Experts Stress Importance of Social Networks for Psychosis and Bipolar Interventions

A team led by Daryl Sweet of Queen’s University Belfast completed an exploratory study mapping personal well-being networks for individuals with SMI diagnoses. The researchers expanded on previous social network research to create maps that include not only social relationships but also connections to community places and involvement in meaningful activities. They propose that their work, published recently in the British Journal of Psychiatry, may eventually be used to support person-centered, recovery-oriented interventions for those diagnosed with SMI.

“Place locations and meaningful activities are important aspects of people’s social worlds,” the authors write. “Mapped alongside social networks, personal well-being networks have important implications for person-centered recovery approaches through providing a broader understanding of an individual’s lives and resources.”

As the authors explain, prior research has shown that individuals with severe mental illness (SMI) diagnoses have more limited social networks – both in the number of connections and strength of emotional ties – than the general population. Past research in this area has typically defined networks solely in terms of the number of social relationships and has explored factors related to social network size. For example, one study found a link between more extensive social networks and decreased rates of hospitalization.

Sweet’s study is novel in its inclusion of other elements (e.g., place and activities) into a social network map that may help provide a fuller picture of participants’ social lives and resources. The team’s decision to include these components was informed by a recently developed personal recovery framework that identifies “connectedness and meaningful activities” as crucial components of recovery journeys.

To develop and analyze personal well-being network maps for individuals with SMI, the researchers collected data from a non-representative sample of 150 participants at two sites in England. All participants were between 16 and 65 years old and had a primary diagnosis of schizophrenia, bipolar, or other psychoses.

The research team used a name-generator method to collect data from participants about the people, places, and meaningful activities (participant-defined) they identified as contributing to their well-being. Information about the frequency, length, and emotional intimacy of relationships was also collected, as were measures of well-being, social capital, and physical health and social functioning. The data was then visualized using a personal well-being network map developed for the study.

Key results included:

Identification of three personal well-being network types:

Based on their analysis, the researchers identified three personal well-being network types – ‘formal and sparse,’ ‘family and stable,’ and ‘diverse and active.’ 31% of participants fit under the ‘formal and sparse’ network type, which was characterized by relatively fewer social connections, a larger proportion of medical provider contacts, more time at home, and fewer links to community places and involvement in meaningful activities. Those with ‘formal and sparse’ networks tended to be older, recipients of long-term disability aid, and to have schizophrenia or a psychotic disorder diagnosis. They were also more likely to be male.

32% of participants’ networks were classified as ‘family and stable,’ meaning that their social worlds were primarily composed of family and friend relationships, as well as more “emotionally close” and steady connections. Although these individuals spent the majority of their time at home, they also had some “community place connections,” and meaningful activity participation. Members of this group were more likely to be female, diagnosed with bipolar disorder, and employed (full or part-time).

Lastly, 36.7% percent of participants fell into the ‘diverse and active’ network group. Those in this group had relatively more social connections as well as greater “relationship type” diversity (i.e., both strong and weak ties, such as neighbors, co-workers, and acquaintances). ‘Diverse and active’ network members spent more time outside the home and were tied to more community places and involved in more meaningful activities than those in other groups. Participants in this category also tended to be younger and more educated than the other two groups.

“Small but significant differences” in mental well-being scores, and significant differences in “self-rated overall health” were found between types, with ‘formal and sparse’ group members experiencing the lowest scores and ‘diverse and active’ exhibiting the highest scores in both cases. However, the authors also underscore their findings of within-group variation – in particular, connectedness, well-being, and quality of life (as measured by physical health and social functioning) were found to highly vary within each of the three network types.

The significance of place and activities:

The authors argue that including place and activity data in an individual’s social map provides a more textured understanding of his or her social world, which can allow health care providers to more effectively identify strengths and possibilities for growth. For example, they discuss the map of one participant who, on the basis of social ties alone, seemed isolated; after incorporating place and activity data into this person’s map, however, it became possible to see that s/he was in fact connected to some places and related activities (e.g., the park, going for walks). Such discoveries could offer practitioners a foundation on which to help patients build further resources, as well as a point of entry for a conversation about resource-building opportunities.

The authors note that their findings are limited in that they are not based on data from a representative sample of individuals with SMI. They are also careful to explain that the three network types they identified are not conclusive, but instead, exist relative to each other. They emphasize that research with other samples could result in the identification of different types.

“Mapping an individual’s PWN [personal well-being network] could support person-centered approaches, both for understanding individuals’ decisions and co-producing plans to change networks in ways that enhance recovery and well-being,” Sweet and his colleagues write.

In closing, the authors write that their expanded approach to understanding the social networks of individuals with SMI diagnoses may enhance practitioners work with patients. They suggest that this method may offer possibilities for developing personalized, “recovery-focused practice.”

 

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Sweet, D., Byng, R., Webber, M., Enki, D. G., Porter, I., Larsen, J., … & Pinfold, V. (2017). Personal well-being networks, social capital and severe mental illness: exploratory study. The British Journal of Psychiatry, bjp-bp. (Link)