Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China.
JMIR Public Health Surveill. 2024 Apr 22;10:e47428. doi: 10.2196/47428.
Depression is often accompanied by changes in behavior, including dietary behaviors. The relationship between dietary behaviors and depression has been widely studied, yet previous research has relied on self-reported data which is subject to recall bias. Electronic device-based behavioral monitoring offers the potential for objective, real-time data collection of a large amount of continuous, long-term behavior data in naturalistic settings.
The study aims to characterize digital dietary behaviors in depression, and to determine whether these behaviors could be used to detect depression.
A total of 3310 students (2222 healthy controls [HCs], 916 with mild depression, and 172 with moderate-severe depression) were recruited for the study of their dietary behaviors via electronic records over a 1-month period, and depression severity was assessed in the middle of the month. The differences in dietary behaviors across the HCs, mild depression, and moderate-severe depression were determined by ANCOVA (analyses of covariance) with age, gender, BMI, and educational level as covariates. Multivariate logistic regression analyses were used to examine the association between dietary behaviors and depression severity. Support vector machine analysis was used to determine whether changes in dietary behaviors could detect mild and moderate-severe depression.
The study found that individuals with moderate-severe depression had more irregular eating patterns, more fluctuated feeding times, spent more money on dinner, less diverse food choices, as well as eating breakfast less frequently, and preferred to eat only lunch and dinner, compared with HCs. Moderate-severe depression was found to be negatively associated with the daily 3 regular meals pattern (breakfast-lunch-dinner pattern; OR 0.467, 95% CI 0.239-0.912), and mild depression was positively associated with daily lunch and dinner pattern (OR 1.460, 95% CI 1.016-2.100). These changes in digital dietary behaviors were able to detect mild and moderate-severe depression (accuracy=0.53, precision=0.60), with better accuracy for detecting moderate-severe depression (accuracy=0.67, precision=0.64).
This is the first study to develop a profile of changes in digital dietary behaviors in individuals with depression using real-world behavioral monitoring. The results suggest that digital markers may be a promising approach for detecting depression.
抑郁症常伴有行为改变，包括饮食行为。饮食行为与抑郁症之间的关系已得到广泛研究，但先前的研究依赖于自我报告数据，而这些数据容易受到回忆偏差的影响。基于电子设备的行为监测具有在自然环境中客观、实时地采集大量连续、长期行为数据的潜力。
本研究旨在描述抑郁症患者的数字化饮食行为，并确定这些行为是否可用于检测抑郁症。
共招募了 3310 名学生（2222 名健康对照者[HCs]、916 名轻度抑郁症患者和 172 名中重度抑郁症患者），通过电子记录在一个月内记录他们的饮食行为，并在月中评估抑郁症严重程度。采用协方差分析（ANCOVA）比较 HCs、轻度抑郁症和中重度抑郁症患者的饮食行为差异，协变量包括年龄、性别、BMI 和教育水平。采用多元逻辑回归分析检验饮食行为与抑郁症严重程度之间的关联。采用支持向量机分析（Support Vector Machine analysis）判断饮食行为变化是否可用于检测轻度和中重度抑郁症。
研究发现，与 HCs 相比，中重度抑郁症患者的饮食模式更不规律，进食时间波动更大，晚餐花费更多，食物选择更单一，早餐吃得更少，更倾向于只吃午餐和晚餐。中重度抑郁症与每日 3 餐规律模式（早餐-午餐-晚餐模式；OR 0.467，95%CI 0.239-0.912）呈负相关，轻度抑郁症与每日午餐和晚餐模式呈正相关（OR 1.460，95%CI 1.016-2.100）。这些数字化饮食行为的变化能够检测出轻度和中重度抑郁症（准确率=0.53，精确率=0.60），对中重度抑郁症的检测准确率更高（准确率=0.67，精确率=0.64）。
这是第一项使用真实世界行为监测来开发抑郁症患者数字化饮食行为变化特征的研究。结果表明，数字化标志物可能是一种有前途的检测抑郁症的方法。
