On average, each January sees a 23% increase in fitness search interest – and April brings the second climb of 13% as people prepare for summer.
However, a new study from Surfshark reveals that the digital fitness boom comes with a hidden cost: popular apps collect data linked to your identity and train their AI.
Strava, for example, collects 20 data types linked to your identity and uses the information to train its AI and ML models. Whereas collectors like Peloton, which gather two data types linked to your identity, state that any personal data processed by AI is strictly used to enhance their services.
“It’s no surprise that AI is coming to fitness and similar applications,” says Luis Costa, research lead at Surfshark. “However, training AI on personal data without explicit, informed consent is not innovation – it’s an invasion of privacy and trust. Such practices make people unknowingly submit their data when using AI features without a clear understanding of how their data is being processed.”
Google Trends reveals a clear pattern
The new study reveals that the search terms “fitness” and “personal training” spike globally in winter and have a notable increase in spring, starting from April. Since 2022, the search interest for “fitness” reached its highest value (100) in January 2026. Interest in “personal training” has also been notable, peaking at 100 in the winter of 2026 – up from 37 in January 2025, an impressive 2,7-fold increase.
A second wave of interest in “fitness” comes in April, averaging a growth of approximately 13%. The trends for “personal training” are similar, with growth beginning in April and peaking in August at 75 out of 100.
Strava collects 20, Nike Training Club – 19 data types linked to your identity
AI is transforming fitness by personalising workouts using user data. This data, while improving individual fitness experiences, can also be utilised for AI training.
For example, Strava uses gathered information from users to enhance the quality, reliability, and/or accuracy of their AI features by creating, developing, training, testing, improving, and maintaining AI and ML models run by Strava or its service providers.
However, they state that, where possible, they use aggregated, de-identified information for AI features. On the other hand, Peloton uses collected data to build, train, analyse, and improve the accuracy of their services, enhance products, and increase operational efficiency. While Peloton may use third-party AI service providers, they explicitly state that any personal data processed by these technologies is strictly for enhancing their services.
Among the workout apps analysed, Strava collects the most data linked to user identity – gathering 20 out of 35 data types listed in the Apple App Store. These data types include location, purchase and search history, photos and videos, and other user content. Nike Training Club collects 19 data types, while Peloton collects only two.
Although many of these data types may be essential for app functionality, they can also be used for purposes such as advertising, analytics, product personalisation, and more. For example, Ladder uses only three out of 10 data types linked to users for app functionality, but collects seven data types for product personalisation and employs six for analytics. Companies may also access and use additional sensitive biometric data when these apps connect to wearables or third-party services.
“AI in fitness is introducing a new method for extensive information collection, enabling detailed user profiling,” says Costa. “This raises concerns about the privacy of sensitive personal information – which is often never shared even with friends – as it is analysed and used to train AI models. People overlook security risks, believing that AI is an impersonal entity. However, this information can be exposed in breaches and ultimately exploited by Big Tech or even bad actors.”
Furthermore, four out of the five analysed apps also use data for tracking, as stated by app developers in the information provided on the Apple App Store, with Apple Fitness+ being the exception. “Tracking” refers to linking user or device data collected from the app – such as a user ID, device ID, or profile – with user or device data collected from other apps, websites, or offline properties for targeted advertising purposes.
Tracking also refers to sharing user or device data with data brokers.