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Despite growing popularity, associations between dosage and outcomes in meditation-app interventions have not been established. We examined this relationship using a range of operationalizations of dosage (e.g., minutes of use, days of use, number and type of activities completed) and strategies for modeling outcomes (e.g., ordinary least squares regression, multilevel modeling, latent class analysis). We used data from a recently completed randomized controlled trial that tested a meditation app (N= 662; 80.4% with elevated depression/anxiety) that included psychological distress as its preregistered primary outcome. Across 41 models, whether an association was detected and the shape and direction of this association varied. Although several models indicated that higher dosage was associated with larger decreases in psychological distress, many models failed to show this relationship, and some even showed the opposite. These results may have implications for optimizing and studying dosage in meditation apps and for open-science practices.

Bidirectional associations between changes in symptoms and alliance are established for in-person psychotherapy. Alliance may play an important role in promoting engagement and effectiveness within unguided mobile-health (mHealth) interventions. Using models disaggregating alliance and psychological distress into within- and between-persons components (random intercept cross-lagged panel model), we report bidirectional associations between alliance and distress over the course of a 4-week smartphone-based meditation intervention (n= 302, 80.0% elevated depression/anxiety). Associations were stable across time, and effect sizes were similar to those observed for psychotherapy (distress to alliance: βs = −0.13 to −0.14; alliance to distress: βs = −0.09 to −0.10). Alliance may be worth measuring to improve the acceptability and effectiveness of mHealth tools. Further empirical and theoretical work characterizing the role and meaning of alliance in unguided mHealth is warranted.