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against.

Methodology

What we measure

Three numbers: current streak, longest streak, total relapses to date. A calendar view of when relapses occurred. Optional encrypted notes per relapse. That’s it.

Why these and not more

Self-monitoring works when it’s frictionless. Tools that ask for trigger taxonomies, mood scores, and journaling fields every time tend to be abandoned within weeks (Eysenbach, 2005; Krebs & Duncan, 2015). We tested heavier schemas in early prototypes and saw what every other app sees: 80%+ drop-off by week three (Meyerowitz-Katz et al., 2020). Single-action logging is what survives.

Why streaks

Streak feedback reinforces target behaviors by combining behavioral repetition with contextual cues and a growing sense of accomplishment. Those are the core mechanisms of habit formation identified in the literature (Lally et al., 2010; Curran et al., 2024). Self-monitoring itself is a well-established active ingredient in behavior change interventions across multiple domains (Burke et al., 2011; Michie et al., 2011). We surface streaks because they’re useful, not because we want to gamify the work. There are no rewards for hitting milestones, no shareable badges, no leaderboards. Just the number.

What we do not claim

We make no claim that against. cures, treats, or substitutes for clinical care. The app is a self-monitoring adjunct. People with significant distress, comorbid mental health concerns, or relational impact should work with a qualified clinician. The app is one tool, not a treatment plan.

How we handle data

All sensitive data (notes, trigger annotations) is encrypted on-device with AES (CBC mode, 256-bit key). The key is generated on first run and stored in the operating system’s secure enclave (Keychain on iOS, EncryptedSharedPreferences on Android). The only “remote” service is Firebase Cloud Messaging for optional notifications, which never carries user data. See the Privacy & Security page for the full technical detail.

Updates and corrections

We update this page when our methodology changes. A revision log lives at the bottom of the page.

Last reviewed: 2026-05-07. Methodology version 1.0.

References

  • Burke, L. E., Wang, J., & Sevick, M. A. (2011). Self-monitoring in weight loss: A systematic review of the literature. Journal of the American Dietetic Association, 111(1), 92-102. https://doi.org/10.1016/j.jada.2010.10.008
  • Curran, M., Larade, N., Özakinci, G., Tymowski-Gionet, G., & Dombrowski, S. U. (2024). Look, over there! A streaker! — Qualitative study examining streaking as a behaviour change technique for habit formation in recreational runners. Health Psychology and Behavioral Medicine, 12(1). https://doi.org/10.1080/21642850.2024.2416505
  • Eysenbach, G. (2005). The law of attrition. Journal of Medical Internet Research, 7(1), e11. https://doi.org/10.2196/jmir.7.1.e11
  • Krebs, P., & Duncan, D. T. (2015). Health app use among US mobile phone owners: A national survey. JMIR mHealth and uHealth, 3(4), e101. https://doi.org/10.2196/mhealth.4924
  • Lally, P., van Jaarsveld, C. H. M., Potts, H. W. W., & Wardle, J. (2010). How are habits formed: Modelling habit formation in the real world. European Journal of Social Psychology, 40(6), 998-1009. https://doi.org/10.1002/ejsp.674
  • Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6, 42. https://doi.org/10.1186/1748-5908-6-42
  • Meyerowitz-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), e20283. https://doi.org/10.2196/20283

Background reading: What the science says about habit-tracking and recovery.