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 R² = 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.

This trial evaluated the feasibility of podiatrist-led health coaching (HC) to facilitate smart-insole adoption and foot monitoring in adults with diabetes-related neuropathy. Adults aged 69.9 ± 5.6 years with diabetes for 13.7 ± 10.3 years participated in this 4-week explanatory sequential mixed-methods intervention. An HC training package was delivered to podiatrists, who used HC to issue a smart insole to support foot monitoring. Insole usage data monitored adoption. Changes in participant understanding of neuropathy, foot care behaviours, and intention to adopt the smart insole were measured. Focus group and in-depth interviews explored quantitative data. Initial HC appointments took a mean of 43.8 ± 8.8 min. HC fidelity was strong for empathy/rapport and knowledge provision but weak for assessing motivational elements. Mean smart-insole wear was 12.53 ± 3.46 h/day with 71.2 ± 13.9% alerts not effectively off-loaded, with no significant effect for time on usage F(3,6) = 1.194 (p = 0.389) or alert responses F(3,6) = 0.272 (p = 0.843). Improvements in post-trial questionnaire mean scores and focus group responses indicate podiatrist-led HC improved participants’ understanding of neuropathy and implementation of footcare practices. Podiatrist-led HC is feasible, supporting smart-insole adoption and foot monitoring as evidenced by wear time, and improvements in self-reported footcare practices. However, podiatrists require additional feedback to better consolidate some unfamiliar health coaching skills. ACTRN12618002053202.