Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation.

BACKGROUND: 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. 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 [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. RESULTS: 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). 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. The results suggest that digital markers may be a promising approach for detecting depression.

Psychodynamic-based virtual reality cognitive training system with personalized emotional arousal elements for mild cognitive impairment patients.

BACKGROUND AND OBJECTIVE: Mild cognitive impairment (MCI) is a serious threat to the physical health and quality of life of the elderly, as well as a heavy burden on families and society. The current computer-based rehabilitation training ignores the role of emotions in cognitive impairment rehabilitation, making it difficult to improve patient engagement and efficiency. To address this, a psychodynamics-based cognitive rehabilitation training method with personalized emotional arousal elements was proposed using virtual reality technology. METHODS: Our proposed method contains four training tasks, which cover (audiovisual memory, attention & processing, working memory, abstract & Logic, spatial pathfinding) and six positive emotional arousal elements (sensory feedback, achievement system, multiplayer interaction, score comparison, relaxation scenarios, and peaceful videos) to motivate participants to persist during cognitive training continuously and maintain a positive mental attitude toward training. The six emotional arousal elements were divided into two personalized combinations-full combination and half combination-based on the results of the pre-assessment and were dynamically distributed throughout both the training tasks and post-training. RESULTS: Fifteen participants with MCI were recruited to complete the proposed experiment and validate the effectiveness of the system. They were first asked to complete two assessments (e.g., the big five scale and the positive and negative affect scale) to investigate their personalities. Based on the results of the assessments, they were provided with a full or half combination of arousal elements in the training tasks and post-training. Finally, the acceptability of the system and task experience were assessed using questionnaires. Notably, there was a significant increase in training scores for participants who completed a six-week training period (66.7%, 33.4%, and 25.0% for attention and processing, working memory, and abstraction and logic, respectively). The results show that positive emotional arousal had a positive effect on the MCI participants. The training tasks and arousal elements can improve cognitive function and enhance the confidence and engagement of participants. There were no significant differences in cognitive domain training scores between the two groups. CONCLUSIONS: This personalized cognitive training system has the potential to serve as a convenient solution for complementary treatment of MCI.