Zhao Kang, Xu Xinyi, Zhu Hanfei, Ren Ziqi, Zhang Tianzi, Yang Ningli, Zhu Shuqin, Xu Qin
School of Nursing, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.
Faculty of Health, The Queensland University of Technology, Brisbane, Queensland, Australia.
Diabetes Metab Syndr Obes. 2021 Dec 29;14:4959-4970. doi: 10.2147/DMSO.S347032. eCollection 2021.
The weight loss in Chinese patients after sleeve gastrectomy is different, and the differences can be evaluated through the trajectories of the percentage of body fat (BF%). Patients' baseline psychosocial factors may be associated with these trajectories.
We selected 267 patients who received sleeve gastrectomy for the first time. The BF% at baseline and 1, 3, 6, 12 months after surgery and baseline psychosocial variables were retrospectively collected. The trajectory model was established according to BF% based on the growth mixture model. The baseline psychosocial variables were compared among different trajectory classes.
Four types of trajectory classes were obtained. The differences in preoperative dietary self-efficacy, exercise self-efficacy, depression, social support, working status, alcohol consumption, and gender among the classes were statistically significant. The pairwise comparison of the above variables revealed that the differences of gender, dietary self-efficacy and exercise self-efficacy among classes were highly effective.
Female gender, low dietary self-efficacy and low exercise self-efficacy were predictors for poor BF% trajectory in sleeve gastrectomy patients. Health professionals can early identify patients who are most likely to lose weight in a not-ideal manner based on the above predictors.
中国患者接受袖状胃切除术后的体重减轻情况各不相同,可通过体脂百分比(BF%)轨迹进行评估。患者的基线心理社会因素可能与这些轨迹相关。
我们选取了267例首次接受袖状胃切除术的患者。回顾性收集了患者术前、术后1个月、3个月、6个月、12个月的BF%以及基线心理社会变量。基于生长混合模型,根据BF%建立轨迹模型。比较不同轨迹类别之间的基线心理社会变量。
获得了四种轨迹类别。不同类别之间术前饮食自我效能感、运动自我效能感、抑郁、社会支持、工作状态、饮酒情况及性别差异具有统计学意义。上述变量的两两比较显示,类别间性别、饮食自我效能感和运动自我效能感差异具有高度显著性。
女性、低饮食自我效能感和低运动自我效能感是袖状胃切除术患者BF%轨迹不佳的预测因素。卫生专业人员可基于上述预测因素早期识别出最有可能体重减轻不理想的患者。