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Record W2769239381 · doi:10.2196/resprot.7700

In-Person Versus eHealth Mindfulness-Based Intervention for Adolescents With Chronic Illness: Protocol for a Randomized Controlled Trial

2017· article· en· W2769239381 on OpenAlexafffundvenue
Nicholas Chadi, Miriam Kaufman, Elli Weisbaum, Catherine Malboeuf‐Hurtubise, Sara Ahola Kohut, C. Viner, Jake Locke, Dzung X. Vo

Bibliographic record

VenueJMIR Research Protocols · 2017
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsHospital for Sick ChildrenUniversity of British ColumbiaBC Children's HospitalUniversity of TorontoSickKids FoundationUniversité du Québec en Outaouais
FundersUniversity of TorontoMind and Life Institute
KeywordseHealthMindfulnessRandomized controlled trialFacilitatorPsychological interventionAnxietyMoodMental healthMedicineHealth careIntervention (counseling)PsychologyClinical psychologyNursingPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Eight-week mindfulness-based interventions (MBIs) have a beneficial impact on mental health and well-being in adolescents with chronic health conditions. Usually delivered in person in a group setting, these programs are difficult to access for teens with disabilities or who do not have in-person MBIs available in their communities. OBJECTIVE: This paper outlines the rationale, development, and design of a randomized controlled trial comparing the effects of an MBI delivered in person or via eHealth in adolescents with a chronic illness. Quantitative outcomes will include mindfulness skills acquisition (primary outcome), effects of the MBI on self-reported mood, anxiety, self-esteem, illness perception, and physiological stress (via salivary cortisol), and qualitative outcomes will include individual practice, participant appreciation, and adaptation of the MBI for eHealth. METHODS: This is a randomized noninferiority mixed methods study comparing 2 MBI arms: in-person and eHealth. Participants are eligible to participate if they are aged 13 to 18 years, have a diagnosis of chronic medical condition, live close enough to the recruitment hospital to participate in the in-person arm of the study, and are currently followed by a health care provider. Each participant will receive an adapted 8-week MBI delivered either in person at a tertiary pediatric hospital or via a secure audio-visual platform allowing group interactions in real time. Groups will be facilitated by 2 experienced mindfulness providers. Quantitative and qualitative data will be collected through standardized research questionnaires administered via a secure, youth-friendly online platform and through semistructured interviews, participant log books, facilitator log books, and salivary cortisol analysis. Qualitative data will be analyzed using a grounded theory model. RESULTS: Data collection is currently underway. Data analysis, manuscript writing, and additional publications are expected to be completed in the winter and spring of 2018. CONCLUSIONS: Based on previous results from in-person trials conducted in adolescents and eHealth trials conducted in adults, we anticipate that both modes of delivery will significantly improve mindfulness skills acquisition, mood, anxiety, self-esteem, illness perception, and stress and that the magnitude of the effects will be correlated to the level of home practice. We predict that participants in both arms will show similar levels of home practice and that both modes of delivery will have high levels of feasibility and acceptability. If successful, this study could provide evidence for the use of eHealth in the delivery of 8-week MBIs in clinical adolescent populations, potentially increasing availability to MBIs for a large group of youth with mobility issues or living away from large urban centers. TRIAL REGISTRATION: ClinicalTrials.org NCT03067207; https://clinicaltrials.gov/ct2/show/NCT03067207 (archived by WebCite at http://www.webcitation.org/6v4ZK8RBH).

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.212
GPT teacher head0.574
Teacher spread0.362 · 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

Citations16
Published2017
Admission routes3
Has abstractyes

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