Currently submitted to: JMIR Formative Research
Date Submitted: Jul 29, 2026
Open Peer Review Period: Jul 29, 2026 - Sep 23, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Building a Sensor-Based Research App Without Software Development Expertise: Lessons from Using Apple’s Study App Template
ABSTRACT
Background:
Widespread use of smartphones and wearable devices has led to the expansion of mobile health (mHealth) technology for clinical research, allowing for remote, real-time, continuous health tracking. However, most studies developing research applications (apps) to capture these data rely on in-house programming experience or costly partnerships with app developers, posing barriers for many researchers. To address this challenge, Apple introduced a templated framework for study apps intended for researchers without formal software development experience.
Objective:
To describe our team’s experience leveraging Apple’s templated framework to build and deploy a research app for an emergency physician sleep and wellbeing study. We highlight the development process, implementation challenges, additional lessons learned, and implications for those conducting similar research, and we demonstrate proof of concept using preliminary feasibility data.
Methods:
Using Apple’s Study App Template to design the app, we developed and customized the app’s welcome page, e-consent process, onboarding flow, and permissions screens for HealthKit and SensorKit data sharing. We integrated 15 individual surveys with survey-specific deployment schedules. Data collected from surveys included participant characteristics (i.e., demographics), clinically relevant participant-reported sleep and insomnia scores derived using validated questionnaires, chronotype, physical health and habits, stress and mental health, and nutrition. Data were stored securely with Amazon Web Services. We report on our development process, data architecture and storage, and pilot testing.
Results:
Our team, composed of physician scientists, biostatisticians, and research staff, all of whom had no prior app development experience, successfully created and deployed a research app in 10 months. As of June 2026, 279 participants have completed the study. We identified critical technical and participant-related challenges: 8 participants replaced their iPhone during the 90-day study period which resulted in early stoppage of the protocol, 5 took unanticipated leave, 3 needed onboarding assistance related to system permissions required by SensorKit, 2 reported the app crashing, and one had their watch stolen. We share lessons learned and suggestions for future studies.
Conclusions:
Apple’s templated framework for study apps enabled our physician-scientist led team to independently create a consumer-grade research app without software development expertise, significantly reducing barriers to collecting sensor data in clinical research. Our experience demonstrates how industry-academic partnerships can accelerate innovation and help address outstanding feasibility concerns regarding the collection of real-world sensor data to inform clinical care and research. Clinical Trial: N/A
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