Journal of foot and ankle research · 2025
Knowledge-Behavior Relationships and Technology Adoption Among Patients With Diabetes: A Mixed-Methods Analysis of Smart Foot Care Technology.
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Yeh Ting-Ting, Huang Jawl-Shan, Chou Yun-Chieh
Аннотация
BACKGROUND: Although recent systematic reviews indicate low adherence to foot care practices among patients with type 2 diabetes compared to other self-management behaviors, smart healthcare technologies offer potential solutions for improving foot care management. The smart diabetic foot screening system represents an innovative approach to diabetic foot care. However, the factors influencing its adoption, particularly the relationship between knowledge, behavior, and technology acceptance, remain poorly understood.
METHODS: A mixed-methods design was employed, integrating quantitative and qualitative data. Quantitative data were collected from 80 patients with type 2 diabetes using validated instruments: the foot care knowledge questionnaire, diabetic foot self-management behavior scale, and the unified theory of acceptance and use of technology questionnaire. Pearson correlation and regression analyses examined relationships between knowledge, behavior, and technology adoption intention. In-depth, semistructured interviews with 20 participants explored adoption factors. Thematic analysis was conducted on qualitative data.
RESULTS: Despite high levels of foot care knowledge (86.2% correct response rate), actual self-management behaviors remained suboptimal, with a modest correlation between knowledge and behavior (r = 0.31 and p < 0.01). Regression analysis identified attitude and facilitating conditions as significant predictors of smart system adoption intention, explaining 57% of the variance. Qualitative analysis revealed three main themes: technology acceptance perceptions, implementation support system, and self-management patterns, highlighting the complex interplay between knowledge, attitudes, and behavioral factors.
CONCLUSIONS: This study reveals that despite improved knowledge levels compared to previous decades, the knowledge-behavior gap in diabetic foot care persists. The findings suggest that successful implementations of smart healthcare technologies require addressing both attitudinal factors and facilitating conditions, rather than focusing solely on knowledge enhancement. These insights contribute to understanding technology adoption in chronic disease self-management and inform the development of more effective implementation strategies.
METHODS: A mixed-methods design was employed, integrating quantitative and qualitative data. Quantitative data were collected from 80 patients with type 2 diabetes using validated instruments: the foot care knowledge questionnaire, diabetic foot self-management behavior scale, and the unified theory of acceptance and use of technology questionnaire. Pearson correlation and regression analyses examined relationships between knowledge, behavior, and technology adoption intention. In-depth, semistructured interviews with 20 participants explored adoption factors. Thematic analysis was conducted on qualitative data.
RESULTS: Despite high levels of foot care knowledge (86.2% correct response rate), actual self-management behaviors remained suboptimal, with a modest correlation between knowledge and behavior (r = 0.31 and p < 0.01). Regression analysis identified attitude and facilitating conditions as significant predictors of smart system adoption intention, explaining 57% of the variance. Qualitative analysis revealed three main themes: technology acceptance perceptions, implementation support system, and self-management patterns, highlighting the complex interplay between knowledge, attitudes, and behavioral factors.
CONCLUSIONS: This study reveals that despite improved knowledge levels compared to previous decades, the knowledge-behavior gap in diabetic foot care persists. The findings suggest that successful implementations of smart healthcare technologies require addressing both attitudinal factors and facilitating conditions, rather than focusing solely on knowledge enhancement. These insights contribute to understanding technology adoption in chronic disease self-management and inform the development of more effective implementation strategies.