METHODS: This study developed machine learning classification algorithms to classify binge eating and vomiting among 22 adults with binge-spectrum EDs using CGM data. Participants wore Dexcom G6 CGMs and reported eating episodes and disordered eating symptoms using ecological momentary assessment for 2 weeks. Group-level random forest models were generated to distinguish binge eating from typical eating episodes and to classify instances of vomiting.
RESULTS: The binge eating model had accuracy of 0.88 (95% CI: 0.83, 0.92), sensitivity of 0.56, and specificity of 0.90. The vomiting model demonstrated accuracy of 0.79 (95% CI: 0.62, 0.91), sensitivity of 0.88, and specificity of 0.71.
CONCLUSIONS: Results suggest that CGM may be a promising avenue for passively classifying binge eating and vomiting, with implications for innovative research and clinical applications.
方法:这项研究开发了机器学习分类算法,使用CGM数据对22名暴饮暴食和呕吐进行分类。参与者穿着DexcomG6CGMs,并使用2周的生态瞬时评估报告了饮食发作和饮食紊乱症状。生成组级别的随机森林模型以区分暴饮暴食与典型的进食发作,并对呕吐的情况进行分类。
结果:暴食模型的准确性为0.88(95%CI:0.83,0.92),敏感性为0.56,特异性为0.90。呕吐模型的准确性为0.79(95%CI:0.62,0.91),敏感性为0.88,特异性为0.71。
结论:结果表明,CGM可能是被动分类暴饮暴食和呕吐的有希望的途径,对创新研究和临床应用具有重要意义。