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.

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.

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.

Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant’s progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.