A Novel Mobile Health App to Educate and Empower Young Adults With Type 1 Diabetes to Exercise Safely: Prospective Single-Arm Pre-Post Noninferiority Clinical Trial
Bibliographic record
Abstract
Background: A novel mobile health (mHealth) app "acT1ve," developed using a co-design model, provides real-time support during exercise for young people with type 1 diabetes (T1D). Objective: This study aimed to demonstrate the noninferiority of acT1ve compared with "treatment as usual" with regard to hypoglycemic events. Methods: Thirty-nine participants living with T1D (age: 17.2, SD 3.3 years; HbA1c: 64, SD 6.0 mmol/mol) completed a 12-week single-arm, pre-post noninferiority study with a follow-up qualitative component. During the intervention, continuous glucose monitoring (CGM) and physical activity were monitored while participants used acT1ve to manage exercise. CGM data were used to assess the number of hypoglycemic events (<3.9 mmol/L for ≥15 minutes) in each phase. Using a mixed effects negative binomial regression, the difference in the rates of hypoglycemia between the preapp and app-use phases was analyzed. Participants completed both a semistructured interview and the user Mobile Application Rating Scale (uMARS) questionnaire postintervention. All interviews were audio-recorded for transcription, and a deductive content analysis approach was used to analyze the participant interviews. The uMARS Likert scores for each subscale (engagement, functionality, esthetics, and information) were calculated and reported as medians with IQRs. Results: The rates of hypoglycemia were similar for both the preapp and app-use phases (0.79 and 0.83 hypoglycemia events per day, respectively). The upper bound of the CI of the hypoglycemia rate ratio met the prespecified criteria for noninferiority (rate ratio=1.06; 95% CI 0.91-1.22). The uMARS analysis showed a high rating (≥4 out of 5) of acT1ve by 80% of participants for both functionality and information, 72% for esthetics, and 63% for overall uMARS rating. Content analysis of the interview transcripts identified 3 main themes: "Provision of information," "Exercising with the App," and "Targeted Population." Conclusions: The mHealth app "acT1ve," which was developed in collaboration with young people with T1D, is functional, acceptable, and safe for diabetes management around exercise. The study supports the noninferiority of acT1ve compared with "treatment as usual" with regards to hypoglycemic events.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".