A smartphone-based mindful breathing task with assessment of heart-rate variability for clinically relevant chronic pain, depression, and anxiety: Protocol for a randomized-controlled trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Mindfulness meditation is a commonly used psychological intervention for pain, mood, and anxiety conditions, but can be challenging to practice when dealing with severe symptoms without proper training experience. The Mindfulness Meditation App (MMA) is a supportive training tool specifically developed for the present study, to aid in the practice of mindful breathing using a smartphone. OBJECTIVE The aim of the present study is to evaluate the psychophysiological effects of the MMA. Specifically, the study aims to assess parasympathetic functioning using heart-rate variability (HRV; primary outcome), pain and mood symptoms, mind-wandering and present awareness, and breath focus in groups of participants who self-report clinically significant symptoms of chronic pain (CP), depression and/or anxiety (DA), as well as control participants (C) who do not meet criteria for either. METHODS The present study is a two-arm randomized-controlled trial (registration #NCT03296007), taking place at York University in Toronto, Canada. Sixty participants in each group of CP, DA, or C (N=180 total) will be pre-screened and randomly assigned by a 1:1 ratio to a mindfulness meditation app (MMA+) condition or a mindfulness meditation condition without the app (MMA-) after a brief stress-induction procedure. In MMA+, participants will practice mindful breathing with a smartphone and press “breath” or “other” buttons at the sound of audio tones if their awareness was on breathing or another experience, respectively. HRV and respiration data will be obtained during rest (5 minutes), stress-induction (5 minutes), and meditation condition (12 minutes). Participants will complete psychological self-report inventories before and after the stress-induction, and after the meditation condition. RESULTS Recruitment for the study began in November 2017 and is expected to be completed in July of 2019. CONCLUSIONS This RCT will inform the design of mindfulness meditation training tools delivered by apps and web platforms for the treatment of chronic pain, depression, and anxiety conditions. CLINICALTRIAL ClinicalTrials.gov: NCT03296007
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.090 | 0.014 |
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".