EC Nutrition

Healthy eating involves more than nutritional recommendations, as everyday meal choices also depend on practical considerations such as preparation time, taste, affordability, and household preferences. This Perspective explores how Mind Genomics could be used to investigate the combined influence of these attributes on perceived meal suitability. An illustrative framework comprising four domains and sixteen candidate elements is presented as a starting point for future empirical research. This approach may help investigate individual differences in how people evaluate meal options and inform future research on nutritional communication. However, empirical validation and independent replication are needed to establish its practical value for everyday meal planning and healthier eating.

 Keywords: Mind Genomics; Everyday Meal Planning; Healthy Eating; Food Choice; Personalized Nutrition; Nutritional Communication

1. World Health Organization. “Healthy Diet”. World Health Organization, Accessed 16 October 2026. 
2. Jabs J and Devine CM. “Time Scarcity and Food Choices: An Overview”. Appetite2 (2006): 196-204. 
3. Bailey C., et al. “Food Choice Drivers in the Context of the Nutrition Transition in Delhi, India”. Journal of Nutrition Education and Behavior7 (2018): 675-686. 
4. Ducrot P., et al. “Meal Planning Is Associated with Food Variety, Diet Quality and Body Weight Status in a Large Sample of French Adults”. International Journal of Behavioral Nutrition and Physical Activity1 (2017): 12. 
5. Sobal J and Bisogni CA. “Constructing Food Choice Decisions”. Annals of Behavioral Medicine 38 (Suppl. 1) (2009): S37-S46. 
6. Jones BL. “Making Time for Family Meals: Parental Influences, Home Eating Environments, Barriers and Protective Factors”. Physiology and Behavior 193 (Part B) (2018): 248-251. 
7. Cesareo M., et al. “The Effectiveness of Nudging Interventions to Promote Healthy Eating Choices: A Systematic Review and an Intervention among Italian University Students”. Appetite 168 (2022): 105662. 
8. Gynell I and Kemps E and Prichard I. “The Effectiveness of Implicit Interventions in Food Menus to Promote Healthier Eating Behaviours: A Systematic Review”. Appetite 173 (2022): 105997. 
9. Zeisel SH. “Precision (Personalized) Nutrition: Understanding Metabolic Heterogeneity”. Annual Review of Food Science and Technology 11 (2020): 71-92. 
10. Jinnette R., et al. “Does Personalized Nutrition Advice Improve Dietary Intake in Healthy Adults? A Systematic Review of Randomized Controlled Trials”. Advances in Nutrition3 (2021): 657-669. 
11. Celis-Morales C., et al. “Effect of Personalized Nutrition on Health-Related Behaviour Change: Evidence from the Food4Me European Randomized Controlled Trial”. International Journal of Epidemiology2 (2017): 578-588. 
12. Barkmeijer A., et al. “Towards Effective Dietary Counseling: A Scoping Review”. Patient Education and Counseling7 (2022): 1801-1817. 
13. de Hoogh IM., et al. “Design Issues in Personalized Nutrition Advice Systems”. Journal of Medical Internet Research 25 (2023): e37667. 
14. Moskowitz HR. “‘Mind Genomics’: The Experimental, Inductive Science of the Ordinary, and Its Application to Aspects of Food and Feeding”. Physiology and Behavior4 (2012): 606-613. 
15. Davidov S., et al. “Mind Genomics: Accelerating and Scaling Our Understanding of the Everyday Mind and Its Prospects for Efforts in Nutrition”. Acta Scientific Nutritional Health 4 (2020): 72-80. 
16. Moskowitz HR., et al. “Founding a New Science: Mind Genomics”. Journal of Sensory Studies3 (2006): 266-307. 
17. Kornstein B., et al. “Communication Styles Regarding Child Obesity: Investigation of a Health and Communication Issue by a High School Student Researcher, Using Mind Genomics and Artificial Intelligence”. Mind Genomics Studies in Psychology and Experience1 (2023): 1-14. 
18. Papajorgji P. “Mind Genomics and Its Applications: A Survey”. MOJ Food Processing & Technology1 (2024): 91-92. 
19. Gere A., et al. “The Mind Genomics Metaphor: From Measuring the Every-Day to Sequencing the Mind”. International Journal of Genomics and Data Mining 1 (2017): 110.