AI and the Health and Fitness Industry: Can AI Really Replace Trainers, Coaches, and Nutritionists?

Key Points: 

• AI is revolutionising many industries by automating tasks that used to need human  input. 

• In the health and fitness world, AI is used in apps to create personalised workout  and meal plans, making expert advice more accessible and affordable.

• While AI is great at certain tasks, it often struggles to pick up on subtle information that a human coach or nutritionist might notice. 

• Future use of AI in health and fitness probably involves a collaborative approach  between AI and experts to provide the best outcomes for clients. 

  
  

Introduction

Artificial intelligence (AI) is impacting many industries in a similar manner as the Industrial Revolution. But instead of big machines automating physical tasks, AI is taking over more cognitive, decision-making work that is typically performed by humans. While this sparks some concern about job security, I believe that the observed advancements in AI are a good thing. AI can handle repetitive and time-consuming tasks, freeing up time for professionals to focus on things AI can’t do—like interacting with clients, offering personalised advice, and thinking strategically.

Interestingly, while AI has recently gained a lot of attention, many of the key ideas aren’t new. For instance, "neural networks" have existed since the 1960s but weren't widely used due to the lack of computing power and large datasets required to make them work. With today’s technological advancements, neural networks have been brought back into fashion and have even evolved into more complex “deep learning” systems. But what does AI really mean, and how is it relevant to coaches, trainers, or nutritionists? Let’s dive in!

What is AI?

There’s no single definition of AI that everyone agrees on, but in simple terms, AI refers to systems that can do things which usually require human intelligence—things like making decisions, recognising patterns, and solving problems. In essence, AI gets machines to take in data, process it, and produce useful outcomes.

Broadly speaking, AI can be divided into two types: rule-based systems and machine learning-based systems. Rule-based systems rely on a set of rules created by humans, such as "if-then" conditions (e.g., IF the weather is cold AND windy, THEN suggest wearing a coat). These systems work well for structured, predictable problems and are often found in chatbots or expert systems used in healthcare, where an AI might suggest treatment based on predefined criteria. 

Machine learning, on the other hand, takes a different approach. Instead of being programmed with specific rules, these systems learn from large amounts of data. Machine learning on its own is a broad category, and includes techniques like supervised learning (which uses labelled data to teach the system) and unsupervised learning (which finds patterns in data without labels). With enough data, these systems can identify complex patterns and improve over time, commonly tackling problems like image and speech recognition or making recommendations in social media and streaming platforms.

How is AI Being Used in the Health & Fitness Industry?

AI is already making its mark on the health and fitness world. Many of the apps you’ve likely heard of, or maybe even used, employ some form of AI to help you progress towards your goals. Apps like Runna, Fitbod, and TrainAsOne use AI to create customised workout plans for users, while nutrition apps like MyFitnessPal and MacroFactor use AI to recommend tailored dietary plans based on your data. Even your smart watches or other wearable devices use AI to predict data like how many calories you have burned, how many steps you have taken, and provide estimates on your recovery (more on wearable devices another time as they deserve their own article!). The mentioned apps make it easier and cheaper to access expert-level advice, making personal fitness more accessible to everyone. For example, an AI-powered workout builder app may ask you various questions ranging from age, sex, fitness level, and goals. Based on your inputted information, the AI system can generate a workout plan tailored just for you. Pretty cool, right?

Examples of Rule-based and Machine Learning-based Approaches

Health and fitness apps use a variety of AI techniques to personalise their recommendations. Let’s revisit the two main approaches—rule-based systems and machine learning.

A rule-based system uses guidelines defined by experts like trainers or nutritionists. For example, an app used to build workouts for users might classify you as a beginner if you’ve been weightlifting for less than six months and then generate a workout plan suited to that level. If you’ve been lifting for over two years, the app might suggest an advanced program. While these systems are reliable and easy to interpret, they are limited by the set rules and can’t adapt to more personalised needs.

Machine learning-based systems are much more flexible. Rather than relying on preset rules, machine learning models analyse data from thousands of users to determine which workouts or nutrition plans are most effective. For instance, using the same workout app example, an app might learn that users with two years of lifting experience perform better with a specific type of workout style (e.g., moderate-volume, high-intensity workouts) and adjust future recommendations accordingly, based on user feedback. It can continuously improve, offering more personalised insights than a rule-based system.

While it might seem like machine learning approaches are the clear winner, they aren’t always the best option. Machine learning requires large and diverse datasets to function effectively and without enough data, they are not useable. Developers may then need to rely on a rule-based systems to provide a useable AI system. This might not be a set back by any means, as some problems can be easily solved using rule-based approaches, and using more complex machine learning approaches might be unnecessary. Additionally, as the app collects user data, developers can transition to machine learning if they are unsatisfied with the current model or want to investigate trends within their collected dataset. 

Shortcomings of AI

You might now be thinking that AI is pretty impressive and could singlehandedly provide comprehensive and expert-like guidance. However, AI does have limitations that are worth noting. One of the biggest issues is that AI doesn’t always understand nuances that can make a huge difference in a training or nutrition plan. Sure, it can handle data like age, sex, and fitness history, but what about things that are harder to quantify? For instance, how do you measure someone's mental state when they walk into the gym, or whether they have the mobility to perform a certain exercise effectively? How does it know if the individual is training hard enough, eating appropriately, or whether their personal or work life is affecting their recovery? 

While you could, in theory, build a system that asks for all this information, it might become so complicated that users are reluctant to use it altogether. Let’s be honest, the average gym goer might be using most of their will-power to go to the gym, and having to constantly complete pre- and post-session questionnaires might be a step too far. Instead, this level of nuance and analysis is traditionally provided by good trainers and coaches through regular conversations with clients and visually observing their exercise performance, all of which are beyond current AI capabilities.

There are also broader concerns around privacy and bias within the developed system. People might be uncomfortable sharing personal data with an app, especially if they’re not sure how it will be used. Additionally, an AI system is only suitable when it is applied in settings it has been trained or developed to handle. For instance, if a machine learning model is only exposed to specific demographic within the target population (e.g., young, well-trained, male athletes), it could end up giving advice that doesn’t work well for other demographics (e.g., women, middle or older aged individuals, untrained lifters). This level of bias is not just a concern with machine learning models, as rule-based models may also present bias depending on the type of expertise or knowledge used to develop it. 

Finally, AI lacks the personal touch. A personal trainer can encourage you to push harder, adjust your form, or even just chat with you about your goals. AI can’t replicate the same sense of motivation or build the kind of relationship a human coach or trainer can.

Possible Integrations of AI in Health and Fitness

Don’t get me wrong—this isn’t an article dismissing AI and its use in the health and fitness industry. On the contrary, I think there’s a huge potential for AI to enhance the services offered in the industry, particularly when it’s used to complement, rather than replace, human expertise.

For example, AI could handle the initial assessment and provide a baseline training program based on the information you provide. From there, a human trainer could step in and fine-tune the program, asking additional questions or making in-the-moment adjustments based on their expertise. They could also identify if the developed AI model makes an strange suggestion, and correct the program accordingly. AI could also be used to collect data during a program, offering trainers suggestions for tweaks based on progress or even a lack of.

So while AI might not be ready to take over completely, it could serve as a powerful tool for professionals, automating some of the more routine tasks and allowing them to focus on what they do best—helping people reach their goals through personalised support. On that notion, I think that coaches and trainers, if they aren’t already, should be embracing AI techniques in their practice to try and gain a possible competitive edge and provide the highest level of service to their clients.

Summary

In conclusion, AI has incredible potential to change the health and fitness industry, but it is not at the point where it can replace human professionals (coaches and trainers can breathe a sigh of relief). AI still struggles to handle the complex, nuanced needs of individuals, and issues like data privacy, bias towards the data they are trained on, and the lack of a personal element can’t be ignored. However, AI could be a great partner for health and fitness professionals, helping to streamline the planning process for exercise programs or prescribed diets while leaving room for the expertise and personal touch that only a human can provide. Looking ahead, the best results will likely come from a combination of AI and human guidance, offering clients the best of both worlds: the efficiency of technology and the invaluable insights of a trained expert.