Engaging diverse colorectal cancer survivors in the design of an adaptive text message-based intervention to improve diet quality

让不同的结直肠癌幸存者参与设计基于自适应短信的干预措施，以改善饮食质量

• 批准号: 10673783
• 负责人: Erin Lynn Van Blarigan
• 金额: $ 18.47万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10673783
• 关键词: Address; Affect; Age; Algorithms; Artificial Intelligence; Asian Americans; Behavior; Behavior Therapy; Black race; Cancer Etiology; Cancer Survivor; Categories; Cereals; Cessation of life; Characteristics; Colorectal Cancer; Consumption; Cultural Backgrounds; Data; Diabetes Mellitus; Diagnosis; Diet; Dietary Factors; Dietary Fiber; Dietary Intervention; Ethnic Origin; Fiber; Focus Groups; Food; Frequencies; Future; Gender; Goals; Grain; Guidelines; Health; Health behavior; Intake; Intervention; Interview; Language; Latinx; Learning; Level of Evidence; Libraries; Low Income Population; Machine Learning; Malignant Neoplasms; Mental Depression; Methods; Modeling; Motivation; Pacific Islander; Participant; Persons; Physical activity; Population Heterogeneity; Psychological reinforcement; Public Health; Questionnaires; Race; Randomized, Controlled Trials; Recommendation; Research; Role; Structure; Subgroup; Surveys; Testing; Text Messaging; Time; United States; Update; Woman; Work; acceptability and feasibility; age group; arm; behavior change; bilingualism; cancer prevention; cancer survival; caucasian American; colorectal cancer risk; design; dietary; ethnic diversity; follow-up; good diet; improved; intervention effect; learning algorithm; malignant breast neoplasm; mortality; multidisciplinary; preference; programs; racial diversity; response; text messaging intervention

Colorectal cancer (CRC) is the 2nd leading cause of cancer death in the United States. Interventions that are accessible and effective in the large, culturally diverse populations of people affected by CRC will greatly improve public health. Dietary interventions hold particular promise for reducing CRC mortality because strong evidence supports that a high-fiber diet rich in whole grains is associated with lower mortality among people with CRC. Yet, <15% of CRC survivors consume a high-fiber diet. Therefore, our goal is to develop a scalable intervention to increase intake of whole grains and reduce intake of refined grains that is acceptable to CRC survivors of varied cultural backgrounds. Text messages are a promising intervention component for reaching a wide range of CRC survivors. However, most studies have developed and tested “one size fits all” static programs with limited generalizability. To address this gap, we propose to develop a continuously adaptive text message-based intervention using reinforcement learning (RL). RL is a type of machine learning that can be used to optimize text messages over time based on participant characteristics (e.g., race/ethnicity, language), engagement, and/or behavior. Dr. Aguilera (co-I) has developed a text message intervention that uses RL to increase physical activity in low income people with depression and diabetes; messages are sent in English or Spanish. While promising, it is not known if a RL algorithm can be used to improve diet behavior. In this proposal, we will develop a RL algorithm to optimize text message content and timing based on whole grain intake assessed through text messages. Informed by the Capability Opportunity Motivation – Behavior (COM- B) model, we propose an embedded mixed method study. In Aim 1, we will conduct four focus groups among Black, Latinx, non-Latinx white, and Asian American/Pacific Islander CRC survivors (5-8/group) and 20 semi- structured interviews. We aim to understand CRC survivors’ capabilities, opportunities, and motivations for a healthy diet and preferences for text messages. Using these data, we will refine our theoretical framework and revise/update our library of >200 diet-focused text messages from previous work in non-Latinx white cancer survivors. In Aim 2, informed by Aim 1, we will develop an adaptive text message intervention using RL to increase the proportion of grains consumed that are whole grains among CRC survivors. We will determine the intervention’s feasibility and acceptability and estimate its effect on diet (intake of whole grains, refined grains, total fiber) in a 12-week single-arm study among 60 CRC survivors, approximately equal numbers of whom identify as Black, Latinx, non-Latinx white, and Asian American/Pacific Islander. Our multidisciplinary and multicultural team has the expertise needed to complete this study. Our proposal will inform a scalable dietary intervention with broad generalizability that we will ultimately test in a randomized controlled trial. Improving CRC survivors’ diet quality has potential to reduce CRC mortality and have great public health impact.

结直肠癌（CRC）是美国癌症死亡的第二大原因。干预措施 在受CRC影响的人群中，可访问且有效 改善公共卫生。饮食干预措施特别有望降低CRC死亡率，因为很强 有证据支持富含全谷物的高纤维饮食与人们的死亡率较低有关 与CRC。然而，<15％的CRC表面饮食饮食。因此，我们的目标是开发可扩展的 干预以增加全谷物的摄入量并减少CRC可以接受的精制谷物的摄入量 各种文化背景的幸存者。短信是可以到达的承诺干预组件 广泛的CRC存活。但是，大多数研究已经开发并测试了“一件尺寸适合所有人”的静态 具有有限的推广性的程序。为了解决这一差距，我们建议开发一个连续的自适应文本 使用增强学习（RL）的基于消息的干预。 RL是一种可以是的机器学习 用于根据参与特征（例如种族/民族，语言）来优化文本消息， 参与和/或行为。 Aguilera博士（CO-I）开发了一种文本消息干预措施，该干预使用RL 增加抑郁症和糖尿病患者的体育锻炼；消息以英文或 西班牙语。尽管有希望，但尚不清楚RL算法是否可以用于改善饮食行为。在这个 提案，我们将开发一种RL算法，以优化基于全谷物的文本消息内容和计时 通过短信进行评估的进气口。以能力机会动机的方式告知 - 行为（com- b）模型，我们提出了一项嵌入式混合方法研究。在AIM 1中，我们将在AIM 1中进行四个焦点小组 黑色，拉丁裔，非LATINX白色和亚裔美国/太平洋岛民CRC表面（5-8/组）和20个半 结构化访谈。我们旨在了解CRC幸存的能力，机遇和动机 健康饮食和短信的偏好。使用这些数据，我们将完善我们的理论框架和 修订/更新我们的库> 200个以饮食为中心的文本消息的库中，来自非Latinx白癌的库 幸存者。在AIM 1告知AIM 2中，我们将使用RL开发自适应文本消息干预措施 增加CRC存活中全谷物所消耗的谷物的比例。我们将确定 干预的可行性和可接受性，并估计其对饮食的影响（全谷物的摄入，精制谷物， 总纤维）在60个CRC表面的一项为期12周的单臂研究中，其中大约相等的数量 识别为黑色，拉丁裔，非拉特犬白色和亚裔美国/太平洋岛民。我们的多学科和 多元文化团队拥有完成这项研究所需的专业知识。我们的建议将为可扩展的饮食提供信息 干预具有广泛的概括性，我们最终将在随机对照试验中测试。改进 CRC幸存者的饮食质量有可能降低CRC死亡率并产生巨大的公共卫生影响。