Kumah-Crystal Yaa A, Hood Korey K, Ho Yu-Xian, Lybarger Cindy K, O'Connor Brendan H, Rothman Russell L, Mulvaney Shelagh A
1 Department of Pediatrics, Vanderbilt University Medical Center , Nashville, Tennessee.
2 Department of Pediatrics, Stanford University , Palo Alto, California.
Diabetes Technol Ther. 2015 Jul;17(7):449-54. doi: 10.1089/dia.2014.0422. Epub 2015 Mar 31.
This study examines technology use for problem solving in diabetes and its relationship to hemoglobin A1C (A1C).
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.
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).
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.
本研究探讨了在糖尿病问题解决中技术的使用情况及其与糖化血红蛋白(A1C)的关系。
112名1型糖尿病青少年完成了评估糖尿病问题解决技术使用情况的测量,包括移动应用程序、社交技术和血糖软件。进行分层回归分析,以确定新的九项1型糖尿病问题解决技术使用量表(TUPS)对A1C的贡献,并考虑已知的A1C临床影响因素。
样本的平均年龄为14.5(标准差1.7)岁,平均A1C为8.9%(标准差1.8%),50%为女性,糖尿病病程为5.5(标准差3.5)年。TUPS的Cronbach's α信度为0.78。在回归分析中,与A1C显著相关的变量是社会经济地位(β = -0.26,P < 0.01)、糖尿病青少年问题解决问卷(β = -0.26,P = 0.01)和TUPS(β = 0.26,P = 0.01)。除了修订后的糖尿病自我护理量表外,每个模块对模型R(2)都有显著贡献。用于A1C建模的最终模型R(2)为0.22(P < 0.001)。
结果表明,在问题解决中技术使用频率较高与A1C较高之间存在违反直觉的关系。血糖控制较差的青少年可能以被动而非预防的方式使用技术。需要更好地理解随着时间推移技术在自我管理中的使用本质,以指导为1型糖尿病青少年开发技术介导的问题解决工具。