Alòs Francesc, Puig-Ribera Anna, Bort-Roig Judit, Chirveches-Pérez Emilia, Berenguera Anna, Martin-Cantera Carlos, Colomer Ma Àngels
Centre d'Atenció Primària Passeig de Sant Joan. Gerència Territorial de Barcelona Muntanya-Dreta, Institut Català de la Salut, Barcelona, Spain; Member of the redGDPS Foundation, Madrid, Spain; Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
Sport and Physical Activity Research Group, Institute for Research and Innovation in Life and Health Sciences in Central Catalonia, University of Vic-Central University of Catalonia, Vic, Spain.
Prim Care Diabetes. 2025 Jun;19(3):214-220. doi: 10.1016/j.pcd.2025.03.001. Epub 2025 Mar 10.
Type 2 diabetes mellitus (DM2) is one of the main public health threats of the 21st century. Half of the people with DM2 worldwide are not diagnosed. The high prevalence, underdiagnosis and complications of diabetes highlight the need for identifying people at risk. Sedentary behaviour (SB) or prolonged sitting is a major predisposing risk factor for the increasing prevalence of DM2. Incorporating SB measures into clinical practice systems for identifying individuals more likely to have DM2 should be considered.
To develop a mathematical model for clinical practice that allows early identification of office employees at risk of DM2 based on objective data on SB.
A cross-sectional study with a cross-validation procedure was conducted. Anthropometric variables (sex, age and body mass index, BMI), sleep time (hours; measured by ActivPAL3M devices), and SB patterns (sedentary breaks and time spent in sedentary bouts of four different lengths; measured by ActivPAL3M devices) of two groups of office employees (adults with and without diabetes) were compared. Eighty-one participants had DM2 and 132 had normal glucose metabolism (NGM). The risk of having DM2 was modelled using generalized linear models (GLM), particularly a logistic regression model.
Five non-invasive clinical variables that were significantly correlated to DM2 with no collinearity were included in the mathematical model: sex, age, BMI, sleep time (hours) and sedentary breaks < 20 minutes (number/day). The validated model correctly classified 94.58 % of the participants with DM2 and 97.99 % of participants with NGM. The sensitivity was 94.58 % and the specificity 97.99 %. Additionally, the model allowed the design of a preventive tool to recommend changes in the SB pattern based on the participant's anthropometric profile, aiming to reduce the risk of developing DM2 in office employees.
This study highlights the importance of incorporating SB measures in primary care clinical practice. Our mathematical model suggests that including SB could enhance the early identification of adults at risk of DM2. Further research is needed to validate these findings and assess the practical application of the mathematical model in clinical practice.
2型糖尿病（DM2）是21世纪主要的公共卫生威胁之一。全球一半的DM2患者未被诊断出来。糖尿病的高患病率、诊断不足和并发症凸显了识别高危人群的必要性。久坐行为（SB）或长时间坐着是DM2患病率上升的主要诱发风险因素。应考虑将SB测量纳入临床实践系统，以识别更可能患有DM2的个体。
开发一种临床实践的数学模型，以便根据关于SB的客观数据早期识别有DM2风险的办公室员工。
进行了一项带有交叉验证程序的横断面研究。比较了两组办公室员工（患有和未患有糖尿病的成年人）的人体测量变量（性别、年龄和体重指数，BMI）、睡眠时间（小时；通过ActivPAL3M设备测量）和SB模式（久坐休息时间以及在四种不同时长的久坐时段所花费的时间；通过ActivPAL3M设备测量）。81名参与者患有DM2，132名具有正常糖代谢（NGM）。使用广义线性模型（GLM），特别是逻辑回归模型，对患DM2的风险进行建模。
数学模型纳入了五个与DM2显著相关且无共线性的非侵入性临床变量：性别、年龄、BMI、睡眠时间（小时）和久坐休息时间<20分钟（次数/天）。经过验证的模型正确分类了94.58%的DM2参与者和97.99%的NGM参与者。敏感性为94.58%，特异性为97.99%。此外，该模型允许设计一种预防工具，根据参与者的人体测量特征推荐改变SB模式，旨在降低办公室员工患DM2的风险。
本研究强调了将SB测量纳入初级保健临床实践的重要性。我们的数学模型表明，纳入SB可以加强对有DM2风险成年人的早期识别。需要进一步研究来验证这些发现并评估该数学模型在临床实践中的实际应用。
