A Pilot Study of Intervention With a Mobile Application Visualizing the Macronutrient Content for Type 2 Diabetes at a Japanese Center
Bibliographic record
Abstract
Background: Estimating the nutritional content of food is essential for self-management in people with type 2 diabetes mellitus, but it is a difficult skill to learn. The aim of this study was to investigate how diabetes management was impacted by the ability of patients to search for items they ate from a database of 26,300 different foods, and to visualize nutritional intake using the Japanese mobile application (app) "Calomeal." Methods: This was a single-arm, single-center, pilot study. Eighteen outpatients with type 2 diabetes mellitus used the "Calomeal" app for 3 months. The primary endpoint was change in hemoglobin A1c (HbA1c). Secondary endpoints were changes in body weight (BW), lipid parameters, and quality of life scores. Results: The baseline characteristics of the study subjects were as follows: age: 53.4 ± 7.8 years; male/female ratio: 11/7; HbA1c: 7.9 (7.58 - 8.23)%; and body mass index (BMI): 25.17 (21.63 - 28.59) kg/m 2 . Significant reductions in HbA1c and BMI were observed over 3 months (HbA1c: 7.9 (7.58 - 8.23)% to 7.6 (7.3 - 8.23)%, P = 0.0410; BMI: 25.17 (21.63 - 28.59) to 24.54 (21.57 - 27.81) kg/m 2 , P = 0.0057). Reductions in HbA1c and BMI both correlated with decreased carbohydrate intake estimated by the mobile app. Conclusions: Japanese patients who used their smartphones to visualize their nutritional intake using the "Calomeal" app demonstrated improved short-term glycemic control and BMI. Although the validity of the results should be tested in future randomized controlled trials, this approach may be a clinical option for improving self-management in Japanese patients with type 2 diabetes mellitus. J Clin Med Res. 2021;13(8):425-433 doi: https://doi.org/10.14740/jocmr4558
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".