# title
Understanding Patient Beliefs in Using Technology to Manage Diabetes: Path Analysis Model From a National Web-Based Sample (Preprint)

# 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 (R ²=0.65; F 12,199=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.

# authors[].organizations[].label
Center for Remote Health Technologies and Systems

# authors[].organizations[].label
Texas A&M Engineering Experiment Station (TEES)

# authors[].organizations[].label
Center for Health Organization Transformation

# authors[].organizations[].label
Institute for Engineering Education and Innovation

# authors[].organizations[].label
Institute for Science, Technology, and Public Policy

# authors[].organizations[].label
Bush School of Government and Public Service

# authorList[]
Zahed, K., Mehta, R., Erraguntla, M., Qaraqe, K., & Sasangohar, F.