Mobile Mental Health: A Review of Applications for Depression Assistance

A. Teles, I. Rodrigues, Davi Viana, Francisco Silva, L. Coutinho, M. Endler, R. A. Rabelo
{"title":"Mobile Mental Health: A Review of Applications for Depression Assistance","authors":"A. Teles, I. Rodrigues, Davi Viana, Francisco Silva, L. Coutinho, M. Endler, R. A. Rabelo","doi":"10.1109/CBMS.2019.00143","DOIUrl":null,"url":null,"abstract":"Depression is a mental disorder characterized by persistent sadness, loss of interest, and a set of behavioral changes. The high prevalence of depression imposes a significant burden on the world population, demanding methods capable of monitoring and treating this mental disorder. Currently, a large number of mobile applications have been designed to provide support to depressive people. This paper aims to identify, analyze and characterize the current state of mobile applications focused on depression. To do so, we conducted a systematic review of applications for depression assistance. The two most popular mobile app stores (Google Play Store and Apple App Store) have been explored to find the most relevant apps. After applying the inclusion and exclusion criteria and performing the quality assessment of the results, 216 applications were selected for the data extraction phase, where we summarized their benefits and limitations and identified gaps and trends. The results of this review evidenced that there is a growth in the diversity of apps' purposes such as chatbot, online therapy, educational tools, mood tracker, testing, and self-help.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2019.00143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22

Abstract

Depression is a mental disorder characterized by persistent sadness, loss of interest, and a set of behavioral changes. The high prevalence of depression imposes a significant burden on the world population, demanding methods capable of monitoring and treating this mental disorder. Currently, a large number of mobile applications have been designed to provide support to depressive people. This paper aims to identify, analyze and characterize the current state of mobile applications focused on depression. To do so, we conducted a systematic review of applications for depression assistance. The two most popular mobile app stores (Google Play Store and Apple App Store) have been explored to find the most relevant apps. After applying the inclusion and exclusion criteria and performing the quality assessment of the results, 216 applications were selected for the data extraction phase, where we summarized their benefits and limitations and identified gaps and trends. The results of this review evidenced that there is a growth in the diversity of apps' purposes such as chatbot, online therapy, educational tools, mood tracker, testing, and self-help.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
流动心理健康:抑郁症援助应用综述
抑郁症是一种精神障碍,其特征是持续悲伤,失去兴趣和一系列行为改变。抑郁症的高患病率给世界人口带来了沉重负担,需要能够监测和治疗这种精神障碍的方法。目前,大量的移动应用程序被设计为为抑郁症患者提供支持。本文旨在识别、分析和描述当前专注于抑郁症的移动应用程序的状态。为此,我们对抑郁症援助申请进行了系统审查。我们在两个最流行的手机应用商店(Google Play Store和Apple app Store)中寻找最相关的应用。在应用纳入和排除标准并对结果进行质量评估后,我们选择了216个应用程序进入数据提取阶段,在此阶段我们总结了它们的优点和局限性,并确定了差距和趋势。这项调查的结果证明,应用程序的用途越来越多样化,比如聊天机器人、在线治疗、教育工具、情绪追踪器、测试和自助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models Performance of Data Enhancements and Training Optimization for Neural Network: A Polyp Detection Case Study I Know How you Feel Now, and Here's why!: Demystifying Time-Continuous High Resolution Text-Based Affect Predictions in the Wild Identifying Diabetic Retinopathy from OCT Images using Deep Transfer Learning with Artificial Neural Networks Towards an Analysis of Post-Transcriptional Gene Regulation in Psoriasis via microRNAs using Machine Learning Algorithms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1