AI Wearables

Role: Qualitative Researcher

Qualitative research to understand the effects of AI-integration into fitness wearables for exercise tracking, personalized training, and health optimization.

10 week

Project Timeline

5 brands

Of fitness wearables

7 participants

18 - 55 tech-savvy adults, works out at least 3x week, consistantly wears fitness wearable, has a fitness goal

Tooling: Miro, In-depth interviews, Zoom

Research Goal

Understand how AI incorporated features in fitness wearables affect user behavior and perception of health performance.

Research Questions

What are the aspects of the experience of using AI generated fitness plans that might influence perceptions of their quality or trustworthiness?
How do AI-generated interactions and feedback influence user understanding of their biometric data and motivation to continue training?
How do AI fitness recommendations influence the user’s level of confidence in accessing accurate and personalized fitness plans?

Project Context

The integration of AI into fitness tracking wearables has the potential to affect the workout styles, fitness planning, and goal setting most user groups currently utilize.

30% of U.S. adults now use fitness trackers so what are the impacts of AI integration into future fitness wearables and how do these systems shape user experience and promote behavior change?

User Criteria

Tech savvy adults 18 - 25: most likely to utilize wearable technology, and early adoption of AI-integrated features.
Exercised at least 3x per week: have an established routine that their wearable is able to track longitudinal data and has a deeper understanding of their fitness progress.
Own and regularly wear a fitness tracker: we are not introducing this technology. These users are people who consistently interact with their data and have more touchpoints with AI-features.
Has a fitness goal: long-term motivation that they’re progressing towards based off of biometric or performance data (pace, speed, weight, etc).

Methods & Strategy

Competitive Analysis

Utilized AI to create a competitve analysis of fitness wearable brands like Oura, Garmin, and Apple to create a hollistic understanding of where certain product succeed, where they stand using AI, and what consumers they go after.

Netnography

Researched online communities and forums like reddit, substack, and youtube to see what wear testers were saying about the products, where they experienced pain points, and how well AI-integration worked for their goals.

Semi-structured Interviews

Interviewed participants regarding the reasons why their bought their wearbes, when they wear it, how they interact with it, and how their habits have changed with their wearable.

Codebook Analysis

Each interview transcript had two researchers analyze the data. From the analysis, we compared notes to generate a code book. Some things we kept in mind were the participants' fitness experience, how familiar they were with the product ecosystem, and when they wore their product.

Codebook Examples

Behavioral Change

“Because I think if I had a really good workout or I hit a PR– I do remember there was a time or a time or two when it was like, “Oh, this is your best” or like “You had a certain segment on this run that was really fast compared to how it has been”, and that felt good.” [P5]

1

Cognitive Load

“But there's not a whole lot of like fact checking like counting the amount of steps I take, and then checking the app. I just trust it.” [P2]

2

Accuracy

“The distance is pretty accurate, which makes me believe that the step count is pretty accurate, and they obviously take, like your height and weight and gender” [P6]

3

AI Perception

“If you just have AI, just give you a workout, use all that blindly. That is just a recipe for disaster. You're going to get injured." [P1]

4

Major Themes:

Personalized AI recommendations act as a motivating factor:
- Data visualizations drives motivation.
- Timing of recommendations is key.
User’s trust in AI features, recommendations and tracking is dependent on their fitness experience and workout routine:
- Novice fitness enthusiasts view AI recommendations as accurate and a reliable tool for training.
- Experiences fitness enthusiasts are less likely to listen to their wearable if it did not align with their structured training or past experiences.
Users are open to sharing AI-tracked data for community engagement:
- By sharing their data via the wearable’s AI tracking algorithm, user information has impacted how they perceive their fitness in comparison to others in their community.
AI aggregated data reduces the user’s cognitive load of logging biometric performance over time:
- Users view the purpose of their fitness wearable as a method to log physical activity and performance metrics. Even if the AI feature wasn’t able to accurately provide fitness recommendations or evaluations, users saw it as a tool that relieved the cognitive load of manual tracking.

What does this mean for the future of fitness wearables?
Two possible routes:

1. Individual enhancements and hollistic overviews. With The rise of concierge medicine and health opitimization, users are looking for even more hollistic and personalized approaches to their fitness routines.

2. Deeper community engagement. Fitness apps are social media. Companies could leverage AI as a way to match make fitness partners. Individuals with similar fitness goals or levels in order to encourage users.

Market Insight

The global fitness market industry was valudes at 72.08 billion USD in 2025 with the US taking a little less than half of the market share. As technological advances in hardware continue to increase, we will see more fitness trackers being utilized in both healthcare and the overall wellness industry.

In the past few years, there has been a huge boom in health optimization through biotracking. This trend quickly became a status symbol; illustrating disposable income through expensive gym memberships, expensive races, and a variety of restrictive diets.

As we enter 2027, the way to differentiate in the fitness and wellness space will require companies to quickly advance their hardware technology and create a more seamless AI integration, or use their products as a way to focus on fostering community building.