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Do streaks help mental health apps?

By Alvina · Last updated · 5 min read

The streak is perhaps the most familiar feature in consumer apps: a counter that grows each day and resets to zero the moment you miss one. It is borrowed from games, where it is a reasonable way to hold attention, and it has spread to language lessons, fitness trackers and, more recently, apps for mood and mental health. What is less clear is whether a streak does anything useful for people trying to understand their own mood or habits, or whether it mostly turns an ordinary human lapse into a small failure. This post summarizes what the research suggests, including the parts that cut in favour of streaks.

Key takeaways

Do streaks make mental health apps work better?

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 kept using them (Six et al., 2021).

That review compared apps with more and fewer game-like features and found that the number of gamification elements did not predict changes in depressive symptoms or adherence. Both kinds of apps were associated with similar benefits. In other words, the useful ingredient in these apps appears to be the content, such as skills practice, rather than the points and counters wrapped around it. A 2025 meta-analysis of 92 randomized trials reached a similar conclusion about persuasive design more broadly, finding no significant link between the number of persuasive principles in an app and either efficacy or engagement (Valentine et al., 2025).

Is there evidence against streaks?

Some, although it is mixed. In one meta-analysis of depression and anxiety app trials, attrition was lower in trials that offered reminders, human contact and no gamification features (Liu et al., 2026).

Two further studies point to side effects. A CHI study found that streaks and reminders can build dependency in habit apps, with behaviour collapsing once the app is dropped (Renfree et al., 2016). Moreover, when GitHub removed its streak counter, researchers used the change as a natural experiment and concluded that streaks had been steering developers' behaviour in ways they did not intend (Moldon et al., 2021). Neither study is about mental health, so they are best read as cautionary signals rather than proof of harm.

Do streaks ever work?

Yes, in some settings. In seven consumer experiments, seeing an intact streak in a behavioural log made people more likely to repeat the behaviour than seeing a broken one (Silverman & Barasch, 2023).

The same paper is also a caution. The effect depended on how the log represented past behaviour rather than on what people had actually done, it was stronger when people blamed themselves for a break, and it weakened when they could repair a broken streak. This suggests that a streak works by making the streak itself a goal. For fitness or vocabulary practice that may be harmless. For someone logging mood on a low day, a counter that has just reset may be an unhelpful thing to see.

Why do people quit trackers anyway?

Lapsing appears to be normal. A scoping review found that a median of 70% of users stopped using lifestyle and mental health apps within the first 100 days (Kidman et al., 2024).

That review covered 18 studies and more than 500,000 participants, and the reasons for abandonment included poor user experience, privacy concerns and changing needs.

Use also tends to come in waves. In a year of data from about 41,000 users of a commercial walking app, people commonly took breaks and came back, which the authors described as a "multiple lives" pattern of streaks and breaks (Lau et al., 2022). If lapsing is the usual pattern, a design that treats every gap as a failure may be working against how people actually behave.

Does missing a day break a habit?

Probably not. In a 12-week study of 96 volunteers, missing one opportunity to perform a daily behaviour did not materially affect habit formation (Lally et al., 2010).

The same study found wide variation in how long habits took to settle, from 18 to 254 days. Automaticity grew gradually as the behaviour was repeated in a consistent context, and it did not reset after a single miss. This is hard to square with the all-or-nothing logic of a streak counter, which treats one missed day as though months of practice had vanished.

Why might guilt after a lapse backfire?

Self-criticism does not appear to be the best motivator. In four experiments, people who were encouraged to treat a failure with self-compassion showed more motivation to improve than people in comparison conditions, for instance by spending more time studying after an initial failure (Breines & Chen, 2012).

These were short experiments, not studies of apps, so the link to streaks is an inference. Still, they suggest that a gentler response to a missed day may be at least as motivating as a harsh one, and that the red "you broke your streak" message may be a poor trade.

What is missing from the evidence?

Quite a lot. Most trials test mental-health apps as a package, so streaks are rarely isolated from other features, and effects may vary by app, condition and person.

A systematic review of gamification in mental health apps found that applications were often not driven by health behaviour change theory, and that many researchers may treat gamification as a black box (Cheng et al., 2019). A separate review of health apps found wide variation in the behaviour change techniques they used, and no clear relationship between the number of techniques and user ratings (Edwards et al., 2016). Few studies randomize people to a streak versus no streak in a mood-tracking context, so the strongest claim available is that the research gives no clear reason to add the pressure. Tabula does not claim that streaks harm everyone.

What does Tabula do about it?

Tabula has no streak counter. A missed day is not shown in red, past days can be backfilled, and coming back is met with a welcome rather than a broken chain.

Progress is cumulative and does not reset. Where counts appear, they forgive, as in "3 of the last 7". Logging is minimal and one-tap, and mood uses emotion words rather than scores to chase. Data and the on-device AI stay on your phone. 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.

References

Breines, J. G., & Chen, S. (2012). Self-compassion increases self-improvement motivation. Personality and Social Psychology Bulletin, 38(9), 1133–1143. https://doi.org/10.1177/0146167212445599

Cheng, V. W. S., Davenport, T., Johnson, D., Vella, K., & Hickie, I. B. (2019). Gamification in apps and technologies for improving mental health and well-being: Systematic review. JMIR Mental Health, 6(6), Article e13717. https://doi.org/10.2196/13717

Edwards, E. A., Lumsden, J., Rivas, C., Steed, L., Edwards, L. A., Thiyagarajan, A., Sohanpal, R., Caton, H., Griffiths, C. J., Munafò, M. R., Taylor, S., & Walton, R. T. (2016). Gamification for health promotion: Systematic review of behaviour change techniques in smartphone apps. BMJ Open, 6(10), Article e012447. https://doi.org/10.1136/bmjopen-2016-012447

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

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

Lau, E. Y., Mitchell, M. S., & Faulkner, G. (2022). Long-term usage of a commercial mHealth app: A “multiple-lives” perspective. Frontiers in Public Health, 10, Article 914433. https://doi.org/10.3389/fpubh.2022.914433

Liu, C., Torous, J., Fuller-Tyszkiewicz, M., Messer, M., Anderson, C., Soliman, O. M., & Linardon, J. (2026). Uptake, adherence, and attrition in clinical trials of depression and anxiety apps: A systematic review and meta-analysis. JAMA Psychiatry, 83(1), 43–50. https://doi.org/10.1001/jamapsychiatry.2025.3439

Moldon, L., Strohmaier, M., & Wachs, J. (2021). How gamification affects software developers: Cautionary evidence from a natural experiment on GitHub. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) (pp. 549–561). IEEE. https://doi.org/10.1109/ICSE43902.2021.00058

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

Silverman, J., & Barasch, A. (2023). On or off track: How (broken) streaks affect consumer decisions. Journal of Consumer Research, 49(6), 1095–1117. https://doi.org/10.1093/jcr/ucac029

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

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