Feasibility Trial of a Problem-Solving Weight Loss Mobile Application

描述(申请人提供)：生活方式干预措施虽然在减轻体重和糖尿病风险方面有效，但强度很大(即需要16次或更多面对面的访问)，这阻碍了广泛的实施。移动技术可能会降低干预强度，同时通过帮助传递行为策略来保存结果，但很少有研究探索这一点。在开放市场上，减肥移动应用程序激增；然而，我们的工作和其他2表明，这些应用程序解决的循证策略的范围很窄，主要包括自我监控、提示、目标设置，有时还包括社交网络。3市场和研究中都缺少的一个关键策略是解决问题，这是行为减肥干预措施的基本组成部分。4我们建议开发和测试Smart Coach的可行性，这是一款减肥移动应用程序，包括自我监控、目标设置和社交网络等常见功能，但更重要的是，它是一种虚拟化身促进的具体问题解决功能，可以智能地处理信息，帮助患者识别减肥问题的解决方案。我们假设Smart Coach结合较低强度(一半疗程)的减肥干预将比单独进行较低强度的减肥干预更有效，面对面访问结束后可以观察到最大的差异。使用“众包”模式，我们将通过1)专家提供的问题解决会议，对试图减肥的肥胖参与者进行会议，以及2)应用程序的预试运行测试，用问题和解决方案填充数据库。运用人工智能原理，将问题求解的算法转化为 为移动应用程序提供咨询，以便它可以独立执行这一策略，但要基于专家和众包信息。然后，我们将使用一系列涉及定性研究方法(可用性测试、焦点小组和预试运行)的迭代步骤来改进工具。随机试点可行性试验将测试Smart Coah移动应用程序与缩短的(8周)行为减肥干预措施配合使用时的可行性和初步效果，相对于单独使用缩短的行为减肥干预措施。可行性结果包括移动应用程序的使用频率和持续时间以及每项功能、招聘和保留。我们还将进行探索性分析，比较8周和16周时解决问题的技能和体重减轻的情况。数据将支持智能教练辅助的短暂行为减肥干预相对于单纯短暂行为减肥干预的更大有效性试验。我们的总体目标是开发移动技术，在保持减肥结果的同时，尽可能减少生活方式干预的强度，最终扩大目前几乎无法接触到的人群和环境的覆盖范围。

DESCRIPTION (provided by applicant): Lifestyle interventions, while effective at reducing weight and diabetes risk, are intensive (i.e., requiring 16 or more face-to-face visits) which has prevented widespread implementation. Mobile technology may reduce intervention intensity while preserving outcomes by assisting in the delivery of behavioral strategies, but very little research has explored this. Weight loss mobile applications are proliferating in the open market; however our work and others2 show that the range of evidence-based strategies addressed by these apps is narrow, primarily including self-monitoring, prompts, goal-setting, and sometimes a social network.3 A key strategy missing across apps in both the market and research is problem solving, an essential component of behavioral weight loss interventions.4 We propose to develop and test the feasibility of Smart Coach, a weight loss mobile application that includes common features such as self-monitoring, goal setting, and a social network, but even more importantly, an avatar-facilitated, idiographic problem solving feature that processes information intelligently to help patients identify solutions to their weight loss problems. We hypothesize tha Smart Coach when combined with a lower intensity (half the sessions) weight loss intervention will be more effective than a lower intensity weight loss intervention alone, with biggest differences observable after face-to- face visits end. Using a "crowd-sourcing" model, we will populate a database with problems and solutions via 1) expert-delivered problem solving sessions with a sample of obese participants trying to lose weight and 2) a pre-pilot test of the app. Using principles of "artificial intelligence" we will convert the algorithm of problem solving counseling into the mobile application so that it may perform this strategy on its own, but based on expert and crowd-sourced information. We will then use a series of iterative steps involving qualitative research methods (usability testing, focus groups, and pre-piloting) to refine the tool A randomized pilot feasibility trial will test the feasibility and initial effects of the Smart Coah mobile application when paired with a shortened (8- week) behavioral weight loss intervention relative to a shortened behavioral weight intervention alone. Feasibility outcomes include frequency and duration of usage of the mobile app and each feature, recruitment, and retention. We will also do exploratory analyses comparing conditions on problem solving skills and weight loss at 8- and 16-weeks. Data will support a larger efficacy trial of a Smart Coach-assisted brief behavioral weight loss intervention relative to a brief behavioral weight loss intervention alone. Our overarching goal is to develop mobile technology that reduces the intensity of lifestyle interventions as far as possible while preserving weight loss outcomes, to ultimately broaden the reach to people and settings that currently have little access.