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Understanding Patient Beliefs in Using Technology to Manage Diabetes: A Path Analysis Model from a National Online Sample
ABSTRACT
Background:
With 425 million individuals globally suffering from a life-threatening condition such as diabetes, it is critical for them to use technologies to help self-manage their condition. However, adherence and engagement with existing technologies is inadequate and needs further research.
Objective:
The objectives of our study were (a) to identify the significant constructs in predicting intention to use a diabetes self-management device for detecting the onset of hypoglycemia, and (b) to test the accuracy of such a model.
Methods:
Adults with type 1 diabetes living in the United States were recruited through Qualtrics to take an online questionnaire that assessed their preferences for a device that monitors their tremors and alerts them of an 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 =.627, F(12,199)=27.19, P<.001) by four main constructs. The most significant constructs were perceived usefulness (β=.33, P<.001) and perceived health threat (β=.55, P<.001) followed by cues to action (β=.17, P<.001), and a negative effect from resistance to change (β=-.19, P<.001). Older age (β=.24, P<.05) 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 their diabetes as threatening to their life, need to regularly remember to perform actions to manage their condition, and be less resistant to change. The model predicted intention to use a diabetes self-management device 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.
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