Developers
August 6, 2020

How Machine Learning Is Advancing Nutrition Apps

If you thought Machine Learning was only being used for hard sciences and highly technical research, think again. ML is finding its way into new industries and whole economies. The health and nutrition industry is no exception.

Today we are going to discuss health apps and how Machine Learning is impacting the nutrition and health landscape in today’s digital era.

It is no question that health, nutrition, and moreover dieting has become very “trendy” in the 21st century. In the earlier 2000s, the South Beach Diet was a New York Times Bestselling book. It was only until a few years later that several of the claims made in that book, about how to regiment carbs specifically, were apparently disproven by the medical and nutrition establishment.

Since, there’s been a range of new health crazes, including Ketogenic diets, the Paleo, or “caveman” diet, pescatarian diets, vegan diets, vegetarian diets, and so much more. In whatever dieting moment the health and wellness economy is centered on, there’s also been a birthing of new health and nutrition mobile applications to “track” what we’re eating and when we’re eating.

These iOS and Android applications are the latest success in helping to quantify everything we eat, creating a data addiction with food and everything we consume on any given day of the week. Nutrition has now been incorporated into the palm of our hands.

Algorithms for what we eat

In order to understand how some of these applications are working, let’s first take a look at the architecture of nutrition applications, and the data that ML is processing for us as we go about our eating schedules.

Some of the main data that is used in mobile nutrition applications include the following: the time of each meal we eat, food and beverage amount and content, physical activity (either daily or weekly), height, weight and sleep time. From here, all sorts of factors are calculated that relate to our health, including, but not limited to: glycemic index, calories burned, calories consumed, how our fitness is affecting out metabolic rates, etc.

Part of the reason these nutrition and some would call fitness apps are so addicting is because the amount of data computed makes consumers curious, and triggers that serotonin response everytime we get a good reading of something healthy we did or ate.

Some of the newest Apps on the Market

Here are some examples of organizations revolutionizing the health industry as of late:

FitGenie is an iOS app billed as a “smart calorie counter,” which applies machine learning algorithms to simplify nutrition planning for individuals aiming to reach a certain weight or fitness goal. The AI of this app also gives users meal recommendations which is a huge plus for user satisfaction. One user mentioned that the App always knows what kind of food they want for their next meal.

HealthyOut, another fairly new iOS app helps people to identify nutritious options while dining out at restaurants, allowing users to filter based on their dietary goals/restrictions. Users recommend this App because of the macronutrient breakdown in a colorful pie chart. Another plus is that this App helps to uncover how much Fat is used in cooking dishes in American restaurants.

AVA is another app that harnesses AI’s power to analyze the types of meals that will help its users to manage their health. It screens for allergies and food sensitivities as well as stress, sleep, and physical activity to formulate personalized recipe recommendations and meal planning tools, as well as daily coaching and guidance.

Overall, we can see that ML is being used in very strategic ways in these iOS applications. The ML is not just giving people data outputs about food, but rather giving people insights for how to think about nutrition in ways that are non-conventional, interesting, and unique to each person’s abilities and routines. This starts with a team of developers and engineers that think critically about how they want their ML to impact their audiences, and how food trends and our modern existence is affected by the food we enjoy and interact with on a daily basis.

TagsNutrition AppsHealthMachine Learning
Michael Robbins
Writer
Michael is a writer that helps organizations align their mission and values to a wide audience.

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DevelopersAugust 6, 2020
How Machine Learning Is Advancing Nutrition Apps
If you thought Machine Learning was only being used for hard sciences and highly technical research, think again. ML is finding its way into new industries and whole economies. The health and nutrition industry is no exception.

Today we are going to discuss health apps and how Machine Learning is impacting the nutrition and health landscape in today’s digital era.

It is no question that health, nutrition, and moreover dieting has become very “trendy” in the 21st century. In the earlier 2000s, the South Beach Diet was a New York Times Bestselling book. It was only until a few years later that several of the claims made in that book, about how to regiment carbs specifically, were apparently disproven by the medical and nutrition establishment.

Since, there’s been a range of new health crazes, including Ketogenic diets, the Paleo, or “caveman” diet, pescatarian diets, vegan diets, vegetarian diets, and so much more. In whatever dieting moment the health and wellness economy is centered on, there’s also been a birthing of new health and nutrition mobile applications to “track” what we’re eating and when we’re eating.

These iOS and Android applications are the latest success in helping to quantify everything we eat, creating a data addiction with food and everything we consume on any given day of the week. Nutrition has now been incorporated into the palm of our hands.

Algorithms for what we eat

In order to understand how some of these applications are working, let’s first take a look at the architecture of nutrition applications, and the data that ML is processing for us as we go about our eating schedules.

Some of the main data that is used in mobile nutrition applications include the following: the time of each meal we eat, food and beverage amount and content, physical activity (either daily or weekly), height, weight and sleep time. From here, all sorts of factors are calculated that relate to our health, including, but not limited to: glycemic index, calories burned, calories consumed, how our fitness is affecting out metabolic rates, etc.

Part of the reason these nutrition and some would call fitness apps are so addicting is because the amount of data computed makes consumers curious, and triggers that serotonin response everytime we get a good reading of something healthy we did or ate.

Some of the newest Apps on the Market

Here are some examples of organizations revolutionizing the health industry as of late:

FitGenie is an iOS app billed as a “smart calorie counter,” which applies machine learning algorithms to simplify nutrition planning for individuals aiming to reach a certain weight or fitness goal. The AI of this app also gives users meal recommendations which is a huge plus for user satisfaction. One user mentioned that the App always knows what kind of food they want for their next meal.

HealthyOut, another fairly new iOS app helps people to identify nutritious options while dining out at restaurants, allowing users to filter based on their dietary goals/restrictions. Users recommend this App because of the macronutrient breakdown in a colorful pie chart. Another plus is that this App helps to uncover how much Fat is used in cooking dishes in American restaurants.

AVA is another app that harnesses AI’s power to analyze the types of meals that will help its users to manage their health. It screens for allergies and food sensitivities as well as stress, sleep, and physical activity to formulate personalized recipe recommendations and meal planning tools, as well as daily coaching and guidance.

Overall, we can see that ML is being used in very strategic ways in these iOS applications. The ML is not just giving people data outputs about food, but rather giving people insights for how to think about nutrition in ways that are non-conventional, interesting, and unique to each person’s abilities and routines. This starts with a team of developers and engineers that think critically about how they want their ML to impact their audiences, and how food trends and our modern existence is affected by the food we enjoy and interact with on a daily basis.

Nutrition Apps
Health
Machine Learning
About the author
Michael Robbins -Writer
Michael is a writer that helps organizations align their mission and values to a wide audience.

Related Articles