Protocol for a randomized controlled trial investigating an intervention to boost decentering in response to distressing mental experiences during adolescence: The Decentering in Adolescence Study (DECADES)

Introduction. Decentering describes the ability to voluntarily adopt an objective self-perspective from which to notice internal, typically distressing, stressors (e.g. difficult thoughts, memories, and feelings). The reinforcement of this skill may be an active ingredient through which different psychological interventions accrue reductions in anxiety and/or depression. However, it is unclear if decentering can be selectively trained at a young age and if this might reduce psychological distress. The aim of the current trial is to address this research gap. Methods and analysis. Adolescents, recruited from partnering schools in the UK and the EU (n = 48 per group, age range = 16-19 years), will be randomised to complete of five-weeks of decentering training, or form an active control group that will take part in in light physical exercise and cognitive training. The co-primary training outcomes include a self-reported decentering inventory (i.e. the Experiences Questionnaire) and the momentary use of decentering in response to psychological stressors, using experience sampling. The secondary mental health outcomes will include self-reported inventories of depression and anxiety symptoms, as well as psychological wellbeing. The initial statistical analysis will use mixed-model analysis of variance (ANOVA) to estimate the effect of training condition on self-rated inventories across three timepoints: baseline, mid-intervention and post-intervention. Additionally, experience sampling data will be initially interrogated using hierarchical linear models. Ethics and dissemination. This study was approved by the Cambridge Psychology Research Ethics Committee, University of Cambridge (PRE.2019.109). Findings will be disseminated through typical academic routes including poster/paper presentations at (intern)-national conferences, academic institutes and through publication in peer-reviewed journals.

Latent Markov Factor Analysis: A Mixture Modeling Approach for Evaluating Within- and Between-Person Measurement Model Differences in Intensive Longitudinal Data

SUMMARY DOCTORAL DISSERTATION: Experience sampling methodology, in which participants are repeatedly questioned via smartphone apps, is popular for studying psychological constructs or “factors” (e.g., well-being or depression) within persons over time. The validity of such studies (e.g., concerning treatment decisions) may be hampered by distortions of the measurement of the relevant constructs due to response styles or item interpretations that change over time and differ across persons. In this PhD project, we developed a new approach to evaluate person- and time-point-specific distortions of the construct measurements, taking into account the specific characteristics of (time-intensive) longitudinal data inherent to experience sampling studies. Our new approach, latent Markov factor analysis, extends mixture factor analysis and clusters time-points within persons according to their factor model. The factor model describes how well items measure the constructs. With the new approach, researchers can examine how many and which factor models underlie the data, for which persons and time-points they apply, and thus which observations are validly comparable. Such insights can also be interesting in their own right. In personalized healthcare, for example, detecting changes in response styles is critical for accurate decisions about treatment allocation over time, as response styles may be related to the occurrence of depressive episodes.

Polygenic risk, familial liability and stress reactivity in psychosis: an experience sampling study

Background There is evidence for a polygenic contribution to psychosis. One targetable mechanism through which polygenic variation may impact on individuals and interact with the social environment is stress sensitization, characterized by elevated reactivity to minor stressors in daily life. The current study aimed to investigate whether stress reactivity is modified by polygenic risk score for schizophrenia (PRS) in cases with enduring non-affective psychotic disorder, first-degree relatives of cases, and controls. Methods We used the experience sampling method to assess minor stressors, negative affect, positive affect and psychotic experiences in 96 cases, 79 first-degree relatives, i.e. siblings, and 73 controls at wave 3 of the Dutch Genetic Risk and Outcome of Psychosis (GROUP) study. Genome-wide data were collected at baseline to calculate PRS. Results We found that associations of momentary stress with psychotic experiences, but not with negative and positive affect, were modified by PRS and group (all pFWE<0.001). In contrast to our hypotheses, siblings with high PRS reported less intense psychotic experiences in response to momentary stress compared to siblings with low PRS. No differences in magnitude of these associations were observed in cases with high v. low level of PRS. By contrast, controls with high PRS showed more intense psychotic experiences in response to stress compared to those with low PRS. Conclusions This tentatively suggests that polygenic risk may operate in different ways than previously assumed and amplify reactivity to stress in unaffected individuals but operate as a resilience factor in relatives by attenuating their stress reactivity.

Continuous measurement of stress levels in naturalistic settings using heart rate variability: An experience-sampling study driving a machine learning approach

<p class="Abstract"><span id="page629R_mcid43" class="markedContent"><span dir="ltr">Developing automatic methods to measure psychological stress in everyday life has become an important research challenge. Here, we describe the design and implementation of a personalized mobile system for the detection of psychological stress episodes based on Heart-Rate Variability (HRV) indices. The system’s architecture consists of three main modules: a mobile acquisition module; an analysis-decision module; and a visualization-reporting module. Once the stress level is calculated by the mobile system, the visualization-reporting module of the mobile application displays the current stress level of the user. We carried out an experience-sampling study, involving 15 participants, monitored longitudinally, for a total of 561 ECG analyzed, to select the HRV features which best correlate with self-reported stress levels. Drawing on these results, a personalized classification system is able to automatically detect stress events from those HRV features, after a training phase in which the system learns from the subjective responses given by the user. Finally, the performance of the classification task was evaluated on the empirical dataset using the leave one out cross-validation process. Preliminary findings suggest that incorporating self-reported psychological data in the system’s knowledge base allows for a more accurate and personalized definition of the stress response measured by HRV indices.</span></span></p>

Understanding User Perceptions of Proactive Smart Speakers

Voice assistants, such as Amazon's Alexa and Google Home, increasingly find their way into consumer homes. Their functionality, however, is currently limited to being passive answer machines rather than proactively engaging users in conversations. Speakers' proactivity would open up a range of important application scenarios, including health services, such as checking in on patient states and triggering medication reminders. It remains unclear how passive speakers should implement proactivity. To better understand user perceptions, we ran a 3-week field study with 13 participants where we modified the off-the-shelf Google Home to become proactive. During the study, our speaker proactively triggered conversations that were essentially Experience Sampling probes allowing us to identify when to engage users. Applying machine-learning, we are able to predict user responsiveness with a 71.6% accuracy and find predictive features. We also identify self-reported factors, such as boredom and mood, that are significantly correlated with users' perceived availability. Our prototype and findings inform the design of proactive speakers that verbally engage users at opportune moments and contribute to the design of proactive application scenarios and voice-based experience sampling studies.

Methodological Innovations for the Study of Spirituality

One of the most important decisions in any study of spirituality is the method used to collect information about spiritual life. This methodological choice frames later conceptual analysis—making possible some types of conclusions but preventing others. Accordingly, methodological innovation in the study of spiritualty holds the promise of conceptual innovation. This chapter puts forth three methodological innovations available to spirituality researchers. They are (1) using smartphones to collect experience-sampling method data about day-to-day spiritual experiences, (2) conducting field experiments in which spiritual experiences are randomly assigned, and (3) analyzing big data to observe societal-wide trends and patterns in spiritual expressions. Each of these methods promises to produce rich and novel data that hold the potential for conceptual breakthroughs in our understanding of spiritual processes.