- and high contextual variability. Furthermore, resampling was used to assess whether rQoL is a distinct concept from positive affect. Virtual ESM beep datasets were extracted from 1,058 valid ESM observations for virtual and real subjects. Results: Behavior settings defined by Who-What contextual information were most informative. Simulations of at least 100 ESM observations are needed for reliable assessment. Virtual ESM beep datasets of a real subject can be defined by Who-What-Where behavior setting combinations. Large sample sizes are necessary for reliable rQoL assessments, except for subjects with low contextual variability. rQoL is distinct from positive affect. Conclusion: rQoL is a feasible concept. Monte Carlo experiments should be used to assess the reliable implementation of an ESM statistic. Future research in ESM should asses the behavior of summary statistics under different sampling situations. This exploration is especially relevant in clinical implementation, where often only small datasets are available.

Reward and punishment learning in daily life: A replication study

Day-to-day experiences are accompanied by feelings of Positive Affect (PA) and Negative Affect (NA). Implicitly, without conscious processing, individuals learn about the reward and punishment value of each context and activity. These associative learning processes, in turn, affect the probability that individuals will re-engage in such activities or seek out that context. So far, implicit learning processes are almost exclusively investigated in controlled laboratory settings and not in daily life. Here we aimed to replicate the first study that investigated implicit learning processes in real life, by means of the Experience Sampling Method (ESM). That is, using an experience-sampling study with 90 time points (three measurements over 30 days), we prospectively measured time spent in social company and amount of physical activity as well as PA and NA in the daily lives of 18-24-year-old young adults (n = 69 with anhedonia, n = 69 without anhedonia). Multilevel analyses showed a punishment learning effect with regard to time spent in company of friends, but not a reward learning effect. Neither reward nor punishment learning effects were found with regard to physical activity. Our study shows promising results for future research on implicit learning processes in daily life, with the proviso of careful consideration of the timescale used. Short-term retrospective ESM design with beeps approximately six hours apart may suffer from mismatch noise that hampers accurate detection of associative learning effects over time.

Integrating Personality Structure, Personality Process, and Personality Development

In this target article, we argue that personality processes, personality structure, and personality development have to be understood and investigated in integrated ways in order to provide comprehensive responses to the key questions of personality psychology. The psychological processes and mechanisms that explain concrete behaviour in concrete situations should provide explanation for patterns of variation across situations and individuals, for development over time as well as for structures observed in intra–individual and inter–individual differences. Personality structures, defined as patterns of covariation in behaviour, including thoughts and feelings, are results of those processes in transaction with situational affordances and regularities. It cannot be presupposed that processes are organized in ways that directly correspond to the observed structure. Rather, it is an empirical question whether shared sets of processes are uniquely involved in shaping correlated behaviours, but not uncorrelated behaviours (what we term ‘correspondence’ throughout this paper), or whether more complex interactions of processes give rise to population–level patterns of covariation (termed ‘emergence’). The paper is organized in three parts, with part I providing the main arguments, part II reviewing some of the past approaches at (partial) integration, and part III outlining conclusions of how future personality psychology should progress towards complete integration. Working definitions for the central terms are provided in the appendix. Copyright © 2017 European Association of Personality Psychology

Imagine your mood: Study design and protocol of a randomized controlled micro-trial using app-based experience sampling methodology to explore processes of change during relapse prevention interventions for recurrent depression

Background

Relapse prevention strategies include continuation of antidepressant medication and preventive psychological interventions. This study aims to gain understanding that may inform tailoring of relapse prevention to individual differences, to improve their effects. Such treatment personalization may be based on repeated assessments within one individual, using experience sampling methodology. As a first step towards informing decisions based on this methodology, insight is needed in individual differences in risk of relapse and response to treatment, and how relapse prevention strategies may differentially target vulnerability for relapse.

Methods

The smartphone application ‘Imagine your mood’ has been developed specifically for this study to assess emotions, imagery, cognitions, and behaviors in daily life. Parallel to the randomized controlled trial ‘Disrupting the rhythm of depression’, 45 remitted recurrently depressed individuals taking continuation antidepressant medication will be randomly assigned to either continuing antidepressant medication (n = 15), continuing antidepressant medication combined with an eight-session preventive cognitive therapy (n = 15), or tapering of antidepressant medication in combination with preventive cognitive therapy (n = 15). Relapse and return of depressive symptomatology over a 24-month follow-up will be assessed. Additionally, matched never depressed individuals (n = 15) will be recruited as controls.

Discussion

This innovative study combines the strengths of a randomized controlled trial and experience sampling methodology in a micro-trial to explore individual differences in risk of relapse and what works for whom to prevent relapse. Results may ultimately pave the way for therapists to tailor relapse prevention strategies to individual (affective) vulnerability.

Trial registration

ISRCTN15472145, retrospectively registered.

Striatal dopaminergic modulation of reinforcement learning predicts reward-oriented behavior in daily life

Much human behavior is driven by rewards. Preclinical neurophysiological and clinical positron emission tomography (PET) studies have implicated striatal phasic dopamine (DA) release as a primary modulator of reward processing. However, the relationship between experimental reward-induced striatal DA release and responsiveness to naturalistic rewards, and therefore functional relevance of these findings, has been elusive. We therefore combined, for the first time, a DA D_2/3 receptor [¹⁸F]fallypride PET during a probabilistic reinforcement learning (RL) task with a six day ecological momentary assessments (EMA) of reward-related behavior in the everyday life of 16 healthy volunteers. We detected significant reward-induced DA release in the bilateral putamen, caudate nucleus and ventral striatum, the extent of which was associated with better behavioral performance on the RL task across all regions. Furthermore, individual variability in the extent of reward-induced DA release in the right caudate nucleus and ventral striatum modulated the tendency to be actively engaged in a behavior if the active engagement was previously deemed enjoyable. This study suggests a link between striatal reward-related DA release and ecologically relevant reward-oriented behavior, suggesting an avenue for the inquiry into the DAergic basis of optimal and impaired motivational drive.

An exploratory study on the impact of daily activities on the pleasure and physical activity of older adults

Background

Pleasure is one determinant of intrinsic motivation and yet a dimension often forgotten when promoting physical activity among the older population. In this study we investigate the relation between daily activities and physical activity, experience of pleasure, and the interaction between pleasure and physical activity in the daily lives of community-dwelling older adults.

Methods

Participants carried a hip-worn accelerometer during 30 consecutive days resulting in a total of 320 days of data collection. Current activity, location, companion and experience of pleasure during each activity were assessed through experience sampling on a smartphone every 1–2 h. Between- and within-individual differences were analysed with multi-level models and 10xN = 1 regression analysis.

Results

Outdoor activities were associated with higher physical activity than indoor activities (p < 0.001). Performing leisure activities, outdoors and not alone significantly predicted pleasure in daily life (all p’s < 0.05). Being more active while performing leisure activities resulted in higher experiences of pleasure (p < 0.001). However, when performing basic activities of daily living (e.g. commuting or households) this relation was inverted. Results provide meaningful indication for individual variance. The 30 days of data collected from each participant allow for identification of individual differences.

Conclusions

Daily activities and their contexts do influence the experience of pleasure and physical activity of older adults in daily life of older adults, although similar research with larger population is recommended. Results are in accordance with the literature, indicating that the method adopted (accelerometry combined with experience sampling) provides reliable representation of daily life. Identification of individual differences can eventually be automatically performed through data mining techniques. Further research could look at innovative approaches to promote Active Ageing using mobile technology in the daily life, by promoting physical activity through recommendation of pleasurable activities, and thus likely to increase the intrinsic motivation to become physically active.