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

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

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

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

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

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.

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.</p