Text Mining and Grounded Theory for Appraising the Self-Management Indicators of Diabetes Mobile Apps

Q3 Medicine
Chinedu I. Ossai , Nilmini Wickramasinghe
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引用次数: 3

Abstract

Background: Understanding diabetes mobile apps functionality is fundamental to diabetes self-management because of the reliance of many patients with diabetes on these apps.

Objectives: The aim of this study is to perform a review of diabetes mobile apps to discover users’ sentiments and qualitatively examine the review comments to understand the perceptions of positive, neutral, and negative sentimental users of the apps.

Method: A total of 2678 user review comments obtained from the google play store were analysed from 47 diabetes mobile apps to understand user sentiments following clinical Self-management Indicators (SMIs) shown in previous research. Pearson correlation analysis was conducted to determine the association between the SMIs present in the apps’ and user review indicators such as rating score, user sentiment and the number of downloads. The users’ review comments were thematically screened using grounded theory to establish the themes to describe their perception of the apps.

Results: After evaluating SMIs such as weight tracking/BMI, sugar level monitoring, diet/Calories management, medication reminder, etc., 74.47% of the apps were found to have Sugar Level Monitoring(SLM) capabilities with 10.64% designed to track weight/BMI. There are 53.19% of the apps that can manage diet/calories and have data storage and security SMIs, however, less than 30% of them provide medication adherence, exercise management, doctor's appointment scheduling, and diabetes information repository. The number of the SMIs included in apps did not influence users’, but the value derived from the functionality of the apps.

Conclusions: Users are satisfied with the apps that are easy to use, setup, provide good analytics for blood sugar monitoring and have uncrowded graphical outputs and user interface. Proper data management and contemporary information about diabetes are among the identified challenges of the apps that were found to crash relentlessly on downloading, uploading, installing, and setup.

糖尿病手机app自我管理指标评价的文本挖掘与扎根理论
背景:了解糖尿病移动应用程序的功能是糖尿病自我管理的基础,因为许多糖尿病患者依赖这些应用程序。目的:本研究的目的是对糖尿病移动应用程序进行审查,以发现用户的情绪,并定性地检查审查评论,以了解应用程序的积极,中立和消极情感用户的看法。方法:分析47个糖尿病手机应用程序中从谷歌play store获取的2678条用户评论,了解用户按照前期研究的临床自我管理指标(clinical Self-management Indicators, SMIs)的感受。通过Pearson相关性分析来确定应用中存在的smi与用户评价指标(如评分、用户情绪和下载量)之间的关联。使用扎根理论对用户的评论进行主题筛选,以建立描述他们对应用程序的看法的主题。结果:通过对体重跟踪/BMI、糖水平监测、饮食/卡路里管理、用药提醒等SMIs进行评估,发现74.47%的app具有糖水平监测(SLM)功能,10.64%的app具有体重/BMI跟踪功能。有53.19%的应用程序可以管理饮食/卡路里,并具有数据存储和安全SMIs,然而,只有不到30%的应用程序提供药物依从性、运动管理、医生预约安排和糖尿病信息库。应用程序中包含的smi数量不会影响用户,但会影响应用程序功能产生的价值。结论:用户对应用程序的易用性、设置性、血糖监测分析能力强、图形输出和用户界面不拥挤等特点感到满意。适当的数据管理和有关糖尿病的最新信息是应用程序面临的挑战之一,这些应用程序在下载、上传、安装和设置时被发现会无情地崩溃。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Endocrine and Metabolic Science
Endocrine and Metabolic Science Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
2.80
自引率
0.00%
发文量
4
审稿时长
84 days
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