Feasibility Trial of a Problem-Solving Weight Loss Mobile Application

描述（由申请人提供）：生活方式干预，虽然有效地降低体重和糖尿病风险，是密集的（即，需要16次或更多次面对面访问），这阻碍了广泛实施。移动的技术可能会降低干预强度，同时通过协助提供行为策略来保护结果，但很少有研究对此进行了探讨。减肥移动的应用在公开市场上激增;然而，我们的工作和其他人2表明，这些应用程序所解决的基于证据的策略范围很窄，主要包括自我监控，提示，目标设定，有时还包括社交网络。3市场和研究中的应用程序都缺少一个关键策略是解决问题，行为减肥干预的重要组成部分。4我们建议开发和测试智能教练的可行性，这是一种减肥移动的应用程序，包括自我监控，目标设定和社交网络等常见功能，但更重要的是，一个化身促进，具体的问题解决功能，智能地处理信息，以帮助患者确定他们的减肥问题的解决方案。我们假设智能教练与较低强度（一半的课程）减肥干预相结合时，将比单独的较低强度减肥干预更有效，在面对面访问结束后观察到的差异最大。使用“众包”模式，我们将通过1）专家提供的问题解决会议（具有尝试减肥的肥胖参与者的样本）和2）应用程序的预试点测试，用问题和解决方案填充数据库。使用“人工智能”的原则，我们将转换问题解决的算法 向移动的应用提供咨询，使得它可以自己执行该策略，但是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，焦点小组和预试点）的迭代步骤来完善该工具。随机试点可行性试验将测试Smart Coah移动的应用程序与缩短的（8周）行为减肥干预配对时相对于单独缩短的行为减肥干预的可行性和初步效果。可行性结果包括移动的应用程序和每个功能的使用频率和持续时间、招募和保留。我们还将进行探索性分析，比较8周和16周时解决问题技能和减肥的情况。数据将支持一个更大的有效性试验的智能教练辅助简短的行为减肥干预相对于一个简短的行为减肥干预单独。我们的总体目标是开发移动的技术，尽可能降低生活方式干预的强度，同时保持减肥效果，最终扩大到目前几乎没有机会的人群和环境。

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.