Usability and Feasibility of a Smartphone App to Assess Human Behavioral Factors Associated with Tick Exposure (The Tick App): Quantitative and Qualitative Study
Bibliographic record
Abstract
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; P<.001), did frequent outdoor activities (recreational or peridomestic; 75.24%, 1094/1454; P<.001 and 64.58%, 941/1457; P<.001, respectively), and lived in the Midwest (56.55%, 824/1457) and Northeast (33.0%, 481/1457) regions in the United States, more specifically in Wisconsin, southern New York, and New Jersey. Users lived more frequently in high-incidence counties for Lyme disease (incidence rate ratio [IRR] 3.5, 95% CI 1.8-7.2; P<.001) and in counties with cases recently increasing (IRR 1.8, 95% CI 1.1-3.2; P=.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; P<.01). The number of Tick Diaries submitted per user (median 2, interquartile range [IQR] 1-11) was higher for older age groups (aged >55 years; IRR 3.4, 95% CI 1.5-7.6; P<.001) and lower in the Northeast (IRR[NE] 0.4, 95% CI 0.3-0.7; P<.001), whereas the number of tick reports (median 1, IQR 1-2) increased with the frequency of outdoor activities (IRR 1.5, 95% CI 1.3-1.8; P<.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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".