Acceptability, engagement, outcomes, and dose–response associations of a mindfulness-based meditation app in individuals waiting for psychological services
Bibliographic record
Abstract
Abstract Background While mindfulness apps have received growing clinical attention, their integration within health systems has received limited investigation. In this study, we evaluated a mindfulness app in adults waiting for psychological services. A non-randomized clinical trial was conducted with a 4-week intervention period and 8-week follow-up. At baseline, adults ( N = 193) with moderate depression and anxiety symptoms completed an assessment and received access to a mobile mindfulness app called AmDTx. Additional assessments were completed at 2-, 4-, 8-, and 12-weeks post-baseline. Descriptive statistics of attrition, adoption, acceptability, and engagement were computed. Linear mixed models estimated treatment outcomes for functional disability (primary outcome), depression, anxiety, stress, rumination, and mindful awareness/acceptance. We also evaluated the dose–response association between app use and functional disability. Results Using intent-to-treat (ITT) analyses, there was a 75% adoption rate and a 30% attrition rate at post-treatment (i.e., 4 weeks post-baseline). In addition, 1.09 hours of meditation time and 9.16 exercises were recorded on average at post-treatment. During follow-up, less than a third of participants remained active users, but they reported increases in meditation hours and number of exercises. Participants reported positive ratings of credibility, acceptability, and usability ratings. Treatment effects were observed in the expected direction for all outcomes but one (mindful awareness). Dose-response relationships indicated that increases in app engagement correlated with decreases in functional disability Conclusions The findings reinforce the potential for AmDTx, and mindfulness apps more broadly, to serve as low-intensity tools to alleviate unmet service needs and impart clinically meaningful benefit for a significant subset of those waiting for psychological services. Trial registration ClinicalTrials.gov NCT05211960, Registered 2022–01-26.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".