Bridging the Gap in Carbohydrate Counting With a Mobile App: Needs Assessment Survey
Bibliographic record
Abstract
BACKGROUND: Carbohydrate counting (CC) can be burdensome and difficulty with adherence has been reported. Automated CC through mobile apps offers innovative solutions to ease this burden. OBJECTIVE: This cross-sectional web-based survey aims to identify (1) perceived barriers to CC by Canadians living with type 1 diabetes (T1D) and (2) app features that would help reduce these barriers. The secondary objective aims to compare apps used by participants with the suggested app features. METHODS: People with T1D aged 14 years and older, living in Canada, were recruited through the BETTER Canadian registry, diabetes organizations, and social media. Participants completed a 39-question web-based survey (closed- and open-ended) to identify barriers in CC, preferred CC app features, and current app use. Respondents rated barriers and app features using a 5-point Likert scale. The features were cross-referenced in each app reported being used by participants. Descriptive statistics summarized barriers and app feature preferences, and statistical analyses identified differences by age, app use, and insulin modality. Mean scores (out of 5) were compared using 2-tailed t tests or nonparametric tests. Open-ended questions were analyzed using inductive thematic analysis. RESULTS: Participants (N=196; woman: n=145, 74%; mean age 40 [SD 17] years; mean diabetes duration 22 (14) years; relied on CC to determine insulin doses at mealtimes: n=178, 90.8%) reported barriers related to carbohydrate identification, nutrient interaction, and insulin dose calculation, as well as psychosocial factors. Preferred app features included nutrient analysis (165/196, 84.2%), personalization (151/196, 77.1%), insulin bolus calculation (145/196, 74%), and health care professional support (135/196, 68.8%). Among the 16 apps used by participants, most (12/16, 75%) supported nutrient analysis but only one offered bolus calculations or health care professional support, and none offered personalization. Users on injections reported greater barriers to blood glucose monitoring for insulin adjustments compared to exclusive pump users (mean score of 3.87, SD 1.22 vs mean 3.30, SD 1.28; P=.001). They also expressed higher needs for meal logs in an electronic food journal (mean 4.06, SD 1.18 vs mean 3.69, SD 1.17; P=.01), bolus dose suggestions (mean 4.37, SD 0.98 vs mean 3.84, SD 1.26; P=.001), and app personalization (mean 4.47, SD 0.86 vs mean 3.93, SD 1.21; P<.001). No significant differences were observed based on age or app use. The thematic analysis revealed participants' perceptions of suggested barriers and features, as well as new barriers such as calculation errors from unreliable food data and nutrition labels, fear of eating disorders, limited app reliability, and insufficient health care support, with suggestions for technology-based solutions. CONCLUSIONS: CC mobile apps currently used do not meet the needs of people with T1D. A novel CC app with app features such as photo recognition, reliable nutrient values, and personalized bolus calculations could reduce the CC burden.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".