## 1 Introduction
Establishing a specific treatment plan, regular nutrition counseling, blood glucose (BG) monitoring, and medication administration are all important components of efficient diabetes management [3]. The mHealth applications look promising in the efficient self-management and monitoring of T2 DM patients. The use of mHealth applications, particularly in self-management, is a challenging and time-consuming task [4]. With the rapid growth of several mHealth applications, it becomes difficult for the users to select the best applications. User interface design and user experience plays vital roles in analyzing and determining the best user-friendly mHealth application. Thus, usability evaluation is essential for choosing the best mHealth applications among the available alternatives. Usability, as defined by International Organization for Standardization 9241-11 [5], is the degree to which a product can be utilized by a given individual in a particular circumstance to accomplish realistic goals related to effectiveness, efficiency, and satisfaction. Usability in diabetes applications refers to the user's (e.g., a patient, a clinician, or a caregiver) experience of an interface for using the application thereby expecting good satisfaction, efficiency for saving time, and adequate efficacy that the application functions smoothly and accurately in a preferred usage context (e.g., tracking and analyzing Blood Glucose and carb intake). Automated blood glucose data transfer, incorporation of a diary for tracking and logging food intake, physical activity, setting reminders, facilitating education, establishing communication, and social networking are all possible features of diabetes applications [6; 7]. Identification of the best mHealth application for monitoring T2 DM is a challenging issue. MCDM methodologies can address this issue. MCDM is a structured and multidimensional procedure established to address decision-making problems in a variety of fields and helps to find the most appealing alternative by taking into account all relevant criteria. It studies complex decision-making problems in various fields with its powerful and sophisticated tools. This strategy increases decision-making quality by keeping decision-making reasonable and efficient [8]. According to [PERSON] and [PERSON] [9], the MCDM procedures have the following common characteristics:  
* Multiple criteria: Every problem has a set of criteria, which might be characterized either as objectives or attributes. * Criteria in conflict: Several criteria contradict or disagree with each other. * Incommensurable unit: A criterion maybe measured in different units. * Design/selection: A problem can be solved either by designing the ideal or the best alternative(s) or by choosing the best alternative from the finite alternatives that have already been identified. The two types of criteria are attributes and objectives. As a result, several authors classify MCDM techniques into two categories, on the basis of the problem situations [10]: Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM). * MADM: For evaluation, a discrete set of decision-making variables (finite alternatives and attributes) are commonly utilized [11]. The best option in a MADM problem is chosen from a group of pre-selected options that are presented in the form of attributes, which are usually in conflict. The major aspect of MADM is that there are generally a few predefined alternatives, each of which is related to a level of attribute achievement. The ultimate selection will be based on the attributes. Inter and intra-attribute comparisons are also used to make the ultimate choice of the alternative. On the basis of various types and significant features of information obtained from various decision-makers, [PERSON] and [PERSON] [9] categorized different typical MADM techniques. One of the commonly used methods involving MADM methodology is combinative distance-based assessment (CODAS).