Using the behaviour change wheel and person-based approach to develop a digital self-management intervention for patients with adrenal insufficiency: the Support AI study protocol.

Introduction: Most patients with Adrenal insufficiency (AI) require lifelong glucocorticoid replacement. They need to increase glucocorticoids during physical illness or major stressful situations and require parenteral hydrocortisone in the event of an adrenal crisis. Patients with AI have impaired quality of life and high mortality; approximately 1 in 6-12 patients are hospitalised at least once/year from a potentially preventable adrenal crisis. Adoption of self-management behaviours are crucial; these include adherence to medication, following "sick day rules" and associated behaviours that aid prevention and treatment of adrenal crisis such as symptom monitoring, having extra tablets, carrying a medical-alert ID and injection kit, and self-injecting when necessary. Current patient education is ineffective at supporting self-management behaviour change or reducing adrenal crisis-related hospitalisations. This research study aims to gain an in-depth understanding of the barriers and enablers to self-management for patients with AI and to develop an evidence-based digital self-management behaviour change intervention. Methods: The study is conducted in accordance with the MRC Framework for developing complex interventions. Underpinned by the Behaviour Change Wheel (BCW), the Theoretical Domains Framework (TDF), and the Person-Based Approach, this research will be conducted in two phases: Phase 1 will involve a sequential qualitative/quantitative mixed-methods study involving focus group interviews followed by a cross-sectional survey with patients with AI recruited from patient advocacy groups and endocrine clinics in the UK. Phase 2 will develop the Support AI, a website-based digital behaviour change intervention (DBCI) informed by Phase 1 findings to support self-management for patients with AI. The most appropriate behaviour change techniques (BCTs) will be selected utilising a nominal group technique with an Expert Panel of 10-15 key stakeholders. The design of the Support AI website will be guided by the Person-Based Approach using an Agile iterative "think-aloud" technique with 12-15 participants over 3 usability testing iterations. Conclusion: A theory- and evidence-based digital behaviour change intervention will be developed which will be tested in a feasibility randomised trial following completion of this study. The projected benefit includes cost-effective health care service (reduced hospitalisations and demand for specialist services) and improved health outcomes and quality of life for patients with AI.

Hidden disability: a study of the psychosocial impact of living with pituitary conditions.

BACKGROUND: Pituitary patients often experience psychosocial symptoms associated with their condition. AIMS: To explore the condition management experiences of pituitary patients and their psychosocial symptoms and to explore the impact of these on quality of life. METHODS: A sample of 748 individuals aged 18 to more than 65 years) completed a questionnaire relating to quality of life and the psychosocial impact of pituitary conditions. FINDINGS: Analysis of the qualitative sections using content analysis identified four themes: social isolation, emotional and behavioural issues, appearance distress and physical and cognitive effects including fatigue and pain. An overarching theme of hidden disability emerged. CONCLUSION: The study identified multiple biopsychosocial factors that impact quality of life, with symptoms not visible to others most likely to impact negatively. Nurses are well placed to provide support and information to patients about the possible psychosocial impact of pituitary conditions to enable positive adjustment.

Towards a greater understanding of the psychosocial impact of the symptoms of pituitary conditions.

Management of pituitary conditions can be problematic with many patients experiencing long-term psychological and social difficulties that impact on their quality of life. This study aimed to identify psychosocial symptoms associated with pituitary conditions that lead to poor quality of life and identify differences in symptomatology between patient groups. A survey using measures of psychological and social symptoms was sent to 2000 members of the Pituitary Foundation in January 2016. The survey was completed by 1062 patients (683 female), aged under 18 to over 65 years, using categorical age ranges. Physical and psychosocial symptoms including appearance issues, fatigue, anxiety and depression were reported. Using correlational and regression analyses, significant variation in symptoms were identified across gender, age range and condition type that were impairing patients' long-term functioning and impacting quality of life. There is a need for greater patient information and advice surrounding psychosocial symptoms of pituitary conditions.