In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Szigeti:2021:10.7554/eLife.62878,
author = {Szigeti, B and Kartner, L and Blemings, A and Rosas, F and Feilding, A and Nutt, DJ and Carhart-Harris, RL and Erritzoe, D},
doi = {10.7554/eLife.62878},
journal = {eLife},
pages = {1--26},
title = {Self-blinding citizen science to explore psychedelic microdosing},
url = {http://dx.doi.org/10.7554/eLife.62878},
volume = {10},
year = {2021}
}
TY - JOUR
AB - Microdosing is the practice of regularly using low doses of psychedelic drugs. Anecdotal reports suggest that microdosing enhances well-being and cognition; however, such accounts are potentially biased by the placebo effect. This study used a ‘self-blinding’ citizen science initiative, where participants were given online instructions on how to incorporate placebo control into their microdosing routine without clinical supervision. The study was completed by 191 participants, making it the largest placebo-controlled trial on psychedelics to-date. All psychological outcomes improved significantly from baseline to after the 4 weeks long dose period for the microdose group; however, the placebo group also improved and no significant between-groups differences were observed. Acute (emotional state, drug intensity, mood, energy, and creativity) and post-acute (anxiety) scales showed small, but significant microdose vs. placebo differences; however, these results can be explained by participants breaking blind. The findings suggest that anecdotal benefits of microdosing can be explained by the placebo effect.
AU - Szigeti,B
AU - Kartner,L
AU - Blemings,A
AU - Rosas,F
AU - Feilding,A
AU - Nutt,DJ
AU - Carhart-Harris,RL
AU - Erritzoe,D
DO - 10.7554/eLife.62878
EP - 26
PY - 2021///
SN - 2050-084X
SP - 1
TI - Self-blinding citizen science to explore psychedelic microdosing
T2 - eLife
UR - http://dx.doi.org/10.7554/eLife.62878
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000626291400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://elifesciences.org/articles/62878
UR - http://hdl.handle.net/10044/1/90014
VL - 10
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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