Diabetes Educators’ Insights Regarding Connecting Mobile Phone– and Wearable Tracker–Collected Self-Monitoring Information to a Nationally-Used Electronic Health Record System for Diabetes Education: Descriptive Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Diabetes educators are integral to a clinical team in providing diabetes self-management education and support; however, current mobile and Web-based self-management tools are not integrated into clinical diabetes care to support diabetes educators' education efforts. OBJECTIVE: The objective of our study was to seek diabetes educators' insights regarding the development of an interface within the Chronicle Diabetes system, a nationally used electronic health record (EHR) system for diabetes education documentation with behavioral goal-setting functions, to transfer mobile phone- and wearable tracker-collected self-monitoring information from patients to diabetes educators to facilitate behavioral goal monitoring. METHODS: A descriptive qualitative study was conducted to seek educators' perspectives on usability and interface development preferences in developing a connected system. Educators can use the Chronicle Diabetes system to set behavioral goals with their patients. Individual and group interviews were used to seek educators' preferences for viewing mobile phone- and wearable tracker-collected information on diet, physical activity, and sleep in the Chronicle Diabetes system using open-ended questions. Interview data were transcribed verbatim and analyzed for common themes. RESULTS: Five common themes emerged from the discussion. First, educators expressed enthusiasm for and concerns about viewing diet and physical activity data in Chronicle Diabetes system. Second, educators valued viewing detailed dietary macronutrients and activity data; however, they preferred different kinds of details depending on patients' needs, conditions, and behavioral goals and educators' training background. Third, all educators liked the integration of mobile phone-collected data into Chronicle Diabetes system and preferably with current EHR systems. Fourth, a need for a health care team and a central EHR system to be formed was realized for educators to share summaries of self-monitoring data with other providers. Fifth, educators desired advanced features for the mobile app and the connected interface that can show self-monitoring data. CONCLUSIONS: Flexibility is needed for educators to track the details of mobile phone- and wearable tracker-collected diet and activity information, and the integration of such data into Chronicle Diabetes and EHR systems is valuable for educators to track patients' behavioral goals, provide diabetes self-management education and support, and share data with other health care team members to faciliate team-based care in clinical practice.
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 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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".