Measuring the Effects of Sharing Mobile Health Data During Diabetes Consultations: Protocol for a Mixed Method Study
Bibliographic record
Abstract
BACKGROUND: There is rising demand for health care's limited resources. Mobile health (mHealth) could be a solution, especially for those with chronic illnesses such as diabetes. mHealth can increases patients' options to self-manage their health, improving their health knowledge, engagement, and capacity to contribute to their own care decisions. However, there are few solutions for sharing and presenting patients' mHealth data with health care providers (HCPs) in a mutually understandable way, which limits the potential of shared decision making. OBJECTIVE: Through a six-month mixed method feasibility study in Norway, we aim to explore the impacts that a system for sharing patient-gathered data from mHealth devices has on patients and HCPs during diabetes consultations. METHODS: Patients with diabetes will be recruited through their HCPs. Participants will use the Diabetes Diary mobile phone app to register and review diabetes self-management data and share these data during diabetes consultations using the FullFlow data-sharing system. The primary outcome is the feasibility of the system, which includes HCP impressions and expectations (prestudy survey), usability (System Usability Scale), functionalities used and data shared during consultations, and study-end focus group meetings. Secondary outcomes include a change in the therapeutic relationship, patient empowerment and wellness, health parameters (HbA1c and blood pressure), and the patients' own app-registered health measures (blood glucose, medication, physical activity, diet, and weight). We will compare measures taken at baseline and at six months, as well as data continuously gathered from the app. Analysis will aim to explain which measures have changed and how and why they have changed during the intervention. RESULTS: The Full Flow project is funded for 2016 to 2020 by the Research Council of Norway (number 247974/O70). We approached 14 general practitioner clinics (expecting to recruit 1-2 general practitioners per clinic) and two hospitals (expecting to recruit 2-3 nurses per hospital). By recruiting through the HCPs, we expect to recruit 74 patients with type 2 and 33 patients with type 1 diabetes. Between November 2018 and July 2019, we recruited eight patients and 15 HCPs. During 2020, we aim to analyze and publish the results of the collected data from our patient and HCP participants. CONCLUSIONS: We expect to better understand what is needed to be able to share data. This includes potential benefits that sharing patient-gathered data during consultations will have on patients and HCPs, both individually and together. By measuring these impacts, we will be able to present the possibilities and challenges related to a system for sharing mHealth data for future interventions and practice. Results will also demonstrate what needs to be done to make this collaboration between HCPs and patients successful and subsequently further improve patients' health and engagement in their care.
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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 | 0.079 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".