English abstract
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.
