Lived experience at the core: A classification system for risk-taking behaviours in bipolar

Objective: Clinical observations suggest that individuals with a diagnosis of bipolar face difficulties regulating emotions and impairments to their cognitive processing, which can contribute to high-risk behaviours. However, there are few studies which explore the types of risk-taking behaviour that manifest in reality and evidence suggests that there is currently not enough support for the management of these behaviours. This study examined the types of risk-taking behaviours described by people who live with bipolar and their access to support for these behaviours.

Methods: Semi-structured interviews were conducted with n = 18 participants with a lived experience of bipolar and n = 5 healthcare professionals. The interviews comprised open-ended questions and a Likert-item questionnaire. The responses to the interview questions were analysed using content analysis and corpus linguistic methods to develop a classification system of risk-taking behaviours. The Likert-item questionnaire was analysed statistically and insights from the questionnaire were incorporated into the classification system.

Results: Our classification system includes 39 reported risk-taking behaviours which we manually inferred into six domains of risk-taking. Corpus linguistic and qualitative analysis of the interview data demonstrate that people need more support for risk-taking behaviours and that aside from suicide, self-harm and excessive spending, many behaviours are not routinely monitored.

Conclusion: This study shows that people living with bipolar report the need for improved access to psychologically informed care, and that a standardised classification system or risk-taking questionnaire could act as a useful elicitation tool for guiding conversations around risk-taking to ensure that opportunities for intervention are not missed. We have also presented a novel methodological framework which demonstrates the utility of computational linguistic methods for the analysis of health research data.