App-Based Delivery of Clinical Emotional Freedom Techniques: Cross-Sectional Study of App User Self-Ratings
Bibliographic record
Abstract
BACKGROUND: The burgeoning area of mobile health (mHealth) has experienced rapid growth in mobile apps designed to address mental health issues. Although abundant apps offer strategies for managing symptoms of anxiety and stress, information regarding their efficacy is scarce. OBJECTIVE: This study aimed to assess the effect of an mHealth app on user self-ratings of psychological distress in a sample of 270,461 app users. The Tapping Solution App guides users through the therapeutic protocols of Clinical Emotional Freedom Techniques (EFT), an evidence-based psychophysiological intervention that combines acupressure with elements of cognitive and exposure therapies. METHODS: App users provided self-ratings of emotional intensity before and after app sessions (termed "tapping meditations") using an 11-point Subjective Units of Distress scale. App user data for 23 tapping meditations, which addressed psychological symptoms of anxiety and stress, were gathered between October 2018 and October 2019, totaling 380,034 completed app sessions. RESULTS: Across 12 anxiety-tapping meditations, the difference in emotional intensity ratings from presession (mean 6.66, SD 0.25) to postsession (mean 3.75, SD 0.30) was statistically significant (P<.001; 95% CI -2.92 to -2.91). Across 11 stress-tapping meditations, a statistically significant difference was also found from presession (mean 6.91, SD 0.48) to postsession (mean 3.83, SD 0.54; P<.001; 95% CI -3.08 to -3.07). The results are consistent with the literature on the efficacy of Clinical EFT for anxiety and stress when offered in conventional therapeutic formats. CONCLUSIONS: The findings provide preliminary support for the effectiveness of the mHealth app in the immediate reduction of self-rated psychological distress. As an adjunct to professional mental health care, the app promises accessible and convenient therapeutic benefits.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".