Evidence map›Paper›PMID 37327031›Full record

ArticleJMIR mHealth and uHealth2023

Using Smartphone Survey and GPS Data to Inform Smoking Cessation Intervention Delivery: Case Study.

Amanda Luken, Michael R Desjardins, Meghan B Moran, Tamar Mendelson, Vadim Zipunnikov, Thomas R Kirchner, Felix Naughton, Carl Latkin, Johannes Thrul

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

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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 3 institutions in 3 countries.

Amanda LukenDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-8100-2176
Michael R DesjardinsSpatial Science for Public Health Center, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-2789-5460
Meghan B MoranDepartment of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0001-6745-6668
Tamar MendelsonDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-6085-3043
Vadim ZipunnikovDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0001-9864-2420
Thomas R KirchnerDepartment of Social and Behavioral Sciences, New York University School of Global Public Health, New York, NY, United States.ORCID 0000-0001-5764-4980
Felix NaughtonBehavioural and Implementation Science Research Group, University of East Anglia, Norwich, United Kingdom.ORCID 0000-0001-9790-2796
Carl LatkinDepartment of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-7931-2116
Johannes ThrulDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0001-8929-9579
Johns Hopkins University · USNew York University · USUniversity of East Anglia · GB

Funding

NIDA Epidemiology Training Program: Johns Hopkins UniversityT32DA007292 · NIDA · JOHNS HOPKINS UNIVERSITY · PI RENEE M. JOHNSON, Brion S Maher · 1993 to 2026
$17.7M
Testing the impact of smartphone-based messaging to support young adult smoking cessationR01CA246590 · NCI · JOHNS HOPKINS UNIVERSITY · PI THRUL, JOHANNES · 2020 to 2025
$1.8M
NCI NIH HHS R01 CA246590NIDA NIH HHS T32 DA007292
6 · The paper itself

Abstract

backgroundInterest in quitting smoking is common among young adults who smoke, but it can prove challenging. Although evidence-based smoking cessation interventions exist and are effective, a lack of access to these interventions specifically designed for young adults remains a major barrier for this population to successfully quit smoking. Therefore, researchers have begun to develop modern, smartphone-based interventions to deliver smoking cessation messages at the appropriate place and time for an individual. A promising approach is the delivery of interventions using geofences-spatial buffers around high-risk locations for smoking that trigger intervention messages when an individual's phone enters the perimeter. Despite growth in personalized and ubiquitous smoking cessation interventions, few studies have incorporated spatial methods to optimize intervention delivery using place and time information.

objectiveThis study demonstrates an exploratory method of generating person-specific geofences around high-risk areas for smoking by presenting 4 case studies using a combination of self-reported smartphone-based surveys and passively tracked location data. The study also examines which geofence construction method could inform a subsequent study design that will automate the process of deploying coping messages when young adults enter geofence boundaries.

methodsData came from an ecological momentary assessment study with young adult smokers conducted from 2016 to 2017 in the San Francisco Bay area. Participants reported smoking and nonsmoking events through a smartphone app for 30 days, and GPS data was recorded by the app. We sampled 4 cases along ecological momentary assessment compliance quartiles and constructed person-specific geofences around locations with self-reported smoking events for each 3-hour time interval using zones with normalized mean kernel density estimates exceeding 0.7. We assessed the percentage of smoking events captured within geofences constructed for 3 types of zones (census blocks, 500 ft

resultsThe number of reported past 30-day smoking events ranged from 12 to 177 for the 4 cases. Each 3-hour geofence for 3 of the 4 cases captured over 50% of smoking events. The 1000 ft

conclusionsOur findings suggest that this geofence construction method can identify high-risk smoking situations by time and place and has potential for generating individually tailored geofences for smoking cessation intervention delivery. In a subsequent smartphone-based smoking cessation intervention study, we plan to use fishnet grid geofences to inform the delivery of intervention messages.

Indexed as

Mobile ApplicationsSmoking CessationHumansSelf ReportSmartphoneSmokersYoung Adultadultapplicationcase studycessationdeliveryGISGPShealth interventionsmHealthmobile phonesmartphonesmartphone applicationsmokersmokingsmoking cessation

Identifiers

PMID37327031
PMCPMC10337446
OpenAlexW4362714751

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.