A randomized wait-list control trial to evaluate the impact of a mobile application to improve self-management of individuals with type 2 diabetes: a study protocol
Bibliographic record
Abstract
BACKGROUND: Management of diabetes through improved glycemic control and risk factor modification can help prevent long-term complications. Much diabetes management is self-management, in which healthcare providers play a supporting role. Well-designed e-Health solutions targeting behavior change can improve a range of measures, including glycemic control, perceived health, and a reduction in hospitalizations. METHODS: The primary objective of this study is to evaluate if a mobile application designed to improve self-management among patients with type 2 diabetes (T2DM) improves glycemic control compared to usual care. The secondary objectives are to determine the effects on patient experience and health system costs; evaluate how and why the intervention worked as observed; and gain insight into considerations for system-wide scale-up. This pragmatic, randomized, wait-list-control trial will recruit adult participants from three Diabetes Education Programs in Ontario, Canada. The primary outcome is glycemic control (measured by HbA1c). Secondary outcomes include patient-reported outcomes and patient-reported experience measures, health system utilization, and intervention usability. The primary outcome will be analyzed using an ANCOVA, with continuous secondary outcomes analyzed using Poisson regression. Direct observations will be conducted of the implementation and application-specific training sessions provided to each site. Semi-structured interviews will be conducted with participants, healthcare providers, organizational leaders, and system stakeholders as part of the embedded process evaluation. Thematic analysis will be applied to the qualitative data in order to describe the relationships between (a) key contextual factors, (b) the mechanisms by which they effect the implementation of the intervention, and (c) the impact on the outcomes of the intervention, according to the principles of Realist Evaluation. DISCUSSION: The use of mobile health and virtual tools is on the rise in health care, but the evidence of their effectiveness is mixed and their evaluation is often lacking key contextual data. Results from this study will provide much needed information about the clinical and cost-effectiveness of a mobile application to improve diabetes self-management. The process evaluation will provide valuable insight into the contextual factors that influence the application effectiveness, which will inform the potential for adoption and scale. TRIAL REGISTRATION: Clinicaltrials.gov NCT02813343 . Registered on 24 June 2016 (retrospectively registered). Trial Sponsor: Ontario Telemedicine Network.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".