The Tick App

Research initiatives that engage the public (i.e., community science or citizen science) increasingly provide insights into tick exposures in the United States. However, these data have important caveats, particularly with respect to reported travel history and tick identification. Here, we assessed whether a smartphone application, The Tick App, provides reliable and novel insights into tick exposures across three domains — travel history, broad spatial and temporal patterns of species-specific encounters, and tick identification. During 2019–2021, we received 11,424 tick encounter submissions from across the United States, with nearly all generated in the Midwest and Northeast regions. Encounters were predominantly with human hosts (71%); although one-fourth of ticks were found on animals. Half of the encounters (51%) consisted of self-reported peri?domestic exposures, while 37% consisted of self-reported recreational exposures. Using phone-based location services, we detected differences in travel history outside of the users' county of residence along an urbanicity gradient. Approximately 75% of users from large metropolitan and rural counties had travel out-of-county in the four days prior to tick detection, whereas an estimated 50–60% of users from smaller metropolitan areas did. Furthermore, we generated tick encounter maps for Dermacentor variabilis and Ixodes scapularis that partially accounted for travel history and overall mirrored previously published species distributions. Finally, we evaluated whether a streamlined three-question sequence (on tick size, feeding status, and color) would inform a simple algorithm to optimize image-based tick identification. Visual aides of tick coloration and size engaged and guided users towards species and life stage classification moderately well, with 56% of one-time submitters correctly selecting photos of D. variabilis adults and 76% of frequent-submitters correctly selecting photos of D. variabilis adults. Together, these results indicate the importance of bolstering the use of smartphone applications to engage community scientists and complement other active and passive tick surveillance systems.

Background: Mobile health (mHealth) technology takes advantage of smartphone features to turn them into research tools, with the potential to reach a larger section of the population in a cost-effective manner, compared with traditional epidemiological methods. Although mHealth apps have been widely implemented in chronic diseases and psychology, their potential use in the research of vector-borne diseases has not yet been fully exploited. Objective: This study aimed to assess the usability and feasibility of The Tick App, the first tick research–focused app in the United States. Methods: The Tick App was designed as a survey tool to collect data on human behaviors and movements associated with tick exposure while engaging users in tick identification and reporting. It consists of an enrollment survey to identify general risk factors, daily surveys to collect data on human activities and tick encounters (Tick Diaries), a survey to enter the details of tick encounters coupled with tick identification services provided by the research team (Report a Tick), and educational material. Using quantitative and qualitative methods, we evaluated the enrollment strategy (passive vs active), the user profile, location, longitudinal use of its features, and users' feedback. Results: Between May and September 2018, 1468 adult users enrolled in the app. The Tick App users were equally represented across genders and evenly distributed across age groups. Most users owned a pet (65.94%, 962/1459; PPPPP=.03). Recurring users (49.25%, 723/1468) had a similar demographic profile to all users but participated in outdoor activities more frequently (80.5%, 575/714; P55 years; IRR 3.4, 95% CI 1.5-7.6; PPP<.001). Conclusions: This assessment allowed us to identify what fraction of the population used The Tick App and how it was used during a pilot phase. This information will be used to improve future iterations of The Tick App and tailor potential tick prevention interventions to the users' characteristics.