Evaluation of the SUCCESS Health Literacy App for Australian Adults With Chronic Kidney Disease: Protocol for a Pragmatic Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: We developed a smartphone app-the SUCCESS (Supporting Culturally and Linguistically Diverse CKD Patients to Engage in Shared Decision-Making Successfully) app-to support Australian adults with kidney failure undertaking dialysis to actively participate in self-management and decision-making. The content of the SUCCESS app was informed by a theoretical model of health literacy that recognizes the importance of reducing the complexity of health information as well as providing skills necessary to access, understand, and act on this information. OBJECTIVE: The purpose of this study is to investigate the efficacy of the SUCCESS app intervention. METHODS: We designed a multicenter pragmatic randomized controlled trial to compare the SUCCESS app plus usual care (intervention) to usual care alone (control). A total of 384 participants receiving in-center or home-based hemodialysis or peritoneal dialysis will be recruited from six local health districts in the Greater Sydney region, New South Wales, Australia. To avoid intervention contamination, a pragmatic randomization approach will be used for participants undergoing in-center dialysis, in which randomization will be based on the days they receive hemodialysis and by center (ie, Monday, Wednesday, and Friday or Tuesday, Thursday, and Saturday). Participants undergoing home-based dialysis will be individually randomized centrally using simple randomization and two stratification factors: language spoken at home and research site. Consenting participants will be invited to use the SUCCESS app for 12 months. The primary endpoints, which will be assessed after 3, 6, and 12 months of app usage, are health literacy skills, evaluated using the Health Literacy Questionnaire; decision self-efficacy, evaluated using the Decision Self-Efficacy Scale; and rates of unscheduled health encounters. Secondary outcomes include patient-reported outcomes (ie, quality of life, evaluated with the 5-level EQ-5D; knowledge; confidence; health behavior; and self-management) and clinical outcomes (ie, symptom burden, evaluated with the Palliative care Outcome Scale-Renal; nutritional status, evaluated with the Patient-Generated Subjective Global Assessment; and intradialytic weight gain). App engagement will be determined via app analytics. All analyses will be undertaken using an intention-to-treat approach comparing the intervention and usual care arms. RESULTS: The study has been approved by Nepean Blue Mountains Human Research Ethics Committee (2020/ETH00910) and recruitment has begun at nine sites. We expect to finalize data collection by 2023 and publish the manuscript by 2024. CONCLUSIONS: Enhancing health literacy skills for patients undergoing hemodialysis is an important endeavor, given the association between poor health literacy and poor health outcomes, especially among culturally diverse groups. The findings from this trial will be published in peer-reviewed journals and disseminated at conferences, and updates will be shared with partners, including participating local health districts, Kidney Health Australia, and consumers. The SUCCESS app will continue to be available to all participants following trial completion. TRIAL REGISTRATION: Australia New Zealand Clinical Trials Registry (ANZCTR) ACTRN12621000235808; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380754&isReview=true. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39909.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.049 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".