The collective dynamics of smoking in a large social network
Open accessAbstract
BACKGROUND: The prevalence of smoking has decreased substantially in the United States over the past 30 years. We examined the extent of the person-to-person spread of smoking behavior and the extent to which groups of widely connected people quit together. METHODS: We studied a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. We used network analytic methods and longitudinal statistical models. RESULTS: Discernible clusters of smokers and nonsmokers were present in the network, and the clusters extended to three degrees of separation. Despite the decrease in smoking in the overall population, the size of the clusters of smokers remained the same across time, suggesting that whole groups of people were quitting in concert. Smokers were also progressively found in the periphery of the social network. Smoking cessation by a spouse decreased a person's chances of smoking by 67% (95% confidence interval [CI], 59 to 73). Smoking cessation by a sibling decreased the chances by 25% (95% CI, 14 to 35). Smoking cessation by a friend decreased the chances by 36% (95% CI, 12 to 55 ). Among persons working in small firms, smoking cessation by a coworker decreased the chances by 34% (95% CI, 5 to 56). Friends with more education influenced one another more than those with less education. These effects were not seen among neighbors in the immediate geographic area. CONCLUSIONS: Network phenomena appear to be relevant to smoking cessation. Smoking behavior spreads through close and distant social ties, groups of interconnected people stop smoking in concert, and smokers are increasingly marginalized socially. These findings have implications for clinical and public health interventions to reduce and prevent smoking.
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Smoking is a social behaviour.
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The dynamics of health behavior sentiments on a large online social network
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- Authors
- Christakis, N. A., & Fowler, J. H.
- Affiliations
- Harvard University (US)
- University of California San Diego (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2008-05-22
- Volume
- 358
- Issue
- 21
- Pages
- 2249-2258
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC2822344
- OpenAlex
- W2111896942
- Cited by
- 2,390
- References
- 52
- Citations by year
- 2026: 65 · 2025: 71 · 2024: 78 · 2023: 90 · 2022: 97 · 2021: 174 · 2020: 162 · 2019: 129 · 2018: 128 · 2017: 198 · 2016: 148 · 2015: 143 · 2014: 169 · 2013: 185 · 2012: 173
- FWCI
- 84.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Issue Lab (Candid) (submittedVersion)
- Funders
- National Institutes of Health (n01hc25195-41-0-1)
- National Institute on Aging (R01AG24448, P01AG031093, R01AG024448)
- National Heart, Lung, and Blood Institute (N01‐HC25195, N01-HC-025195)
- Topics
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- smoking behavior, smoking cessation, social networks, social contagion, social ties, spousal influence, sibling influence, coworker influence, friendship, educational attainment, social marginalization, smoking prevalence decline, Framingham Heart Study
- MeSH terms
- Adult, Aged, Algorithms, Female, Humans, Longitudinal Studies, Male, Middle Aged, Smoking, Social Behavior, Social Support, Sociology, Medical, Logistic Models, Smoking Cessation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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