August 11, 2022
2 minutes to read
Source / Disclosures
Facilitator reported receiving speaking and consulting fees from Dexcom and Tandem Diabetes Care, and works as a consultant at Capillary Biomedical. The Messer Foundation receives grants for research and projects from Abbott, Beta Bionics, Dexcom, and Insulet Corp. and Medtronic and Tandem Diabetes Care. Please see the study for all relevant financial disclosures by other authors.
Teenagers and young adults with type 1 diabetes They had lower glucose levels on the days they reported feeling more involved in managing their diabetes, according to results published in diabetes medicine.
In adolescents and young adults with diabetes, daily personal factors such as self-esteem, healthy perception, and plans or desire to self-manage diabetes all influence daily blood sugar. Laurel H. Messer, Ph.D., RNAnd the An assistant professor of pediatrics at the Barbara Davis Diabetes Center, University of Colorado Anschutz School of Medicine, told Healio. “These circadian predictors may be considered new targets for interventions to help support adolescents in diabetes care, particularly in terms of helping to stimulate planning and willingness to manage diabetes first in the morning.”
Messer and colleagues conducted a 2-week longitudinal observational study of 88 adolescents and young adults with type 1 diabetes who used Continuous glucose monitoring, an insulin pump or smart pen, and a smartphone for diabetes management (mean age, 17.6 years; 54% female; 90% white). Participants downloaded continuous glucose monitoring and insulin delivery data at the end of the two-week period to assess time in range, average sensor glucose and user-initiated bolus. On six randomly selected days, participants completed a 25-item Participation Prediction Survey evaluating biopsychosocial factors that can influence daily engagement in diabetes management. The survey was conducted in the morning and participants answered each question on a 4-point Likert scale. At the end of each assessment day, participants completed an end-of-day goal survey on a 5-point Likert scale to assess achievement of diabetes management goals.
Time in range predictions, blood sugar variability
Mean time-in-range fluctuation of 16% and mean absolute sensor glucose fluctuation of 30.4 mg/dL per day per participant (s < .001 for both). The mean absolute change in the number of doses administered fluctuated by 2.2 per day (s <.001).
Seven questions were identified in the Participation Prediction Survey as most predictive of glycemic outcomes. These responses to items predicted 16.7% of the time in range change, 18.6% of the mean sensor glucose variability, 2.1% of the variance in the number of doses, 14% of the variance in the hyperglycemic response, and 28.7% of the variance in the hyperglycemic response. Achieving diabetes management goals.
Positive association with lower glucose levels
Participants who positively reported sleeping longer (beta = -6.6; s < .001), slept well the night before (beta = −12.6; s = .001), planning for diabetes management on that day (beta = -17.7; s = .004), with the goal of managing their diabetes (beta = −16.6; s = .008), desire to manage their diabetes (beta = -15.7; s = .001) and that they were healthy (beta = -22.1; s <.001) are more likely to have lower glucose levels. Conversely, those who said they were too sick to manage their diabetes (beta = 29.1; s < .001) or felt they needed additional support to manage their diabetes (beta = 11.9; s = .004) had higher glucose levels in the morning.
The group estimated that they spent an average of 30 minutes in diabetes care and an average of 30 minutes thinking about their diabetes each day. The number of minutes thought about diabetes was associated with the percentage of hyperglycemic alerts that responded to (s = .035) and the perceived goal achievement score (s = .044). The number of minutes of diabetes care was also positively associated with goal achievement (s = .022). Time in the range was inversely related to the number of doses per day (beta = -0.9; s <.001).
“We were very surprised that these factors predicted the total time in the range; however, we were unable to explain how often the adolescent gave insulin doses for the meals,” Messer said. “Usually, you’d assume it’s the meal doses of insulin that optimize the time in range, so we need to do more to understand that.”
for more information:
Laurel H. Messer, Ph.D., RNcan be accessed at [email protected].