The New Personalized AI Nutritionist

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Jun 28, 2024

In recent years, artificial intelligence (AI) has witnessed an unprecedented acceleration in capabilities and commercial adoption, including a particularly impactful yet under-discussed area: the nutritional health industry. The emerging potential of AI to personalize nutrition advice is significantly altering how individuals can manage their food choices.1 These AI-powered nutritionists often take the form of websites or apps, leveraging machine learning to help users improve eating habits, tailor meal plans, and create recipes that align with personal preferences and health objectives.

With just a simple user input, AI meal planners can generate recipes that not only cater to dietary restrictions but also optimize the use of available ingredients. These tools also track caloric intake and monitor dietary goals. Moreover, AI nutritionists can analyze users' current health data and goals to create customized nutrition guidance and educational resources.2 They can even help users with specific health problems such as chronic diseases to manage their conditions. 

Although the integration of AI into nutrition and health promotion offers numerous advantages, it also presents certain pitfalls. Limitations in data modeling can introduce functional biases and raise significant privacy concerns. Many AI tools currently lack the ability to forge complex interactions with users. Even with the ‘perfect’ AI nutritionist, human behavioral factors crucially influence adherence to guidance and plans, commonly leading to complete withdrawal from the tools.

This article delves into both the positive impacts and potential drawbacks of AI nutritionists. We also discuss strategies for incorporating behavioral perspectives into AI tools to effectively promote a balanced and healthy diet, aligning technological advancements with human behaviors and needs.

Advantages of AI in nutrition and health promotion

Easy access to low-cost, highly efficient solutions

Nutrition management has traditionally been a self-directed endeavor, often hindered by busy schedules that leave little room for individuals to seek professional guidance or consistently track their dietary habits. However, the emergence of AI nutritionists overcomes these challenges by increasing people’s accessibility to comprehensive nutrition knowledge and providing healthy eating solutions that are both sustainable and adaptable to individual schedules. 

In contrast to traditional nutrition planning, which typically requires multiple in-person consultations that can be both costly and time-consuming, AI nutritionists streamline this process. They automate the collection and analysis of personal health data, significantly boosting efficiency and reducing costs by providing individualized nutritional guidance that is affordable and timely. Equipped with machine learning capabilities, they can further adapt diet plans and meal recommendations to accommodate individual dietary sensitivities, allergies, or health conditions.

Behavioral Science, Democratized

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Example of real-time conversation between AI nutritionist and user

Photo source: SnapCalorie

Example of real-time conversation between AI nutritionist and user

Photo source: SnapCalorie

Democratized information and enhanced equality   

Many AI nutritionists are powered by natural language processing and cloud computing infrastructure. The former helps AI to understand, interpret, and respond to human language in a way that is both meaningful and useful, and the latter grants it ample data storage and processing power. Combined, these technologies allow AI to participate in real-time conversations with users who seek health-related information or specific nutritional advice.3 This capability is more straightforward than a typical Google search, facilitating immediate and relevant interactions.

These AI tools democratize access to information that was once only available through specialized dietitians, making it easier for individuals from varied backgrounds to access tailored nutrition advice and dietary strategies, as well as enhancing equity in health information access.2 Users can access AI nutritionists for virtual consultations at any time, which encourages them to deepen their understanding of a healthy lifestyle overall.

Data-driven precision and adaptive capabilities

AI nutritionists’ potential is further realized through the richness of data they utilize and their constantly evolving capabilities. These systems are trained on extensive datasets that encompass health history and environmental factors, enabling them to analyze individual health needs with great precision. With abundant data, AI can perform thorough analyses of individuals and develop highly customized, sustainable nutrition plans. They can also monitor food intake and offer immediate, ongoing feedback through photo recognition technology. This allows for continuous adjustments to nutrition plans, ensuring they remain relevant and effective amidst users’ changing lifestyle circumstances. This mechanism keeps individuals actively engaged in their health journeys, helping to maintain their health goals over time.4 

Pitfalls of AI in nutrition and health promotion

Data concerns: privacy and quality issues

Although revolutionary, AI-powered nutrition and health promotion tools are not without problems. One of the primary concerns for AI nutritionists is related to data privacy and transparency. These mechanisms often require access to sensitive personal health information for customized guidance. Nevertheless, individuals who provide the data likely have a limited understanding of how securely their data is handled. Without stringent data protection measures and clear transparency about the necessity, storage, usage, and sharing of such data, people will build uncertainty and fear of privacy loss, discouraging individuals from continuing to use these AI tools.5 

Another major issue for an effective AI nutritionist is the quality of its input data. Imbalanced population data or limited datasets can lead to skewed or harmful AI model recommendations. Furthermore, the data training process may inadvertently introduce implicit bias if it fails to account for critical factors like ethnicity and social determinants of health in the algorithms.6 These biases in the data source and algorithm would result in an AI tool that is not only incompetent in making appropriate recommendations for diverse populations but also poses a risk of exacerbating existing health disparities. 

Example of a problematic AI calorie counting feature

Example of a problematic AI calorie counting feature

Photo source: Food Beast

Limited accuracy and interaction capabilities

The current state of AI in nutrition often falls short of maintaining accuracy and enabling natural, complex interactions similar to what one would have with a real nutritionist. Deep learning is prevalent in automated vision-based dietary assessment systems that analyze meals from photos. However, these systems struggle with accurate food volume and nutritional content analysis. The deep camera systems of these tools have difficulties in precisely scaling certain foods with weak textures like yogurt, and they may not distinguish between foods with visual similarities, such as melted cheese and butter.7 Additionally, AI chatbots are generally designed with constrained algorithms. This rule-based content delivery approach hampers their flexibility and adaptability during conversations with humans, making interactions feel unnatural and detached.3  

Low adherence rates and behavioral challenges

While digital solutions for healthy eating are convenient and readily accessible, adherence rates for these AI tools are consistently less than 5%. This means that out of 100 individuals using AI nutritionists to improve their diets, typically fewer than 5 continue using them over time.2 Tools like diet loggers and personalized nutrition plans usually see high drop-off rates because they demand consistent engagement, which can be difficult to maintain. Adopting healthier eating habits involves complex behavioral modifications, including purchasing healthy food and making healthy meals. These requirements can seem task-demanding and daunting, leading to higher dropout rates when individuals face such lifestyle changes.8

Moreover, the effectiveness of AI nutritionists can be limited by their failure to account for the importance of humanistic motivation and feedback in fostering behavioral change. While these tools provide extensive knowledge and personalized plans, they typically lack the ability to enhance users' self-efficacy and offer continuous encouragement needed for setting and pursuing goals.9 The impersonal nature of digital interactions, coupled with a tendency to provide generic and repetitive feedback, can significantly deter people from sustained engagement.

Incorporating Behavioral Perspectives into AI Nutrition

Individual food choices and eating behavior are multifactorial. These individual, social, and environmental factors may interplay and result in failures in achieving optimal nutrition and health growth. Empirical research emphasizes that, according to the extensively examined Theory of Planned Behavior, several factors are crucial to dietary decisions. The theory explains people’s behavior, such as making healthy food choices, is a result of their attitudes, the social expectations they perceive from their environment, and their perceived ability to control their behavior.10 While AI nutritionists may not control other external factors that also play significant roles, such as economic variables and physical environment, they can take advantage of several behavioral aspects behind human eating behavior under the Theory of Planned Behavior to increase overall compliance with the recommended nutrition plans.

Healthy eating as a behavioral chain

Healthy eating consists of a series of choice responses. People must experience a temporally delayed behavioral chain that includes selection and preparation before consumption.11 A person’s overall attitude towards healthy eating may be positive; however, their situational attitudes, grounded beliefs, and concurrent responses to the options in the behavioral chain may be the opposite. For instance, a person might choose donuts over fruits after a bad day for emotional comfort, or a chocolate lover might indulge in dessert even after a healthy meal. AI nutritionists must recognize that these behaviors are common and acceptable as part of daily human life and that behavioral change towards healthier eating decisions can occur at multiple locations on the behavioral chain. 

Therefore, instead of fostering discouragement, AI could honor individual decisions and improve conscious eating and healthier food selection at people’s various behavioral choice points to boost behavioral outcomes. From eating locations to food purchases, AI can take part in recommending the healthier option along the behavioral chain when faced with choices like a fast food restaurant versus a salad shop, or a side of apple versus chips. Even when unhealthy food choices are made, AI nutritionists may still offer healthier alternatives by suggesting smaller portions or healthier preparation methods, such as avoiding extra toppings on ice cream.11 

As AI systems follow behavioral chains of eating, they can learn from users’ situational attitudes and eating habits. With this information, AI nutritionists can adjust nutrition plans around personal preferences, making healthy eating more natural and adhering to these plans more feasible. This approach allows AI to support gradual behavioral changes across the eating behavior chain, enhancing the effectiveness of dietary recommendations.

Reduce efforts toward healthy eating 

Along the behavioral chain of choice responses towards healthy eating, one critical evaluation of behavioral success is the response effort an individual must take at each choice point. Response effort often takes the form of the time and labor required to acquire and prepare healthy food. If people exert greater effort to achieve healthy eating, they will likely consume less healthy food or choose unhealthy options.11 For instance, a person would likely choose mobile order pizza for delivery if they were faced with the alternative of spending an hour cooking a meal from scratch. 

Therefore, AI nutritionists could prioritize methods to decrease the response effort required to obtain healthy food, making healthy choices more accessible and appealing. This behavioral strategy would also strengthen people’s perceived ability to control behaviors around healthy eating and to make preferred nutritional decisions. AI can help eliminate certain burdensome steps in the behavioral chain of healthy eating by reducing the effort required during the selection and preparation stages. 

For instance, AI can place simple recipes with fewer ingredients and cooking steps as the default option during nutrition planning, thus shortening the time and cognitive energy individuals must spend on purchasing different types of ingredients and cooking them. This approach is especially beneficial for those with limited cooking skills.12 

AI tools may also recommend pre-packaged or store-prepared healthy options when cooking is not an option. Such strategies not only simplify healthy eating but also enhance individuals' confidence in their ability to maintain healthy eating habits. This can lead to more sustained behavior change, as people feel more capable of controlling their dietary choices through manageable efforts.

Promote social norms in healthy eating

People’s normative beliefs are as important as their attitudes and perceived competence toward healthy eating. When an AI nutritionist supports numerous individuals with their nutrition goals, it also cultivates a community bound by shared wellness values and health objectives. Within this community, when certain members have difficulty following their plans, AI systems may leverage the established social norms to motivate adherence to community beliefs and practices. Since individuals often conform to the social norms of relatable communities, other members' behaviors are particularly effective in fostering retention and maintaining engagement.13

AI can further strengthen individual goal commitments by showcasing community progress and achievements. For instance, when a user of an AI nutritionist logs their daily nutritional intake, the system can generate social validation by displaying how many others have also completed their daily log and maintained a healthy nutritional balance. This visibility of peer actions encourages individuals to persist with their plans, leveraging the power of social norms to promote healthy eating.

Conclusion

The emergence of AI nutritionists is pivotal in dietary management, as they provide customized, accessible, and efficient nutritional advice tailored to individuals. Despite facing challenges, the continuous advancements in AI capabilities and regulatory frameworks, along with the strategic incorporation of behavioral insights, will enable these tools to become more adept at meeting user needs. As such, the ongoing evolution of AI nutritionists holds the promise of making comprehensive, personalized nutrition accessible to all, ultimately thereby fostering a more equitable and healthier dietary lifestyle across larger populations.

References

  1. An, R. and X. Wang, Artificial Intelligence Applications to Public Health Nutrition. Nutrients, 2023. 15(19).
  2. Hamideh, D., et al., Your digital nutritionist. Lancet, 2019. 393(10166): p. 19.
  3. Oh, Y.J., et al., A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. International Journal of Behavioral Nutrition and Physical Activity, 2021. 18(1): p. 160.
  4. Boland, M. and J. Bronlund, eNutrition - The next dimension for eHealth? Trends in Food Science & Technology, 2019. 91: p. 634-639.
  5. Karimian, G., E. Petelos, and S.M.A.A. Evers, The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI and Ethics, 2022. 2(4): p. 539-551.
  6. Russo, S. and S. Bonassi, Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology. Nutrients, 2022. 14(9).
  7. Konstantakopoulos, F.S., E.I. Georga, and D.I. Fotiadis, A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems. IEEE Rev Biomed Eng, 2024. 17: p. 136-152.
  8. Vandeputte, J., et al., Principles and Validations of an Artificial Intelligence-Based Recommender System Suggesting Acceptable Food Changes. J Nutr, 2023. 153(2): p. 598-604.
  9. Dias, S.B., et al., Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach. Front Nutr, 2022. 9: p. 898031.
  10. Chen, P.J. and M. Antonelli, Conceptual Models of Food Choice: Influential Factors Related to Foods, Individual Differences, and Society. Foods, 2020. 9(12).
  11. Rafacz, S.D., Healthy Eating: Approaching the Selection, Preparation, and Consumption of Healthy Food as Choice Behavior. Perspect Behav Sci, 2019. 42(3): p. 647-674.
  12. Ensaff, H., A nudge in the right direction: the role of food choice architecture in changing populations' diets. Proc Nutr Soc, 2021. 80(2): p. 195-206.
  13. Bauer, J.M. and L.A. Reisch, Behavioural Insights and (Un)healthy Dietary Choices: a Review of Current Evidence. Journal of Consumer Policy, 2019. 42(1): p. 3-45.

About the Author

Yuzhen (Valerie) Guo

Yuzhen is an applied behavioral scientist in healthcare. She leverages scientific methods and behavioral techniques to foster positive behavioral changes in health-related programs. In her work, she focuses on how behavioral sciences in communication and visual representation influence human decision-making. Yuzhen holds a Master's in Behavioral and Decision Sciences from the University of Pennsylvania, and a BA in Psychology and Organizational Sciences from The George Washington University. In her free time, Yuzhen enjoys expanding her knowledge of art history and exploring national parks.

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