Zou Ning, Xie Bo, He Daqing, Hilsabeck Robin, Aguirre Alyssa
School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States.
School of Nursing, The University of Texas at Austin, Austin, TX, United States.
JMIR Aging. 2024 Nov 20;7:e58517. doi: 10.2196/58517.
Informal caregivers of persons living with dementia are increasingly using mobile health (mHealth) apps to obtain care information. mHealth apps are seen as promising tools to better support caregivers' complex and evolving information needs. Yet, little is known about the types and quality of dementia care information that these apps provide. Is this information for caregivers individually tailored; if so, how?
We aim to address the aforementioned gaps in the literature by systematically examining the types and quality of care-related information provided in publicly available apps for caregivers of persons living with dementia as well as app features used to tailor information to caregivers' information wants and situations.
In September 2023, we used a multistage process to select mobile apps for caregivers of persons living with dementia. The final sample included 35 apps. We assessed (1) types of dementia care information provided in the apps, using our 3-item Alzheimer disease and related dementias daily care strategy framework, which encompasses educational information, tangible actions, and referral information; (2) quality of apps' care information, using the 11 indicators recommended by the National Library of Medicine; and (3) types of tailoring to provide personalization, feedback, and content matching, which are common tailoring strategies described in the literature.
Educational information was the most prevalent type of information provided (29/35 apps, 83%), followed by information about tangible actions (18/35, 51%) and referrals (14/35, 40%). All apps presented their objectives clearly and avoided unrealistic or emotional claims. However, few provided information to explain whether the app's content was generated or reviewed by experts (7/35, 20%) or how its content was selected (4/35, 11%). Further, 6 of the 35 (17%) apps implemented 1 type of tailoring; of them, 4 (11%) used content matching and the other 2 (6%) used personalization. No app used 2 types of tailoring; only 2 (6%) used all 3 types (the third is feedback).
Existing dementia care apps do not provide sufficient high-quality, tailored information for informal caregivers. Caregivers should exercise caution when they use dementia care apps for informational support. Future research should focus on designing dementia care apps that incorporate quality-assured, transparency-enhanced, evidence-based artificial intelligence-enabled mHealth solutions for caregivers.
痴呆症患者的非正式照护者越来越多地使用移动健康(mHealth)应用程序来获取护理信息。移动健康应用程序被视为有望更好地满足照护者复杂且不断变化的信息需求的工具。然而,对于这些应用程序提供的痴呆症护理信息的类型和质量,我们却知之甚少。这些信息是否是为照护者量身定制的;如果是,又是如何定制的呢?
我们旨在通过系统地研究公开可用的痴呆症患者照护者应用程序中提供的护理相关信息的类型和质量,以及用于根据照护者的信息需求和情况量身定制信息的应用程序功能,来填补文献中上述空白。
2023年9月,我们采用多阶段流程为痴呆症患者的照护者选择移动应用程序。最终样本包括35个应用程序。我们评估了:(1)应用程序中提供的痴呆症护理信息的类型,使用我们的包含教育信息、具体行动和转诊信息的3项阿尔茨海默病及相关痴呆症日常护理策略框架;(2)应用程序护理信息的质量,使用美国国立医学图书馆推荐的11项指标;(3)为实现个性化、反馈和内容匹配而进行的定制类型,这些是文献中描述的常见定制策略。
教育信息是提供的最普遍的信息类型(29/35个应用程序,83%),其次是关于具体行动的信息(18/35,51%)和转诊信息(14/35,40%)。所有应用程序都清晰地展示了其目标,避免了不切实际或情绪化的表述。然而,很少有应用程序提供信息来解释其内容是否由专家生成或审核(7/35,20%),或者其内容是如何选择的(4/35,11%)。此外,35个应用程序中有6个(17%)实施了1种定制类型;其中,4个(11%)使用了内容匹配,另外2个(6%)使用了个性化。没有应用程序使用2种定制类型;只有2个(6%)使用了所有3种类型(第三种是反馈)。
现有的痴呆症护理应用程序没有为非正式照护者提供足够的高质量、量身定制的信息。照护者在使用痴呆症护理应用程序获取信息支持时应谨慎。未来的研究应专注于设计痴呆症护理应用程序,为照护者纳入质量有保证、透明度更高、基于证据的人工智能支持的移动健康解决方案。