Benchmarking Four Large Generative AI Models on Pediatric Nutrition Queries: A Comparative Study

Objectives: This study aimed to investigate the relationship between night eating and mental health among Korean adults.Methods: This cross-sectional study was conducted with 200 individuals (91 men and 109 women) aged 20-64 in Korea.Mental health status was assessed using the General Health Questionnaire (GHQ-12), while night eating patterns were estimated through self-reported 7-days dietary records.Night eating was defined as consuming any food or beverages after 21:00 on 3 or more days within a 7-day period.Results: Participants were categorized into three groups: non-night eaters (night eating for < 3 days), those consuming < 25% of total energy after 21:00, and those consuming 25% of total energy after 21:00.Among the 200 participants, 147 were classified as non-night eaters, 29 as consuming <25% of their total energy intake from night eating, and 24 as consuming 25%.The mean GHQ-12 score for all participants was 7.06 (SD, 4.40), with men averaging 6.23 (SD, 4.53) and women averaging 7.74 (SD, 4.19).Night eating was positively correlated with mental health across all participants (r 0.17, 95% CI 0.03, 0.30, P < 0.05), with a stronger correlation observed among men (r 0.35, 95% CI 0.15, 0.52, P < 0.001).Participants consuming 25% of total energy intake from night eating exhibited higher GHQ-12 scores than non-night eaters (P 0.05).Conclusions: The results indicate that eating at night is associated with poorer mental health among Korean adults.Further research is needed to examine the relationship between mental health status and diverse eating patterns across larger population groups.

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Publication Details

Journal
Current Developments in Nutrition
Published
2025-05-01
DOI
https://doi.org/10.1016/j.cdnut.2025.106577
Primary Topic
Edcuational Technology Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Benchmarking Four Large Generative AI Models on Pediatric Nutrition Queries: A Comparative Study

Jeffrey S. Smith, Claire Dvorak, Jennifer Rickerds
Current Developments in Nutrition
Edcuational Technology Systems
article

Benchmarking Four Large Generative AI Models on Pediatric Nutrition Queries: A Comparative Study

Jeffrey S. Smith, Claire Dvorak, Jennifer Rickerds
article en

Abstract

Objectives: This study aimed to investigate the relationship between night eating and mental health among Korean adults.Methods: This cross-sectional study was conducted with 200 individuals (91 men and 109 women) aged 20-64 in Korea.Mental health status was assessed using the General Health Questionnaire (GHQ-12), while night eating patterns were estimated through self-reported 7-days dietary records.Night eating was defined as consuming any food or beverages after 21:00 on 3 or more days within a 7-day period.Results: Participants were categorized into three groups: non-night eaters (night eating for < 3 days), those consuming < 25% of total energy after 21:00, and those consuming 25% of total energy after 21:00.Among the 200 participants, 147 were classified as non-night eaters, 29 as consuming <25% of their total energy intake from night eating, and 24 as consuming 25%.The mean GHQ-12 score for all participants was 7.06 (SD, 4.40), with men averaging 6.23 (SD, 4.53) and women averaging 7.74 (SD, 4.19).Night eating was positively correlated with mental health across all participants (r 0.17, 95% CI 0.03, 0.30, P < 0.05), with a stronger correlation observed among men (r 0.35, 95% CI 0.15, 0.52, P < 0.001).Participants consuming 25% of total energy intake from night eating exhibited higher GHQ-12 scores than non-night eaters (P 0.05).Conclusions: The results indicate that eating at night is associated with poorer mental health among Korean adults.Further research is needed to examine the relationship between mental health status and diverse eating patterns across larger population groups.

Current Developments in NutritionVol. 9
Orlando Health (US)
Samsung
Zero hunger
Openalex Percentile: Top 94%
Edcuational Technology Systems
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