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