Optimizing an mHealth Intervention to Change Food Purchasing Behaviors for Cancer Prevention

摘要 饮食摄入量是影响癌症风险的一个强大的、可改变的因素。不幸的是，大多数美国成年人。 发现很难坚持预防癌症的饮食指南。一条有希望的改进途径 饮食坚持是针对杂货店的购物习惯，即在家中购买供消费的食品。两个- 每天摄入的食物中有三分之一来自或在家里吃，因此提高了家庭食物的质量 环境应提高整体饮食质量。当健康食品被购买，而不健康食品被 不，在家里做出健康的饮食选择需要最低限度的自我控制。在购买时，它是 很难抵抗美味食物的诱惑，但干预措施可能会通过以下方式促进健康选择) 在杂货店购物时实时提高饮食目标的重要性，b)增强制作和 保持饮食的变化，以及c)增加家庭对健康食品的支持和问责 采购。在研究的这个阶段，对可以改变食物的干预成分进行有系统的测试 需要购买行为，以便制定一个可行、可接受、 并针对效率和可扩展性进行了优化。建议的研究将纳入患有低血压病的成年人(N=) 坚持癌症预防饮食建议。所有参与者都将参加一个3小时的营养课程 教育工作坊。在研讨会结束后的6个月内，所有参与者都将收到短信通知 为食品采购提供量身定制的提醒和建议。这项研究将使用析因设计来 通过实验测试四个额外的干预组件，并检查其可行性、可接受性和 在3个月和6个月时对食物购买和饮食摄入量的影响。(每个组件被随机分配为 为50%的参与者激活。)要测试的四个组件是：1)位置触发文本 通知：食品购买的提醒和建议在到达时“及时”送达 杂货店购物地点，以增强目标突出性。2)对变革的好处的思考：提高 为了鼓励人们思考健康饮食的预期益处，在信息中添加了内容。 3)教练监控：由教练自动监控食物购买(通过收集 项目级商店数据)，他们发送个性化的购买后消息，旨在增强支持 责任，从而激励。4)家庭短信：家中其他成年人收到信息 旨在争取对健康食品购买的支持，并提供另一个支持性问责来源。 这项研究的初步目的是评估干预组成部分的可行性和可接受性。这个 这项研究的主要目的是量化每个干预组成部分的影响，分别和 关于杂货店食品购买(用商店数据进行客观评估)和饮食摄入量的组合 (通过24小时食品召回进行评估)。还将进行调解分析。这件事的首要目标是 该项目旨在优化这一mHealth干预措施，未来可以在全功率临床试验中进行测试。

Abstract Dietary intake is a powerful, modifiable factor that influences cancer risk. Unfortunately, most adults in the U.S. find it difficult to adhere to dietary guidelines for cancer prevention. One promising pathway for improving dietary adherence is to target grocery shopping habits, i.e., foods purchased for consumption at home. Two- thirds of daily food intake is sourced from or eaten in the home, so improving the quality of the home food environment should improve overall diet quality. When healthy foods are purchased and unhealthy foods are not, minimal self-control is needed to make healthy eating choices in the home. At the point of purchase, it is difficult to resist the temptation of palatable foods, but interventions might facilitate healthy choices by a) promoting dietary goal salience in real-time while grocery shopping, b) enhancing motivation to make and sustain changes to the diet, and c) increasing household support and accountability for healthy food purchasing. At this stage of research, methodical testing of intervention components that can change food purchase behaviors is needed, in order to craft an mHealth intervention package that is feasible, acceptable, and optimized for efficacy and scalability. The proposed study will enroll adults (N = 64) who have low adherence to cancer prevention dietary recommendations. All participants will attend a 3-hour nutrition education workshop. For 6 months following the workshop, all participants will receive text notifications that provide tailored reminders and recommendations for food purchasing. The study will use a factorial design to experimentally test four additional intervention components and examine their feasibility, acceptability, and effect on food purchases and dietary intake at 3 and 6 months. (Each component is randomly assigned to be activated for 50% of participants.) The four components to be tested are: 1) Location-triggered text notifications: Reminders and recommendations for food purchases are delivered “just-in-time,” when arriving at grocery shopping locations, to enhance goal salience. 2) Reflections on the benefits of change: To enhance motivation, content is added to messages to encourage reflection on the anticipated benefits of healthy eating. 3) Coach monitoring: Food purchases are automatically monitored by a coach (through a system that collects item-level store data) who sends personalized post-purchase messages designed to enhance supportive accountability and thus motivation. 4) Household text: Other adults in the household receive messages designed to elicit support for healthy food purchasing and provide another source of supportive accountability. The preliminary aim of the study is to assess feasibility and acceptability of the intervention components. The primary aim of the study is to quantify the effect of each intervention component, individually and in combination, on grocery store food purchases (objectively assessed with store data), and dietary intake (assessed with 24-hour food recalls). Mediation analyses also will be conducted. The overarching goal of this project is to optimize this mHealth intervention, which can be tested in the future in a fully powered clinical trial.