Accepted for/Published in: JMIR Formative Research
Date Submitted: Jan 2, 2021
Date Accepted: Nov 27, 2021
©ARVPredictor: Android-based mobile application for the detection of HIV drug resistance mutations and treatment at the point of care.
ABSTRACT
Background:
HIV/AIDS is still one of the major global human health challenges especially in resource limited environments. By 2017, over 77.3 million people were infected with the disease and about 35.4 million individuals already died from AIDS-related illnesses. Around the same time over 21.7 million people were accessing ART with significant clinical outcomes. However, numerous challenges are experienced in delivery and accurate interpretation of HIV patients’ data by various care givers at different care levels. Mobile health technology is progressively making inroads into the health sector as well as medical research. Different mobile devices have become common in health care settings leading to rapid growth in the development of downloadable softwares specifically designed to fulfill particular health related purposes.
Objective:
We developed a mobile based application called ©ARVPredictor and showed that it can accurately define HIV-1 drug resistance mutations targeting the HIV pol gene for use at the point of care
Methods:
©ARVPredictor was designed using Android Studio with Java as the programming language and set for both Android and iOS. The application system is hosted on Nginx Server and network calls built on PHP’s Lavarel framework handled by Retrofit Library. Digital Ocean offers high performance and stable cloud computing platform for ©ARVPredictor. This mobile application is enlisted in the play store (https://play.google.com/store/apps/details?id=co.ke.ikocare) as “ARV Predictor” and Source code available under MIT permissive License at the following GitHub repository https://github.com/bongadi/ARV_Predictor_App_OngadiBA
Results:
The mobile based application (©ARVPredictor) takes in a set of sequences or known mutations (protease, reverse transcriptase and integrase). It then returns inferred levels of resistance to selected nucleoside, non-nucleoside protease and integrase inhibitors for accurate HIV/AIDS management at the point of care.
Conclusions:
The application achieves the overall aim of providing a solution to HIV/AIDS care givers at the point of care. Within a record turnaround time the caregiver is capable of determining the HIV drug resistance mutation and identify the patients’ appropriate line of management.
Citation
Per the author's request the PDF is not available.
Copyright
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