Written By: Reese Dait, MPH, RD, LDN
Written by an American Sports and Performance Dietitians Association Registered Dietitian (RD). To learn more about sports nutrition and ASPDA, go to www.sportsrd.org.
A student-athlete meets with their team dietitian for the first time and expresses frustration in failing to maintain weight and perform well at practice despite following a meal plan their favorite professional athlete posted on Instagram. In a world where ChatGPT can generate a 7-day meal plan at the click of a button or TikTok users can see a fitness influencer’s full day of eating in a 30 second video, why would anyone spend the time or money to meet with a dietitian? The hard truth is that nutrition is not one size fits all. And, artificial intelligence (AI) can’t take the place of personalized, precision nutrition. Let’s look at 3 key areas where AI misses the mark.
1. Nutrition Care Process
To individualize nutrition prescriptions, registered dietitians (RDs) meet with an athlete to complete a comprehensive assessment of genetics, dietary habits, food behaviors, physical activity, and even microbiota composition. RDs are trained to investigate potential associations between feeding behaviors and metabolic outcomes using a systematic method called the Nutrition Care Process. This process involves four steps: assessment, diagnosis, intervention, and monitoring and evaluation.
During assessment, RDs evaluate an individual’s dietary habits and food behaviors through evidence-based methods like food frequency questionnaires or diet recalls. RDs working in a performance or tactical based setting may have access to physical activity data from fitness tracking devices or they may estimate needs through equations. RDs also assess personal history by asking questions about chronic disease, disordered eating behaviors, and body image concerns.
Human interaction in the field of nutrition is truly irreplaceable, especially in the uniquely changing environment of athletics. Face-to-face interactions with athletes allow sports dietitians to interpret body language, ask deeper questions about food choices, and build rapport which helps them make more personalized nutrition suggestions. Sports dietitians also consider thoughts and concerns an athlete may have regarding nutrition and body image and work closely with mental health professionals to help athletes overcome potential barriers to making nutrition changes. In addition to these traditional methods of assessing personal history, recent advancements in genomics can provide a deeper look at an individual’s health.
2. Sport Nutrigenomics and Nutrigenetics
Several genetic studies have shown that differences in genotyping among individuals can result in variable responses to some nutrients and compounds, like caffeine. Caffeine is used by many athletes as an ergogenic aid, but it may not always be beneficial to performance. Variations in the CYP1A2, caffeine-metabolizing gene, have helped researchers identify individuals as “fast” or “slow” metabolizers. “Slow” metabolizers, who have AC or CC genotype, have an increased risk of myocardial infarction, hypertension, elevated blood pressure, and pre-diabetes with caffeine consumption. Only those who are type AA “fast” metabolizers have experienced improvements in performance with caffeine (Guest et al., 2019).
Realistically, many RDs are not assessing specific genes to make dietary recommendations. A sports dietitian may not know their athlete’s genotype for CYP1A2, but they can inform their athlete of the recommended dose of caffeine for ergogenic benefit, advise them to choose a third party tested or natural source, and disclaim that not all individuals are responders to caffeine. Sports dietitians take an evidence-based approach to each consult and use clinical judgment to make personalized recommendations for each athlete.
3. Personalized and Precision Nutrition
Personalized nutrition means making customized nutrition recommendations that aim to promote or maintain health and prevent disease. Personalized nutrition considers important factors like an individual’s genetics and microbiome as well as external factors like dietary habits and physical activity. Studies have shown that characteristics like age, genetics, gender, lifestyle, environmental exposure, gut microbiome, epigenetics, and metabolism lead to variability in an individual’s response to dietary intervention (Verma et al., 2018). This means that one nutrition intervention may be beneficial to a specific individual and not beneficial, and potentially even detrimental, to another. Precision nutrition aims to provide recommendations which are comprehensive and dynamic to account for changing personal and environmental factors (De Toro-Martin et al.,2017). In the world of sports, things are always changing; opponents, game play strategies,
training hours, timelines, locations, injuries, access to food, and the list goes on. These factors can change nutrition recommendations and prescriptions. Athletes get injured and need to alter their training and nutrition to promote recovery and prevent future injury. Access to food changes when teams travel for competitions. RDs work hard to keep up with their athletes’ changing needs through consistent communication with the athlete and collaboration with other members of the performance team including physicians, mental health professionals, strength and conditioning coaches, athletic trainers, and coaching staff.
The Role of Advanced Technology in Nutrition Intervention
So where does AI fit? Current use of AI along with machine learning (ML) technologies is limited in the field of nutrition; however, AI may be helpful in creating data-driven modeling systems to better understand nutrition intervention and health outcomes. AI along with ML techniques can develop synthetic patient populations from large-scale clinical data, conduct computer-based simulation clinical trials, and compare the response to various treatment options and health outcomes. It is important to note that AI programs are simply gathering data from electronic health records and physical activity records for an RD to interpret for practical application (Verma et al., 2018).
Some other uses of technology for nutrition purposes include food tracking apps, fitness tracking devices, and AI meal planning. Evaluating nutrient intake is an essential component of managing chronic disease and achieving body composition goals (Verma et al., 2018). Apps that allow users to upload pictures of their food and wearable fitness trackers that offer energy expenditure information can ease the burden of manual data entry. That being said, some apps may be able to determine the food contents from a picture uploaded by the user but other apps are not as advanced. Even with accurate accounts of nutrient intake, there is a need for an RD to review the entries and make recommendations to support the individual’s goals.
ChatGPT is a program created by Open AI. Although ChatGPT can generate a daily meal plan within a specific caloric limit when prompted, its meal plans have not been assessed for individuals with chronic conditions or for athletes with performance goals. ChatGPT also does not conduct medical evaluations, review personal medical history, or consider drug nutrient interactions when providing dietary suggestions (Harman et al., 2024). Although advanced technology may be integrated into an RD’s practice, clinical judgment is a unique benefit that comes with experience in the field of healthcare, and that’s something that AI can never replace.
Overall, even with the integration of data driven technologies within healthcare growing in popularity, there is a need for professionals, like RDs, who are trained in clinical and translational approaches to plan and implement appropriate interventions. Although AI can be helpful in gathering and computing data, human interface from these trained individuals is essential for the goals of personalized and precision nutrition (Verma et al., 2018). Sports dietitians take a holistic approach and collaborate with a multidisciplinary team of professionals who all strive for the same goal: supporting athletes for optimal performance and overall health.
References:
De Toro-Martin, J., Arsenault, B. J., Despres, J.-P., & Vohl, M.-C. (2017). Precision
Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and
Management of Metabolic Syndrome. Nutrients, 9(8). https://doi.org/10.3390/nu9080913
Guest, N. S., Horne, J., Vanderhout, S. M., & El-Sohemy, A. (2019). Sport
Nutrigenomics: Personalized Nutrition for Athletic Performance. Frontiers in Nutrition, 6.
https://doi.org/10.3389/fnut.2019.00008
Harman Ph.D., M., Skolnik Ph.D., M., & Lostak Ph.D., M. (2024). AI dietician: Unveiling
the accuracy of ChatGPT’s nutritional estimations. Nutrition, 119.
https://doi.org/10.1016/j.nut.2023.112325
Verma, M., Hontecillas, R., Tubau-Juni, N., Abedi, V., & Bassaganya-Riera, J. (2018).
Challenges in Personalized Nutrition and Health. Frontiers in Nutrition, 5.
https://doi.org/10.3389/fnut.2018.00117