Caregiver experiences of an integrative patient-centered digital health application for pediatric type 1 diabetes care: findings from a pilot clinical trial
Bibliographic record
Abstract
Abstract Diabetes technology generates vital health data, but healthcare professionals (HCP) and patients must navigate multiple platforms to access it. We developed a digital health platform, co-designed with patients and families living with type 1 diabetes (T1D) and their HCPs, that aim to support a collaborative care experience through shared access to diabetes data, clinical recommendations, and resources. We describe caregivers’ views on the platform’s impact on clinic visits and child self-management in children with T1D. A six-month observational pilot study at BC Children’s Hospital Diabetes Clinic in British Columbia, Canada, gathered data through surveys and interviews. Surveys were administered to caregivers and HCPs at different time points throughout the study; 18 qualitative interviews were conducted with caregivers at the conclusion of the study. Quantitative data were summarized descriptively. Interview data were transcribed, coded using open and systematic coding, and subsequent inductive thematic analysis. Eighteen caregivers completed the surveys, and 11 HCP participants submitted 41 surveys (approximately 3-4 each) after using the platform. Most caregivers (61%; 11/18) found the platform helpful, and 56% (10/18) reported that using the platform made their clinical visits and recommendations more personalized. Nearly all HCPs (90%; 37/41) were satisfied with the platform’s ability to support clinical visits. Themes identified from caregiver qualitative interviews revealed that (1) the platform provided a convenient connection that improved preparedness and empowered caregivers in managing their child’s T1D; (2) the platform’s value was driven by the healthcare team’s usage of it; and (3) caregivers felt hopeful that the platform could better support their child’s T1D management. The platform could foster a collaborative and personalized care experience that enables caregivers to engage in diabetes self-management and feel connected to their healthcare team. These results will guide the future development, evaluation, and implementation of the platform. Author Summary Managing type 1 diabetes (T1D) involves keeping track of a lot of health information, like blood sugar levels, insulin doses, and food intake. Right now, families and healthcare providers often need to use several different apps or systems to access this information, which can be confusing and hard to manage. To make this easier, we created a new digital platform that puts all the important diabetes data in one place. We designed it together with families of children with T1D and their healthcare providers, so it could truly meet their needs. The goal was to help families and doctors work together more easily by sharing information, treatment recommendations, and helpful resources. We tested the platform during a six-month pilot study at BC Children’s Hospital Diabetes Clinic in British Columbia, Canada. We asked parents and caregivers, as well as healthcare professionals, to share their thoughts through surveys and interviews. In total, 18 caregivers completed surveys, and 11 healthcare providers filled out 41 surveys. At the end of the study, we also interviewed 18 caregivers to hear more about their experience using the platform. The results were promising. Most caregivers (61%) said the platform was helpful in managing their child’s diabetes. Over half (56%) felt that their visits with their doctor and diabetes team became more personal and tailored to their child’s specific needs. Nearly all of the healthcare providers (90%) said the platform helped improve their clinical visits. Caregivers also shared some deeper insights during interviews. They said the platform helped them feel more prepared for appointments and more confident in managing their child’s diabetes. They also noted that the platform worked best when their healthcare team actively used it. Many felt hopeful that this kind of tool could make a big difference in their child’s day-to-day diabetes care. In short, this digital platform shows real potential to improve the way families and healthcare teams manage type 1 diabetes together. It could lead to more personalized care, improved communication and connection, and better support for both children and their caregivers. These early results will help us improve the platform and guide how it’s used in the future.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".