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Record W4381598814 · doi:10.2196/41876

Cancer Resource and Information Support (CRIS) for Bladder Cancer Survivors and Their Caregivers: Development and Usability Testing Study

2023· article· en· W4381598814 on OpenAlexvenueno aff
Michael A. Diefenbach, Allison Marziliano, Elizabeth J. Siembida, Thomas Mistretta, Halie Pfister, Andrea Yacoub, Kelli Aibel, Priya Patel, Emmanuel Lapitan, Erin K. Tagai, Marc C. Smaldone, Suzanne M. Miller

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicStoma care and complications
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institutes of Health
KeywordsCystectomyUsabilityMedicinePsychosocialBladder cancerCancerComputer scienceInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Bladder cancer survivors and their caregivers face profound practical (eg, use of stoma appliances and care for urinary diversion methods) and psychosocial (eg, depression and anxiety) challenges after surgical treatment with cystectomy. OBJECTIVE: To improve the health-related quality of life and postsurgical outcomes of both bladder cancer survivors and their caregivers, the team, in collaboration with Sourcetop, Inc (software design) and Dappersmith (graphic design), developed the Cancer Resource and Information Support (CRIS) software. The purpose of this manuscript is to report on the development and usability testing of the CRIS software. METHODS: The development of the CRIS software was guided by the Obesity-Related Behavioral Intervention Trials (ORBIT) model for developing behavioral treatments for chronic diseases. The ORBIT model is unique in that it proposes a flexible and progressive process with prespecific clinically significant milestones for forward movement and returns to earlier stages for refinement, and it facilitates communication among diverse groups by using terminology from the drug development model. This paper focuses on 2 phases of the ORBIT model: phase IA: define and IB: refine. During phase IA, the study team developed solutions for the stated clinical problem-adjustment to life post cystectomy-by reviewing the literature and collecting feedback from clinicians, professional organizations, bladder cancer survivors, and their caregivers. During Phase IB, the study team focused on tailoring content in the CRIS software to the user as well as usability testing with 7 participants. RESULTS: The finished product is CRIS, a web-based software for survivors of bladder cancer and their caregivers to serve as a health management and lifestyle resource after surgery. Overarching themes from phase IA (participant feedback) included how to use new medical equipment, tips and tricks for easier living with new medical equipment, questions about health maintenance, and questions about lifestyle modifications. To accommodate our target population, we also incorporated recommendations from the Americans with Disabilities Act for website design, such as large text size, large paragraph spacing, highly contrasting text and background colors, use of headings and labels to describe the purpose of the content, portrait orientation without the need for horizontal scrolling, multiple ways to access a web page within a set of pages, ability to navigate web pages in sequential order, and in-text links that are descriptive. Usability participants evaluated CRIS very positively, indicating that it was easy to use, the functions were well-integrated, and if available, they would use CRIS frequently. CONCLUSIONS: CRIS, developed over the course of 18 months by integrating feedback from experts, literature reviews, and usability testing, is the first web-based software developed for bladder cancer survivors and their caregivers to help them adjust to life following cystectomy. The efficacy of CRIS in improving patients' and caregivers' quality of life is currently being evaluated in a randomized controlled trial.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.130
GPT teacher head0.435
Teacher spread0.305 · 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 designObservational
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

Citations3
Published2023
Admission routes1
Has abstractyes

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