Medical wearable devices as a revolutionary technology gained more attraction by most dominant organizations like Apple and Google. Advanced wearable devices are used along with Artificial intelligence for the cardiology and other purposes. Thus, Machine learning helped to test the patient's vital signs like heart rate. This study aims to examine different factors that influence the adoption of wearable technologies in Oman healthcare for patients with chronic diseases. Also, to propose a model based on integrated theories (UTAUT2 and Protection Motivation Theory) to investigate how patients can adopt wearable technology in the healthcare sector. A quantitative methodology is followed using a survey distributed to around 350 patients to assess each factor proposed by the model. Data collected has been analysed using SPSS package AMOS. Structural Equation Modelling (SEM) was employed to test the proposed hypotheses. The results showed Performance Expectancy (PE), Hedonic Motivation (HM), Perceived Irreplaceability (PIR), Perceived Privacy Risk (PPR), and Regulatory Expectations (RE) have a significant influence on the patient's acceptance of using wearable technologies. This study is the first research which is empirically investigating the acceptance of healthcare wearable technology focusing on Omani patients. Wearables enable technical and healthcare decision-makers to form better decisions in order to implement wearable technologies in the most efficient and useful way.