Evidence map›Paper›PMID 24009742›Full record

ArticlePloS one2013

Promoting smoke-free homes: a novel behavioral intervention using real-time audio-visual feedback on airborne particle levels.

Neil E Klepeis, Suzanne C Hughes, Rufus D Edwards, Tracy Allen, Michael Johnson, Zohir Chowdhury, Kirk R Smith, Marie Boman-Davis, John Bellettiere, Melbourne F Hovell

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 2 pooled it
2.8field-weighted citation impact, top 10% 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

38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 65 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Trial
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  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Family informatics.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  13. Effects of aerosol particle size on the measurement of airborne PMEnvironmental monitoring and assessment · 2022
    Article
  14. Article
  15. Article
  16. Smoke-Free Home Intervention in Permanent Supportive Housing: A Multifaceted Intervention Pilot.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2021
    Article
  17. Article
  18. Article
  19. Radon, an invisible killer in Canadian homes: perceptions of Ottawa-Gatineau residents.Canadian journal of public health = Revue canadienne de sante publique · 2019
    Article
  20. 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

10 authors at 3 institutions in 1 country.

Neil E KlepeisCenter for Behavioral Epidemiology and Community Health (C-BEACH), Graduate School of Public Health, SDSU Research Foundation, San Diego State University, San Diego, California, USA. nklepeis@projects.sdsu.edu
Suzanne C Hughes
Rufus D Edwards
Tracy Allen
Michael Johnson
Zohir Chowdhury
Kirk R Smith
Marie Boman-Davis
John Bellettiere
Melbourne F Hovell
San Diego State University · USUniversity of California, Irvine · USUniversity of California, Berkeley · US

Funding

Innovation for Smoke-Free Homes: Real-Time FeedbackR01HL103684 · NHLBI · SAN DIEGO STATE UNIVERSITY · PI HOVELL, MELBOURNE F · 2011 to 2015
$5.4M
Clinician Promotion of Healthy Diet and Activity to Reduce Obesity Among AdolesceR01CA138192 · NCI · SAN DIEGO STATE UNIVERSITY · PI HOVELL, MELBOURNE F · 2009 to 2014
$4.0M
NCI NIH HHS R01 CA138192NCI NIH HHS R01 CA138192-04NHLBI NIH HHS R01 HL103684NHLBI NIH HHS R01 HL103684-02
6 · The paper itself

Abstract

Interventions are needed to protect the health of children who live with smokers. We pilot-tested a real-time intervention for promoting behavior change in homes that reduces second hand tobacco smoke (SHS) levels. The intervention uses a monitor and feedback system to provide immediate auditory and visual signals triggered at defined thresholds of fine particle concentration. Dynamic graphs of real-time particle levels are also shown on a computer screen. We experimentally evaluated the system, field-tested it in homes with smokers, and conducted focus groups to obtain general opinions. Laboratory tests of the monitor demonstrated SHS sensitivity, stability, precision equivalent to at least 1 µg/m(3), and low noise. A linear relationship (R(2) = 0.98) was observed between the monitor and average SHS mass concentrations up to 150 µg/m(3). Focus groups and interviews with intervention participants showed in-home use to be acceptable and feasible. The intervention was evaluated in 3 homes with combined baseline and intervention periods lasting 9 to 15 full days. Two families modified their behavior by opening windows or doors, smoking outdoors, or smoking less. We observed evidence of lower SHS levels in these homes. The remaining household voiced reluctance to changing their smoking activity and did not exhibit lower SHS levels in main smoking areas or clear behavior change; however, family members expressed receptivity to smoking outdoors. This study established the feasibility of the real-time intervention, laying the groundwork for controlled trials with larger sample sizes. Visual and auditory cues may prompt family members to take immediate action to reduce SHS levels. Dynamic graphs of SHS levels may help families make decisions about specific mitigation approaches.

Indexed as

FamilyParticulate MatterAdolescentAdultAir Pollution, IndoorAttitude to HealthEnvironmental MonitoringFemaleHealth BehaviorHumansMiddle AgedTobacco Smoke PollutionYoung AdultParticulate MatterTobacco Smoke Pollution

Identifiers

PMID24009742
PMCPMC3751871
OpenAlexW2011138254

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.