用可穿戴设备动作数据，机器学习识别进食成瘾，准确率超95%
Objective Features Extracted from Motor Activity Time Series for Food Addiction Analysis Using Machine Learning -- A Pilot Study
- 从手腕活动时间序列提取256个统计与熵特征
- 区分进食成瘾的准确率达95.3%，特异性达98%
- 为隐私保护的临床评估提供客观数字标志物
可穿戴传感器和物联网平台实现持续实时监测，但进食障碍的客观数字标记仍有限。本研究探讨了加速度计与机器学习（ML）能否提供食物成瘾（FA）及症状数量（SC）的客观标准。对78名参与者（平均年龄22.1±9.5岁；73.1%女性）进行一周非优势腕部加速度计记录及心理量表数据采集（YFAS、DEBQ、ZSDS）。时间序列按昼夜分段，计算统计与熵特征（FuzzyEn、DistEn、SVDEn、PermEn、PhaseEn；共256特征）。采用基于K近邻（KNN）的五折分层交叉验证管道（重复100次；500次评估），以均值马修斯相关系数（MCC）为主评价指标，结合SHAP辅助解释。二分类FA任务中，活动期特征表现最佳（MCC = 0.78±0.02；准确率~95.3%±0.5；敏感性~0.77±0.03；特异性~0.98±0.004），优于客观-主观特征组合（OaS，MCC=0.69±0.03）和仅夜间休息特征（MCC=0.50±0.03）。四类症状数量预测中，OaS略胜于仅活动特征（MCC=0.40±0.01 vs 0.38±0.01；准确率~58.1% vs 56.9%）。情绪性进食与克制性进食与加速度特征相关。结果支持腕戴式加速度计作为食物成瘾的数字生物标志物，可补充问卷并推动隐私保护型临床转化。
原文摘要 · Abstract (English)
Wearable sensors and IoT/IoMT platforms enable continuous, real-time monitoring, but objective digital markers for eating disorders are limited. In this study, we examined whether actimetry and machine learning (ML) could provide objective criteria for food addiction (FA) and symptom counts (SC). In 78 participants (mean age 22.1 +/- 9.5 y; 73.1% women), one week of non-dominant wrist actimetry and psychometric data (YFAS, DEBQ, ZSDS) were collected. The time series were segmented into daytime activity and nighttime rest, and statistical and entropy descriptors (FuzzyEn, DistEn, SVDEn, PermEn, PhaseEn; 256 features) were calculated. The mean Matthews correlation coefficient (MCC) was used as the primary metric in a K-nearest neighbors (KNN) pipeline with five-fold stratified cross-validation (one hundred repetitions; 500 evaluations); SHAP was used to assist in interpretation. For binary FA, activity-segment features performed best (MCC = 0.78 +/- 0.02; Accuracy ~ 95.3% +/- 0.5; Sensitivity ~ 0.77 +/- 0.03; Specificity ~ 0.98 +/- 0.004), exceeding OaS (Objective and Subjective Features) (MCC = 0.69 +/- 0.03) and rest-only (MCC = 0.50 +/- 0.03). For SC (four classes), OaS slightly surpassed actimetry (MCC = 0.40 +/- 0.01 vs 0.38 +/- 0.01; Accuracy ~ 58.1% vs 56.9%). Emotional and restrained eating were correlated with actimetric features. These findings support wrist-worn actimetry as a digital biomarker of FA that complements questionnaires and may facilitate privacy-preserving clinical translation.
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