Psychosocial impacts of hybrid closed-loop systems in the management of diabetes: a review.
C FarringtonPublished in: Diabetic medicine : a journal of the British Diabetic Association (2018)
There is a pressing need for new treatment regimens that enable improved glycaemic control and reduced diabetes self-management burdens. Closed-loop, or artificial pancreas, systems represent one of the most promising avenues in this regard. Closed-loop systems connect wearable continuous glucose monitor (CGM) sensors to smartphone- or tablet-mounted algorithms that process and model CGM data to deliver precise and frequently updated doses of fast-acting insulin (and glucagon in dual-hormone systems) to users via wearable pumps. Recent studies have demonstrated that closed-loop systems offer significant benefit in terms of improved glycaemic control. However, less attention has been paid to the psychosocial impact on users of closed-loop systems. This article reviews recent research on psychosocial aspects of closed-loop usage in light of preceding research on user experience of currently available technologies such as insulin pumps and CGM sensors. The small, but growing body of research in this field reports generally positive user experience and a number of experienced benefits including: reassurance and reduced anxiety, improved sleep and confidence, and 'time off' from diabetes demands. However, these benefits are counterbalanced by important challenges, ranging from variable levels of trust to concerns about physical bulk, technical glitches and difficulties incorporating closed-loop systems into everyday life. Future research should explore psychosocial aspects of closed-loop usage in more diverse groups and with regard to clinicians, as well as users, to ensure that the clinical benefits of closed-loop systems are realized at scale in routine medical care.
Keyphrases
- type diabetes
- mental health
- cardiovascular disease
- glycemic control
- physical activity
- emergency department
- randomized controlled trial
- heart rate
- depressive symptoms
- metabolic syndrome
- systematic review
- deep learning
- sleep quality
- blood pressure
- big data
- blood glucose
- weight loss
- combination therapy
- replacement therapy
- data analysis