JMIR Publications

ABSTRACT

Background:

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.

Objective:

The study aims to characterize digital dietary behaviors in depression, and to determine whether these behaviors could be used to detect depression.

Methods:

A total of 3310 students (2222 healthy controls, 916 with mild depression, and 172 with moderate-severe depression) were recruited for the study. Daily electronic records of meals consumed over one month were used to analyze differences in dietary behaviors between groups through analyses of covariance. Support vector machine analysis was used to determine if changes in dietary behaviors could detect mild and moderate-severe depression.

Results:

The study found that individuals with moderate-severe depression had more irregular eating patterns, more fluctuated feeding times, spending more money on dinner, less diverse food choices, as well as eating breakfast less frequently and preferred to eat only lunch and dinner, compared to healthy controls. The study found that these changes in digital dietary behaviors were able to detect mild and moderate-severe depression, with better accuracy for detecting moderate-severe depression.

Conclusions:

This is the first study to develop a profile of changes in digital dietary behaviors in individuals with depression using real-world behavioral monitoring rather than self-reported data. The results suggest that digital markers may be a promising approach for detecting depression.