Exploring temporal relationships among worrying, anxiety, and somatic symptoms

OBJECTIVES: The role of anxiety symptoms in the development of functional somatic symptoms (FSS) is unknown. Somatic symptoms may be triggered by or give rise to anxiety symptoms. This study aimed to 1) explore interrelationships among within-day worrying, feeling anxious, and somatic symptoms, and 2) investigate the association between these interrelationships and overall level of FSS. METHODS: This study included 767 participants (83% females, mean age 39 years), who were recruited through an online crowdsourcing study in the Dutch general population. Somatic, and anxiety symptoms were reported thrice daily (6-h intervals) for 30 days using electronic diaries. FSS were assessed at baseline (PHQ-15). Temporal relationships among worrying, feeling anxious, and somatic symptoms were modeled using a multilevel vector autoregressive model. RESULTS: We observed large heterogeneity in the within-person interrelationships among worrying, feeling anxious and somatic symptoms. Averaged over participants, higher-than-usual somatic symptoms were associated with increases in levels of worrying six hours later (B = 0.017, 95% CI [0.006, 0.027]). At the between-person level, FSS levels predicted the persistence of feeling anxious (B = 0.230 95% CI [0.105, 0.350]) and the carry-over of worrying to feeling anxious over six-hours (B = 0.159, 95% CI [0.031, 0.283]). CONCLUSIONS: In contrast to what we expected, higher levels of somatic symptoms over multiple weeks were associated with the persistence and carry-over of within-day anxiety-related symptoms. One within-person association between psychological and somatic symptoms during the day was observed, suggesting that, over a time span of 6-h, anxiety symptoms relate to somatic symptoms only in a minority of people from the general population

The u-can-act Platform:A Tool to Study Intra-individual Processes of Early School Leaving and Its Prevention Using Multiple Informants

We present the u-can-act platform, a tool that we developed to study the individual processes of early school leaving and the preventative actions that mentors take to steer these processes in the right direction. Early school leaving is a significant problem, particularly in vocational education, and can have severe consequences for both the individual and society. However, the prevention of early school leaving is hampered by a mismatch between research and practice: research tends to focus on identifying risk factors using group averages and cross-sectional studies, while practitioners focus on intervening in individual processes. We aim to help solve this mismatch with our project u-can-act. In this project we have developed a platform that helps to gain insight into both the individual processes that precede early school leaving as well as the actions that mentors take to prevent it. In this paper we introduce the u-can-act platform, which consists of three technology-based, reusable methodological innovations. Specifically, our innovations concern: (i) an open source web application for longitudinal personalized data-collection, (ii) an automated study protocol that optimizes adherence in a difficult target group (adolescents at risk for early school leaving), and (iii) a technologically assisted coupling between mentor and student that allows us to study dyadic interactions over time. We present performance results of our platform, including participant adherence, the behavior of the questionnaire items over time, and the way that our web application is experienced by the participants. We conclude that our innovative platform is successful in collecting multi-informant time-series data on intervention processes among students in vocational education, both for at-risk students and control students, and for their mentors. Moreover, our platform is suitable for broader applications: it can be used to study any malleable individual process including the efforts of a second individual who aims to influence this process. Because of the unique insights that the u-can-act platform is able to generate, the platform may ultimately contribute to solving the mismatch between research and practice, and to more effective interventions in individual processes

Let's get Physiqual - An intuitive and generic method to combine sensor technology with ecological momentary assessments

The emergence of wearables and smartwatches is making sensors a ubiquitous technology to measure daily rhythms in physiological measures, such as movement and heart rate. An integration of sensor data from wearables and self-report questionnaire data about cognition, behaviors, and emotions can provide new insights into the interaction of mental and physiological processes in daily life. Hitherto no method existed that enables an easy-to-use integration of sensor and self-report data. To fill this gap, we present 'Physiqual', a platform for researchers that gathers and integrates data from commercially available sensors and service providers into one unified format for use in Ecological Momentary Assessments (EMA) or Experience Sampling Methods (ESM), and Quantified Self (QS). Physiqual currently supports sensor data provided by two well-known service providers and therewith a wide range of smartwatches and wearables. To demonstrate the features of Physiqual, we conducted a case study in which we assessed two subjects by means of data from an EMA study combined with sensor data as aggregated and exported by Physiqual. To the best of our knowledge, the Physiqual platform is the first platform that allows researchers to conveniently aggregate and integrate physiological sensor data with EMA studies. (C) 2016 Elsevier Inc. All rights reserved

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology:The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

OBJECTIVE: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual’s emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS: To evaluate this, we crowdsourced the analysis of one individual patient’s ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0–16) and nature of selected targets varied widely. CONCLUSION: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation

Generating personalized advice for schizophrenia patients

The results of routine patient assessments in psychiatric healthcare in the Northern Netherlands are primarily used to support clinicians. We developed Wegweis, a web-based advice platform, to make this data accessible and understandable for patients. Objective: We show that a fully automated explanation and interpretation of assessment results for schizophrenia patients, which prioritizes the information in the same way that a clinician would, is possible and is considered helpful and relevant by patients. The goal is not to replace the clinician but rather to function as a second perspective and to enable patient empowerment through knowledge. Methods: We have developed and implemented an ontology-based approach for selecting and ranking information for schizophrenia patients based on their routine assessment results. Our approach ranks information by severity of associated schizophrenia-related problems and uses an ontology to decouple problems from advice, which adds robustness to the system, because advice can be inferred for problems that have no exact match. Results: We created a problem ontology, validated by a group of experts, to combine and interpret the results of multiple schizophrenia-specific questionnaires. We designed and implemented a novel ontology-based algorithm for ranking and selecting advice, based on questionnaire answers. We designed, implemented, and illustrated Wegweis, a proof of concept for our algorithm, and, to the best of our knowledge, the first fully automated interpretation of assessment results for patients suffering from schizophrenia. We evaluated the system vis-a-vis the opinions of clinicians and patients in two experiments. For the task of identifying important problems based on MANSA questionnaires (the MANSA is a satisfaction questionnaire commonly used in schizophrenia assessments), our system corresponds to the opinion of clinicians 94% of the time for the first three problems and 72% of the time, overall. Patients find two out of the first three advice topics selected by the system to be relevant and roughly half of the advice topics overall. Conclusions: Our findings suggest that an approach that uses problem severities to identify important problems for a patient corresponds closely to the way a clinician thinks. Furthermore, after applying a severity threshold, the majority of advice units selected by the system are considered relevant by the patients. Our findings pave the way for the development of systems that facilitate patient-centered care for chronic illnesses by automating the sharing of assessment results between patient and clinician. (C) 2013 Elsevier B.V. All rights reserved

Resilience in sports: a multidisciplinary, dynamic, and personalized perspective

Athletes are exposed to various psychological and physiological stressors, such as losing matches and high training loads. Understanding and improving the resilience of athletes is therefore crucial to prevent performance decrements and psychological or physical problems. In this review, resilience is conceptualized as a dynamic process of bouncing back to normal functioning following stressors. This process has been of wide interest in psychology, but also in the physiology and sports science literature (e.g. load and recovery). To improve our understanding of the process of resilience, we argue for a collaborative synthesis of knowledge from the domains of psychology, physiology, sports science, and data science. Accordingly, we propose a multidisciplinary, dynamic, and personalized research agenda on resilience. We explain how new technologies and data science applications are important future trends (1) to detect warning signals for resilience losses in (combinations of) psychological and physiological changes, and (2) to provide athletes and their coaches with personalized feedback about athletes' resilience