Empowering Caregiver Well-Being With the Adhera Caring Digital Program for Family Caregivers of Children Living With Type 1 Diabetes: Mixed Methods Feasibility Study
Bibliographic record
Abstract
Background: Caregivers of children living with type 1 diabetes (T1D) face multiple challenges that significantly impact their mental health and quality of life. The well-being of caregivers directly affects the management of the child's condition. The Adhera Caring Digital Program (ACDP) is a comprehensive, digitally delivered program, designed to support family caregivers in enhancing self-management and well-being. This study aims to assess how the ACDP influences caregivers' mood, emotional well-being, and health-related quality of life within the context of T1D. Objective: This study aimed to evaluate the impact of ACDP on caregivers' psychological well-being and caregiving outcomes. Methods: This was a two-step, prospective, mixed methods study targeting caregivers of children living with T1D who were under the care of a pediatric endocrinologist at Miguel Servet Children's University Hospital in Zaragoza, Spain. In substudy 1 (SS1), qualitative and quantitative data were collected to optimize the ACDP. In substudy 2 (SS2), caregivers used the optimized ACDP for three months. Psychometric assessments were conducted at baseline and follow-up to evaluate positive mood states, general well-being, self-efficacy, and lifestyle behaviors. This paper focuses on SS2. Results: Ninety caregivers participated in SS2. Positive affect significantly increased (P<.001), and negative affect decreased (P<.001) on the Positive and Negative Affect Schedule (PANAS). Depression, anxiety, and stress scores were reduced (P<.001) on the Depression, Anxiety and Stress Scale-21 Items (DASS-21). General well-being, measured by the Mental Health Continuum-Short Form (MHC-SF) and self-efficacy, assessed using General Self-Efficacy Scale (GSE), improved significantly (P<.001). Health-related quality of life (HrQoL) scores and Mediterranean Diet Quality Index scores increased modestly (P=.03, and P=.04, respectively). Conclusions: The ACDP intervention improved caregivers' psychological well-being and self-efficacy. These findings highlight the potential of digital solutions to support caregiver mental health and positively influence diabetes management. Future research should explore long-term outcomes and scalability.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".