Adaptive data collection for intraindividual studies affected by adherence.

Autor: Monacelli G; School of Computing, Dublin City University, Dublin, Ireland.; Insight SFI Research Centre for Data Analytics, Dublin, Ireland., Zhang L; School of Computing, Dublin City University, Dublin, Ireland.; Insight SFI Research Centre for Data Analytics, Dublin, Ireland., Schlee W; Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany., Langguth B; Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany., Ward TE; School of Computing, Dublin City University, Dublin, Ireland.; Insight SFI Research Centre for Data Analytics, Dublin, Ireland., Murphy TB; Insight SFI Research Centre for Data Analytics, Dublin, Ireland.; School of Mathematics and Statistics, University College Dublin, Dublin, Ireland.
Jazyk: angličtina
Zdroj: Biometrical journal. Biometrische Zeitschrift [Biom J] 2023 Oct; Vol. 65 (7), pp. e2200203. Date of Electronic Publication: 2023 Apr 21.
DOI: 10.1002/bimj.202200203
Abstrakt: Recently, the use of mobile technologies in ecological momentary assessments (EMAs) and interventions has made it easier to collect data suitable for intraindividual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subject. In this paper, we address this problem for a specific EMA setting that aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection that considers both the individual variability of the self-reported variable and of the adherence. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon signed rank test shows that the algorithm tends to have both a higher F 1 score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially in studies where data collection is impacted by adherence.
(© 2023 Insight SFI Research Centre for Data Analytics and The Authors. Biometrical Journal published by Wiley-VCH GmbH.)
Databáze: MEDLINE
Externí odkaz: