PURPOSE: Deteriorating type 1 diabetes management and control are common among adolescents; however, clinical evidence suggests that individual trajectories can vary. The aim of this study was to examine patterns and predictors of blood glucose monitoring (BGM) frequency and glycemic control (hemoglobin A1c). METHODS: Prospective data analysis spanning 18-24 months was conducted with 150 adolescent-parent pairs. Latent group-based trajectory modeling identified subgroups and determined medical, demographic, psychological, and family predictors of subgroup membership. RESULTS: Three subgroups emerged, representing diabetes management and control that are "meeting treatment targets" (40%; A1c at baseline = 7.4%, BGM frequency at baseline = 4.8 checks/day) and two levels "not meeting targets":"normatively similar" youth (40%; A1c = 9.2%, BGM frequency = 2.8 checks/day), and "high-risk" youth (20%; A1c = 11.2%, BGM frequency = 2.9 checks/day). Subgroup membership was maintained over 18-24 months. There was minimal change across time, although only one-third of adolescents met treatment targets. Older age, longer diabetes duration, ethnic minority status, unmarried caregiver status, insulin delivery via injections versus continuous subcutaneous insulin infusion, greater depressive symptoms, negative affect about BGM, and diabetes-specific family conflict each predicted membership in a subgroup with poorer diabetes management and control. CONCLUSIONS: Among the nearly two-thirds of adolescents with management and control that do not meet treatment targets, modifiable and nonmodifiable factors may signal the need for prevention or intervention. Demographic and medical factors may call for proactive efforts to prevent deterioration, and psychological symptoms and family conflict signal opportunities for clinical intervention to promote improved diabetes management and control in adolescence.
PURPOSE: Deteriorating type 1 diabetes management and control are common among adolescents; however, clinical evidence suggests that individual trajectories can vary. The aim of this study was to examine patterns and predictors of blood glucose monitoring (BGM) frequency and glycemic control (hemoglobin A1c). METHODS: Prospective data analysis spanning 18-24 months was conducted with 150 adolescent-parent pairs. Latent group-based trajectory modeling identified subgroups and determined medical, demographic, psychological, and family predictors of subgroup membership. RESULTS: Three subgroups emerged, representing diabetes management and control that are "meeting treatment targets" (40%; A1c at baseline = 7.4%, BGM frequency at baseline = 4.8 checks/day) and two levels "not meeting targets":"normatively similar" youth (40%; A1c = 9.2%, BGM frequency = 2.8 checks/day), and "high-risk" youth (20%; A1c = 11.2%, BGM frequency = 2.9 checks/day). Subgroup membership was maintained over 18-24 months. There was minimal change across time, although only one-third of adolescents met treatment targets. Older age, longer diabetes duration, ethnic minority status, unmarried caregiver status, insulin delivery via injections versus continuous subcutaneous insulin infusion, greater depressive symptoms, negative affect about BGM, and diabetes-specific family conflict each predicted membership in a subgroup with poorer diabetes management and control. CONCLUSIONS: Among the nearly two-thirds of adolescents with management and control that do not meet treatment targets, modifiable and nonmodifiable factors may signal the need for prevention or intervention. Demographic and medical factors may call for proactive efforts to prevent deterioration, and psychological symptoms and family conflict signal opportunities for clinical intervention to promote improved diabetes management and control in adolescence.
Authors: Deborah A Butler; Jessica B Zuehlke; Alison Tovar; Lisa K Volkening; Barbara J Anderson; Lori M B Laffel Journal: Pediatr Diabetes Date: 2008-08 Impact factor: 4.866
Authors: Elaine C Moreland; Lisa K Volkening; Margaret T Lawlor; Karen A Chalmers; Barbara J Anderson; Lori M B Laffel Journal: Arch Intern Med Date: 2006-03-27
Authors: Tim Wysocki; Michael A Harris; Lisa M Buckloh; Debbie Mertlich; Amanda S Lochrie; Nelly Mauras; Neil H White Journal: Diabetes Care Date: 2007-03 Impact factor: 19.112
Authors: A M Jacobson; S T Hauser; P Lavori; J B Willett; C F Cole; J I Wolfsdorf; R H Dumont; D Wertlieb Journal: Psychosom Med Date: 1994 Sep-Oct Impact factor: 4.312
Authors: Dayna E McGill; Lori M Laffel; Lisa K Volkening; Deborah A Butler; Wendy L Levy; Rachel M Wasserman; Barbara J Anderson Journal: Diabetes Technol Ther Date: 2020-05 Impact factor: 6.118
Authors: Susana R Patton; Stephen A DeLurgio; Amanda Fridlington; Cyndy Cohoon; Angela L Turpin; Mark A Clements Journal: Diabetes Technol Ther Date: 2014-04-28 Impact factor: 6.118
Authors: Kimberly L Savin; Emily R Hamburger; Alexandra D Monzon; Niral J Patel; Katia M Perez; Jadienne H Lord; Sarah S Jaser Journal: J Fam Psychol Date: 2018-02
Authors: Jennifer M Rohan; Bin Huang; Jennifer Shroff Pendley; Alan Delamater; Lawrence Dolan; Grafton Reeves; Dennis Drotar Journal: J Pediatr Psychol Date: 2015-07-07