What Factors Predict the Adoption of Type 2 Diabetes Patients to Wearable Activity Trackers-Application of Diffusion of Innovation Theory

Globally, diabetes has brought an enormous burden to public health resources, and the situation of disease burden caused by diabetes in China is especially severe. China is currently facing the dual threat of aging and diabetes, and wearable activity trackers could promote elderly diabetic patients' physical activity levels and help them to manage blood glucose control. Therefore, examining the influencing factors of elderly patients' adoption intention is critical as wearing adoption determines actual wearing behaviors. This study aims to explore the predicting factors of Chinese elderly type 2 diabetic patients' adoption intention to wearable activity trackers and their actual wearing behavior, using diffusion of innovation theory as the theoretical framework. We hope to provide insights into future interventions using wearable activity trackers as tools to improve the outcome of patients. Wearable activity trackers were freely distributed to type 2 diabetic patients in Beijing, China. A questionnaire survey was conducted to examine predicting factors of adoption intention after a week's try-on. Actual wearing behavior for 3-month was obtained from the exclusive cloud. Data were analyzed with structural equation modeling. A total of 725 patients completed the questionnaire. Patients had a mean age of 60.3 ± 7.6 years old and the educational level was generally lower. The results indicated that observability was the primary influencing factor of patients' adoption intention (β = 0.775, < 0.001). Relative advantage (β = 0.182, = 0.014) and perceived social image (β = 0.080, = 0.039) also had a positive influence while perceived risk (β = -0.148, < 0.001) exerted a negative influence. In addition, results showed that the more intention led to the better actual wearing behavior (β = 0.127, = 0.003). Observability (β = 0.103, = 0.005), perceived ease (β = 0.085, = 0.004), and relative advantage (β = 0.041, = 0.009) also indirectly influenced the wearing behavior. The intentions of Chinese elderly type 2 diabetic patients to wearable activity trackers directly influenced the actual wearing behavior. In addition, their adoption intention to wearable activity trackers was mainly influenced by observability, perceived ease to use, relative advantage, perceived risk, and social image.