Evaluating a Digital Chronic Condition Prevention Intervention (THRIVE) in Australian General Practice: Protocol for a Mixed Methods Feasibility Study (ePREVENT-360)
Bibliographic record
Abstract
Background: Chronic conditions are responsible for a growing burden of morbidity, mortality, and cost globally. Despite widespread recognition of the need for preventive care, general practice remains underresourced and primarily focused on treatment. Digital health interventions (DHIs) present a scalable solution to support person-centered preventive care, but evidence regarding the feasibility and acceptability of multirisk consumer-facing interventions in general practice remains limited. Objective: This study (ePREVENT-360) aims to evaluate the feasibility, acceptability, sustainability, and preliminary impact on health activation of a consumer-facing DHI, THRIVE (Tailored Health Risk Insights for Vital Empowerment) in Australian general practices. Methods: A mixed methods, pre-post feasibility study will be conducted in 5 general practices across New South Wales, Queensland, and Victoria. Adult consumers aged 30 to 65 years will use the THRIVE digital platform to receive chronic condition risk assessments, health scores, and action plans. Quantitative data will include engagement metrics, surveys, and chronic condition risk scores. Qualitative semistructured interviews with consumers and clinicians will provide data about acceptability, engagement, and sustainability. Quantitative data will be analyzed using descriptive and multilevel regression methods, while qualitative data will be analyzed thematically. Results: The study has secured funding in 2024 through an Australian General Practice Research Foundation and Hospital Contribution Fund of Australia Research Foundation Health Services Research Grant. Consumer recruitment commenced in December 2025. Recruitment of the 5 participating general practices was completed in March 2026. As of April 2026, all clinician preintervention interviews have been completed, and consumer recruitment has commenced, with 25 consents obtained. Data collection is ongoing, with follow-up expected to be completed by December 2026. Outcomes will inform the iterative refinement of interventions and future trial designs to assess effectiveness. Conclusions: This study will address a key evidence gap in the digital prevention space by evaluating the feasibility, acceptability, and sustainability of a multicondition DHI embedded in general practices. The findings will support the development of a larger adaptive controlled trial and inform future implementation.
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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 | 0.082 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".