Evidence map›Paper›PMID 31157881›Full record

Trial reportAnnals of behavioral medicine : a publication of the Society of Behavioral Medicine2020

Multiple Health Risk Behaviors in Young Adult Smokers: Stages of Change and Stability over Time.

Danielle E Ramo, Johannes Thrul, Erin A Vogel, Kevin Delucchi, Judith J Prochaska

Registry-linked trialOpen access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Annals of behavioral medicine : a publication of the Society of Behavioral Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02207036 (Social Media Intervention for Young Adult Smokers), which is not on this map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.2field-weighted citation impact, top 21% 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.

NCT02207036 nacompletednot on this map

Social Media Intervention for Young Adult Smokers

TypeinterventionalSponsorUniversity of California, San FranciscoRan2014 to 2016Enrolled501ConditionsSmokingArmsFacebook intervention, Smokefree.gov
3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 19 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Digital interventions for substance use disorders in young people: rapid review.Substance abuse treatment, prevention, and policy · 2023
    Review
  9. Article
  10. Article
  11. Living with a Smoker and Multiple Health-Risk Behaviors.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2021
    Observational
  12. Observational
  13. 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

5 authors at 3 institutions in 1 country.

Danielle E RamoDepartment of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, Suite, San Francisco, CA, USA.
Johannes ThrulDepartment of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Erin A VogelDepartment of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, Suite, San Francisco, CA, USA.
Kevin DelucchiDepartment of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, Suite, San Francisco, CA, USA.
Judith J ProchaskaStanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA.
University of California, San Francisco · USJohns Hopkins University · USStanford University · US

Funding

TREATMENTS FOR COMPLEX PATIENTS IN NEW SETTINGSP50DA009253 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WEISNER, CONSTANCE M. · 1994 to 2014
$28.2M
Substance Use Disorders Treatment/Services Research Training ProgramT32DA007250 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Derek D Satre, Janice Y Tsoh · 1991 to 2026
$7.4M
Postdoctoral training in tobacco controlR25CA113710 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GLANTZ, STANTON ARNOLD · 2006 to 2015
$5.4M
Technology Innovations for Supporting Health in Alaska Native PeopleR01HL117736 · NHLBI · STANFORD UNIVERSITY · PI BENOWITZ, NEAL L, PROCHASKA, JUDITH J. · 2014 to 2018
$3.8M
Postdoctoral training in tobacco controlT32CA113710 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LING, PAMELA MAY · 2016 to 2020
$2.6M
Evaluation of Tobacco Treatment Strategies for Inpatient PsychiatryR01MH083684 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PROCHASKA, JUDITH J. · 2009 to 2013
$2.6M
Social Media Intervention for Young Adult SmokersK23DA032578 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI RAMO, DANIELLE E · 2012 to 2016
$927k
NCI NIH HHS R25 CA113710NCI NIH HHS T32 CA113710NHLBI NIH HHS R01 HL117736NIDA NIH HHS K23 DA032578NIDA NIH HHS P50 DA009253NIDA NIH HHS T32 DA007250NIMH NIH HHS R01 MH083684
6 · The paper itself

Abstract

backgroundHealth risk behaviors (HRBs) are common, yet not well understood in young adult smokers. PURPOSE: We examined HRB profiles over 12 months in young adult smokers participating in a Facebook smoking cessation intervention clinical trial.

methodsParticipants (N = 500; age M = 20.9 years; 54.6% women) were recruited online and randomized to receive either a 3-month Facebook smoking cessation intervention or referral to Smokefree.gov (control). A Health Risk Assessment determined risk for 10 behaviors at baseline and 3, 6, and 12 months. Latent class analysis (LCA) and latent transition analysis (LTA) were used to identify patterns of HRBs and changes over time.

resultsAt baseline, participants reported an average of 5.4 (standard deviation [SD] = 1.7) risk behaviors, including smoking (100%), high-fat diet (84.8%), poor sleep hygiene (71.6%), and low fruit and vegetable intake (69.4%). A 3-class model fit the data best at baseline and all follow-up time points: low risk (28.8% at baseline) with low likelihood of risk on all behaviors except smoking, substance use risk (14.0% at baseline) characterized by heavy episodic drinking, cannabis use, and other illicit drug use, and metabolic risk (57.2% at baseline), with a high percentage of members at risk for a low fruit and vegetable intake, high-fat diet, inactivity, stress, and poor sleep hygiene. Classes were very stable at 3, 6, and 12 months, with few participants transitioning between classes.

conclusionsMost young adult smokers engaged in multiple risk behaviors, with meaningful clustering of behaviors, and demonstrated stability over a year's time. In addition to smoking, targets for intervention are co-occurring substance use and metabolic risk behaviors. CLINICAL TRIALS REGISTRATION: NCT02207036.

Indexed as

Sleep HygieneAdultDietFemaleFollow-Up StudiesHealth Risk BehaviorsHumansMaleRisk AssessmentSmokingSmoking CessationSubstance-Related DisordersYoung AdultInterventionlatent transition analysisMultiple health risk behaviorSocial mediaYoung adults

Identifiers

PMID31157881
PMCPMC7170731
OpenAlexW2948500695

What OpenQuestion holds

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Registered trials

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.