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. 1
• Main Authors: Bastiaansen, Jojanneke A, Kunkels, Yoram K, Blaauw, Frank J, Boker, Steven M, Ceulemans, Eva, Chen, Meng, Chow, Sy-Miin, Jonge, Peter de, 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 GC, Albers, Casper J, Bringmann, Laura F
• Format: Journal Article
• Language: English
• Published: London Elsevier Science Ltd 01.10.2020
• Subjects: Clustering; Crowdsourcing; Ecological momentary assessment; Emotions; Methodological problems; Missing data; Research methodology; Sampling; Statistical analysis; Teams
• ISSN: 0022-3999; 1879-1360

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.