Implementing Diabetes Distress Screening in a Pediatric Endocrinology Clinic Using a Digital Health Platform: Quantitative Secondary Data Analysis Article Swipe
YOU?
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· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/65107
Background Type 1 diabetes (T1D) management requires following a complex and constant regimen relying on child or caregiver behaviors, skills, and knowledge. Psychological factors such as diabetes distress (DD), depression, and burnout are pertinent considerations in the treatment of pediatric T1D. Approximately 40% of youth and 61% of caregivers experience DD. Implementation of DD screening as part of clinical best practice is recommended and may facilitate treatment referral, perhaps leading to improved health or well-being for youth with T1D and their caregivers. By building on existing institutional infrastructure when available, screening via digital health platforms (applications, or “apps”) may allow for timely screening of, and response to, DD. Objective This work details the creation, implementation, and refinement of a process to screen for DD in youth and their caregivers in the context of routine T1D care using a digital health platform. Methods DD screening was implemented in an outpatient endocrinology clinic over 1 year as part of a larger screen-to-treat trial for children aged 8‐12.99 years and their caregivers. Validated measures were sent via digital health platform to be completed prior to the clinic visit. Results were initially reviewed manually, but a digital best practice alert (BPA) was later built to notify staff of elevated scores. Families experiencing DD received resources sent via the digital health platform. For this secondary analysis, child demographics and glycated hemoglobin A 1c (HbA 1c ) were collected. Results During the screening period, absolute completion rates were 36.78% and 38.83%, with adjusted screening rates at 52.02% and 54.48%, for children and caregivers, respectively. A total of 21 children (mean HbA 1c 8.04%, SD 1.39%) and 26 caregivers (child mean HbA 1c 8.04%, SD 1.72%) reported elevated DD. Prior to BPA development, resources were sent to all but 1 family. After BPA implementation, all families were sent resources. Conclusions Early findings indicate that DD education, screening, and response can be integrated via digital platforms in a freestanding outpatient endocrinology clinic, thereby facilitating timely treatment referral and provision of resources for those identified with distress. Notably, in the observed 1-year screening period, screening rates were low, and barriers to implementation were identified. While some implementation challenges were iteratively addressed, there is a need for future quality improvement initiatives to improve screening rates and the identification of, or response to, DD in our pediatric patients and their families.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/65107
- OA Status
- gold
- Cited By
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- References
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- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4405219074Canonical identifier for this work in OpenAlex
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https://doi.org/10.2196/65107Digital Object Identifier
- Title
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Implementing Diabetes Distress Screening in a Pediatric Endocrinology Clinic Using a Digital Health Platform: Quantitative Secondary Data AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-12-11Full publication date if available
- Authors
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Nicole Kahhan, Larry A. Fox, Matthew Benson, Susana R. PattonList of authors in order
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https://doi.org/10.2196/65107Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.2196/65107Direct OA link when available
- Concepts
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Medicine, Referral, Glycated hemoglobin, Distress, Context (archaeology), Digital health, Family medicine, Health care, Diabetes mellitus, Type 2 diabetes, Clinical psychology, Biology, Paleontology, Economic growth, Endocrinology, EconomicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.54.48%, | 253 |
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