Injectable insulin therapy is a valuable therapeutic option for millions of people with diabetes worldwide. However, many people with diabetes undergoing insulin therapy experience suboptimal outcomes and/or have complications because of inadequate injection technique and training. Practical, current, evidence-based recommendations are mandatory for primary care practitioners and diabetes specialists alike to address unmet needs in insulin injection technique, education, and consequent outcomes. The most recent global insulin injection technique best practices were published in 2016 by the Forum for Injection Technique and Therapy Expert Recommendations (FITTER). While injection technique efforts in different regions have reflected some developments since 2016, a global effort was warranted to comprehensively capture new evidence and modern expert perspectives. In this article, we share the output of the "FITTER Forward" initiative, authored by 16 diabetes specialists from 13 countries who met virtually in 2023-2024. FITTER Forward provides an updated rationale for the importance of proper injection technique training and its impact on diabetes management. The FITTER Forward recommendations are organized for use in clinical practice and include 4 sections describing (1) the foundational science informing injection device design, experiences, and outcomes, (2) proper injection technique procedures for insulin pens and syringes from insulin storage to needle disposal, (3) lipodystrophy risk reduction, with a focus on lipohypertrophy, and (4) structured injection technique training programs for people with diabetes. Overall, FITTER Forward aims to better equip health care professionals to advance diabetes care by empowering people with diabetes and their caregivers to correctly and safely deliver insulin.
Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models (LLMs) provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: ChatGPT-3.5, ChatGPT-4.0, Google Bard, LlaMA-7B, LlaMA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, MedGPT, HuatuoGPT, and Chinese LlaMA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China (NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians (PCPs) without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that ChatGPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, LlaMA-7B, and LlaMA2-7B. For the NCE-CPFC examination, ChatGPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, MedGPT, and ChatGPT-3.5 successfully passed, whereas LlaMA2-7B, HuatuoGPT, Chinese LLaMA2-7B, and LlaMA-7B failed. ChatGPT-4.0 (84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination (improving by 1 %-6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.