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Record W2992323807 · doi:10.2196/14119

Effects of a 12-Minute Smartphone-Based Mindful Breathing Task on Heart Rate Variability for Students With Clinically Relevant Chronic Pain, Depression, and Anxiety: Protocol for a Randomized Controlled Trial

2019· article· en· W2992323807 on OpenAlexaffvenueabout
Muhammad Abid Azam, Vered Latman, Joel Katz

Bibliographic record

VenueJMIR Research Protocols · 2019
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsYork University
Fundersnot available
KeywordsAnxietyRandomized controlled trialHeart rate variabilityDepression (economics)Heart ratePhysical therapyBreathingChronic painProtocol (science)PsychologyMedicineTask (project management)Clinical psychologyPhysical medicine and rehabilitationPsychiatryAlternative medicineBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Mindfulness meditation (MM) is a commonly used psychological intervention for pain, mood, and anxiety conditions, but can be challenging to practice with severe symptoms without proper training. The Mindfulness Meditation app (MMA) is a supportive training tool specifically developed for this study to aid in the practice of mindful breathing using a smartphone. OBJECTIVE: This study aims to evaluate the psychophysiological effects of the MMA. Specifically, the study will assess parasympathetic functioning using heart rate variability (HRV; primary outcome), pain and mood symptoms, mind-wandering and present moment awareness, and breath focus in groups of undergraduate participants who self-report clinically-relevant symptoms of chronic pain (CP) and depression or anxiety (DA) and condition-free (CF) participants who do not meet either criteria. We hypothesize that use of the MMA by study groups will lead to improved HRV, pain, and mood symptoms compared with groups who do not use the app. METHODS: This study is a two-arm randomized controlled trial (RCT) recruiting through a Web-based research participation pool at York University in Toronto, Canada. We are aiming for minimum 60 participants in each of CP, DA, and CF groups. Upon arriving to the laboratory, participants will be prescreened for classification into groups of CP, DA, or CF. Groups will be randomly assigned by a 1:1 ratio to an MMA (MMA+) condition or MM 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 min), stress induction (5 min), and meditation conditions (12 min). Participants will complete psychological self-report inventories before and after the stress induction and after the meditation condition. Separate linear mixed models will be used to examine HRV and self-report inventories comparing groups and treatment conditions. RESULTS: Recruitment for the study began in November 2017 and is expected to be completed in winter of 2019-2020. As of July 2019, 189 participants have been recruited. The study's main findings are expected to reveal a positive pattern of HRV responses in the CP, DA, and CF groups, such that a significant increase in HRV (P<.05) is detected in those randomized to the MMA+ condition in comparison with those randomized to the MMA- condition. CONCLUSIONS: This RCT will contribute to the burgeoning health psychology literature regarding the clinical relevance of HRV in assessment and treatment of psychological and medical conditions. Furthermore, possible ways to inform designs of MM training tools delivered by apps and Web platforms for CP, depression, and anxiety conditions' treatment will be discussed. TRIAL REGISTRATION: Clinicaltrials.gov NCT03296007; https://clinicaltrials.gov/ct2/show/NCT03296007. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14119.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.037
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.068
GPT teacher head0.512
Teacher spread0.444 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreProtocol

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".

Quick stats

Citations19
Published2019
Admission routes3
Has abstractyes

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