title: Technology Use for Diabetes Problem Solving in Adolescents with Type 1 Diabetes: Relationship to Glycemic Control.
abstract: BACKGROUND
  This study examines technology use for problem solving in diabetes and its relationship to hemoglobin A1C (A1C).
  
  
  METHODS
  A sample of 112 adolescents with type 1 diabetes completed measures assessing use of technologies for diabetes problem solving, including mobile applications, social technologies, and glucose software. Hierarchical regression was performed to identify the contribution of a new nine-item Technology Use for Problem Solving in Type 1 Diabetes (TUPS) scale to A1C, considering known clinical contributors to A1C.
  
  
  RESULTS
  Mean age for the sample was 14.5 (SD 1.7) years, mean A1C was 8.9% (SD 1.8%), 50% were female, and diabetes duration was 5.5 (SD 3.5) years. Cronbach's α reliability for TUPS was 0.78. In regression analyses, variables significantly associated with A1C were the socioeconomic status (β = -0.26, P < 0.01), Diabetes Adolescent Problem Solving Questionnaire (β = -0.26, P = 0.01), and TUPS (β = 0.26, P = 0.01). Aside from the Diabetes Self-Care Inventory--Revised, each block added significantly to the model R(2). The final model R(2) was 0.22 for modeling A1C (P < 0.001).
  
  
  CONCLUSIONS
  Results indicate a counterintuitive relationship between higher use of technologies for problem solving and higher A1C. Adolescents with poorer glycemic control may use technology in a reactive, as opposed to preventive, manner. Better understanding of the nature of technology use for self-management over time is needed to guide the development of technology-mediated problem solving tools for youth with type 1 diabetes.
authors.affiliation: 1 Department of Pediatrics, Vanderbilt University Medical Center , Nashville, Tennessee.
authors.forename: Yaa A
authors.initials: YA
authors.lastname: Kumah-Crystal
authors.affiliation: 2 Department of Pediatrics, Stanford University , Palo Alto, California.
authors.forename: Korey K
authors.initials: KK
authors.lastname: Hood
authors.affiliation: 3 Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee.
authors.forename: Yu-Xian
authors.initials: YX
authors.lastname: Ho
authors.affiliation: 1 Department of Pediatrics, Vanderbilt University Medical Center , Nashville, Tennessee.
authors.forename: Cindy K
authors.initials: CK
authors.lastname: Lybarger
authors.affiliation: 4 School of Nursing, Vanderbilt University Medical Center , Nashville, Tennessee.
authors.forename: Brendan H
authors.initials: BH
authors.lastname: O'Connor
authors.affiliation: 5 Department of Medicine, Vanderbilt University Medical Center , Nashville, Tennessee.
authors.forename: Russell L
authors.initials: RL
authors.lastname: Rothman
authors.affiliation: 1 Department of Pediatrics, Vanderbilt University Medical Center , Nashville, Tennessee.|3 Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee.|4 School of Nursing, Vanderbilt University Medical Center , Nashville, Tennessee.
authors.forename: Shelagh A
authors.initials: SA
authors.lastname: Mulvaney
mesh_terms: D000293:Adolescent; D001786:Blood Glucose / Q000032:analysis; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000628:therapy; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis*; D006801:Humans; D008297:Male; D011340:Problem Solving; D012044:Regression Analysis; D015203:Reproducibility of Results; D012648:Self Care / Q000379:methods* / Q000706:statistics & numerical data; D012923:Social Class; D012984:Software / Q000706:statistics & numerical data; D011795:Surveys and Questionnaires
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
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