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The research behind Tabula

"Kind by design: the research behind Tabula"

By Alvina · Last updated · 7 min read

In this era of constant connectivity, a great deal of daily life is already logged somewhere: steps, spending, sleep, mood. What is less clear is whether logging helps, and for whom. I'm Alvina, a psychology PhD student, and I built Tabula because every tracker I tried either shamed me for a missed day or sent my data somewhere I could not see. Self-tracking, sometimes called personal informatics, is the practice of collecting data about yourself in order to understand yourself better. This page sets out what the research suggests about it at a broad level, and how those findings shaped the app. Each section links to a longer post with the sources.

Key takeaways

What is the main finding about tracking apps?

Tracking appears to work as a tool for insight, not as a treatment. A 2026 review found that mood monitoring on its own had no robust effect in bipolar disorder and only a small, borderline effect on depression at 12 months (Astill Wright et al., 2026).

That review, which is the subject of a longer post on whether mood tracking actually helps, is consistent with the wider literature on mental-health apps. A meta-analysis of 66 randomized trials found that app-supported smartphone interventions outperformed control conditions for depressive symptoms (g = 0.28) and anxiety symptoms (g = 0.30), with larger effects for apps that delivered cognitive behavioural therapy and offered guidance and reminders (Linardon et al., 2019). Those were interventions that taught skills, not diaries, and the authors noted that apps are not intended to replace professional services (Linardon et al., 2019). A meta-analysis of 92 trials also reported a pooled effect of g = 0.43 for mental-health apps (Valentine et al., 2025). As such, the sensible reading is that a tracker may help you notice patterns, and small optional skills next to the data may add to that, but logging itself should not be sold as a cure.

What did the research change in Tabula?

Five findings shaped the app, and each has its own post with the sources.

The last of these is easy to underrate. Privacy concerns are one of six categories of reasons people give for abandoning lifestyle and mental-health apps (Kidman et al., 2024), and an assessment of 36 depression and smoking-cessation apps detected data transmission to third parties in 33 of them (Huckvale et al., 2019). That is why Tabula keeps your journal on your device.

Do streaks and points help?

The evidence does not suggest that they do. A review of 38 studies found that gamification made no difference to how well depression apps worked or how long people used them (Six et al., 2021).

The same pattern appears in a 2025 meta-analysis of 92 randomized trials, which found no significant link between the number of persuasive design principles and either efficacy or engagement (Valentine et al., 2025). A qualitative study of a popular habit app found that reminders and streaks support repetition of a new behaviour but also create a dependency, so that people often drop the behaviour once they drop the app (Renfree et al., 2016). Lapsing is also common in any case: a median of about 70% of users stop using lifestyle and mental-health apps within the first 100 days (Kidman et al., 2024). A design that treats every gap as a failure may be working against how people actually behave. The longer post on streaks includes the parts of the evidence that cut in favour of them.

How much logging is too much?

Probably less than most apps ask for. Researchers who study quantified-self enthusiasts listed tracking too many things as a common pitfall (Choe et al., 2014), and low-burden logging can support awareness at the right moments (Barker-Canler et al., 2024).

Those findings sit within a longer line of work. An early stage-based model of personal informatics described preparation, collection, integration, reflection and action, and found that barriers in one stage cascade into later stages (Li et al., 2010). A later lived informatics model proposed that lapsing and resuming is a normal part of tracking rather than a failure of it (Epstein et al., 2015). A field trial of a deliberately minimal, flexible app suggested that low-burden logging of self-defined data can support self-awareness, reflection and regulation at opportune moments (Barker-Canler et al., 2024). Together these suggest that burden is a design problem, not a character flaw, which is the argument of the post on keeping a tracker simple enough to stick with.

Can tracking itself become a burden?

Yes, for some people. In interviews with 22 patients living with multiple chronic conditions, tracking health data felt like work, and the data provoked strong positive and negative emotions and value judgments (Ancker et al., 2015).

Self-tracking may also turn into rumination, where reflection becomes anxious, repetitive thought about the negative parts of the self (Eikey et al., 2021). This is one reason Tabula's wording is kind and neutral, and its charts are optional. The research on words points the other way for a different reason: putting a feeling into words, called affect labelling, has been described as a form of implicit emotion regulation (Torre & Lieberman, 2018). That is why Tabula's check-ins use words like heavy, steady and bright. The post on naming a feeling covers the caveats, including that labelling does not help every feeling or every person.

How does my own research shape it?

My research on parasocial relationships, wisdom and political bias in language models informs each design choice, mostly by telling me what to leave out.

My research is on political parasocial relationships - what are known as one-sided emotional bonds with public figures (Horton & Wohl, 1956) - on wisdom, and on political bias in large language models. Parasocial research is about why people bond with media, so Tabula has no fake-friend voice and no guilt. Wisdom research is about reflection, so the app favours looking back kindly over scoring yourself. Work on bias in LLMs is why Tabula's AI runs on your device, shows what it understood before anything is logged, and does not advise or diagnose.

What is missing from the evidence?

Quite a lot. Most studies concern mental-health and tracking apps in general, rather than Tabula itself.

Effects are often small, and some of the evidence, especially for AI and voice, is still thin. Many of the usability findings come from interviews and small field studies, such as 22 patients (Ancker et al., 2015) or recordings of talks by enthusiasts (Choe et al., 2014), which may not generalize to everyone. Few trials isolate one design feature, such as a streak, from the rest of an app, so the strongest honest claim is often that the research gives no clear reason to add a feature, not that it proves harm. I will say "studies suggest" rather than "proves", and I will update these pages as the literature does.

What does Tabula do about it?

Tabula is built around these findings without claiming more than they support. It has no streaks, forgives missed days, and keeps logging to one tap.

Check-ins use emotion words, with the number optional. Your journal and Tabula's AI features stay on your device by default. Tabula is a private everything-tracker, not a medical tool or a treatment, and it does not claim that using it will change how you feel. If something feels heavy, a person or a professional is the right place for it. Thank you for reading this far; the five linked posts have the details and the sources.

References

Ancker, J. S., Witteman, H. O., Hafeez, B., Provencher, T., Van de Graaf, M., & Wei, E. (2015). “You get reminded you’re a sick person”: Personal data tracking and patients with multiple chronic conditions. Journal of Medical Internet Research, 17(8), Article e202. https://doi.org/10.2196/jmir.4209

Astill Wright, L., Shajan, G., Purewal, D., Stone, J., Majid, M., Guo, B., & Morriss, R. (2026). Mood monitoring, mood tracking, and ambulatory assessment interventions in depression and bipolar disorder: Systematic review and meta-analysis of randomized controlled trials. JMIR Mental Health, 13, Article e84020. https://doi.org/10.2196/84020

Barker-Canler, M., Gooch, D., Van Der Linden, J., & Petre, M. (2024). Flexible minimalist self-tracking to support individual reflection. ACM Transactions on Computer-Human Interaction, 31(3), 1–35. https://doi.org/10.1145/3660339

Choe, E. K., Lee, N. B., Lee, B., Pratt, W., & Kientz, J. A. (2014). Understanding quantified-selfers’ practices in collecting and exploring personal data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1143–1152). ACM. https://doi.org/10.1145/2556288.2557372

Eikey, E. V., Caldeira, C. M., Figueiredo, M. C., Chen, Y., Borelli, J. L., Mazmanian, M., & Zheng, K. (2021). Beyond self-reflection: Introducing the concept of rumination in personal informatics. Personal and Ubiquitous Computing, 25(3), 601–616. https://doi.org/10.1007/s00779-021-01573-w

Epstein, D. A., Ping, A., Fogarty, J., & Munson, S. A. (2015). A lived informatics model of personal informatics. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 731–742). ACM. https://doi.org/10.1145/2750858.2804250

Horton, D., & Wohl, R. R. (1956). Mass communication and para-social interaction: Observations on intimacy at a distance. Psychiatry, 19(3), 215–229. https://doi.org/10.1080/00332747.1956.11023049

Huckvale, K., Torous, J., & Larsen, M. E. (2019). Assessment of the data sharing and privacy practices of smartphone apps for depression and smoking cessation. JAMA Network Open, 2(4), Article e192542. https://doi.org/10.1001/jamanetworkopen.2019.2542

Kidman, P. G., Curtis, R. G., Watson, A., & Maher, C. A. (2024). When and why adults abandon lifestyle behavior and mental health mobile apps: Scoping review. Journal of Medical Internet Research, 26, Article e56897. https://doi.org/10.2196/56897

Li, I., Dey, A., & Forlizzi, J. (2010). A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 557–566). ACM. https://doi.org/10.1145/1753326.1753409

Linardon, J., Cuijpers, P., Carlbring, P., Messer, M., & Fuller-Tyszkiewicz, M. (2019). The efficacy of app-supported smartphone interventions for mental health problems: A meta-analysis of randomized controlled trials. World Psychiatry, 18(3), 325–336. https://doi.org/10.1002/wps.20673

Renfree, I., Harrison, D., Marshall, P., Stawarz, K., & Cox, A. (2016). Don't kick the habit: The role of dependency in habit formation apps. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2932–2939). ACM. https://doi.org/10.1145/2851581.2892495

Six, S. G., Byrne, K. A., Tibbett, T. P., & Pericot-Valverde, I. (2021). Examining the effectiveness of gamification in mental health apps for depression: Systematic review and meta-analysis. JMIR Mental Health, 8(11), Article e32199. https://doi.org/10.2196/32199

Torre, J. B., & Lieberman, M. D. (2018). Putting feelings into words: Affect labeling as implicit emotion regulation. Emotion Review, 10(2), 116–124. https://doi.org/10.1177/1754073917742706

Valentine, L., Hinton, J. D. X., Bajaj, K., Boyd, L., O’Sullivan, S., Sorenson, R. P., Bell, I. H., Vega, M. S., Liu, P., Peters, W., Mangelsdorf, S. N., Wren, T. W., Moller, C., Cross, S., McEnery, C., Bendall, S., Nicholas, J., & Alvarez-Jimenez, M. (2025). A meta-analysis of persuasive design, engagement, and efficacy in 92 RCTs of mental health apps. npj Digital Medicine, 8(1), Article 229. https://doi.org/10.1038/s41746-025-01567-5