Improving diabetes and depression self-management via adaptive mobile messaging

通过自适应移动消息传递改善糖尿病和抑郁症的自我管理

• 批准号: 10204099
• 负责人: Adrian Aguilera
• 金额: $ 37.87万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10204099
• 关键词: Improving; diabetes; depression; self; management

Project Summary/Abstract Diabetes and depression are major public health problems that disproportionately affect racial/ethnic minorities and low-income individuals in the US. Efficacious interventions for depression and diabetes exist but are not often combined despite similar treatment recommendations (specifically physical activity) for both conditions. Especially in resource-constrained environments, mobile health (mHealth) technologies are cost effective and feasible methods for delivering self-management support given the more ubiquitous penetration across socioeconomic status. Existing mHealth interventions have shown preliminary success but have had difficulty sustaining engagement. When combined with machine learning algorithms, health messages can be adapted to specifically motivate individuals based on their unique profiles. In Aim 1, we will integrate content from interventions targeting diabetes, depression, and physical activity applying user design methods. We will utilize the existing HealthySMS platform as the basis for this intervention. This will be called the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation (DIAMANTE) study. In Aim 2, we will test an mHealth intervention for diabetes and depression that will generate messages using an adaptive machine learning algorithm that learns from patient step count data (collected passively via a smartphone app) and patient entered blood glucose and mood ratings. We will compare this adaptive, personalized intervention with a static messaging intervention, typical of many existing text messaging interventions. In Aim 3, we will rerandomize non-responsive participants to receiving nurse outreach using a sequential, multiple assignment, randomized trial (SMART) design. We will leverage the SMART design to conserve more expensive one-on-one nurse outreach for the patients who are no longer engaged in the program and need the most support. We will test this intervention with 350 patients from a safety net setting in English and Spanish. The primary outcomes for this study are HbA1c levels and PHQ-9 scores. The results of this study will help us understand the impact of personalizing content utilizing machine learning algorithms as well as the impact of providing clinician support for those receiving mobile health interventions. Since we are testing this intervention in a resource-constrained environment, the results of this study will be relevant for a broader population.

项目摘要/摘要 糖尿病和抑郁症是主要影响种族/族裔少数民族的主要公共卫生问题 和美国的低收入个人。存在有效的抑郁和糖尿病干预措施，但不存在 尽管在两种情况下进行了类似的治疗建议（特别是体育活动），但通常会合并。 特别是在资源受限环境中，移动健康（MHealth）技术具有成本效益，并且 鉴于跨越更加普遍的穿透力，可以提供自我管理支持的可行方法 社会经济地位。现有的MHealth干预措施已显示出初步的成功，但遇到了困难 维持参与。与机器学习算法结合使用时，可以调整健康消息 特别是根据个人独特的个人资料来激励个人。在AIM 1中，我们将整合来自 针对糖尿病，抑郁症和体育锻炼的干预措施应用用户设计方法。我们将利用 现有的Healthysms平台作为此干预的基础。这将被称为糖尿病和精神 健康自适应通知跟踪和评估（DIAMANTE）研究。在AIM 2中，我们将测试MHealth 干预糖尿病和抑郁症，将使用自适应机器学习产生消息 从患者步骤计数数据中学习的算法（通过智能手机应用被动收集）和患者 输入血糖和情绪等级。我们将将这种适应性的个性化干预与静态进行比较 消息干预，许多现有文本消息干预措施的典型特征。在AIM 3中，我们将重新调整 使用连续的多个分配，随机分配的无反应参与者接受护士外展 试用（智能）设计。我们将利用智能设计来保护更昂贵的一对一护士 不再从事该计划并且需要最大支持的患者的外展活动。我们将测试 与350名来自英语和西班牙语安全网的患者进行干预。主要结果 这项研究是HBA1C水平和PHQ-9分数。这项研究的结果将有助于我们了解 使用机器学习算法的个性化内容以及提供临床医生支持的影响 对于那些接受移动健康干预措施的人。由于我们正在对资源约束的研究进行测试 环境，这项研究的结果将与更广泛的人群有关。

项目成果

Conducting Internet-Based Visits for Onboarding Populations With Limited Digital Literacy to an mHealth Intervention: Development of a Patient-Centered Approach.

• DOI: 10.2196/25299
• 发表时间: 2021-04-29
• 期刊: JMIR formative research
• 影响因子: 2.2
• 作者: Hernandez-Ramos R;Aguilera A;Garcia F;Miramontes-Gomez J;Pathak LE;Figueroa CA;Lyles CR
• 通讯作者: Lyles CR

A flexible micro-randomized trial design and sample size considerations.

灵活的微随机试验设计和样本量考虑。

• DOI: 10.1177/09622802231188513
• 发表时间: 2023
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Xu,Jing;Yan,Xiaoxi;Figueroa,Caroline;Williams,JosephJay;Chakraborty,Bibhas
• 通讯作者: Chakraborty,Bibhas

Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.

用于优化移动应用程序以实现行为健康的自适应学习算法：设计决策指南。

• DOI: 10.1093/jamia/ocab001
• 发表时间: 2021
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Figueroa,CarolineA;Aguilera,Adrian;Chakraborty,Bibhas;Modiri,Arghavan;Aggarwal,Jai;Deliu,Nina;Sarkar,Urmimala;JayWilliams,Joseph;Lyles,CourtneyR
• 通讯作者: Lyles,CourtneyR