The Impact of Mobile Health Use on the Self-care of Patients With Type 2 Diabetes: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Background Chronic diseases are a substantial public health issue worldwide and affect an individual’s quality of life. Due to the alarming rise in type 2 diabetes, health care that was primarily focused on diagnosis and treatment of the disease is increasingly focused on prevention and self-care. Patients who adhere to a constant and strict treatment regimen (physical activity, diet, medication) and regularly monitor their health are more likely to maintain self-care and health, prevent the exacerbation of the disease, and avoid the complications of diabetes (retinopathy, diabetic feet, etc). In recent years, many innovative devices that have become increasingly present in inpatient health care, such as mobile apps, are available to help patients maintain consistency in monitoring their health status. Mobile apps make it easier for individuals to monitor their self-care and illness and follow instructions regarding disease control. Objective This study aims to determine the impact of mobile app use on self-care in patients with type 2 diabetes. We will evaluate and test the usefulness of the forDiabetes app as a tool to improve the self-care of individuals with type 2 diabetes. Methods We will perform a double-blind randomized controlled trial. The study will include individuals aged over 18 years diagnosed with or have regulated type 2 diabetes who are treated in family medicine practices. Additionally, the individuals included in the study should not have any acute complications due to the consequences of type 2 diabetes. They will use an Android or iOS mobile phone and a blood glucose meter during the investigation. With the help of simple randomization, individuals will be divided into the intervention and control groups. Individuals in the intervention group will use the forDiabetes mobile app to monitor their self-care for type 2 diabetes. Individuals in the control group will not receive a particular intervention. Data will be collected using the Self-care of Diabetes Inventory questionnaire and Brief Illness Perception Questionnaire. Blood sugar, blood pressure, glycated hemoglobin (HbA1c), and weight measurements will be monitored using calibrated instruments during the study by the nurses employed at the family medicine practice. Data will be collected at the beginning of the study and after a patient visits the family medicine practice. Results In the first half of 2020, we have prepared a translation of the mobile app that will be used by the participants of the intervention group, as well as more detailed instructions for using the app. We have also prepared a translation of the questionnaires in Slovene. The research results will be published in 2023. Conclusions This research contributes to greater visibility and usability of mobile apps for the self-care of patients with type 2 diabetes and raises awareness of the possible use of innovative methods. Trial Registration Clinicaltrials.gov NCT04999189; https://clinicaltrials.gov/ct2/show/NCT04999189 International Registered Report Identifier (IRRID) PRR1-10.2196/31652
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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.076 | 0.011 |
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".