Journal of Consulting and Clinical Psychology
September 28, 2023
Christian A. Webb, Matthew J. Hirshberg, Oscar González et al.
6 citations
Mechanisms explaining why meditation training works may differ across patient subgroups. Prior research often collapsed heterogeneous groups, obscuring these differences. Using data from 662 participants, researchers developed a Personalized Advantage Index (PAI) to identify individuals likely to benefit more from a meditation app. A moderated mediation analysis showed that mindfulness acquisition mediated better outcomes only for those with higher PAI scores. This suggests that subgroup-specific mediators should be considered to clarify how psychosocial interventions work and to match individuals to the most beneficial treatment.
medRxiv
August 28, 2024
Zishan Jiwani, Simon B. Goldberg, Jack Stroud et al.
3 citations
preprint
Most meditators who use psychedelics perceive them as beneficial for their meditation practice. Among 863 regular meditators (practicing at least three times weekly for the past year) who also used psychedelics, machine learning identified four factors most likely to predict this positive perception: greater frequency of psychedelic use, setting intentions before use, higher agreeableness, and having used N,N-Dimethyltryptamine (DMT). The model explained about 27% of the variance. The findings suggest that intentional and personality factors may shape how psychedelics influence meditation, but causality remains unestablished.
PLoS One
February 12, 2025
Zishan Jiwani, Simon B. Goldberg, Jack Stroud et al.
1 citation
Most meditators who also use psychedelics report that the drugs improve their meditation practice. In a survey of 863 regular meditators who had used psychedelics, 73.5% said psychedelics positively influenced the quality of their meditation. Machine learning analysis of 53 variables identified the strongest predictors of this perceived benefit: greater frequency of psychedelic use, setting intentions before taking psychedelics, having an agreeable personality, and having used N,N-Dimethyltryptamine (N,N-DMT). The results suggest that individual traits and patterns of use shape whether psychedelics are seen as helpful for meditation, but causality cannot be established from this cross-sectional data.
October 17, 2023
Christian A. Webb, Lori M. Hilt, Caroline M. Swords et al.
1 citation
preprint
Ecological momentary assessment (EMA) measures of rumination are only modestly correlated with conventional self-report measures, especially for change over time, partly due to lower reliability of EMA. Changes in rumination were larger for conventional self-report than EMA. Both types of measures accounted for unique variance in depressive symptom improvement, showing incremental predictive validity. The findings suggest that EMA and conventional self-report provide distinct, clinically meaningful information. Researchers using EMA should consider psychometric properties and the precise construct they intend to capture.
July 31, 2022
Christian A. Webb, Matthew J. Hirshberg, Richard J. Davidson et al.
1 citation
An algorithm was developed to predict who benefits most from a meditation app. Using data from a randomized controlled trial of a 4-week meditation app versus a control condition in 662 school system employees, a machine learning model created a Personalized Advantage Index (PAI) that estimated each person's expected reduction in distress. The PAI scores significantly predicted which individuals improved more with the app than without. A simpler model using only repetitive negative thinking as a predictor performed similarly well. The algorithm could help individuals make informed decisions about whether a meditation app is right for them.