Category Archive: Publications

ULTEMAT: A mobile framework for smart ecological momentary assessments and interventions

Van de Ven, P., O’Brien, H., Henriques, R., Klein, M., Msetfi, R., Nelson, J., …, Ruwaard, J., Riper, H. (2017). ULTEMAT: A mobile framework for smart ecological momentary assessments and interventions. Internet Interventions, 9, 74–81. doi: 10.1016/j.invent.2017.07.001

In this paper we introduce a new Android library, called ULTEMAT, for the delivery of ecological momentary assessments (EMAs) on mobile devices and we present its use in the MoodBuster app developed in the H2020 E-COMPARED project. We discuss context-aware, or event-based, triggers for the presentation of EMAs and discuss the potential they have to improve the effectiveness of mobile provision of mental health interventions as they allow for the delivery of assessments to the patients when and where these are most appropriate. Following this, we present the abilities of ULTEMAT to use such context-aware triggers to schedule EMAs and we discuss how a similar approach can be used for Ecological Momentary Interventions (EMIs).

A feature representation learning method for temporal datasets

Van Breda, W., Hoogendoorn, M., Eiben, A. E., Andersson, G., Riper, H., Ruwaard, J., & Vernmark, K. (2017). A feature representation learning method for temporal datasets. 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. doi: 10.1109/SSCI.2016.7849890

Predictive modeling of future health states can greatly contribute to more effective health care. Healthcare professionals can for example act in a more proactive way or predictions can drive more automated ways of therapy. However, the task is very challenging. Future developments likely depend on observations in the (recent) past, but how can we capture this history in features to generate accurate predictive models? And what length of history should we consider? We propose a framework that is able to generate patient tailored features from observations of the recent history that maximize predictive performance. For a case study in the domain of depression we find that using this method new data representations can be generated that increase the predictive performance significantly.

Technologische vernieuwingen in de geestelijke gezondheidszorg

Ruwaard, J. (2017). Technologische vernieuwingen in de geestelijke gezondheidszorg [Technological innovation in mental healthcare]. Tijdschrift voor Klinische Psychologie, 47, 1. journal site

De e-mental health, de toepassing van computer- en internettechnologie voor de beoordeling en behandeling van psychische aandoeningen, is in de afgelopen twintig jaar sterk gegroeid. Effectieve toepassingen voor een scala aan psychische aandoeningen zijn in wetenschappelijk onderzoek geïdentificeerd, waaronder internetbehandelingen, telezorg en virtual reality exposure-therapie. Deze toepassingen worden in de Nederlandse GGZ-praktijk echter nog weinig ingezet. Blended therapie, een practice-based hybride vorm waarin online en face-to-facecontact wordt gecombineerd, lijkt in de praktijk wel aan te slaan. Of deze vorm ook effectief is, moet echter nog blijken. Ondertussen dienen mobiele toepassingen en geautomatiseerde gepersonaliseerde interventies zich aan, in een tempo dat de wetenschap niet bij lijkt te houden. In de e-mental health worden validatie, toepassing en innovatie niet altijd goed gescheiden en op elkaar aangesloten. Praktijkorganisaties doen er goed aan om na de introductie van e-Health toepassingen via routine outcome monitoring zicht te houden op de kwaliteit van zorg.

Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study (2016)

Asselbergs, J., Ruwaard, J., Ejdys, M., Schrader, N., Sijbrandij, M. & Riper, H. (2016). Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study. J Med Internet Res 2016;18(3):e72 DOI: 10.2196/jmir.5505 [Open access at journal site ]

Background: Ecological momentary assessment (EMA) is a useful method to tap the dynamics of psychological and behavioral phenomena in real-world contexts. However, the response burden of (self-report) EMA limits its clinical utility.

Objective: The aim was to explore mobile phone-based unobtrusive EMA, in which mobile phone usage logs are considered as proxy measures of clinically relevant user states and contexts

Methods: This was an uncontrolled explorative pilot study. Our study consisted of 6 weeks of EMA/unobtrusive EMA data collection in a Dutch student population (N=33), followed by a regression modeling analysis. Participants self-monitored their mood on their mobile phone (EMA) with a one-dimensional mood measure (1 to 10) and a two-dimensional circumplex measure (arousal/valence, –2 to 2). Meanwhile, with participants’ consent, a mobile phone app unobtrusively collected (meta) data from six smartphone sensor logs (unobtrusive EMA: calls/short message service (SMS) text messages, screen time, application usage, accelerometer, and phone camera events). Through forward stepwise regression (FSR), we built personalized regression models from the unobtrusive EMA variables to predict day-to-day variation in EMA mood ratings. The predictive performance of these models (ie, cross-validated mean squared error and percentage of correct predictions) was compared to naive benchmark regression models (the mean model and a lag-2 history model).

Results: A total of 27 participants (81%) provided a mean 35.5 days (SD 3.8) of valid EMA/unobtrusive EMA data. The FSR models accurately predicted 55% to 76% of EMA mood scores. However, the predictive performance of these models was significantly inferior to that of naive benchmark models.

Conclusions: Mobile phone-based unobtrusive EMA is a technically feasible and potentially powerful EMA variant. The method is young and positive findings may not replicate. At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings. Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA’s true potential.

Online Structured Writing Therapy for Post-traumatic Stress Disorder and Complicated Grief (2016)

Ruwaard, J. & Lange, A. (2016). Online structured writing therapy for post-traumatic stress disorder and complicated grief. In N. Lindefors and G. Andersson (eds), Guided Internet-Based Treatments in Psychiatry. Switzerland, Springer International Publishing. doi:10.1007/978-3-319-06083-5_7 [ Chapter at publisher’s site ]


Post-traumatic stress disorder (PTSD) and complicated grief are related disorders for which well-described and effective cognitive-behavioural therapeutic procedures exist that are firmly rooted in theoretical work. As a result, several research groups have been able to successfully translate these procedures into e-mental health applications for the prevention, detection and treatment of the disorders. This chapter reviews online structured writing therapy (oSWT), a standardised therapist-guided Internet-based cognitive-behavioural treatment (ICBT) for post-traumatic stress disorder and complicated grief, which can be fully delivered online, without face-to-face contact between the patient and therapist. This protocol integrates three principal elements of trauma-focused therapy: (1) exposure through self-confrontation, (2) cognitive reappraisal and (3) strengthening of social support. A unique characteristic of oSWT is that it implements these three elements through writing assignments. In the past two decades, oSWT has been validated in a series of controlled studies and in routine clinical practice, with positive results. This chapter reviews these efficacy and effectiveness trials, elaborates on the details of the therapeutic procedures of the treatment protocol and identifies future research themes.

Gelezen: Andersson, G. (2014). The Internet and CBT: A Clinical Guide.

Ruwaard, J. (2015). Andersson, G. (2014). The Internet and CBT: A Clinical Guide (Boek Recensie). Tijdschrift voor Psychiatrie, 41(5). Manuscript | Published URL

Wie start met online hulpverlenen, koopt een boek. Vaak wordt dat het ‘Handboek Online Hulpverlening’ van stichting (Schalken e.a., 2012). Een prima keuze, maar daarin komen behandelinhoudelijke overwegingen en de wetenschappelijke onderbouwing maar beperkt aan de orde. Gerhard Andersson, een bekende Zweedse eMental Health expert, vult dit gat met zijn boek ‘The Internet and CBT: A Clinical Guide’. Cognitieve gedragstherapie via het internet: wat is dat, werkt dat, zo ja, voor wie dan en waar moet ik op letten in de behandelkamer? Andersson vat twee decennia onderzoek naar deze vragen samen. Dat is ambitieus: u kent de kloof tussen wetenschap en praktijk. Maar Andersson schreef een constructieve aanvulling op het beschikbare aanbod. Misschien net iets te constructief, maar dat kan aan mij liggen.