RELATIONSHIPS BETWEEN DIABETES DISTRESS, TECHNOLOGY EXPECTATIONS, TECHNOLOGY EXPERIENCE AND TECHNOLOGY USE
- Linda Gonder-frederick, United States of America
- Linda Gonder-frederick, United States of America
- Alessandro Bisio, United States of America
- Sue A. Brown, United States of America
- Marc Breton, United States of America
- Stacey Anderson, United States of America
- Laura Kollar, United States of America
- Jessica Robic, United States of America
- Emma Emory, United States of America
- Mary C. Oliveri, United States of America
- Christian Wakeman, United States of America
- Boris Kovatchev, United States of America
Abstract
Background and Aims
Adoption and use of diabetes technology may be associated with pre-existing psychosocial factors, including expectations and emotional status. This study investigated the relationships between diabetes distress and user expectations, experience, and utilization of Closed-Loop Control (CLC) and Decision Support Systems (DSS).
Methods
In Study 1, 61 T1D adults (60% F, age=42.5±11.6, T1Dyrs=22.1±12, HbA1c =7.4±1.0, regimen=insulin pump) participated in a CLC clinical trial using SAP therapy, overnight CLC (ON-CLC), and 24-hr CLC (24-CLC) over three 8-week periods. In Study 2, 57 T1D adults (59.6% F, age=33±14.1ys, T1Dyrs =16±12.6, HbA1c=7.4±1.2, regimen=MDI + CGM) used a DSS which provided insulin recommendations for meals, exercise and sleep. In both studies, participants completed the Diabetes Distress Scale (DDS), as well as Technology Expectations and Technology Experience questionnaires (Benefit and Burden subscales) at baseline and following treatment conditions.
Results
The table below shows correlations and t-test results between the questionnaires and user score. For CLC, higher Emotional DDS scores correlated with higher experienced burdens in the SAP and ON-CLC conditions, with a similar trend in 24-CLC. For DSS, participants were divided into high and low user groups based on system interactions. High user scores correlated with higher expected and experienced benefits. Lower user scores correlated with higher expected and experienced burdens. Low user scores showed trends in correlations with higher Regimen-related and Physician-related baseline distress.
Conclusions
Relationships between diabetes distress and technology expectations, experience and use should be considered for successful adoption and utilization of different types of diabetes technology.