JMIR Publications

ABSTRACT

Background:

People from underserved communities such as those from lower social economic positions (SEPs) or racial minority groups are often disproportionately targeted by the tobacco industry, including relatively high levels of tobacco retail outlets (TROs) located in their neighborhood or through pro-tobacco marketing and promotional strategies. It is difficult to capture individuals’ smoking behaviors in actual locations, as well as the extent of exposure to tobacco promotional efforts. With the high ownership of smartphones in the U.S., apps—when used alongside data sources on TRO locations—could potentially improve tobacco control efforts. Health apps could be used to assess individual-level exposure to tobacco marketing efforts, particularly in relation to locations of TROs, as well as locations where they were most likely to smoke. To date, it remains unclear how health apps could be used practically by health organizations to better reach underserved communities in their tobacco control efforts.

Objective:

To demonstrate how smartphone apps could augment existing data on locations of TROs within underserved communities in Massachusetts and Texas to help inform tobacco control efforts.

Methods:

Data for this study come from two sources: (a) Geolocations of TROs from the North American Industry Classification System 2016 and (b) 95 participants (aged 18 to 34) from underserved communities who resided in Massachusetts and Texas who took part in an 8-week study using location tracking on their smartphones. We analyzed the data using spatial autocorrelation, optimized hotspot analysis, and fitted power-law distribution to identify the TROs that attract the most traffic using mobility data.

Results:

Participants reported encountering pro-tobacco messages mostly from store signs and displays, and anti-tobacco messages predominantly through television. In Massachusetts, clusters of TROs (Dorchester Center and Jamaica Plain) and reported smoking behaviors (Dorchester Center, Roxbury Crossing, Lawrence) were found in economically disadvantaged neighborhoods. Despite a broad distribution of TROs throughout the communities, participants overwhelming visited a relatively small number of TROs in Roxbury, Methuen. In Texas, clusters of TROs (Spring, Jersey Village, Bunker Hill Village, Sugar Land and Missouri City) were found primarily in Houston, while clusters of reported smoking behaviors were concentrated in West University Place, Aldine, Jersey Village, Spring, and Baytown.

Conclusions:

Smartphone apps can be used to pair geolocation data with self-reported smoking behavior to more fully understand how tobacco product marketing and promotion is influencing behavior within vulnerable communities. Public health officials could take advantage of smartphone data collection capabilities to implement targeted tobacco control efforts in these strategic locations to reach underserved communities in their built environment. Clinical Trial: N.A.