Understanding Patient Beliefs in Using Technology to Manage Diabetes: Path Analysis Model From a National Web-Based Sample. Academic Article uri icon

abstract

  • BACKGROUND: With 425 million individuals globally living with diabetes, it is critical to support the self-management of this life-threatening condition. However, adherence and engagement with existing technologies are inadequate and need further research. OBJECTIVE: The objective of our study was to develop an integrated belief model that helps identify the significant constructs in predicting intention to use a diabetes self-management device for the detection of hypoglycemia. METHODS: Adults with type 1 diabetes living in the United States were recruited through Qualtrics to take a web-based questionnaire that assessed their preferences for a device that monitors their tremors and alerts them of the onset of hypoglycemia. As part of this questionnaire, a section focused on eliciting their response to behavioral constructs from the Health Belief Model, Technology Acceptance Model, and others. RESULTS: A total of 212 eligible participants responded to the Qualtrics survey. Intention to use a device for the self-management of diabetes was well predicted (R2=0.65; F12,199=27.19; P<.001) by 4 main constructs. The most significant constructs were perceived usefulness (=.33; P<.001) and perceived health threat (=.55; P<.001) followed by cues to action (=.17; P<.001) and a negative effect from resistance to change (=-.19; P<.001). Older age (=.025; P<.001) led to an increase in their perceived health threat. CONCLUSIONS: For individuals to use such a device, they need to perceive it as useful, perceive diabetes as life-threatening, regularly remember to perform actions to manage their condition, and exhibit less resistance to change. The model predicted the intention to use a diabetes self-management device as well, with several constructs found to be significant. This mental modeling approach can be complemented in future work by field-testing with physical prototype devices and assessing their interaction with the device longitudinally.

published proceedings

  • JMIR Diabetes

altmetric score

  • 1.1

author list (cited authors)

  • Zahed, K., Mehta, R., Erraguntla, M., Qaraqe, K., & Sasangohar, F.

citation count

  • 0

complete list of authors

  • Zahed, Karim||Mehta, Ranjana||Erraguntla, Madhav||Qaraqe, Khalid||Sasangohar, Farzan

publication date

  • May 2023