The psychosocial burdens of living with diabetes.

AIM: To better understand the prevalence of self-reported psychosocial burdens and the unmet needs identified by people with diabetes in relation to routine diabetes visits. METHODS: An English language, online survey was distributed via social media, key stakeholder networks, charity and advocacy groups to adults with type 1 diabetes or type 2 diabetes. Survey items were designed by members of the FDA RESCUE Collaborative Community Governing Committee prior to pilot testing with potential participants. Descriptive statistical analyses were conducted, as well as thematic analyses on free-text responses using NVivo v14. RESULTS: Four hundred and seventy-eight participants completed the survey: 373 (78%) had type 1 diabetes, 346 (73%) identified as a woman and 433 (91%) were white. Most participants had experienced self-reported (rather than diagnosed) anxiety and depression (n = 323 and n = 313, respectively), as well as fear of low blood sugars (n = 294), low mood (n = 290) and diabetes-related distress (n = 257). Sixty-eight percent reported that diabetes had negatively affected self-esteem, 62% reported the feelings of loneliness, but 93% reported that friends/family/work colleagues were supportive when needed. Two hundred and seventy-two percent (57%) reported that their diabetes team had never raised the topic of mental health. The overwhelming majority stated that the best thing their diabetes team could do to help was to simply ask about mental well-being, listen with empathy and without judgement, and practice skills to understand psychosocial issues in diabetes. CONCLUSION: Integrating psychosocial discussions and support within routine healthcare visits is crucial to improve outcomes for people with diabetes. Such a biopsychosocial model of healthcare has long been advocated by regulatory bodies.

Delivering Biopsychosocial Health Care Within Routine Care: Spotlight-AQ Pivotal Multicenter Randomized Controlled Trial Results.

BACKGROUND: Annual national diabetes audit data consistently shows most people with diabetes do not consistently achieve blood glucose targets for optimal health, despite the large range of treatment options available. AIM: To explore the efficacy of a novel clinical intervention to address physical and mental health needs within routine diabetes consultations across health care settings. METHODS: A multicenter, parallel group, individually randomized trial comparing consultation duration in adults diagnosed with T1D or T2D for ≥6 months using the Spotlight-AQ platform versus usual care. Secondary outcomes were HbA1c, depression, diabetes distress, anxiety, functional health status, and healthcare professional burnout. Machine learning models were utilized to analyze the data collected from the Spotlight-AQ platform to validate the reliability of question-concern association; as well as to identify key features that distinguish people with type 1 and type 2 diabetes, as well as important features that distinguish different levels of HbA1c. RESULTS: n = 98 adults with T1D or T2D; any HbA1c and receiving any diabetes treatment participated (n = 49 intervention). Consultation duration for intervention participants was reduced in intervention consultations by 0.5 to 4.1 minutes (3%-14%) versus no change in the control group (-0.9 to +1.28 minutes). HbA1c improved in the intervention group by 6 mmol/mol (range 0-30) versus control group 3 mmol/mol (range 0-8). Moderate improvements in psychosocial outcomes were seen in the intervention group for functional health status; reduced anxiety, depression, and diabetes distress and improved well-being. None were statistically significant. HCPs reported improved communication and greater focus on patient priorities in consultations. Artificial Intelligence examination highlighted therapy and psychological burden were most important in predicting HbA1c levels. The Natural Language Processing semantic analysis confirmed the mapping relationship between questions and their corresponding concerns. Machine learning model revealed type 1 and type 2 patients have different concerns regarding psychological burden and knowledge. Moreover, the machine learning model emphasized that individuals with varying levels of HbA1c exhibit diverse levels of psychological burden and therapy-related concerns. CONCLUSION: Spotlight-AQ was associated with shorter, more useful consultations; with improved HbA1c and moderate benefits on psychosocial outcomes. Results reflect the importance of a biopsychosocial approach to routine care visits. Spotlight-AQ is viable across health care settings for improved outcomes.