Turn on, tune in, and drop out: predictors of attrition in a prospective observational cohort study on psychedelic use
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Author(s)
Hübner, Sebastian
Haijen, Eline
Kaelen, Mendel
Carhart-Harris, Robin
Kettner, Hannes Simon
Type
Journal Article
Abstract
Background. The resurgence of research and public interest in the positive psychological effects of psychedelics, together with advancements in digital data collection techniques, have brought forth a new type of research design, gathering large-scale naturalistic data from psychedelic users prospectively, i.e. before and after use of a psychedelic compound. A methodological limitation of such studies is their high attrition rate, caused by participants who stop responding after initial study enrolment. Importantly, study dropout can introduce systematic biases that may affect the interpretability of results. Objective. Based on a previously collected sample (N=654), we here investigated potential determinants of study attrition in prospective psychedelic online research. Methods. Logistic regression models were used to examine demographic, psychological trait and state, and psychedelic-specific predictors of dropout. Predictors were assessed 2 weeks before, one day after, and 2 weeks after the psychedelic experience, with attrition being defined as non-completion of the key endpoint 4 weeks post experience. Results. Predictors of attrition were found among demographic variables, including age and educational level, as well as personality traits, specifically low conscientiousness and high extraversion. Contrary to prior hypotheses, neither baseline attitudes towards psychedelics nor the intensity of acute challenging experiences were predictive of dropout. Conclusions. Baseline predictors of attrition identified here are consistent with those found in longitudinal studies from other scientific disciplines, suggesting their transdisciplinary relevance. Moreover, the lack of an association between attrition and psychedelic advocacy or negative drug experiences in the present sample contextualises concerns about problematic biases in these and related data.
Date Issued
2021-07-28
Date Acceptance
2021-05-04
Citation
Journal of Medical Internet Research, 2021, 23 (7)
ISSN
1438-8871
Publisher
JMIR Publications
Journal / Book Title
Journal of Medical Internet Research
Volume
23
Issue
7
Copyright Statement
©Sebastian Hübner, Eline Haijen, Mendel Kaelen, Robin Lester Carhart-Harris, Hannes Kettner. Originally published in theJournal of Medical Internet Research (https://www.jmir.org), 28.07.2021. This is an open-access article distributed under theterms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricteduse, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical InternetResearch, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/,as well as this copyright and license information must be included.
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Subjects
Medical Informatics
08 Information and Computing Sciences
11 Medical and Health Sciences
17 Psychology and Cognitive Sciences
Publication Status
Published
Article Number
ARTN e25973