The whole study, from one dashboard.
The Health Habit Hub gives you the tools to design studies, guide cohorts and analyse data in a privacy-compliant way. The technical infrastructure runs in the background so you can focus on the research question.
One dashboard for everything: configure studies and questionnaires, maintain the shared knowledge base, track participant progress and export data.
One considered chain, from phone to analysis.
Every building block has a clear job, and all of it runs on its own controlled infrastructure at TU Dresden. That means reproducible results, traceable data paths, and no dependence on an outside vendor that changes the rules tomorrow.
Study setup Admin portal
It all starts in the admin portal: you create your study, define the habits and the consent form, and get a study code for your participants.
App iOS, Android, Web
Participants download the app and enter your study code to join, in about a minute. The same codebase runs on iOS, Android and the browser, so no one is excluded because of their phone. Low entry barriers matter, because app-based studies routinely lose a large share of their participants.12
Login Keycloak SSO
Sign-in and digital consent run through Keycloak. Participants, researchers and admins are cleanly separated, and no one sees more data than their role allows. This is the basis for a clean ethics approval.
API Node service
Only after consent does a central service receive the data, validate it and enforce your study logic. Your study rules live in a single place instead of being scattered across many devices.
Database MongoDB, Neo4j
MongoDB stores the entries, Neo4j the relationship graph between habits. That split makes both classic statistics and network analysis possible, with no compromise.
At the end of the chain, everything runs behind a reverse proxy with TLS on a TU Dresden server. The data never leaves university infrastructure, and you stay independent of commercial providers.
Everything a study needs.
Studies and cohorts
Create studies, split participants into cohorts and steer interventions, with no detours.
Validated questionnaires
Established instruments like the Self-Report Habit Index, collected automatically across the study.5
Knowledge base and recommendations
A shared knowledge base feeds the recommendations participants receive in the app.67
Clean data export
Structured exports for your analysis in R, Python or SPSS, with no direct database access.
Real-time monitoring
Keep operational health and metrics in view via Grafana while the study runs.
Roles and permissions
A clean split between admin, researchers and participants via Keycloak, secured with single sign-on.
Sources
- 1Eysenbach, G. (2005). The law of attrition. Journal of Medical Internet Research, 7(1), Article e11. https://doi.org/10.2196/jmir.7.1.e11
- 2Meyerowitz-Katz, G., Ravi, S., Arnolda, L., Feng, X., Maberly, G., & Astell-Burt, T. (2020). Rates of attrition and dropout in app-based interventions for chronic disease: Systematic review and meta-analysis. Journal of Medical Internet Research, 22(9), Article e20283. https://doi.org/10.2196/20283
- 3De Croon, R., Van Houdt, L., Htun, N. N., Štiglic, G., Vanden Abeele, V., & Verbert, K. (2021). Health recommender systems: Systematic review. Journal of Medical Internet Research, 23(6), Article e18035. https://doi.org/10.2196/18035
- 4Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., Merchant, G. C., Naughton, F., & Blandford, A. (2016). Understanding and promoting effective engagement with digital behavior change interventions. American Journal of Preventive Medicine, 51(5), 833–842. https://doi.org/10.1016/j.amepre.2016.06.015
- 5Verplanken, B., & Orbell, S. (2003). Reflections on past behavior: A self-report index of habit strength. Journal of Applied Social Psychology, 33(6), 1313–1330. https://doi.org/10.1111/j.1559-1816.2003.tb01951.x
- 6Michie, S., West, R., Finnerty, A. N., Norris, E., Wright, A. J., Marques, M. M., Johnston, M., Kelly, M. P., Thomas, J., & Hastings, J. (2021). Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology (Version 2). Wellcome Open Research, 5, Article 123. https://doi.org/10.12688/wellcomeopenres.15902.2
- 7Reinsch, F., Weimann, T., & Stark, J. (2024). Tailoring health: Contextual variables in health recommender systems. In Proceedings of the 6th International Workshop on Health Recommender Systems (HealthRecSys 2024) (CEUR Workshop Proceedings, Vol. 3823, pp. 2–15). CEUR-WS.org. https://ceur-ws.org/Vol-3823/1_Reinsch_tailoring_159.pdf
Anonymous accounts, secured with a recovery phrase like a crypto wallet.
The Health Habit Hub collects behaviour data, not real names. Accounts are anonymous: no email, no name, no phone number, just a random identifier. Access is secured with a 24-word recovery phrase, modelled on a crypto wallet. Sign-in, role separation and transport use established standards, and everything runs GDPR-compliant on TU Dresden servers.
- HabitEvening walk
- Contextafter dinner
- Locationat home
- Emotional statebalanced
The recovery phrase, just like a crypto wallet
On first launch the app generates a 24-word phrase in BIP39 format, exactly like a crypto wallet’s seed phrase. It encodes the anonymous account’s credentials and is shown only once. On the device, the sign-in tokens live in the secure key store (Keychain or Keystore). If the phrase is lost, no one can recover the account, not even us.
Anonymous account
A new account is created with no name, email or phone number, just a random identifier. Behaviour data hangs off that identifier, not off a person.
Want to run a study?
Whether it is your own study, a collaboration, or just a question, send us a few lines and we will get back to you.