Background: Data on the use of Control-IQ, the latest FDA-approved automated insulin delivery (AID) system for people with T1D 6 years of age or older is still scarce, particularly regarding nonglycemic outcomes. Children with T1D and their parents are at higher risk for sleep disturbances. This study assesses sleep, psycho-behavioral and glycemic outcomes of AID compared to sensor-augmented pump therapy (SAP) therapy in young children with T1D and their parents.
Methods: Thirteen parents and their young children (ages 7-10) on insulin pump therapy were enrolled. Children completed an initial 4-week study with SAP using their own pump and a study CGM followed by a 4-week phase of AID. Sleep outcomes for parents and children were evaluated through actigraphy watches. Several questionnaires were administered at baseline and at the end of each study phase. CGM data were used to assess glycemic outcomes.
Results: Actigraphy data did not show any significant change from SAP to AID, except a reduction of number of parental awakenings during the night (p = 0.036). Parents reported statistically significant improvements in Pittsburgh Sleep Quality Index total score (p = 0.009), Hypoglycemia Fear Survey total score (p = 0.011), diabetes-related distress (p = 0.032), and depression (p = 0.023). While on AID, time in range (70-180 mg/dL) significantly increased compared to SAP (p < 0.001), accompanied by a reduction in hyperglycemia (p = 0.001).
Conclusions: These results suggest that use of AID has a positive impact on glycemic outcomes in young children as well as sleep and diabetes-specific quality of life outcomes in their parents.
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http://dx.doi.org/10.1111/pedi.13164 | DOI Listing |
JAMA Pediatr
January 2025
Department of Cardiology, Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts.
Importance: Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening complication of COVID-19 infection. Data on midterm outcomes are limited.
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JAMA Netw Open
January 2025
Buehler Center for Health Policy and Economics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
Importance: Literature suggests that well-being and health status differ by generational status among Asian American individuals.
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Arch Dermatol Res
January 2025
Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, 1150 NW 14th Street, Miami, FL, 33136, USA.
Pityriasis rosea (PR) is an acute exanthematous disease with an uncertain physiopathology, increasingly recognized as potentially drug induced. This study aims to investigate medication triggers associated with PR by analyzing cases reported in the FDA Adverse Event Reporting System (FAERS) database. A retrospective review of 343 PR cases reported in the FAERS database from January 1, 1998, to March 31, 2024, was conducted.
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January 2025
Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington, USA.
Background: Adolescents and young adults (AYA) with cancer experience long-term consequences into survivorship that impact quality of life, including mental health symptoms, substance use, and persistent pain. Given the elevated rates of pain, AYA cancer survivors are at increased risk for opioid pain medication (OPM) exposure, increasing risk for opioid-related negative consequences, particularly for those with mental health symptoms. Minimal research has documented that a considerable proportion of AYAs with cancer receive OPM that continues into survivorship, yet the lack of consensus on the definition of problematic opioid use coupled with the high clinical need for OPM makes it particularly challenging to understand the impact of OPM use in this population.
View Article and Find Full Text PDFPrehosp Emerg Care
January 2025
Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO.
Objectives: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data.
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