Opening the Black Box of an mHealth Patient-Reported Outcome Tool for Diabetes Self-Management: Interview Study Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) tools are used to collect data on patient-reported outcomes (PROs) and facilitate the assessment of patients' self-management behaviors outside the clinic environment. Despite the high availability of mHealth diabetes tools, there is a lack of understanding regarding the underlying reasons why these mHealth PRO tools succeed or fail in terms of changing patients' self-management behaviors. OBJECTIVE: This study aims to identify the factors that drive engagement with an mHealth PRO tool and facilitate patients' adoption of self-management behaviors, as well as elicit suggestions for improvement. METHODS: ) levels and adherence to self-management behaviors at 12 months among patients with uncontrolled type 2 diabetes. Patients randomized to i-Matter participated in semistructured interviews about their experiences at the 3-, 6-, 9-, and 12-month study visits. A qualitative analysis of the interviews was conducted by 2 experienced qualitative researchers using conventional qualitative content analysis. RESULTS: The sample comprised 71 patients, of whom 67 (94%) completed at least one interview (n=48, 72% female patients; n=25, 37% identified as African American or Black; mean age 56.65 [SD 9.79] years). We identified 4 overarching themes and 6 subthemes. Theme 1 showed that the patients' reasons for engagement with i-Matter were multifactorial. Patients were driven by internal motivating factors that bolstered their engagement and helped them feel accountable for their diabetes (subtheme 1) and external motivating factors that helped to serve as reminders to be consistent with their self-management behaviors (subtheme 2). Theme 2 revealed that the use of i-Matter changed patients' attitudes toward their disease and their health behaviors in 2 ways: patients developed more positive attitudes about their condition and their ability to effectively self-manage it (subtheme 3), and they also developed a better awareness of their current behaviors, which motivated them to adopt healthier lifestyle behaviors (subtheme 4). Theme 3 showed that patients felt more committed to their health as a result of using i-Matter. Theme 4 highlighted the limitations of i-Matter, which included its technical design (subtheme 5) and the need for more resources to support the PRO data collected and shared through the tool (subtheme 6). CONCLUSIONS: This study isolated internal and external factors that prompted patients to change their views about their diabetes, become more engaged with the intervention and their health, and adopt healthy behaviors. These behavioral mechanisms provide important insights to drive future development of mHealth interventions that could lead to sustained behavior change.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".