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- Zhang, Erliang, et al.
(författare)
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Dietary Rhythmicity and Mental Health Among Airline Personnel
- 2024
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Ingår i: JAMA Network Open. - : American Medical Association (AMA). - 2574-3805. ; 7:7
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Tidskriftsartikel (refereegranskat)abstract
- IMPORTANCE: Misaligned dietary rhythmicity has been associated with metabolic diseases; however, its association with mental health remains largely unexplored.OBJECTIVE: To examine the association between dietary rhythms and the mental health condition of shift workers, specifically airline crew members.DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study analyzed data collected from the Civil Aviation Health Cohort of China, an ongoing large-scale health survey of pilots, flight attendants, and air security officers employed by major airline companies in China. Participants aged 18 to 60 years were invited through text messages to complete a web-based survey. The data collection period was December 2022 to March 2023. Statistical analysis was performed from July 24, 2023, to April 12, 2024.EXPOSURE: Data on timing of breakfast and dinner on workdays and rest days, daily time windows for food intake, and meal and eating jet lags were collected and calculated.MAIN OUTCOMES AND MEASURES: Anxiety and depressive symptoms were measured using the 7-item Generalized Anxiety Disorder Assessment and the 9-item Patient Health Questionnaire. Multivariate logistic regressions were performed to evaluate the associations of anxiety and depression with meal timing, eating window time, meal jet lag (ie, delayed meals), and eating jet lag (ie, delayed eating). All models were adjusted for individual socioeconomic, demographic, and lifestyle characteristics.RESULTS: Of the 22 617 participants (median [IQR] age, 29.1 [26.3-33.7] years; 13 712 males [60.6%]), 1755 (7.8%) had anxiety and 2768 (12.2%) had depression. After controlling for confounding factors, having dinner after 8 pm on morning-shift days was associated with increased odds of anxiety (adjusted odds ratio [AOR], 1.78; 95% CI, 1.53-2.05) and depression (AOR, 2.01; 95% CI, 1.78-2.27), compared with consuming dinner before 8 pm. Similar results were observed on night-shift days and rest days. An eating window of less than 12 hours was associated with reduced odds of anxiety (AOR, 0.84; 95% CI, 0.75-0.93) and depression (AOR, 0.81; 95% CI, 0.75-0.89) on morning-shift days; the results remained significant on rest days. Delayed dinner on morning-shift days was associated with increased odds of anxiety (AOR, 1.32; 95% CI, 1.13-1.54) and depression (AOR, 1.39; 95% CI, 1.22-1.58). On night-shift days, delayed dinner was associated with higher odds of anxiety (AOR, 1.22; 95% CI, 1.06-1.39) and depression (AOR, 1.21; 95% CI, 1.08-1.36). On morning-shift days, delayed eating rhythms were associated with higher odds of depression (AOR, 1.35; 95% CI, 1.13-1.61), whereas advanced eating rhythms were associated with lower odds of anxiety (AOR, 0.78; 95% CI, 0.70-0.87).CONCLUSIONS AND RELEVANCE: This cross-sectional study found that meal timing, long eating window, and meal jet lags were associated with increased odds of depression and anxiety. These findings underscore the need for interventions and supportive policies that help mitigate the adverse implications of shift work and irregular working hours for the mental health of shift workers.
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| 2. |
- Li, Jie, et al.
(författare)
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Introduction to the Special Section on Big Data and Artificial Intelligence for Network Technologies
- 2020
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Ingår i: IEEE Transactions on Network Science and Engineering. - : IEEE. - 2327-4697. ; 7:1, s. 1-2
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The papers in this special section examines the deployment of Big Data and artificial intelligence for network technologies. The eneration of huge amounts of data, called big data, is creating the need for efficient tools to manage those data. Artificial intelligence (AI) has become the powerful tool in dealing with big data with recent breakthroughs at multiple fronts in machine learning, including deep learning. Meanwhile, information networks are becoming larger and more complicated, generating a huge amount of runtime statistics data such as traffic load, resource usages. The emerging big data and AI technologies may include a bunch of new requirements, applications and scenarios such as e-health, Intelligent Transportation Systems (ITS), Industrial Internet of Things (IIoT), and smart cities in the term of computing networks. The big data and AI driven network technologies also provide an unprecedented patient to discover new features, to characterize user demands and system capabilities in network resource assignment, security and privacy, system architecture, modeling and applications, which needs more explorations. The focus of this special section is to address the big data and artificial intelligence for network technologies. We appreciate contributions to this special section and the valuable and extensive efforts of the reviewers. The topics of this special section range from big data and AI algorithms, models, architecture for networks and systems to network architecture.
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