Innovations in Sports Tech: IoT and Cloud Solutions for Age Management Apps


Alexandra Kazemir-Yampolska


1 minute

From this article, you’ll know

  • What is age management 
  • How IoT sensors enable a holistic approach to individual health
  • Why cloud computing does not solve all challenges, still the majority
  • What is edge computing and how does it save a penny


Age Management: Practices and strategies aimed at promoting healthy aging and improving quality of life as we get older. This can involve aspects like diet, exercise, cognitive training, and preventive healthcare.

Cloud Computing: Delivering on-demand access to computing resources (servers, storage, databases, software) over the internet. Mobile apps can leverage cloud computing to store user data, perform complex analysis, and deliver scalable services.

Edge Computing: Processing data closer to where it’s generated (on devices or local servers) instead of relying solely on the cloud. This can be beneficial for age management apps that deal with real-time sensor data from wearables, reducing latency and improving responsiveness.

Machine Learning: A type of artificial intelligence (AI) that allows computers to learn from data without explicit programming. Machine learning can be used in age management apps to analyze user data, predict health risks, and personalized recommendations.

GPT (General-Purpose Technology): A technology with the potential to significantly impact and improve a wide range of industries and aspects of society. Examples include the steam engine, electricity, and the internet.


GPT (Generative Pre-trained Transformer): A powerful AI language model from OpenAI capable of generating realistic and coherent text. While not directly applicable to core functionalities, GPT could potentially be used in future age management apps to create personalized educational content or chatbot interactions.

Churn Rate: The percentage of users who stop using an app or service over a given period. Digital products can focus on reducing churn by providing engaging features, valuable insights, and personalized experiences.

ARR (Annual Recurring Revenue): The total amount of predictable revenue a subscription-based business expects to receive in a year. Mobile apps using a subscription model can track ARR to measure their financial performance and growth.

In a world where we encounter a new General-Purpose Technology (GPT) seemingly every year, ushering in a new wave of technological revolution, some things remain constant for humanity.

Just as it has been for millennia, sports and health management are some of the most personal and collective aspects of the human experience. Well-being, as an industry, is intertwined with practically every action we take throughout the day. We feel its impact when we decide when to wake up, what to drink, how to sit, or when to socialize with friends over a sporting event and snacks.

Since we are biological systems – susceptible to both optimization and dysfunction – health management remains a necessity, requiring constant attention. And, yes, it’s monetized.

What other option is there, truly?

To provide a snapshot of the current landscape (as of 2024): There are 6.6 million mobile apps in the App Store and Google Play, 106 919 have Health and Fitness as a category,  with 22% nutrition, 22% of them relate to mental health, and predicted digital sports entertainment consumption in Europe more than €7 billion in 2024. Of course, a massive e-commerce ecosystem surrounds all of this.


Sports Apps Statistics Overview in Q1 2024


In total, this market generates 484 billion USD annually, roughly equivalent to the GDP of a country like Australia or India. However, this article focuses on a single, emerging sub-industry within the broader realm of sports, well-being, fitness, nutrition, mental health, and e-commerce – essentially, all of these areas combined. Welcome to Age Management!

Before delving into this complex and technically detailed topic, let’s explore the historical, technological, and human behavioral factors that led to the emergence of this new term. (For further reading, you can refer to these two books (the first one in English, the second – in Ukrainian).)

Sports Industry landscape

Three elephants on which Sports (encompassing well-being, fitness, nutrition, betting, and esports) rests are:

  • The iconic athlete figure.
  • Human engagement around this ideal body shape or results – the game, fun, and communication.
  • A commercial aspect that capitalizes on the gap between reality and the “perfect future state.” Despite this philosophical formula, it remains a commercial machine striving to cut costs and generate more results. This is where, quite literally, every new technology somehow affects the price tag for the better. 

While we’ve observed a decline in the standardized fitness programs field over the past five years, areas like esports and all types of personalized home gym apps have flooded the internet and app stores. Sports are becoming increasingly niche and tailored to specific audiences, with prices dropping and competition rising. 

Why? Thanks to technology, literally any stay-at-home mom can launch a YouTube channel, and later create, say, an app with postpartum bodyweight training programs for a $10 monthly peer membership fee.

Subindustries in Sports

Still having some relative borders, we can divide sports into 8 main subindustries:

  • Gym software
  • Competition software
  • IoT Related Software
  • Betting and E-commerce
  • Personal training apps
  • Nutrition apps
  • Fitness communities
  • Equipment booking

8 Main Subindustries in Sports

Also, some sports-related products or brands digitalize and expand their ecosystem. Here’s a good example: the legendary Nike Training Club and Under Armour Record.

However, we are witnessing the arrival of a new subindustry: Age management. As it interacts with IoT, Nutrition, Sleep management, and fitness, and can massively utilize such GPTs as Machine learning and edge computing, we can soon expect it to separate or even merge with some earlier well-being areas.


When a new technology arrives, some aspects of work are simplified. That’s the core idea of technology itself. Today, creating websites or mobile applications is five times faster than it was 10 years ago. But at the same time, users have become much more demanding.

The variety of different wearable devices multiplies business model combinations for sports apps, while marketing budgets are not limitless.

If we take a look at the majority of mobile apps related to fitness in some way, we’ll notice that their monetization is based on subscriptions, and sometimes selling additional services (clothing, consulting, etc.). The average monthly fee is probably around $15, and the Lifetime Value (LTV) per user is approximately $80 USD.

Apps from betting and E-sports are commission-based, so their LTV is not as important as the number of users (just say “hi” to their aggressive marketing strategy).

Two facts: 

1: Customers’ money is limited. 

2: Today, on average, people have 80 apps installed on their smartphones, 5 of which have some monthly charge, and 1.6 of them are somehow related to Sports or Wellbeing.

This brings us to the two extremes where Sports apps still earn the majority of their ARR (Annual Recurring Revenue). The first category is Betting and E-sports apps, as they sell short-term joy and fun.

The other extreme leverages the power of PERSONALIZED HISTORICAL DATA. Now, hear us out, this is where personal data from wearables (GPS, heart rate, oxygenation, etc.) meets personal pattern recognition and informed decision-making. And users are WILLING to pay for this data to be enriched and stored.


Unlock New Potential for Your Sports App!

Integrate wearable technology and personalized data for enhanced user engagement and revenue.


Digital implementations of the Sports industry have multiple trending directions, and these trends differ quite significantly across each subindustry. Aside from the commercial streams of sports championships and betting on esports, let’s take a look at the areas where Sports meets wearables and other cutting-edge technologies.

Key trends in Fitness, Wellness, and Nutrition:

  • Age Management as a Focus: We’re now seeing influencers massively raising awareness about lifespan prolongation protocols and routines. This topic is complex, but demanding users prefer having everything in one place to collect their data and access a comprehensive knowledge base. Another trend here, indirectly started by Bryan Johnson, is age reduction competition. Similar to the Strava app, Age management apps leverage user engagement by allowing them to compare progress based on historical data and utilize well-known social network mechanics.
  • Wearables and IoT: On the amateur level, sports as a hobby now leverage thousands of individual records for everyone. But on the professional sportsperson level, data is everything. Today, sports labs and institutions can literally measure each breath a professional athlete takes, utilize predictive AI and machine learning models, or even model team competition results. It seems that with today’s technologies, the “bad guy” from Rocky 2 is a reality, and a mass production reality at that, regardless of the kind of sport.
  • Machine Learning: This trend deserves separate mention. Raw data from wearables is now used to find an optimal way for individuals to achieve their desired results, far exceeding the effectiveness of traditional coaches who rely on assumptions about what works for each person. Moreover, technology can also take care of the mental aspects of an athlete’s health, suggesting the next best training session.
  • Ads, Integrations, and Traffic: Every app needs a monetization strategy. This can involve wearables, subscriptions, built-in ads, or even data selling (since let’s be honest, nobody seriously reads Terms and Conditions). This trend inevitably arises across mobile apps in many industries, but the core principle remains the same: users either pay for access to features and are exposed to ads, data selling, and product offerings, or they pay a premium to access exclusive app features without any of the above.
  • Virtual Reality (VR) and Augmented Reality (AR): This technology has already become an integral part of the coaching process in leading sports labs. Most importantly, it helps athletes manage mental barriers and build a foundation before real training begins, especially for activities that are dangerous, new, or expensive.


Key Trends In Fitness, Wellness and Nutrition

Technology signifies the optimization of established ways of doing things – faster, smarter, better. For the first time in the last decade, humanity has witnessed the rise of several General-Purpose Technologies (GPTs) in various fields, such as language models and quantum computers. But how do these high-level concepts impact the things we use daily? Let’s delve a bit deeper into the tech aspects of specific GPTs.

What are GPTs?

These are technologies – known as General-Purpose Technologies (GPTs) – that can be applied to a wide range of problems across different fields. Examples include the printing press, the steam engine, and the internet. GPTs fundamentally change the way we live and work. As history shows with celebrations of GPTs coinciding with massive labor force strikes, these advancements can eliminate some professions while creating entirely new ones.

Looking closer at the past decade’s discoveries, we’ve seen the emergence of nearly 10 new GPTs across various fields. Half of these directly impact Sports, especially when we consider the enormous progress in wearables and sensors.

What has actually changed?

There are two angles on how we can address this question: the user perspective and the product creator’s perspective. Other dimensions include money, usage threshold, personalization, privacy and comfort, and market effects.

Edge Computing:

Edge computing refers to processing data close to where it’s collected. This General-Purpose Technology (GPT) is widely applied in IoT devices, smartphones, and even self-driving cars. With edge computing, users experience lower latency and better privacy as data isn’t transferred to central servers and remains on the device. However, the hardware requirements for performing calculations grow exponentially. Luckily, the latest advancements in semiconductors and graphene applications hold promise for reducing the cost of IoT devices.

AI (Artificial Intelligence):

AI refers to a set of consecutive machine functions designed to mimic human cognitive processes. Essentially, AI involves a chain of interconnected technologies that represent steps in the human thinking flow. The process starts with normalized data inputs, such as images, text, speech, code, or numbers. Then, the learning process begins, either through machine learning or deep learning for complex pattern recognition. Once the model is trained, it can undergo reinforcement training. Finally, depending on the desired outcome, AI provides an output in the form of language generation, image description, or any action based on the underlying business logic.

Now, all this complex AI technology is usually implemented into apps in three ways.

Option #1: For simple apps, there’s the possibility to connect to APIs of pre-trained models, or Generative AIs. This case works perfectly for image recognition, speech recognition, or recommendation generation.

Option #2: Edge computing usually assumes AI calculations right on the device. So, for some apps, this is almost a “built-in” capability, while the app itself is more like a layer on top of the AI insights from a smartphone or smartwatch. Literally, you’ll need to have permission from the device.

Option #3: Train a custom model. Now, this requires tons of normalized data, if available. However, there are some services like X that generate training data. This is definitely an option when the product is new and unique, and it makes sense to invest a couple of months in prompt engineering and data science within the team. On top of that, the training process is a costly undertaking, especially when it comes to image recognition.

ML and Personalization

It’s worth mentioning separately that Machine Learning, Edge Computing, and Cloud Computing ultimately serve personalization – the feature users are willing to pay for in the long run. Statistically verified user preferences reveal personal patterns, which lead to content curation and tailored content recommendations.

However, another area where Machine Learning shines is recommendations and insights. Beyond unbiased (or sometimes intentionally biased) summarizing of user data, ML in sports apps can manage the timing and content of notifications, control the amount of new content in the feed, and predict optimal training pathways based on user’s environmental data and past performance (e.g., suggesting indoor training on a rainy day or showing more ads on payday).

Cloud computing

Overall, cloud computing is a powerful tool for sports mobile apps. It offers scalability for startups, real-time data capabilities for users and product owners, and the potential for a richer user experience when combined with machine learning (ML) or social mechanics.

However, data security and user privacy must be prioritized, and cloud costs need to be managed effectively, especially during peak times. Storage requirements, computational effort demands, and peak app usage can all be major issues. Additionally, poor database architecture can easily multiply the number of API requests by tenfold, skyrocketing cloud computing bills beyond any free quota. Typically, if you plan for more than 50,000 active users, data architecture becomes a top priority for investment.

Whether you choose the Amazon ecosystem, Microsoft, or Google Cloud, they all offer free quotas that can suffice during app creation, as long as you don’t use ML. Each platform has its own strengths and weaknesses.

  • Microsoft Azure: Known for better security and solutions for enterprises.
  • AWS (Amazon Web Services): More cost-effective and flexible for pay-as-you-go cloud computing.
  • Google Cloud: Similar strengths to AWS, with a focus on AI and machine learning tools.

Cloud Computing Platforms – 3 Key Players


The raw data collected from environmental, medical, or any other type of sensor can be processed on the device itself or stored as-is on the cloud or device. Today, we’re talking about more than 100 different measurements and 50+ sensors used in sports, with the most common ones covered in the next chapter.

One common trend with sensors today is miniaturization. For example, the iPhone 15 boasts eight sensors (barometer, Face ID system, three-axis gyroscope, accelerometer, proximity sensor, GPS, LiDAR 3D mapper, and ambient light sensor). When combined with, say, Apple Watch’s heart rate and blood oxygen sensors and software coprocessors, this setup provides a clear picture of one’s health activity statistics.


Sensors Used In Sports Apps 

Battery Low power mode and Bluetooth 5.3

Smartphones and wearables are increasingly becoming essential tools for athletes and fitness enthusiasts. However, tracking workouts and performance can drain battery life quickly. Here’s how low power mode and the latest Bluetooth standard, Bluetooth 5.3, work together to extend battery life while maintaining a reliable connection between your smartphone and wearable.

Together, this allows sports tracking apps to offer extended tracking sessions. Low power mode prioritizes core functionalities like GPS and motion tracking, ensuring your workout data is captured accurately while minimizing distractions from other apps. Bluetooth 5.3 transmits data using less energy, further reducing battery drain on both your smartphone and wearables.

The Power-Saving Techniques In Mobile Apps 

Hardware layer - IoT in Age management apps

Typical sensors of wearable sports devices

From a practical perspective, all sensors used for health monitoring can be divided into four main groups. This classification is based on the ease of access to sensor data. The list starts with sensors readily available on nearly any smartphone and common environmental sensors, while the other end of the spectrum features cutting-edge technologies from experimental labs. Let’s try to cover all of them.

Level 1 - Smartphone sensors and common environmental sensors

  • Accelerometer
  • Gyroscope 
  • Ambient Light Sensor
  • Barometer
  • Microphone
  • Proximity Sensor
  • Temperature Sensor
  • Humidity Sensor

Level 2 - Everyday wearables sensors (apple watch etc)

  • Advanced Motion Sensors: Accelerometer, Gyroscope  for swimming or dancing
  • Electrocardiogram (ECG) Sensor
  • Blood Oxygen Sensor
  • Bioimpedance Sensor
  • Sleep Tracking Features – motion sensors combined  with machine learning

Level 3 - Professional performance sensors (football field sensors, other sports pro devices)

  • Lactate Sensors
  • GPS
  • Foot Pod Sensors
  • Smart Clothing
  • Stadium/Field Sensors with computer visio

Level 4 - Age management and sports lab sensors

Biosensors: Integrated into wearables, these advanced sensors could potentially analyze sweat or interstitial fluid to track biomarkers related to hydration, nutrient levels, and even early disease detection (still under development)).

Smart Pills: Ingestible capsules with sensors that monitor internal body functions like gut health or core body temperature (still under development)).

Advanced Sleep Monitoring Systems: (May use specialized mats or headbands with multiple sensors to provide more detailed sleep stage analysis and potential sleep apnea detection).

Non-invasive Blood Glucose Sensors: (Track blood sugar levels without finger pricking, valuable for diabetics (still under development)).

Brainwave Sensors: (Measure brain activity, potentially useful for monitoring sleep quality or even cognitive function in the future (research stage).)

Typical protocols in Age management apps require the usage of all sensors from Level 1 and some of Level 2. Additionally, the GPS sensor from Level 3 is widely used for daily activity tracking. This functionality tracks walks, hikes, or bike rides with precise location data, helping users monitor the distance covered, analyze routes, and even calculate calorie expenditure.


Hardware Layer – IoT In Age Management Apps

IoT Data with Cloud Computing - Do’s and Dont’s

Once sensor data is acquired, the device or server needs to process and store it. We’ve already discussed edge computing on devices, but for most sports apps and typical user cases, data is fully or partially calculated and then temporarily or permanently stored in the cloud.

Regardless of the specific cloud services provider, there are best practices and considerations to take into account for app architecture.


Enable Data Backup and Recovery: Cloud storage offers a reliable way to back up user data, protecting it from accidental loss or device failure.

Focus on Scalability: Cloud computing allows handling fluctuations in data traffic. The pay-as-you-go model still becomes economically ineffective when the number of users is high. When reaching this threshold it’s time to reconsider monetization policy providing more premium service to fewer high-ticket clients, or doing the absolute opposite if market share is the goal.

Leverage Cloud Analytics: Cloud-based analytics tools to process raw sensor data and generate actionable insights, personalized recommendations, and easy-to-understand visualizations.

Prioritize Data Security: Age management apps often collect sensitive health data. Implement robust security measures in the cloud, including encryption, access controls, and compliance with relevant data privacy regulations. Still, in Cloud hosts T&Cs there are many underwater rocks, make sure your layers have read them carefully.

Integrate with Third-Party Services: The cloud facilitates integration with other health and wellness apps or services. This allows users to consolidate their health data in one place and potentially receive a more holistic view of their well-being. By this, we mean integration between Level 1 and Level 2 sensor data in one app interface.


Neglect User Control: Give users control over their data. Allow them to access, download, and even delete their data as desired. Be transparent about data collection and stay compliant with data regulations like HIPAA or GDPR.

Collect data for the sake of data: this is a very costly approach if you work in any type of Agile methodologies. Define the minimum you are willing to calculate and store.

Overlook Offline Functionality: Not everyone has constant internet access. Allow users to store some essential data locally for offline access to basic app features.

Skimp on Data Cleaning: Sensor data can be noisy or contain errors. Implement data cleaning techniques in the cloud to ensure the accuracy and reliability of the insights generated.

Forget About Cost Optimization: Cloud computing can be cost-effective, but monitor your usage and optimize cloud storage and processing needs to avoid unnecessary expenses. It does make sense to set warnings on limit usage on 10%, 50%, 75% and 90% quota use.

Components of Age Management Apps

Standard mobile app features

By standard app features, we mean functionalities that users always expect to see in the app. These components are not where monetization happens, but rather where users decide whether to stick with the app. In other words, failing to deliver these functionalities in the best quality increases app uninstalls and churn rate, even though these features are common across most apps and developers know how to implement at least 80% of them.

Below is a table that lists features common to 90% of Fitness, Wellness, and Nutrition mobile apps. Additionally, we’ve included an average time estimate for creating each feature as a turnkey solution, including mobile app development, admin dashboard, infrastructure, testing, documentation, communication, and other development and management activities. Keep in mind that these estimates may still be optimistic.

Standard Time -To-Implement Mobile App Features

Unique Features of Age Management Apps

In Age management apps, users pay for a reinvented, unique, and highly personalized combination of features. It includes social mechanics, a UX-polished library of exercise and nutrition advice, displays data from hardware, and summarizes and stores historical personal data. On top of that, Artificial intelligence can take care of even more personalization and provide accurate recommendations.

There are also some less obvious considerations for Age management apps:

  • One of the most complex in-app navigations
  • High Lifetime Value (LTV) and stable Annual Recurring Revenue (ARR) 
  • The huge size of the app itself due to data storage on the device
  • Age management apps constantly work in the background, consuming battery 
  • Cost of infrastructure

Unique Features Of Age Management Apps



Age management apps literally rely on the data from smartphones and wearables. Moreover, some solutions can upload lab test results, integrating even more equipment to build a holistic view of someone’s health and progress.

Besides that, one of the most popular ways to additionally monetize age management apps is by providing or selling specially designed devices. Here are two great examples RedCat used to work with in the past.

This approach assumes almost one additional year of investment in product design, typically done in Taiwan, Singapore, or Japan. It also involves prototype tests with multiple iterations of middleware adjustments. Additionally, unique devices immediately force the need to solve data considerations: what data will be calculated, where will it be stored, and for how long?

The easier way to extract data from wearables is by using commonly used devices and apps like Apple Watch, Xiaomi MiBand, and built-in smartphone health apps. This can be achieved within days using libraries and Extractors like this one for iOS, this one for Kotlin (Android devices), and this one for React Native.

Social network mechanics

Not all age management apps include social features like messaging, ratings, social profiles, and feeds, but the most successful ones often do leverage these features. People are naturally competitive, especially when it comes to fitness and age management goals. Apps like Strava and Peloton have demonstrated the power of social engagement, where social aspects are just as important as athletic achievements.

Building a social network within an app is a well-known and powerful set of features, but it’s also a significant undertaking. It means recreating core Instagram functionality within another app. This is the main reason many product managers choose to prioritize a different approach, positioning their platforms as utilitarian apps focused on individual wellness journeys.

Knowledge Database

At first glance, a knowledge base might seem like the simplest feature on this list. However, architectural shortsightedness during the planning stage of the content catalog database structure can become a significant cost factor if the app becomes successful.

Regardless of the content stored in the knowledge base (video tutorials, exercise instructions, recipes, articles), it’s typically cloud-based content requiring terabytes of storage space. This means data formats, file compression, latency, buffering, and minimizing API requests are all crucial aspects that data architects need to consider.

Personalization and Insights

Age management apps take personalization to a new level. Each user is treated as a holistic data system, similar to admin interfaces where dashboards are the primary screen. This approach can lead to app screens becoming heavily loaded with data. To address this, product managers and UI/UX designers need to evaluate the information displayed constantly. They should question what data can be removed, hidden in less prominent locations, or presented just once through pop-ups or notifications.

Personalization starts much earlier, during the onboarding stage. This is where users fill out a series of questionnaires. The next step involves granting permissions for the app to access data from wearables, smartphone health apps, cameras, and headphones (explain why camera and headphone access might be needed). Step #3 is goal setting and progress tracking. A common method here is comparative analysis, where users are informed about their stats compared to other app users or their own past achievements.

Advanced Personalization with AI

Taking personalization further, the app leverages AI through adaptive learning and predictive algorithms (complex IF statements are likely not used in AI). This allows the app to provide personalized workout recommendations, nutritional advice, and even customized reminders for scheduling, mental exercises, or supplement intake.


The target audience of sports apps is untypically wide, with the potential to have users with any type of check. Moreover, Age management app owners are typically luxurious Clinics, who provide their clients with additional value in the form of digital tools.

Monetization in age management apps usually takes one of two directions:

  • A “closed club” for clinic’s patients with a high ticket for subscription or free-of-charge tool.
  • A multilevel subscription-based health tracker with a knowledge base and shop. 

This means, that such income flows as poorly targeted ads, traffic and data sales, NFT sales, or one-time download fees are used extremely rarely.


Ways To Monentize The Age Management Apps



Overall, cloud computing, IoT, and AI are powerful tools for all types of sports mobile apps. They offer scalability, real-time data capabilities, and the potential for a richer user experience. However, data security and user privacy must be prioritized, and cloud costs need to be managed effectively.

By leveraging these technologies responsibly, app developers can keep users engaged and coming back for more.

By following the mentioned do’s and don’ts, you can create sports mobile apps that leverage the power of IoT data and cloud computing. This will not only enhance the athlete and fan experience but also usher in a new era of data-driven performance analysis, personalized coaching, and deeper fan engagement in the world of sports.

But it’s important to remember that technology is just one piece of the puzzle. The data collected needs to be processed, analyzed, and visually presented in a meaningful way to truly impact sports results or enhance the viewing experience. By using sensor data insightfully, people can gain a deeper understanding of body markers and patterns, optimize their health routines, and empower themselves on a journey towards a longer and healthier life.


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