Members logging workouts manually in your app while all their actual training data sits in Apple Health or their Garmin -- making your platform's recommendations no better than guesswork because you are working from what members remember rather than what they did?
Loyalty rewards tied to self-reported activity that members can inflate with no verification -- so your points budget is spent on claimed behaviour rather than workouts that actually happened?
Fitness Wearable Integration Development
Gyms and fitness apps that pull real biometric data from members' wearables can offer something a generic platform cannot -- recommendations built from actual heart rate, recovery scores, and sleep data rather than self-reported effort that members fill in after the fact.
The engineering challenge is not connecting to the API. Every major device platform publishes an API. The challenge is making the raw data reliable enough to act on -- cleaning inconsistencies, normalising units across devices, and building the data pipeline that turns scattered biometric readings into workout intelligence your platform can use.
Apple Health and Google Fit integration
Garmin, Polar, Whoop, and Fitbit connections
Heart rate zone tracking for class environments
Loyalty reward triggers from verified workout data
RaftLabs builds fitness wearable integrations that connect gym management platforms and member-facing apps to Apple Health, Google Fit, Garmin, Polar, Whoop, and Fitbit. The engineering work is in cleaning, normalising, and mapping raw device data -- heart rate, HRV, sleep, recovery scores, and activity records -- into a consistent format the application can act on. Once the data is reliable, it drives personalised workout recommendations, coach dashboards, and loyalty reward triggers based on verified workout completion rather than self-reported activity. Most wearable integration projects ship in 10 to 14 weeks at a fixed cost with full source code ownership.
100+Products shipped
·24+Industries served
·FixedCost delivery
·10-14Week delivery cycles
The engineering work is in the data, not the connection
Every major wearable platform publishes a developer API. Connecting to Apple HealthKit or the Garmin Connect API is a documented process that any developer can start in an afternoon. The real work begins when the data arrives. Heart rate readings come in different units and sampling intervals across devices. Step counts are calculated differently by Apple and Google. Garmin and Polar report training load using proprietary algorithms. Whoop recovery scores are composites that have no direct equivalent on Fitbit. Raw data pulled from six different device ecosystems cannot be fed directly into a recommendation engine -- it needs cleaning, normalisation, and a consistent schema before any part of the application can act on it reliably.
A wearable integration that produces wrong recommendations because the underlying data is inconsistent is worse than no integration at all. Members who receive suggestions that contradict what their body is telling them stop trusting the platform. The data pipeline -- the layer between the raw device API output and the application logic that uses it -- is where the investment delivers value. Once the pipeline is reliable, the downstream applications are straightforward: personalised workout recommendations, live heart rate displays during class, coach dashboards that surface overtraining risk, and loyalty rewards triggered by verified workout completion rather than self-reporting.
What we build
Apple Health and HealthKit integration
HealthKit read and write permissions scoped to the specific data types the application needs -- activity summaries, heart rate samples, sleep analysis, workout records, HRV measurements, and resting heart rate. Read permissions pull historical data on first authorisation so the member's profile is populated from day one rather than starting empty. Write permissions let the app record workouts back into HealthKit, keeping the member's Apple Health record current and giving the platform a presence in the health data ecosystem the member already uses. Data refresh runs on a configurable schedule to keep the platform current with the member's most recent activity without draining battery through continuous polling.
Google Fit and Android Health Connect integration
Google Fit API and Android Health Connect integration for Android members, covering step count, calories burned, heart rate, active minutes, and workout session records. Health Connect is the current Android health data standard replacing the older Google Fit REST API, and both need to be supported to cover members on different Android versions. Data mapping translates Google's activity type taxonomy into the application's internal format so that a Google Fit "running" session appears correctly in the platform's workout history alongside Apple Watch sessions logged by iOS members. Permissions are requested incrementally -- only the data types the feature being used actually needs, rather than a single broad permissions request that causes members to decline.
Garmin, Polar, Whoop, and Fitbit connections
Device-specific API integrations for the four most common non-Apple, non-Google wearable platforms used in fitness contexts. Garmin Connect API covers the broadest range of training load metrics used by serious athletes -- including Training Stress Score and Body Battery. Polar Accesslink delivers heart rate, recovery, and sleep data with particular accuracy for heart rate variability. Whoop provides recovery scores, strain scores, and sleep performance data -- each a composite metric that needs mapping to the application's data model with care. Fitbit covers active zone minutes, sleep stages, and heart rate data for the large consumer segment using Fitbit devices. Each integration is maintained separately because device platform API versions change independently of each other.
Heart rate zone tracking for class environments
Live heart rate display on gym screens during group fitness classes, pulling data from members' wearables in real time to show each member's current heart rate and zone -- below aerobic threshold, aerobic, threshold, anaerobic, or maximum. Zone calculations are personalised per member using their actual maximum heart rate derived from workout history rather than the generic age-based formula that produces inaccurate zones for trained athletes. Zone time distribution displayed at the end of each class session, showing how much time the member spent in each zone, so the session leaves a training record rather than just a memory. Trainer-facing display aggregates zone data across the class so the coach can see at a glance whether the session is achieving its intended intensity target.
Loyalty reward triggers from workout data
Loyalty point awards and reward triggers connected to verified workout data from wearable devices rather than self-reported activity. A member completing a 45-minute run at or above their aerobic threshold earns the reward -- not a member who manually logs a run they claim to have done. Workout verification logic checks that the wearable data matches the claimed session type, duration, and intensity before releasing the reward, closing the gap that self-reporting leaves open. Configurable trigger rules let the gym set different reward thresholds by activity type -- a high-intensity class earns more points than a recovery walk, matching the reward structure to the training behaviour the programme wants to reinforce. Reward history visible in the member app alongside the biometric data from the session that triggered it.
Coach and trainer dashboards
Aggregated biometric data surfaces across a coach's full client roster in a single dashboard -- resting heart rate trends, weekly training load, HRV direction, and sleep quality scores for each member over the past seven and thirty days. Overtraining risk flags when a member's HRV drops significantly below their personal baseline at the same time their training load is high, prompting the coach to check in before injury rather than after. Members who have been inactive for more than a configurable number of days are surfaced as re-engagement opportunities. Programme load planning built from actual recovery data so coaches can schedule hard sessions when members' wearable data shows they are recovered rather than on a fixed calendar that ignores how the member's body is responding.
Frequently asked questions
The most common integrations we build are Apple Health via HealthKit, Google Fit and Android Health Connect, Garmin Connect, Polar Accesslink, Whoop, and Fitbit. The specific platforms we integrate depend on which devices your member base uses -- a gym where members skew toward serious endurance athletes will need Garmin and Polar prioritised, while a consumer fitness app will see the most coverage from Apple Health and Google Fit. During scoping we review your member demographic and prioritise the device platforms that will cover the largest proportion of your active wearable users before moving to lower-volume integrations.
Connecting to a published API takes a day or two. The complexity is in what happens to the data after it arrives. Each device platform uses different sampling intervals, different units, different activity type taxonomies, and different composite metric definitions. A single member might use an Apple Watch for daily activity tracking and a Garmin for long runs, producing overlapping data for the same time window from two sources with different accuracy characteristics. The data pipeline needs to handle duplicates, gaps, unit conversions, device-specific quirks, and the fact that API data structures change when device manufacturers update their platforms. Building a pipeline that produces consistent, trustworthy output from all of this is where most of the engineering time goes -- and it is what determines whether the application can actually act on the data reliably.
Yes, provided the loyalty programme has an API or integration point that accepts external triggers. We build the wearable data pipeline to validate workout completion against configurable criteria -- session type, duration, intensity -- and fire the reward trigger when the criteria are met. If your loyalty programme is a system we built, the integration is straightforward. If it is a third-party platform, we scope the integration method during discovery based on what the platform's API supports. The outcome is the same: points awarded or rewards unlocked when verified wearable data confirms the member completed the workout, not when they say they did.
Cost depends on the number of device platforms to integrate, the complexity of the data pipeline, and whether the integration needs to drive downstream features such as live class heart rate displays, coach dashboards, or loyalty triggers. A project covering two or three device platforms with a core data pipeline and one downstream feature will be scoped differently from a six-platform integration with a full coach dashboard and real-time class display. We scope every project before pricing -- fixed cost agreed before development starts, no hourly billing. Most wearable integration projects in the fitness context ship in 10 to 14 weeks. Contact us with the device platforms you need to support and the features the data needs to drive.
Talk to us about your wearable integration project.
Tell us which device platforms your members use, what downstream features the data needs to drive, and what your current member app looks like. We will scope an integration built around your platform and your loyalty programme.
