Evaluating Theory-Driven Messaging to Overcome the Barriers to Meditation: Large-Scale Digital Field Experiment
Bibliographic record
Abstract
Background: The general public is largely aware of meditation, and there is compelling evidence the practice has health benefits. But many people who are aware of meditation have not tried it, and those who do often struggle to establish a regular practice. The barriers to meditation are generally understood and include a lack of knowledge, a lack of time, and unclear benefits. These barriers present an impediment to self-efficacy in establishing a meditation practice. Despite these challenges, current strategies for promoting meditation may fail to address these barriers, leaving a gap in our knowledge about health communication efforts aimed at fostering meditation practices. Objective: The objective of this research is to leverage a large-scale, real-world digital platform to understand whether breaking down the theory-based barriers to meditation can serve as an effective strategy for encouraging meditation. Methods: This research is a digital messaging-based experiment that includes approximately 1.33 million people, aged 18 years and older, in the United States. The experiment was conducted on the Spotify mobile app and includes 1 control condition and 4 test conditions. Each of the test conditions was a message that attempted to address a specific barrier to meditation. The control message only included the call-to-action without any theory-based messaging accompaniment. When users clicked the message, they were redirected to meditation content. The click-through rate and the activation rate of each message were the dependent variables in the experiment. Results: The most effective message, which was designed to break down the pragmatic barriers to meditation, had a click-through rate odds ratio (OR) of 1.57 (95% CI 1.52-1.62) and an activation rate OR of 1.55 (95% CI 1.45-1.65), relative to the control. The least effective, which was designed to break down knowledge barriers, had a click-through rate OR of 0.91 (95% CI 0.88-0.94) and an activation rate OR of 0.66 (95% CI 0.61-0.71), compared to the control. After 7 days, the differences in people's engagement with the meditation content itself between experimental conditions had substantively diminished. Conclusions: Theory-driven messaging can potentially encourage people to explore meditation content, but not universally so, given 2 of the experimental conditions performed better than and 2 performed worse than the control. The most successful message broke down the barrier that meditation requires being alone and in a quiet place. Addressing this barrier may have boosted self-efficacy by aligning the practice with everyday settings that fit into people's busy lifestyles. Future researchers might consider how to encourage people to engage in meditation during their daily activities. In addition, breaking down the barriers to meditation through messaging can drive interest and experimentation with meditation content, but may not be enough to compel meaningful behavior change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".