Investigating the Predictors of Women’s Future Intention to Accept and Use Smartphone Applications for Type 2 Diabetic Self-Management in the Kingdom of Saudi Arabia
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Background: Diabetes is one of the most prevalent chronic diseases in many societies, and the Kingdom of Saudi Arabia (SA) has one of the highest incidence rates globally. In SA, close to 28% of women have been diagnosed with Type 2 diabetes. Saudi health authorities have established self-management programs in primary healthcare centres (PHCs) to educate patients, but these programs have many shortcomings. A new approach has emerged in the diabetes field using smartphone technology to enhance patient self-management. While studies have focused on evaluating the use of smartphone technologies among patients with chronic diseases, no study has examined the acceptance of smartphone applications among women with Type 2 diabetes in SA. Purpose: To test a theoretical model examining selected predictors (performance expectancy, effort expectancy, social influence, patient-centred factors) of the future intention to accept and use smartphone applications for Type 2 diabetic self-management among Saudi women. Method: A descriptive, cross-sectional survey design was conducted with a sample of 98 women with Type 2 diabetes in public PHCs in Jeddah, SA. Participants were asked to complete a pen and paper questionnaire including smartphone use, modified Unified Theory of Acceptance and Use of Technology Scale, and scales measuring patient-centred factors. Result: SA women in this study intended to accept and use smartphone applications for diabetes management in the future. The current use of smartphone applications in diabetes management influenced their intention to accept and use diabetes applications. Testing of the theoretical model found 32.1% of the variance in participants’ intention to accept and use smartphone applications to manage Type 2 diabetes was explained by the six predictors. However, only effort expectancy was a significant predictor in the final model.
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