Person-Centered Interactive Self-Management Support in Primary Health Care for People with Type 2 Diabetes: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Background: Type 2 diabetes (T2D) is increasing as the population ages. The development of new medical treatments is promising and important, but the basic treatment remains self-management, even if adherence to lifestyle advice is low. Electronic health (eHealth) or mHealth interventions can increase empowerment among people living with T2D and may compensate for the lack of professional resources and geographical distances. The interactive self-management support (iSMS) project aims at including digital tools to support people living with T2D in their self-management and facilitating their interaction with diabetes specialist nurses (DSNs). This protocol outlines a study with the purpose of developing and evaluating an intervention where people living with T2D can increase self-efficacy and empowerment through digital self-monitoring and interaction with DSNs. Objective: To develop and evaluate a person-centered iSMS intervention in primary health care for people with T2D in addition to their usual diabetes care. Methods: This study is a 12-month, 3-armed, nonblinded randomized controlled trial (RCT), which will be conducted in 6 primary health care centers (HCCs) in northern Sweden. Eligible participants will be randomized to either an intervention group (n=46), a control group (n=46), or an external group (n=46) for comparison. The intervention group will receive the mobile app, and the control group will receive a minimal intervention (diabetes brochure) and the usual standard of care. Changes in glycated hemoglobin (HbA1c) will be the primary outcome measure. Results: This trial is currently open for recruitment. The first results are expected to be submitted for publication in Autumn 2019. Conclusions: This study, with its focus on iSMS, will provide insights regarding suitable ways to promote and develop a person-centered intervention. If successful, the intervention has the potential to become a model for the provision of self-management support to people with T2D.
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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 | 0.042 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.009 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.093 | 0.013 |
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".