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.

A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application.

BACKGROUND: Smartphone self-monitoring of mood, symptoms, and contextual factors through ecological momentary assessment (EMA) provides insights into the daily lives of people undergoing psychiatric treatment. Therefore, EMA has the potential to improve their care. To integrate EMA into treatment, a clinical tool that helps clients and clinicians create personalized EMA diaries and interpret the gathered data is needed. OBJECTIVE: This study aimed to develop a web-based application for personalized EMA in specialized psychiatric care in close collaboration with all stakeholders (ie, clients, clinicians, researchers, and software developers). METHODS: The participants were 52 clients with mood, anxiety, and psychotic disorders and 45 clinicians (psychiatrists, psychologists, and psychiatric nurses). We engaged them in interviews, focus groups, and usability sessions to determine the requirements for an EMA web application and repeatedly obtained feedback on iteratively improved high-fidelity EMA web application prototypes. We used human-centered design principles to determine important requirements for the web application and designed high-fidelity prototypes that were continuously re-evaluated and adapted. RESULTS: The iterative development process resulted in Personalized Treatment by Real-time Assessment (PETRA), which is a scientifically grounded web application for the integration of personalized EMA in Dutch clinical care. PETRA includes a decision aid to support clients and clinicians with constructing personalized EMA diaries, an EMA diary item repository, an SMS text message-based diary delivery system, and a feedback module for visualizing the gathered EMA data. PETRA is integrated into electronic health record systems to ensure ease of use and sustainable integration in clinical care and adheres to privacy regulations. CONCLUSIONS: PETRA was built to fulfill the needs of clients and clinicians for a user-friendly and personalized EMA tool embedded in routine psychiatric care. PETRA is unique in this codevelopment process, its extensive but user-friendly personalization options, its integration into electronic health record systems, its transdiagnostic focus, and its strong scientific foundation in the design of EMA diaries and feedback. The clinical effectiveness of integrating personalized diaries via PETRA into care requires further research. As such, PETRA paves the way for a systematic investigation of the utility of personalized EMA for routine mental health care.

Time to get personal? 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.

ACTman: Automated preprocessing and analysis of actigraphy data.

OBJECTIVES: To introduce a novel software-library called Actigraphy Manager (ACTman) which automates labor-intensive actigraphy data preprocessing and analyses steps while improving transparency, reproducibility, and scalability over software suites traditionally used in actigraphy research practice. DESIGN: Descriptive. METHODS: Use cases are described for performing a common actigraphy task in ACTman and alternative actigraphy software. Important inefficiencies in actigraphy workflow are identified and their consequences are described. We explain how these hinder the feasibility of conducting studies with large groups of athletes and/or longer data collection periods. Thereafter, the information flow through the ACTman software is described and we explain how it alleviates aforementioned inefficiencies. Furthermore, transparency, reproducibility, and scalability issues of commonly used actigraphy software packages are discussed and compared with the ACTman package. RESULTS: It is shown that from an end-user perspective ACTman offers a compact workflow as it automates many preprocessing and analysis steps that otherwise have to be performed manually. When considering transparency, reproducibility, and scalability the design of the ACTman software is found to outperform proprietary and open-source actigraphy software suites. As such, ACTman alleviates important bottlenecks within actigraphy research practice. CONCLUSIONS: ACTman facilitates the current transition towards larger datasets containing data of multiple athletes by automating labor-intensive preprocessing and analyses steps within actigraphy research. Furthermore, ACTman offers many features which enhance user-convenience and analysis customization, such as moving window functionality and period selection options. ACTman is open-source and thus fully verifiable, in contrast with many proprietary software packages which remain a black box for researchers.

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.

Acute stress responses after indirect exposure to the MH17 airplane crash.

People can experience disasters vicariously (indirectly) via conversation, social media, radio, and television, even when not directly involved in a disaster. This study examined whether vicarious exposure to the MH17-airplane crash in Ukraine, with 196 Dutch victims, elicited affective and somatic responses in Dutch adults about 2,600 km away, who happened to participate in an ongoing diary study. Participants (n = 141) filled out a diary three times a day for 30 days on their smartphones. Within-person changes in positive affect (PA) and negative affect (NA) and somatic symptoms after the crash were studied. Additionally, we tested whether between-person differences in response could be explained by age, baseline personality (NEO-FFI-3), and media exposure. The MH17 crash elicited a small within-person decrease in PA and an increase in NA and somatic symptoms. This response waned after 3 days and returned to baseline at day four. The decrease in PA was larger in more extraverted participants but smaller in those higher on neuroticism or conscientiousness. The NA response was smaller in elderly. Personality did not seem to moderate the NA and somatic response, and neither did media exposure. Dutch participants showed small acute somatic and affective responses up till 3 days to a disaster that they had not directly witnessed. Vicariously experienced disasters can thus elicit affective-visceral responses indicative of acute stress reactions. Personality and age explained some of the individual differences in this reaction.

Temporal Dynamics of Health and Well-Being: A Crowdsourcing Approach to Momentary Assessments and Automated Generation of Personalized Feedback.

OBJECTIVE: Recent developments in research and mobile health enable a quantitative idiographic approach in health research. The present study investigates the potential of an electronic diary crowdsourcing study in the Netherlands for (1) large-scale automated self-assessment for individual-based health promotion and (2) enabling research at both the between-persons and within-persons level. To illustrate the latter, we examined between-persons and within-persons associations between somatic symptoms and quality of life. METHODS: A website provided the general Dutch population access to a 30-day (3 times a day) diary study assessing 43 items related to health and well-being, which gave participants personalized feedback. Associations between somatic symptoms and quality of life were examined with a linear mixed model. RESULTS: A total of 629 participants completed 28,430 assessments, with a mean (SD) of 45 (32) assessments per participant. Most participants (n = 517 [82%]) were women and 531 (84%) had high education. Almost 40% of the participants (n = 247) completed enough assessments (t = 68) to generate personalized feedback including temporal dynamics between well-being, health behavior, and emotions. Substantial between-person variability was found in the within-person association between somatic symptoms and quality of life. CONCLUSIONS: We successfully built an application for automated diary assessments and personalized feedback. The application was used by a sample of mainly highly educated women, which suggests that the potential of our intensive diary assessment method for large-scale health promotion is limited. However, a rich data set was collected that allows for group-level and idiographic analyses that can shed light on etiological processes and may contribute to the development of empirical-based health promotion solutions.

HowNutsAreTheDutch (HoeGekIsNL): A crowdsourcing study of mental symptoms and strengths.

HowNutsAreTheDutch (Dutch: HoeGekIsNL) is a national crowdsourcing study designed to investigate multiple continuous mental health dimensions in a sample from the general population (n = 12,503). Its main objective is to create an empirically based representation of mental strengths and vulnerabilities, accounting for (i) dimensionality and heterogeneity, (ii) interactivity between symptoms and strengths, and (iii) intra-individual variability. To do so, HowNutsAreTheDutch (HND) makes use of an internet platform that allows participants to (a) compare themselves to other participants via cross-sectional questionnaires and (b) to monitor themselves three times a day for 30 days with an intensive longitudinal diary study via their smartphone. These data enable for personalized feedback to participants, a study of profiles of mental strengths and weaknesses, and zooming into the fine-grained level of dynamic relationships between variables over time. Measuring both psychiatric symptomatology and mental strengths and resources enables for an investigation of their interactions, which may underlie the wide variety of observed mental states in the population. The present paper describes the applied methods and technology, and presents the sample characteristics. Copyright © 2015 John Wiley & Sons, Ltd.

Automating Vector Autoregression on Electronic Patient Diary Data.

Finding the best vector autoregression model for any dataset, medical or otherwise, is a process that, to this day, is frequently performed manually in an iterative manner requiring a statistical expertize and time. Very few software solutions for automating this process exist, and they still require statistical expertize to operate. We propose a new application called Autovar, for the automation of finding vector autoregression models for time series data. The approach closely resembles the way in which experts work manually. Our proposal offers improvements over the manual approach by leveraging computing power, e.g., by considering multiple alternatives instead of choosing just one. In this paper, we describe the design and implementation of Autovar, we compare its performance against experts working manually, and we compare its features to those of the most used commercial solution available today. The main contribution of Autovar is to show that vector autoregression on a large scale is feasible. We show that an exhaustive approach for model selection can be relatively safe to use. This study forms an important step toward making adaptive, personalized treatment available and affordable for all branches of healthcare.

Ecological Momentary Assessments and Automated Time Series Analysis to Promote Tailored Health Care: A Proof-of-Principle Study.

BACKGROUND: Health promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required. OBJECTIVE: This study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses. METHODS: We developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher's tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4). RESULTS: An illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test. CONCLUSIONS: Results suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis. Analysis of additional datasets is needed in order to validate and refine the application for general use.

E-mental health self-management for psychotic disorders: state of the art and future perspectives.

OBJECTIVE: The aim of this review was to investigate to what extent information technology may support self-management among service users with psychotic disorders. The investigation aimed to answer the following questions: What types of e-mental health self-management interventions have been developed and evaluated? What is the current evidence on clinical outcome and cost-effectiveness of the identified interventions? To what extent are e-mental health self-management interventions oriented toward the service user? METHODS: A systematic review of references through July 2012 derived from MEDLINE, PsycINFO, AMED, CINAHL, and the Library, Information Science and Technology database was performed. Studies of e-mental health self-management interventions for persons with psychotic disorders were selected independently by three reviewers. RESULTS: Twenty-eight studies met the inclusion criteria. E-mental health self-management interventions included psychoeducation, medication management, communication and shared decision making, management of daily functioning, lifestyle management, peer support, and real-time self-monitoring by daily measurements (experience sampling monitoring). Summary effect sizes were large for medication management (.92) and small for psychoeducation (.37) and communication and shared decision making (.21). For all other studies, individual effect sizes were calculated. The only economic analysis conducted reported more short-term costs for the e-mental health intervention. CONCLUSIONS: People with psychotic disorders were able and willing to use e-mental health services. Results suggest that e-mental health services are at least as effective as usual care or nontechnological approaches. Larger effects were found for medication management e-mental health services. No studies reported a negative effect. Results must be interpreted cautiously, because they are based on a small number of studies.

A web-based tool to support shared decision making for people with a psychotic disorder: randomized controlled trial and process evaluation.

BACKGROUND: Mental health policy makers encourage the development of electronic decision aids to increase patient participation in medical decision making. Evidence is needed to determine whether these decision aids are helpful in clinical practice and whether they lead to increased patient involvement and better outcomes. OBJECTIVE: This study reports the outcome of a randomized controlled trial and process evaluation of a Web-based intervention to facilitate shared decision making for people with psychotic disorders. METHODS: The study was carried out in a Dutch mental health institution. Patients were recruited from 2 outpatient teams for patients with psychosis (N=250). Patients in the intervention condition (n=124) were provided an account to access a Web-based information and decision tool aimed to support patients in acquiring an overview of their needs and appropriate treatment options provided by their mental health care organization. Patients were given the opportunity to use the Web-based tool either on their own (at their home computer or at a computer of the service) or with the support of an assistant. Patients in the control group received care as usual (n=126). Half of the patients in the sample were patients experiencing a first episode of psychosis; the other half were patients with a chronic psychosis. Primary outcome was patient-perceived involvement in medical decision making, measured with the Combined Outcome Measure for Risk Communication and Treatment Decision-making Effectiveness (COMRADE). Process evaluation consisted of questionnaire-based surveys, open interviews, and researcher observation. RESULTS: In all, 73 patients completed the follow-up measurement and were included in the final analysis (response rate 29.2%). More than one-third (48/124, 38.7%) of the patients who were provided access to the Web-based decision aid used it, and most used its full functionality. No differences were found between the intervention and control conditions on perceived involvement in medical decision making (COMRADE satisfaction with communication: F1,68=0.422, P=.52; COMRADE confidence in decision: F1,67=0.086, P=.77). In addition, results of the process evaluation suggest that the intervention did not optimally fit in with routine practice of the participating teams. CONCLUSIONS: The development of electronic decision aids to facilitate shared medical decision making is encouraged and many people with a psychotic disorder can work with them. This holds for both first-episode patients and long-term care patients, although the latter group might need more assistance. However, results of this paper could not support the assumption that the use of electronic decision aids increases patient involvement in medical decision making. This may be because of weak implementation of the study protocol and a low response rate.

Generating personalized advice for schizophrenia patients.

UNLABELLED: 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-à-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.

Usability evaluation of a web-based support system for people with a schizophrenia diagnosis.

BACKGROUND: Routine Outcome Monitoring (ROM) is a systematic way of assessing service users' health conditions for the purpose of better aiding their care. ROM consists of various measures used to assess a service user's physical, psychological, and social condition. While ROM is becoming increasingly important in the mental health care sector, one of its weaknesses is that ROM is not always sufficiently service user-oriented. First, clinicians tend to concentrate on those ROM results that provide information about clinical symptoms and functioning, whereas it has been suggested that a service user-oriented approach needs to focus on personal recovery. Second, service users have limited access to ROM results and they are often not equipped to interpret them. These problems need to be addressed, as access to resources and the opportunity to share decision making has been indicated as a prerequisite for service users to become a more equal partner in communication with their clinicians. Furthermore, shared decision making has been shown to improve the therapeutic alliance and to lead to better care. OBJECTIVE: Our aim is to build a web-based support system which makes ROM results more accessible to service users and to provide them with more concrete and personalized information about their functioning (ie, symptoms, housing, social contacts) that they can use to discuss treatment options with their clinician. In this study, we will report on the usability of the web-based support system for service users with schizophrenia. METHODS: First, we developed a prototype of a web-based support system in a multidisciplinary project team, including end-users. We then conducted a usability study of the support system consisting of (1) a heuristic evaluation, (2) a qualitative evaluation and (3) a quantitative evaluation. RESULTS: Fifteen service users with a schizophrenia diagnosis and four information and communication technology (ICT) experts participated in the study. The results show that people with a schizophrenia diagnosis were able to use the support system easily. Furthermore, the content of the advice generated by the support system was considered meaningful and supportive. CONCLUSIONS: This study shows that the support system prototype has valuable potential to improve the ROM practice and it is worthwhile to further develop it into a more mature system. Furthermore, the results add to prior research into web applications for people with psychotic disorders, in that it shows that this group of end users can work with web-based and computer-based systems, despite the cognitive problems they experience.