Centre for Cognitive Neuroscience, University of Salzburg, Austria; Department of Psychology, University of Salzburg, Austria.
Department of Psychology, Division for Clinical Psychology, Psychotherapy, and Health Psychology, University of Salzburg, Austria.
Appetite. 2017 Nov 1;118:168-173. doi: 10.1016/j.appet.2017.08.008. Epub 2017 Aug 7.
Research on eating behavior is limited by an overreliance on self-report. It is well known that actual food intake is frequently underreported, and it is likely that this problem is overrepresented in vulnerable populations. The present research tested a chewing detection method that could assist self-report methods. A trained sample of 15 participants (usable data of 14 participants) kept detailed eating records during one day and one night while carrying a recording device. Signals recorded from electromyography sensors unobtrusively placed behind the right ear were used to develop a chewing detection algorithm. Results showed that eating could be detected with high accuracy (sensitivity, specificity >90%) compared to trained self-report. Thus, electromyography-based eating detection might usefully complement future food intake studies in healthy and vulnerable populations.
对饮食行为的研究受到过度依赖自我报告的限制。众所周知，实际食物摄入量经常被少报，而且这个问题在脆弱人群中可能更为突出。本研究测试了一种咀嚼检测方法，该方法可以辅助自我报告方法。一个受过训练的 15 名参与者样本（14 名参与者的可用数据）在携带记录设备的情况下，在一天一夜的时间里详细记录了饮食情况。利用从右耳后面不显眼位置记录的肌电图传感器记录的信号，开发了一种咀嚼检测算法。结果表明，与训练有素的自我报告相比，这种算法可以非常准确地检测到进食（灵敏度、特异性>90%）。因此，基于肌电图的进食检测可能会在未来健康人群和脆弱人群的食物摄入量研究中发挥重要作用。
