Evidence map›Paper›PMID 32673252›Full record

Observational studyJournal of medical Internet research2020

The Potential of Smartphone Apps in Informing Protobacco and Antitobacco Messaging Efforts Among Underserved Communities: Longitudinal Observational Study.

Edmund Wj Lee, Mesfin Awoke Bekalu, Rachel McCloud, Donna Vallone, Monisha Arya, Nathaniel Osgood, Xiaoyan Li, Sara Minsky, Kasisomayajula Viswanath

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Observational
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 3 countries.

Edmund Wj LeeDana-Farber Cancer Institute, Boston, MA, United States.ORCID 0000-0003-0536-2898
Mesfin Awoke BekaluDana-Farber Cancer Institute, Boston, MA, United States.ORCID 0000-0003-1243-5813
Rachel McCloudDana-Farber Cancer Institute, Boston, MA, United States.ORCID 0000-0003-0174-2260
Donna ValloneSchroeder Institute, Truth Initiative, Washington, DC, United States.ORCID 0000-0001-9841-9333
Monisha AryaBaylor College of Medicine, Houston, TX, United States.ORCID 0000-0001-5417-977X
Nathaniel OsgoodDepartment of Computer Science, College of Arts and Science, University of Saskatchewan, Saskatoon, SK, Canada.ORCID 0000-0002-3994-3298
Xiaoyan LiDepartment of Computer Science, College of Arts and Science, University of Saskatchewan, Saskatoon, SK, Canada.ORCID 0000-0002-4206-098X
Sara MinskyDana-Farber Cancer Institute, Boston, MA, United States.ORCID 0000-0002-7811-5356
Kasisomayajula ViswanathDana-Farber Cancer Institute, Boston, MA, United States.ORCID 0000-0002-4795-5803
Harvard University · USUniversity of Saskatchewan · CAAmerican Legacy Foundation · USDana-Farber Cancer Institute · USMichael E. DeBakey VA Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeople from underserved communities such as those from lower socioeconomic positions or racial and ethnic minority groups are often disproportionately targeted by the tobacco industry, through the relatively high levels of tobacco retail outlets (TROs) located in their neighborhood or protobacco marketing and promotional strategies. It is difficult to capture the smoking behaviors of individuals in actual locations as well as the extent of exposure to tobacco promotional efforts. With the high ownership of smartphones in the United States-when used alongside data sources on TRO locations-apps could potentially improve tobacco control efforts. Health apps could be used to assess individual-level exposure to tobacco marketing, particularly in relation to the 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 promotion organizations to better reach underserved communities in their tobacco control efforts.

objectiveThis study aimed 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.

methodsData for this study were collected from 2 sources: (1) geolocations of TROs from the North American Industry Classification System 2016 and (2) 95 participants (aged 18 to 34 years) from underserved communities who resided in Massachusetts and Texas and took part in an 8-week study using location tracking on their smartphones. We analyzed the data using spatial autocorrelation, optimized hot spot analysis, and fitted power-law distribution to identify the TROs that attracted the most human traffic using mobility data.

resultsParticipants reported encountering protobacco messages mostly from store signs and displays and antitobacco 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 the widespread distribution of TROs throughout the communities, participants overwhelmingly visited a relatively small number of TROs in Roxbury and Methuen. In Texas, clusters of TROs (Spring, Jersey Village, Bunker Hill Village, Sugar Land, and Missouri City) were found primarily in Houston, whereas clusters of reported smoking behaviors were concentrated in West University Place, Aldine, Jersey Village, Spring, and Baytown.

conclusionsSmartphone apps could be used to pair geolocation data with self-reported smoking behavior in order to gain a better understanding of how tobacco product marketing and promotion influence smoking 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.

Indexed as

AdolescentAdultFemaleHumansLongitudinal StudiesMaleMarketingMobile ApplicationsTobacco IndustryVulnerable PopulationsYoung Adultbig datadata sciencemobile healthmobile phonespatial analysistobacco use

Identifiers

PMID32673252
PMCPMC7381035
OpenAlexW3022649065

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.