Popular Glucose Tracking Apps and Use of mHealth by Latinos With Diabetes: Review
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus in the United States is an increasingly common chronic disease, costing hundreds of billions of dollars and contributing to hundreds of thousands of deaths each year. The prevalence of diabetes is over 50% higher in Latinos than in the general population, and this group also suffers from higher rates of complications and diabetes-related mortality than NHWs. mHealth is a promising new treatment modality for diabetes, though few smartphone apps have been designed specifically for Latinos. OBJECTIVE: The objectives of our study were: (1) to identify the most common features of the most popular diabetes apps and consider how such features may be improved to meet the needs of Latinos; (2) to determine the use of diabetes apps among a sample of online Hispanics in the US. METHODS: Our study consisted of two parts. First, 20 of the most popular diabetes apps were reviewed in order to ascertain the most prevalent features and functionalities. Second, an online survey was fielded through a popular health website for Latinos (HolaDoctor) inquiring about respondents' use of diabetes apps. RESULTS: Approximately one-third of apps reviewed were available in Spanish. The most common features were blood glucose recording/annotation and activity logs. The majority of apps permitted exportation of data via e-mail but only a third enabled uploading to an online account. Twenty percent of apps reviewed could connect directly with a glucometer, and 30% had reminder functionalities prompting patients to take medications or check blood glucose levels. Over 1600 online surveys were completed during the second half of April 2014. More than 90% of respondents were from the United States, including Puerto Rico. The majority of respondents used a device running on an Android platform while only a quarter used an iPhone. Use of diabetes apps was approximately 3% among diabetic respondents and 3.6% among diabetic respondents who also had a smartphone. Among app users, blood glucose and medication diaries were the most frequently used functionalities while hemoglobin A1c and insulin diaries were the least used. A significant majority of app users did not share their progress on social media though many of these were willing to share it with their doctor. CONCLUSIONS: Latino diabetics have unique needs and this should be reflected in diabetes apps designed for this population. Existing research as well as our survey results suggest that many Latinos do not possess the prerequisite diabetes knowledge or self-awareness to fully benefit from the most prevalent functionalities offered by the most popular diabetes apps. We recommend developers incorporate more basic features such as diabetes education, reminders to check blood glucose levels or take medications, Spanish language interfaces, and glucometer connectivities, which are relatively underrepresented in the most popular diabetes apps currently available in Spanish.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".