Factors influencing behavioural intention to use a smart shoe insole in regionally based adults with diabetes: a mixed methods study

Background: Smart insole technologies that provide biofeedback on foot health can support foot-care in adults with diabetes. However, the factors that influence patient uptake and acceptance of this technology are unclear. Therefore, the aim of this mixed-methods study was to use an established theoretical framework to determine a model of psychosocial factors that best predicts participant intention to use smart insoles. Methods: Fifty-three adults with diabetes from regional Australia completed the validated Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Multiple regression analysis was used to determine the psychosocial factors that best predict behavioural intention to adopt a smart insole. Additionally, a focus group was conducted and thematic analysis was performed to explore barriers and enablers to adopting this technology. Results: The multiple regression model that best predicted intention to adopt the smart insole (adjusted R = 0.51, p < 0.001) identified that self-efficacy (β = 0.67, p = 0.001) and attitude (β = 0.72, p < 0.001) were significant predictors of behavioural intention, while effort expectancy (β = - 0.52, p = 0.003) and performance expectancy (β = - 0.40, p = 0.040) were moderating factors. Thematic analysis illustrates the importance of attitude and self-efficacy on participants' behavioural intentions, influenced by participant's belief in the device's clinical efficacy and anticipated effort expectancy. Conclusions: This mixed-methods study demonstrates that attitude, self-efficacy, performance expectancy and effort expectancy combine to predict intention to adopt smart insole technology. Clinicians should consider these psychosocial factors when they prescribe and implement smart soles with patients at high risk of foot ulceration. 2