Kelly Ryan Charles, Price Hermione, Phiri Peter, Cummings Michael, Ali Amar, Patel Mayank, Barnard Ethan, Liu Yufan, Mendez Oscar, Barnard-Kelly Katharine
Spotlight Consultations Ltd., Fareham, UK.
Southern Health NHS Foundation Trust, Southampton, UK.
J Diabetes Sci Technol. 2025 Jan;19(1):91-97. doi: 10.1177/19322968231183436. Epub 2023 Jun 23.
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.
To explore the efficacy of a novel clinical intervention to address physical and mental health needs within routine diabetes consultations across health care settings.
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.
= 98 adults with T1D or T2D; any HbA1c and receiving any diabetes treatment participated ( 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.
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.
年度全国糖尿病审计数据一直显示，尽管有大量可用的治疗选择，但大多数糖尿病患者并未持续达到最佳健康状态所需的血糖目标。
探讨一种新型临床干预措施在跨医疗保健机构的常规糖尿病会诊中满足身心健康需求的疗效。
一项多中心、平行组、个体随机试验，比较使用Spotlight-AQ平台与常规护理对诊断为1型糖尿病（T1D）或2型糖尿病（T2D）≥6个月的成年人的会诊时长。次要结局包括糖化血红蛋白（HbA1c）、抑郁、糖尿病困扰、焦虑、功能健康状况和医护人员职业倦怠。利用机器学习模型分析从Spotlight-AQ平台收集的数据，以验证问题-关注关联的可靠性；并识别区分1型和2型糖尿病患者的关键特征，以及区分不同HbA1c水平的重要特征。
98例患有T1D或T2D、任何HbA1c水平且接受任何糖尿病治疗的成年人参与研究（49例为干预组）。干预组参与者的会诊时长在干预会诊中减少了0.5至4.1分钟（3%-14%），而对照组无变化（-0.9至+1.28分钟）。干预组的HbA1c改善了6 mmol/mol（范围0-30），而对照组为3 mmol/mol（范围0-8）。干预组在功能健康状况方面的心理社会结局有适度改善；焦虑、抑郁和糖尿病困扰减轻，幸福感增强。均无统计学意义。医护人员报告称会诊中的沟通得到改善，对患者优先事项的关注增加。人工智能检查突出显示治疗和心理负担在预测HbA1c水平方面最为重要。自然语言处理语义分析证实了问题与其相应关注之间的映射关系。机器学习模型显示1型和2型患者在心理负担和知识方面有不同的关注点。此外，机器学习模型强调不同HbA1c水平的个体表现出不同程度的心理负担和与治疗相关的关注。
Spotlight-AQ与更简短、更有用的会诊相关；HbA1c得到改善，心理社会结局有适度益处。结果反映了生物心理社会方法在常规护理就诊中的重要性。Spotlight-AQ在跨医疗保健机构中可行，可改善结局。
