Use of Mobile Technology in the Care of Adolescents With Diabetes
Bibliographic record
Abstract
Abstract BACKGROUND: The advent of personalised cell phones and mobile technology has created an increasing desire to integrate these resources into the care of pediatric patients with diabetes. Adolescents in particular often have poor diabetes self-management practises and fail to meet glyce-mic targets. Mobile technology use is prevalent among adolescents and the idea of using this technology to assist with diabetes self-care is appealing. However, integrating this technology into clinical practice is challenging and many practitioners do notknow where to start. OBJECTIVES: To describe the use, benefits, and limitations of mobile technology in diabetes care among adolescents and their parents, and to assess if mobile technology use is linked to lower HbA1c levels. DESIGN/METHODS: This cross sectional study involved adolescents age 11-18 years and their parents who were recruited during their regular diabetes patient care visits at two separate pediatric diabetes centres. Patients and parents completed a questionnaire designed by study authors. Patients' two most recent HbA1c levels were recorded following survey completion. RESULTS: 100 adolescents and 80 parents completed the questionnaire. Device ownership was high, with 89% of adolescents and 100% of parents owning at least 1 device. Only one third of the cohort reported using mobile technology for their diabetes care. The commonest reason for non-use was lack of awareness of apps for diabetes care (53% adolescents, 60% parents). Among mobile technology users, texting and calculation were the most frequently used apps. Apps for calorie and carb counting were also frequently used, among which CalorieKing™ was the highest reported (44% adolescents; 47% parents). Insulin pump specific programs including Diasend® and Medtronic CareLink® were reported by 14.8% adolescents and 14.3% parents. The average HbA1c for the entire cohort was 8.0%, with no statistically significant difference between adolescent mobile technology users and non-users (7.8% vs 8.3%; p=0.22). However, mean HbA1c was found to be lower among those adolescents whose parents used mobile technology for their management (7.6 % vs 8.2 %; p=0.04). CONCLUSION: Only a minority of adolescents use mobile technology for their diabetes care, and lack of awareness was the major barrier to mobile technology use. Basic smartphone functions including texting and calculation were the most cited apps used. Parental technology use was associated with improved glycemic control. Considering the widespread use of mobile technology among young people, there remains untapped potential for greater use of this technology towards improved self-care in adolescents with diabetes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".