Diabetes devices stay used but users stop thinking actively 10 words
"Can Computing Systems Eventually Disappear?": A Long-Term Tracking Study on the Use of Diabetes Computing Systems by Patients with Diabetes
Human-Computer Interaction
Summary
People with diabetes use devices like glucose monitors and insulin pumps to manage their health, but over time they often stop actively thinking about the data these devices provide. Instead, they simply react to alarms without interpreting or questioning what the devices say. The authors studied this pattern over eight years and found that while the devices continue to help, users become less engaged mentally. They suggest future devices should help reduce mental workload but still let users understand and question the information.
What this means in practice
- •For medical device designers: Design diabetes devices that reduce unnecessary mental effort yet keep users engaged in understanding and questioning device feedback.
- •For healthcare providers: Adjust clinical practices to recognize when patients rely passively on devices and encourage continued active health judgment.
Authors
Yibo Meng, Jinyuan Zhan, Yao Lyu, Zhuoran Lu, Chengxi Zang
Abstract
Ubiquitous computing has long pursued Weiser's vision of technologies that "disappear" into everyday life. As wearable health systems advance, they increasingly substitute for rather than scaffold users' judgment, yet the long-term consequences remain underexplored. We report an eight-year, five-wave longitudinal interview study from 2017 to 2025 with 37 patients with diabetes, complemented by interviews with 15 clinicians, designers, and vendors. Across three technological generations including glucometers, continuous glucose monitoring, and predictive and automated insulin delivery systems, we identify a novel user state, continued use with cognitive disengagement. In this state, devices remain in use but active interpretation and questioning recede, leaving users to respond only to alarms. This state emerges from functional expansion, delegation of interpretive authority, and information fatigue, and is reinforced by clinical prioritization, information compression, and automation. It results in less frequent independent health judgment and a shift from understanding to responding. We argue that future diabetes computing systems should reduce unnecessary cognitive work while preserving users' opportunities to interpret, contextualize, and question system outputs.