痴呆症、阿尔茨海默病和其他神经认知障碍的移动医疗应用程序:系统搜索与环境扫描

IF 5.4 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES JMIR mHealth and uHealth Pub Date : 2024-07-03 DOI:10.2196/50186
Suad Ali, Hira Alizai, Delal Jemal Hagos, Sindy Ramos Rubio, Dale Calabia, Penelope Serrano Jimenez, Vinuu Aarif Senthil, Lora Appel
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引用次数: 0

摘要

背景:包括运动、睡眠、饮食、压力、精神刺激和社交互动在内的生活方式行为对痴呆症的发病几率有重大影响。移动医疗(mHealth)应用程序是解决这些生活方式行为对一般健康和幸福的影响的重要工具,人们越来越认识到它们在大脑健康和痴呆症预防方面的潜在用途。有效的应用程序必须以证据为基础并保护用户数据,解决目前与痴呆症相关的移动医疗应用程序存在的差距:本研究旨在描述现有痴呆症预防和风险因素应用程序的范围,突出差距并为未来发展提出建议:从 2022 年 10 月 19 日至 2022 年 11 月 2 日,对移动应用程序商店、同行评审文献、痴呆症和阿尔茨海默氏症协会网站以及浏览器进行了系统搜索。共检索到 1044 个移动医疗应用程序。经过筛选,152 个应用程序符合纳入标准,并由配对的独立审查员使用提取框架进行编码。该框架改编自西尔伯格量表、其他针对类似人群的移动医疗应用程序的范围综述以及有关可改变的痴呆症风险因素的背景研究。编码要素包括循证和专家可信度、应用程序功能、重点生活方式要素以及隐私和安全:在符合最终选择标准的 152 款应用程序中,有 88 款(57.9%)涉及与降低痴呆症风险相关的可改变的生活方式行为。然而,其中许多应用程序(59/152,38.8%)只涉及一种生活方式行为,其中最常涉及的是精神刺激。半数以上(84/152,55.2%)的应用程序在西尔伯格量表中获得了 2 分(满分 9 分),平均得分为 2.4 分(标准差为 1.0 分)。152 个应用程序中的大多数没有披露基本信息:120款(78.9%)未公开专家咨询,125款(82.2%)未公开循证信息,146款(96.1%)未公开作者资质,134款(88.2%)未公开信息来源。此外,105 款(69.2%)应用程序未披露数据隐私和安全实践:移动医疗应用程序有机会支持个人参与与降低痴呆症风险相关的行为。虽然这些产品有一定的市场,但目前还缺乏专注于多种生活方式行为的痴呆症相关应用程序。必须解决应用程序开发在证据基础、可信度以及遵守数据隐私和安全标准等方面的不足。与痴呆症相关的应用程序要想取得成效并成功推广,就必须遵循既定的、经过验证的准则。
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mHealth Apps for Dementia, Alzheimer Disease, and Other Neurocognitive Disorders: Systematic Search and Environmental Scan.

Background: Lifestyle behaviors including exercise, sleep, diet, stress, mental stimulation, and social interaction significantly impact the likelihood of developing dementia. Mobile health (mHealth) apps have been valuable tools in addressing these lifestyle behaviors for general health and well-being, and there is growing recognition of their potential use for brain health and dementia prevention. Effective apps must be evidence-based and safeguard user data, addressing gaps in the current state of dementia-related mHealth apps.

Objective: This study aims to describe the scope of available apps for dementia prevention and risk factors, highlighting gaps and suggesting a path forward for future development.

Methods: A systematic search of mobile app stores, peer-reviewed literature, dementia and Alzheimer association websites, and browser searches was conducted from October 19, 2022, to November 2, 2022. A total of 1044 mHealth apps were retrieved. After screening, 152 apps met the inclusion criteria and were coded by paired, independent reviewers using an extraction framework. The framework was adapted from the Silberg scale, other scoping reviews of mHealth apps for similar populations, and background research on modifiable dementia risk factors. Coded elements included evidence-based and expert credibility, app features, lifestyle elements of focus, and privacy and security.

Results: Of the 152 apps that met the final selection criteria, 88 (57.9%) addressed modifiable lifestyle behaviors associated with reducing dementia risk. However, many of these apps (59/152, 38.8%) only addressed one lifestyle behavior, with mental stimulation being the most frequently addressed. More than half (84/152, 55.2%) scored 2 points out of 9 on the Silberg scale, with a mean score of 2.4 (SD 1.0) points. Most of the 152 apps did not disclose essential information: 120 (78.9%) did not disclose expert consultation, 125 (82.2%) did not disclose evidence-based information, 146 (96.1%) did not disclose author credentials, and 134 (88.2%) did not disclose their information sources. In addition, 105 (69.2%) apps did not disclose adherence to data privacy and security practices.

Conclusions: There is an opportunity for mHealth apps to support individuals in engaging in behaviors linked to reducing dementia risk. While there is a market for these products, there is a lack of dementia-related apps focused on multiple lifestyle behaviors. Gaps in the rigor of app development regarding evidence base, credibility, and adherence to data privacy and security standards must be addressed. Following established and validated guidelines will be necessary for dementia-related apps to be effective and advance successfully.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
期刊最新文献
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