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

• Published in: Journal of psychosomatic research Vol. 137; p. 110211
• Main Authors: Bastiaansen, Jojanneke A., Kunkels, Yoram K., Blaauw, Frank J., Boker, Steven M., Ceulemans, Eva, Chen, Meng, Chow, Sy-Miin, de Jonge, Peter, Emerencia, Ando C., Epskamp, Sacha, Fisher, Aaron J., Hamaker, Ellen L., Kuppens, Peter, Lutz, Wolfgang, Meyer, M. Joseph, Moulder, Robert, Oravecz, Zita, Riese, Harriëtte, Rubel, Julian, Ryan, Oisín, Servaas, Michelle N., Sjobeck, Gustav, Snippe, Evelien, Trull, Timothy J., Tschacher, Wolfgang, van der Veen, Date C., Wichers, Marieke, Wood, Phillip K., Woods, William C., Wright, Aidan G.C., Albers, Casper J., Bringmann, Laura F.
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.10.2020
• Subjects: Crowdsourcing science; Electronic diary; Mental disorders; Personalized medicine; Psychological networks; Time-series analysis; Psychological networks; Time-series analysis; Electronic diary; Mental disorders; Personalized medicine; Crowdsourcing science
• ISSN: 0022-3999; 1879-1360
• DOI: 10.1016/j.jpsychores.2020.110211

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. 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. 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. 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. •Intensive longitudinal time-series data can identify personalized treatment targets.•Twelve research labs varied in how they analyzed one patient's time-series data.•Moreover, they varied widely in selected treatment targets and the underlying rationale.•Results of person-specific analyses are currently conditional on subjective choices.