Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study - PubMed (original) (raw)
Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study
James H Fowler et al. BMJ. 2008.
Abstract
Objectives: To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks.
Design: Longitudinal social network analysis.
Setting: Framingham Heart Study social network.
Participants: 4739 individuals followed from 1983 to 2003.
Main outcome measures: Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties.
Results: Clusters of happy and unhappy people are visible in the network, and the relationship between people's happiness extends up to three degrees of separation (for example, to the friends of one's friends' friends). People who are surrounded by many happy people and those who are central in the network are more likely to become happy in the future. Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km) and who becomes happy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation.
Conclusions: People's happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.
Conflict of interest statement
Competing interests: None declared.
Figures
Fig 1 Happiness clusters in the Framingham social network. Graphs show largest component of friends, spouses, and siblings at exam 6 (centred on year 1996, showing 1181 individuals) and exam 7 (year 2000, showing 1020 individuals). Each node represents one person (circles are female, squares are male). Lines between nodes indicate relationship (black for siblings, red for friends and spouses). Node colour denotes mean happiness of ego and all directly connected (distance 1) alters, with blue shades indicating least happy and yellow shades indicating most happy (shades of green are intermediate)
Fig 2 Social distance and happiness in the Framingham social network. Percentage increase in likelihood an ego is happy if friend or family member at certain social distance is happy (instead of unhappy). The relationship is strongest between individuals who are directly connected but remains significantly >0 at social distances up to three degrees of separation, meaning that a person’s happiness is associated with happiness of people up to three degrees removed from them in the network. Values derived by comparing conditional probability of being happy in observed network with an identical network (with topology and incidence of happiness preserved) in which same number of happy people are randomly distributed. Alter social distance refers to closest social distance between alter and ego (alter=distance 1, alter’s alter=distance 2, etc). Error bars show 95% confidence intervals
Fig 3 Happy alters in Framingham social network. Mean probabilities observed in raw data with standard errors. Ego happiness in exams 6 and 7 (dichotomised between those who are maximally happy and everyone else) is positively associated with number of happy alters in previous exam. Generalised estimating equation regression models in appendix (see bmj.com) confirm relation is strongly significant, even with numerous controls
Fig 4 Alter type and happiness in the Framingham social network. Friends, spouses, siblings, and neighbours significantly influence happiness, but only if they live close to ego. Effects estimated with generalised estimating equation logit models of happiness on several different subsamples of the network (see table S6 in appendix on bmj.com)
Fig 5 Physical and temporal separation and spread of happiness in Framingham social network. Figure shows probability that ego is happy given that alter friend is happy, for different subsamples. Top: effect of gradually increasing maximum distance allowed between ego and alter households. Friends who live less than half mile (0.8 km) away have the strongest effect on ego happiness, and effect decreases with distance. Bottom: effect of gradually increasing maximum time allowed between ego and alter exams. Friends who report becoming happy within past half year exert strongest influence on ego happiness, and effect decreases as time between ego and alter exams increases. Effect sizes are based on generalised estimating equation models of happiness in tables S9 and S10 in appendix on bmj.com
Comment in
- Commentary: Understanding social network analysis.
Sainsbury P. Sainsbury P. BMJ. 2008 Dec 4;337:a1957. doi: 10.1136/bmj.a1957. BMJ. 2008. PMID: 19056787 No abstract available. - Happiness, social networks, and health.
Steptoe A, Diez Roux AV. Steptoe A, et al. BMJ. 2008 Dec 4;337:a2781. doi: 10.1136/bmj.a2781. BMJ. 2008. PMID: 19056790 No abstract available. - Happiness networks. What about social politics?
Smith G. Smith G. BMJ. 2009 Jan 28;338:b292. doi: 10.1136/bmj.b292. BMJ. 2009. PMID: 19176664 No abstract available. - Happiness networks. Know your friends.
Mellon JA. Mellon JA. BMJ. 2009 Jan 28;338:b293. doi: 10.1136/bmj.b293. BMJ. 2009. PMID: 19176665 No abstract available.
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