Healthy Mind Lab, Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States.
Department of Pathology and Laboratory Medicine, College of Medicine, Medical University of South Carolina, Charleston, SC, United States.
JMIR Mhealth Uhealth. 2020 Nov 13;8(11):e18609. doi: 10.2196/18609.
Mobile health apps are promising vehicles for delivering scalable health behavior change interventions to populations that are otherwise difficult to reach and engage, such as young adults with psychiatric conditions. To improve uptake and sustain consumer engagement, mobile health interventions need to be responsive to individuals' needs and preferences, which may change over time. We previously created an ecological daily needs assessment to capture microprocesses influencing user needs and preferences for mobile health treatment adaptation.
The objective of our study was to test the utility of a needs assessment anchored within a mobile app to capture individualized, contextually relevant user needs and preferences within the framework of a weight management mobile health app.
Participants with an iOS device could download the study app via the study website or links from social media. In this fully remote study, we screened, obtained informed consent from, and enrolled participants through the mobile app. The mobile health framework included daily health goal setting and self-monitoring, with up to 6 daily prompts to determine in-the-moment needs and preferences for mobile health-assisted health behavior change.
A total of 24 participants downloaded the app and provided e-consent (22 female; 2 male), with 23 participants responding to at least one prompt over 2 weeks. The mean length of engagement was 5.6 (SD 4.7) days, with a mean of 2.8 (1.1) responses per day. We observed individually dynamic needs and preferences, illustrating daily variability within and between individuals. Qualitative feedback indicated preferences for self-adapting features, simplified self-monitoring, and the ability to personalize app-generated message timing and content.
The technique provided an individually dynamic and contextually relevant alternative and complement to traditional needs assessment for assessing individually dynamic user needs and preferences during treatment development or adaptation. The results of this utility study suggest the importance of personalization and learning algorithms for sustaining app engagement in young adults with psychiatric conditions. Further study in broader user populations is needed.
移动健康应用程序是向难以接触和参与的人群（如患有精神疾病的年轻人）提供可扩展健康行为改变干预措施的有前途的载体。为了提高接受度和维持消费者参与度，移动健康干预措施需要对个人的需求和偏好做出反应，而这些需求和偏好可能会随着时间的推移而变化。我们之前创建了一种生态日常需求评估，以捕捉影响用户对移动健康治疗适应需求和偏好的微观过程。
我们的研究目的是测试在移动健康应用程序中锚定的需求评估在体重管理移动健康应用程序框架内捕捉个体化、与上下文相关的用户需求和偏好的实用性。
有 iOS 设备的参与者可以通过研究网站或社交媒体链接从研究应用程序中下载研究应用程序。在这项完全远程研究中，我们通过移动应用程序筛选、获得知情同意并招募参与者。移动健康框架包括每日健康目标设定和自我监测，最多有 6 个每日提示来确定即时的移动健康辅助健康行为改变需求和偏好。
共有 24 名参与者下载了应用程序并提供了电子同意（22 名女性；2 名男性），其中 23 名参与者在 2 周内至少回复了一次提示。参与的平均长度为 5.6（SD 4.7）天，平均每天回复 2.8（1.1）次。我们观察到了个体动态的需求和偏好，说明了个体内部和个体之间的每日可变性。定性反馈表明对自我适应功能、简化自我监测以及个性化应用程序生成消息时间和内容的能力的偏好。
该技术为评估治疗开发或适应过程中个体动态用户需求和偏好提供了一种个体动态和与上下文相关的替代方法和补充方法。这项实用性研究的结果表明，对于维持患有精神疾病的年轻人对应用程序的参与，个性化和学习算法很重要。需要在更广泛的用户群体中进行进一步研究。
