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Record W2595336352 · doi:10.2196/resprot.7151

Writing for Health: Rationale and Protocol for a Randomized Controlled Trial of Internet-Based Benefit-Finding Writing for Adults With Type 1 or Type 2 Diabetes

2017· article· en· W2595336352 on OpenAlexvenueno aff
Joanna Crawford, Kay Wilhelm, Lisa Robins, Judith Proudfoot

Bibliographic record

VenueJMIR Research Protocols · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health via Writing
Canadian institutionsnot available
FundersUniversity of New South Wales
KeywordsThe InternetRandomized controlled trialType 2 diabetesProtocol (science)MedicineComputer scienceAlternative medicineMedical educationWorld Wide WebFamily medicineDiabetes mellitusInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Diabetes mellitus is Australia's fastest growing chronic disease, and has high comorbidity with depression. Both subthreshold depression and diabetes distress are common amongst people with type 1 or type 2 diabetes, and are associated with poorer diabetes self-care. A need exists for low-intensity self-help interventions for large numbers of people with diabetes and diabetes distress or subthreshold depression, as part of a stepped-care approach to meeting the psychological needs of people with diabetes. Benefit-finding writing is a very brief intervention that involves writing about any positive thoughts and feelings about a stressful experience, such as an illness. Benefit-finding writing has been associated with increases in positive affect and positive growth, and has demonstrated promising results in trials amongst other clinical populations. However, benefit-finding writing has not yet been examined in people with diabetes. OBJECTIVE: The aim of this randomized controlled trial (RCT) is to evaluate the efficacy of an Internet-based benefit-finding writing (iBFW) intervention for adults with type 1 or type 2 diabetes (compared to a control writing condition) for reducing diabetes distress and increasing benefit-finding in diabetes, and also improving a range of secondary outcomes. METHODS: A two-arm RCT will be conducted, using the online program Writing for Health. Adults with type 1 or type 2 diabetes living in Australia will be recruited using diabetes-related publications and websites, and through advertisements in diabetes services and general practitioners' offices. Potential participants will be referred to the study-specific website for participant information and screening. All data will be collected online. Participants will be randomized to either iBFW about diabetes, or a control writing condition of writing about use-of-time. Both conditions involve three daily sessions (once per day for three consecutive days) of 15-minute online writing exercises. Outcome measures will be administered online at baseline, one-month, and three-month follow-ups. RESULTS: This trial is currently underway. The primary outcomes will be diabetes distress and benefit-finding in diabetes. Secondary outcomes will be depression, anxiety, diabetes self-care, perceived health, and health care utilization. We aim to recruit 104 participants. All stages of the study will be conducted online using the Writing for Health program. Group differences will be analyzed on an intention-to-treat basis using mixed models repeated measures. Linguistic analyses of the writing exercise scripts, and examinations of the immediate emotional responses to the writing exercises, will also be undertaken. CONCLUSIONS: This RCT will be the first study to examine iBFW for adults with type 1 or type 2 diabetes. If iBFW is found to be efficacious in reducing diabetes distress and improving diabetes self-care and other outcomes, iBFW may offer the potential to be a low-cost, easily accessible self-help intervention to improve the wellbeing of adults with diabetes. TRIAL REGISTRATION: Australia and New Zealand Clinical Trials Registry (ACTRN12615000241538).

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)0.013
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.019
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.318
GPT teacher head0.607
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2017
Admission routes1
Has abstractyes

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