JMIR Publications

ABSTRACT

Background:

The use of spatial methods to inform smartphone-based smoking cessation intervention delivery has been understudied. Such methods can facilitate person- and time-specific intervention delivery.

Objective:

This study aims to demonstrate an exploratory method of generating person-specific geofences around high-risk smoking areas for a smoking cessation intervention by presenting four case studies.

Methods:

Data came from an Ecological Momentary Assessment (EMA) study with young adult smokers conducted from 2016 to 2017 in the San Francisco Bay Area. Participants reported smoking and non-smoking events through a smartphone app for 30 days and GPS data we recorded by the app. We sampled four cases along EMA compliance quartiles and constructed person-specific geofences around locations with self-reported smoking events for each three-hour time interval around zones with normalized mean kernel density estimates exceeding 0.7. We assessed the percentage of smoking events captured within geofences constructed for three types of zones (Census blocks; 500 ft.2 and 1,000 ft.2 fishnet grids).

Results:

Geofences constructed for all three types of zones captured over 50% of smoking events for three of the four cases. Of the three types of zones, the 1,000 ft2 fishnet grid captured the highest percentage of smoking events across the four cases. Across three-hour time intervals, the average percentage of smoking events within geofences ranged from 36.4% – 100.0%.

Conclusions:

This method can identify high-risk smoking situations by time and place and automatically generate individually-tailored geofences for smoking cessation intervention delivery. Our findings suggest that fishnet grid geofences may capture more smoking events compared to Census blocks. We plan to use fishnet grid geofences to inform delivery of a smartphone-based smoking cessation intervention.