Using Retrospective and Real-Time Physical Activity Tracking to Predict Risk of Sunburn in Outdoor Exercisers on Strava

在 Strava 上使用回顾性实时身体活动跟踪来预测户外锻炼者晒伤的风险

• 批准号: 10057198
• 负责人: DAVID B BULLER
• 金额: $ 44.43万
• 依托单位: KLEIN BUENDEL, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-06 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10057198
• 关键词: Accelerometer; Adult; Algorithms; American; Basal Cell; Behavior Therapy; Cancer Control; Cessation of life; Colorado; Connecticut; Consent; Data; Databases; Devices; Diagnosis; Diffusion of Innovation; Electronic Mail; Exercise; Feedback; Financial Hardship; Focus Groups; Funding; Future; Goals; Health system; High Prevalence; Incidence; Individual; Intervention; Libraries; Location; Machine Learning; Malignant Neoplasms; Modeling; National Cancer Institute; Persuasive Communication; Photosensitivity; Physical activity; Population; Prevention; Principal Investigator; Protective Clothing; Published Comment; Recreation; Research; Research Personnel; Research Priority; Resort; Risk; Risk Factors; Seasons; Self Efficacy; Skin; Skin Cancer; Social Network; Software Engineering; Squamous Cell; Sun Exposure; Sunburn; Sunscreening Agents; Surgeon; Technology; Testing; The Sun; Time; UV Radiation Exposure; Ultraviolet Rays; United States; anticancer research; application programming interface; base; cancer prevention; cancer therapy; data access; data format; evidence base; experience; fitbit; forgetting; graduate student; high risk; high risk behavior; high risk population; improved; individualized prevention; innovation; interest; keratinocyte; machine learning algorithm; meetings; melanoma; melanoma biomarkers; mobile application; mobile computing; novel; open source; parallel processing; prediction algorithm; programs; prototype; randomized trial; response; safety practice; skill acquisition; skin cancer prevention; social; social cognitive theory; social media; social networking website; sun protection; sun safety; usability; web site; willingness

Skin cancer is the most common cancer in the United States. Over 5.4 million cases of keratinocyte cancers (basal and squamous cell) are diagnosed annually and incidence of melanoma has increased 3% each year for the past 30 years. In the 2014 Call to Action to Prevent Skin Cancer, the U. S. Surgeon General included strategies for coordinating messages on sun safety and physical activity, recognizing the need for sun safety among populations that engage in physical activity outdoors. Recreational UV exposure is associated with every form of skin cancer; individuals who engage in more physical activity have a higher prevalence of sunburn. Melanoma is the only cancer with which physical activity is positively correlated. Strava, a tracking app and social networking site for athletes, is one of the most popular of mobile technologies for logging physical activity and providing social feedback on activities by other Strava users. The goal of this R21 research is to test the feasibility of interfacing with the Strava website and its mobile app to develop an algorithm that a) predicts when individuals are likely to be engaged in physical activity outdoors in the sun when ultraviolet radiation is high and b) delivers sun safety advice tailored to the time, location, and personal risk (e.g., skin sun sensitivity) of the Strava users. Concept focus groups with Strava users (n=16) and non-users (n=16) will provide input on the feasibility, content, and functions in the Strava Sun (SS) intervention. The algorithm that predicts future outdoor exercise will be developed using machine learning modeling on activity data provided by Strava users (n=1000). The SS intervention will be programmed to deliver ecologically-valid sun safety advice through Strava's open-source Applications Programming Interface (API) via email and comments in the Strava interface. The sun safety advice will be tailored to location, time, season and user's personal risk, employing advice algorithms we developed for a sun safety mobile app, sunZapp, and a social media message library in our Go Sun Smart intervention for outdoor recreation resorts. SS will be tested for usability (n=30 Strava users) prior to conducting a pilot field trial to establish feasibility of SS in practice (n=226 Strava users). The project is significant and innovative. A sun protection interface for the Strava platform will provide individuals who engage in regular physical activity personalized, ecologically-valid advice to help them practice sun safety during outdoor activities. It will contain a novel machine learning algorithm that predicts future high-risk behavior, i.e., outdoor physical activity, to provide precision prevention advice. The intervention will be delivered over an established and popular technology platform with over 42 million registered users.

皮肤癌是美国最常见的癌症。540多万例角质形成细胞癌 (基底细胞和鳞状细胞)每年被诊断为黑色素瘤，#年黑色素瘤发病率每年增加3% 在过去的30年里。在2014年预防皮肤癌行动呼吁中，美国卫生局局长包括 协调关于太阳安全和身体活动的信息的战略，认识到太阳安全的必要性 在从事户外体育活动的人群中。娱乐性紫外线暴露与每一个 皮肤癌；从事更多体育活动的人有更高的晒伤患病率。 黑色素瘤是唯一一种与体力活动呈正相关的癌症。Strava，一款跟踪应用程序和 针对运动员的社交网站，是最流行的移动技术之一，用于登录物理 活动，并对其他Strava用户的活动提供社会反馈。这项R21研究的目标是测试 与Strava网站及其移动应用程序接口以开发a)预测的算法的可行性 当个人很可能在阳光下从事户外体育活动时，紫外线辐射 High和b)提供针对时间、地点和个人风险(例如皮肤的日照敏感度)量身定做的防晒安全建议 Strava的用户。由Strava用户(n=16)和非用户(n=16)组成的概念焦点小组将提供关于 施特拉瓦太阳(SS)干预的可行性、内容和作用。预测未来的算法 户外运动将在Strava用户提供的活动数据上使用机器学习建模进行开发 (n=1000)。SS干预将被编程为通过以下方式提供生态有效的太阳安全建议 Strava的开源应用程序编程接口(API)，通过电子邮件和Strava中的评论 界面。SUN安全建议将根据地点、时间、季节和用户的个人风险进行量身定做，使用 我们为SUN安全移动应用程序sunZapp和社交媒体消息库开发的建议算法 我们的Go Sun Smart干预措施适用于户外娱乐度假村。将对SS进行可用性测试(n=30个Strava 在进行试点现场试验以确定SS在实践中的可行性之前(n=226个Strava用户)。这个 该项目意义重大，具有创新性。Strava平台的防晒接口将为个人提供 经常参加个性化、生态有效的体育活动，帮助他们实践防晒安全 在户外活动期间。它将包含一个新的机器学习算法，可以预测未来的高风险 行为，即户外体力活动，提供精确的预防建议。干预措施将是 通过拥有超过4200万注册用户的成熟流行技术平台交付。