Smartphone-based research · Encrypted data · Global cohorts
The infrastructure for smartphone-based research
SMAAT (Sensor-based Mobile Application for Assessment and Tracking) is built to help researchers, clinicians, and applied teams run modern digital studies without building their own app stack. Design protocols in the web dashboard, reach participants via iOS and Android, and collect encrypted survey and sensor data over time — supported by real-time compliance dashboards, engagement and gamification mechanics, and a validated item library that follows established ESM item repository formats.
Focus
EMA/ESM, digital health, applied research
Data
Self-report, GPS, motion, background streams
Security
Public/private key encryption, on-device storage
Mission
Lower the barrier to high-quality, smartphone-based research by combining robust study tooling with secure, privacy-aware data collection.
Research domains
Psychology, health, market, workplace
Study building blocks
Surveys, sampling, sensors, cohorts
Want to see how everything fits together? Start with Getting Started.
A complete stack for experience sampling and sensor studies
At its core, SMAAT connects a web-based researcher dashboard with a participant-facing mobile app. You configure studies in the browser—surveys, triggers, sensor streams—while participants respond to prompts and passively share data from their everyday smartphones. A compliance dashboard shows response rates by day, week, and survey type, and a validated item library lets you assemble constructs like mood, anxiety, stress, or sleep with drag-and-drop items. Public/private key encryption and on-device storage keep sensitive information protected throughout the pipeline.
Researcher dashboard
Design surveys, define sampling schedules, manage cohorts, and track per-participant adherence in a real-time compliance dashboard with configurable email alerts.
Participant app
A single iOS/Android app for joining studies, responding to prompts, and engaging with points, streaks, badges, levels, missions, and optional team or narrative modes.
Data layer
Encrypted storage, robust synchronization, and exportable datasets suitable for statistical and machine learning analysis, including both survey responses and engagement metrics.
Governance & privacy
Built with privacy by design, GDPR-aligned practices, and clear separation of identifiers and research data, including workflows for contributing and curating validated instruments in the item library.
What defines the SMAAT platform
The design of SMAAT is grounded in three pillars: flexible study design, secure-by-default infrastructure, and sensor-enhanced data collection that works in everyday life.
Flexible study design
Configure EMA/ESM protocols, longitudinal cohorts, and targeted tasks with time-based, random, and event-based prompts plus quiet hours.
Security by design
Public/private key encryption, on-device storage, and careful handling of identifiers help you meet ethical and regulatory expectations.
Sensor-enriched insights
Combine GPS, accelerometer, and other smartphone signals with self-report to understand context, mobility, and behavior over time.
Built for researchers, clinicians, and applied teams
SMAAT is intentionally general-purpose: you can use the same infrastructure for academic studies, clinical monitoring, workplace research, or customer insights—while preserving rigor and privacy.
Academic & clinical research
Run EMA/ESM protocols, observational cohorts, and intervention trials that integrate symptom reports, context, and passive data.
Businesses & organizations
Study customer journeys, workplace experiences, or employee well-being using mobile surveys and optional background signals.
Healthcare & individuals
Support digital health interventions, symptom tracking, and self-monitoring with smartphone-native questionnaires and sensor streams.
Slot SMAAT into your existing research workflow
Define questions and schedule
Translate your protocol into surveys, triggers, and sampling rules inside the SMAAT dashboard, using the validated item library to pull in pre-tagged constructs and submit new tools when needed.
Recruit and monitor
Enroll participants via codes or links, monitor adherence, and check that sensor streams are flowing as expected.
Export and analyze
Export structured datasets for your preferred toolchain—R, Python, SPSS, or custom analysis pipelines.
Built by
A working researcher, for working researchers
Dr. Yury Shevchenko
Postdoctoral Research Associate · Experimental Psychology & Internet Science, University of Konstanz, Germany
Yury holds a PhD in Cognitive Psychology from the University of Mannheim (2018, dissertation on the influence of mood on decision-making) and is currently a postdoc in the Experimental Psychology & Internet Science group at the University of Konstanz. His research spans experience-sampling methodology, online experiments, mobile sensor methods, and decision-making, with 25+ peer-reviewed publications. He developed Open Lab (a web application for running and managing online experiments), created Samply (a notification scheduler used worldwide for ESM studies), is Lead Developer of MindHive (a citizen-science platform for brain and behaviour research), and built SMAAT to give researchers a complete, EU-hosted toolchain for smartphone-based studies.
Selected publications on SMAAT-relevant methods
- Shevchenko, Y., & Reips, U.-D. (2025). Samply Stream API: The AI-enhanced method for real-time event data streaming. Behavior Research Methods, 57(4).
- Shevchenko, Y., & Reips, U.-D. (2023). Geofencing in behavioral research: Methodology, challenges, and implementation. Behavior Research Methods.
- Shevchenko, Y., Kuhlmann, T., & Reips, U.-D. (2021). Samply: A user-friendly smartphone app and web-based means of scheduling and sending mobile notifications for experience-sampling research. Behavior Research Methods.
How to cite
Used SMAAT in your research?
If SMAAT contributed to a publication, presentation, or pre-registration, please cite the platform so other researchers can find and build on the same tooling. APA and BibTeX entries are below — click to copy.
Shevchenko, Y. (2026). SMAAT: Sensor-based Mobile Application for Assessment and Tracking (Version 1.0) [Computer software]. https://smaat.eu
@misc{shevchenko2026smaat,
author = {Shevchenko, Yury},
title = {{SMAAT}: Sensor-based Mobile Application for Assessment and Tracking},
year = {2026},
note = {Version 1.0},
url = {https://smaat.eu}
}Citing the underlying methodology too? If your study leans on the notification-scheduling logic that SMAAT inherits from Samply, also cite Shevchenko, Kuhlmann, & Reips (2021) in Behavior Research Methods. For the geofencing functionality, see Shevchenko & Reips (2023).
Questions about a specific citation? Get in touch and we'll help you frame it for your venue.
Plan your next smartphone-based study with SMAAT
Use the web dashboard to design your protocol, invite participants to the SMAAT app, and move from static questionnaires to rich, time-stamped datasets.