A persuasive technology mhealth self-monitoring system for intervention in diabetic patients medical adherence

Background and Aim of Study: The prevalence of chronic diseases like diabetes has caused unmeasurable strain on many health systems especially in developing countries. Chronically ill patients are traumatised by their incurable illnesses, which adversely affects their adherence to their medical treatment, resulting in serious complications and even death. The aim of the study: to implement an intervention mobile health (mHealth) system by integrating persuasive technologies into mobile applications to empower diabetic patients to adhere to medical prescriptions. Material and Methods: Fogg Behaviour Model (FBM) was leveraged for the integration of mHealth and behaviour aspects. The system was developed with Kotlin programming using the Android Studio working integrated development environment (IDE). Tools including Firebase Real Time Database, Android Studio and Android Mobile Phone were used to afford a fully fledged mHealth self-monitoring system. The system was evaluated using descriptive statistics by medical personnel and social workers to determine the completeness, clarity, logical arrangement, correctness, reliability, usability, as well as content validity. Results: Findings indicated that the mHealth system meets a good degree of the measures that inform patients’ self-monitoring for medicine adherence. The evaluation results also suggested that some functionality of the mHealth self-monitoring system requires an incremental improvement, to provide a seamless healthcare support. The artefact was descriptively evaluated on seven parameters: completeness that showed a mean of 3.75 with a standard deviation of 1.070; functionality with a mean of 4.05 and standard deviation of 0.945; accuracy with a mean of 3.70 and standard deviation of 1.031; reliability a mean of 3.90 and standard deviation of 0.945; consistence a mean of 4.00 and standard deviation of 0.968; performance a mean of 3.75 and standard deviation of 1.250, and usability with a mean of 3.55 and standard deviation of 0.999. Conclusions: The developed system is as effective as face-to-face consultations and personal visits to healthcare facilities. Diabetic patients need to adhere to medicine to avoid further complications that could lead to death.