Building a consumer health app that handles sensitive personal health data but unsure which HIPAA rules apply and which don't?
Integrating with wearable devices and health platforms (Apple Health, Google Fit, CGMs) and hitting undocumented API limitations at every turn?
Custom mobile health apps for digital health companies, chronic disease programs, and consumer wellness platforms -- built around the behaviour change and data flows that health outcomes depend on.
We handle the wearable integrations, PHI data architecture, and clinical workflow logic that make an mHealth app usable in the real world.
Chronic disease management apps for diabetes, hypertension, COPD, and asthma with clinician-facing reporting
Mental wellness tools with CBT exercises, mood tracking, and validated outcome questionnaires (PHQ-9, GAD-7)
Medication adherence apps with dose scheduling, reminders, caregiver visibility, and refill tracking
Wearable and device integration for Apple HealthKit, Google Health Connect, Fitbit, Garmin, and CGMs
RaftLabs builds mHealth applications for digital health companies, chronic disease programs, and wellness platforms. We develop chronic disease management apps, mental wellness tools, medication adherence apps, fitness and nutrition trackers, and wearable integrations (Apple Health, Google Fit, CGMs). Where apps handle protected health information, we apply HIPAA-aware technical safeguards from day one. Most mHealth builds deliver in 10-14 weeks at a fixed cost.
Most mHealth apps fail because they log data without changing behaviour. A diabetes patient who opens an app once to enter a blood glucose reading and never returns is not being helped. The app has to earn daily use by reducing friction, surfacing useful feedback, and fitting the patient's existing routine.
We build mHealth apps with behaviour change mechanics at the centre -- the data capture and wearable integrations serve that goal. Where an app handles protected health information, HIPAA-aware architecture is a requirement from the first sprint, not a compliance checkbox added at the end.
Daily symptom tracking, medication logging, vitals entry, and trend graphs for patients managing diabetes, hypertension, COPD, asthma, and similar long-term conditions. Clinician-facing reporting dashboards show adherence, flagged readings, and week-over-week trends so the care team can act between appointments. Condition-specific logic -- carb-to-insulin ratios for diabetes, peak flow thresholds for asthma -- is built into the data model, not bolted on later.
CBT-based exercises, mood tracking, guided journaling, and meditation content for consumer mental wellness apps. For clinical programs, we add validated outcome questionnaires -- PHQ-9, GAD-7, PCL-5 -- with automatic scoring and a therapist communication layer for follow-up. Engagement mechanics matter here: push notification timing, content personalisation, and streak logic are built with retention data in mind, not as an afterthought.
Dose scheduling with flexible reminder logic -- one-time, recurring, and PRN doses -- across iOS and Android. Refill tracking with pharmacy integration where needed. Caregiver visibility dashboards for paediatric and elderly populations where a family member monitors adherence. Adherence reporting for care coordinators and clinical programs. Missed dose escalation logic that contacts a caregiver or care manager when a patient misses multiple doses in sequence.
Apple HealthKit, Google Health Connect, Fitbit API, and Garmin Connect IQ integrations for step count, heart rate, sleep, and activity data. Continuous glucose monitor integration for Dexcom G7 and Abbott Libre 3 -- real-time glucose readings, trend arrows, and alert thresholds inside the app. Blood pressure cuffs and connected inhalers via Bluetooth LE. We scope every wearable integration during discovery because API access, refresh rates, and data granularity vary significantly across devices and SDK versions.
Activity logging, workout planning, and exercise library with video guidance. Nutritional database lookup with barcode scanning for packaged food and portion-size estimation for whole foods. Calorie, macro, and micronutrient tracking with goal-setting and weekly progress summaries. Sleep and step data pulled from connected wearables to complete the picture. Goal and milestone logic designed for sustained engagement -- not just the first two weeks of a New Year resolution.
Determining which data elements in your app qualify as protected health information is the first step -- not all mHealth data is PHI, and the answer changes based on who the covered entity is and how the data is used. Where PHI is involved, we apply encryption at rest and in transit, role-based access controls, audit logging, and BAAs with all infrastructure providers. We also brief you on App Store and Google Play health data policies, which carry their own disclosure and data-handling requirements separate from HIPAA. For apps that could qualify as Software as a Medical Device under FDA guidance, we identify the risk tier and scope accordingly.
Frequently asked questions
It depends on whether the app handles protected health information and whether the developer qualifies as a covered entity or business associate. A general fitness tracker sold directly to consumers with no connection to a healthcare provider typically does not trigger HIPAA. An app that receives data from a clinician, integrates with an EHR, or is deployed by a health plan almost always does. The safest approach is to map data flows during discovery and determine PHI status before any infrastructure decisions are made. We do this on every health app engagement.
Apple HealthKit and Google Health Connect each expose a set of data types -- steps, heart rate, sleep stages, blood glucose, and others -- that an app can read and write with user permission. Integration requires platform-specific SDKs, a user-facing permission flow that explains which data types the app will access, and background refresh logic to keep data current when the app is not open. Real-world complexity comes from inconsistent data quality across devices, rate limits on background sync, and the fact that Apple and Google update their health data APIs regularly. We scope the specific data types you need during discovery and test on physical devices, not just simulators.
Software as a Medical Device is an FDA classification for software intended to diagnose, treat, cure, or prevent a disease or condition. A medication reminder app is not SaMD. An app that analyses glucose trend data and recommends an insulin dose adjustment likely is. The FDA uses an intended use framework -- what the app claims to do and what condition it acts on -- to determine classification. Apps that fall into a higher-risk SaMD tier require a regulatory strategy that goes beyond HIPAA compliance. We identify the relevant risk tier during scoping so you are not surprised later in development.
A focused mHealth MVP -- one condition or use case, one platform (iOS or Android), basic wearable integration, and no EHR connectivity -- typically runs $35,000--$65,000 and delivers in 10-14 weeks. A full-featured mHealth platform with cross-platform apps, multiple wearable integrations, clinician dashboards, and HIPAA-compliant data architecture runs $70,000--$150,000. Cost is driven primarily by the number of integrations, the depth of clinical logic, and compliance documentation requirements. We price at a fixed cost after a paid discovery phase so there are no change-order surprises mid-build.
Tell us your target condition or use case, your wearable integration needs, and whether PHI is in scope -- we will scope the build.