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Record W3192973299 · doi:10.2196/28322

A Digital Self-management Program (Help to Overcome Problems Effectively) for People Living With Cancer: Feasibility Randomized Controlled Trial

2021· article· en· W3192973299 on OpenAlexaff
Hayley Wright, Faith Martin, Wendy Clyne, Cain C. T. Clark, Gabriela Matouskova, Michael McGillion, Andy Turner

Bibliographic record

VenueJournal of Medical Internet Research · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcMaster University
FundersCoventry University
KeywordsRandomized controlled trialContext (archaeology)MedicineAnxietyIntervention (counseling)Sample size determinationPhysical therapySelf-managementMental healthNursingPsychiatryComputer scienceSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: We present the results of a feasibility, randomized waitlist control group (CG) parallel design study with a 1:1 allocation ratio. Participants were randomized into an intervention group (IG) or a waitlist CG. The intervention was a 6-week digital self-management program, Help to Overcome Problems Effectively (HOPE), for people with cancer. OBJECTIVE: This study aims to test the feasibility of a digitally delivered self-management program for people with cancer. This will inform the design of a definitive randomized controlled trial. In addition, a preliminary assessment of the impact of the HOPE program via secondary outcomes will be used to assess signals of efficacy in a trial context. METHODS: Participants were drawn from an opportunity sample, referred by Macmillan Cancer Support, and were invited via email to participate in the study (N=61). Primary outcomes were rates of recruitment, retention, follow-up, completion and adherence, sample size and effect size estimation, and assessment of progression criteria for a definitive trial. Secondary outcomes were self-report measures of participants' positive mental well-being, depression, anxiety, and patient activation (ie, confidence in managing their cancer). The intervention and data collection took place on the web. RESULTS: The recruitment rate was 77% (47/61). A total of 41 participants completed the baseline questionnaires and were randomized to either the IG (n=21) or the waitlist CG (n=20). The retention rate (attending all program sessions) was greater than 50% (all: 21/41, 51%, IG: 10/21, 48%; and CG: 11/20, 55%). The follow-up rate (completing all questionnaires) was greater than 80% (all: 33/41, 80%; IG: 16/21, 76%; and CG: 17/20, 85%). The completion rate (attending ≥3 sessions and completing all questionnaires) was greater than 60% (all: 25/41, 61%; IG: 13/21, 62%; and CG: 12/20, 60%). Engagement data showed that participants viewed between half (5.1/10, 51%) and three-quarters (12.2/16, 76%) of the pages in each session. CONCLUSIONS: All progression criteria for a definitive trial were met, as supported by the primary outcome data. The IG showed improved postprogram scores on measures of positive mental well-being, depression, anxiety, and patient activation. A full-scale trial of the digital HOPE program for people with cancer will allow us to fully evaluate the efficacy of the intervention relative to a CG. TRIAL REGISTRATION: ISRCTN Registry ISRCTN79623250; http://www.isrctn.com/ISRCTN79623250. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/24264.

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)0.012
metaresearch head score (Gemma)0.008
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.402
Teacher spread0.367 · 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
GenreEmpirical

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

Citations24
Published2021
Admission routes1
Has abstractyes

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