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Where does your tracking data go?

By Alvina · Last updated · 6 min read

A tracker holds some of the most personal data a person produces: when they slept badly, how they felt, what they took, who they saw. In the era of free apps and constant connectivity, it is easy to forget that every one of those entries has to live somewhere, and that "somewhere" is often a server owned by a company the person has never heard of. Whether that data stays with the person is not only an ethical question; the research suggests it also shapes whether people trust an app enough to keep using it. This post sets out what published studies say about privacy, trust and data sharing in health and mental-health apps, where the evidence runs out, and what Tabula does about it.

Key takeaways

Why does privacy matter for tracking apps?

Because the data is sensitive and the trust it requires is easily lost. Privacy concerns are one of six categories of reasons people give for abandoning health apps (Kidman et al., 2024), so a tracker people do not trust is a tracker people may stop using.

That review of lifestyle and mental-health apps also found that a median of about 70% of users stop within the first 100 days (Kidman et al., 2024). Privacy is only one reason among several, alongside poor user experience and changing needs, so it would be wrong to treat it as the whole story. A scoping review of 278 studies on trust in digital health reached a compatible picture: privacy was among the enablers of trust, and fear of data exploitation was among the impediments (Adjekum et al., 2018). Moreover, research on long-term mental-health self-tracking suggests that people want control over what is shared and with whom (Murnane et al., 2018).

Do health and mood apps share data with third parties?

Many appear to. In one assessment of 36 top-ranked depression and smoking-cessation apps, data transmission to a third party was detected in 33 of them, or 92% (Huckvale et al., 2019).

In that study, 81% of the apps sent data for advertising, marketing or analytics to just two companies, Google and Facebook, and the researchers highlighted that disclosures in privacy policies did not always match what the apps did (Huckvale et al., 2019). A traffic analysis of 24 medicines-related apps found that 79% shared user data, with 55 different entities receiving or processing it, two thirds of them providing services for collecting and analyzing user data such as analytics or advertising (Grundy et al., 2019). At a much larger scale, a study of nearly 21,000 health apps on Google Play found that 88% included code that could potentially collect user data, that most data collection and transmission involved external service providers, and that nearly a quarter of transmissions used insecure protocols (Tangari et al., 2021). Mental health is a context where stigma and the effects of inadvertent disclosure raise the stakes (Huckvale et al., 2019).

Do mental-health apps have clear privacy policies?

Often not. In a sample of mood-tracking and related mental-health apps, privacy policies could be accessed for only 18% of iOS apps and 4% of Android apps (Robillard et al., 2019).

Those policies were mostly written at a post-secondary reading level, and many said that information may be shared with third parties (Robillard et al., 2019). Other audits point the same way. Among 61 prominent mental-health apps, about 41% had no privacy policy at all, and many requested so-called dangerous device permissions (Parker et al., 2019). Among the 600 most commonly used health apps, only 30.5% had a policy, the average reading grade level was 16, and two thirds of policies did not specifically address the app itself (Sunyaev et al., 2015). In a heuristic evaluation of 64 popular self-tracking services, most failed to give users full access to their own data, did not obtain sufficient consent, or inadequately controlled disclosures to third parties, and apps collecting health data performed worse than other self-trackers (Hutton et al., 2018).

What do users say they worry about?

Most appear to have some concern. In a questionnaire and interview study of 117 people, most reported privacy concerns about health data in apps, and they wanted features such as user consent and access control (Zhou et al., 2019).

The same participants named a lack of security features as a barrier to adopting health apps, while strong but easy-to-use security and clear privacy policies might encourage use (Zhou et al., 2019). This is a modest sample, but it fits a wider pattern: users appear to ask for control and clarity rather than for perfection. Only 1.3% of user reviews of health apps raised privacy concerns (Tangari et al., 2021), which suggests that stated worry and visible complaint are not the same thing.

What about AI features?

The evidence is still thin. Early work on generative AI and personal health data points to opportunities for planning, tracking and reflecting, but accuracy and trust remain open questions (Chopra et al., 2025).

AI features raise a further privacy question, which is where the processing happens. If a person's entries are sent to a remote model, then they travel beyond the device, and the third-party patterns described above become relevant to an AI feature as well. As such, whether the AI runs locally is a design choice with real consequences for privacy, even though I have not found studies that test on-device processing against cloud processing in mood-tracking apps specifically.

What is missing from the evidence?

Quite a lot. Most of these studies are snapshots of app stores at one moment, and apps change.

Many of the audits are a few years old and analyze Android apps, and policy audits describe what a company says rather than what any individual user experienced. What people say they worry about may also differ from what they do, and the user studies are small. Importantly, I have not found trials showing that stronger privacy raises long-term use in mood-tracking apps, so the link between privacy and retention remains plausible rather than proven. The reasons for abandonment also overlap, and privacy is only one of them (Kidman et al., 2024). Finally, almost no research tests on-device storage or on-device AI against cloud alternatives in this setting.

What does Tabula do about it?

Your journal stays on your device and never reaches Tabula, and you do not need an account. Anything that would leave your device needs your explicit say-so first.

Tabula's AI features run on your device, on devices that support Apple Intelligence. Voice capture shows you what it understood before anything is logged, never invents things, and can always be undone. The oracles reflect; they never advise or diagnose. Tabula keeps a person in charge. It is a private everything-tracker, not a medical tool or a treatment. On-device also has limits: your data is only as safe as your device and your backups, and Tabula does not claim more than that. If privacy is the reason you have stopped using other trackers, I hope this one earns a second look, and I will keep updating this page as the research does.

References

Adjekum, A., Blasimme, A., & Vayena, E. (2018). Elements of trust in digital health systems: Scoping review. Journal of Medical Internet Research, 20(12), Article e11254. https://doi.org/10.2196/11254

Chopra, S., Juarez, K., Fogarty, J., & Munson, S. A. (2025). Engagements with generative AI and personal health informatics: Opportunities for planning, tracking, reflecting, and acting around personal health data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(3), 1–33. https://doi.org/10.1145/3749503

Grundy, Q., Chiu, K., Held, F., Continella, A., Bero, L., & Holz, R. (2019). Data sharing practices of medicines related apps and the mobile ecosystem: Traffic, content, and network analysis. BMJ, 364, Article l920. https://doi.org/10.1136/bmj.l920

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

Hutton, L., Price, B. A., Kelly, R., McCormick, C., Bandara, A. K., Hatzakis, T., Meadows, M., & Nuseibeh, B. (2018). Assessing the privacy of mHealth apps for self-tracking: Heuristic evaluation approach. JMIR mHealth and uHealth, 6(10), Article e185. https://doi.org/10.2196/mhealth.9217

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

Murnane, E. L., Walker, T. G., Tench, B., Voida, S., & Snyder, J. (2018). Personal informatics in interpersonal contexts: Towards the design of technology that supports the social ecologies of long-term mental health management. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–27. https://doi.org/10.1145/3274396

Parker, L., Halter, V., Karliychuk, T., & Grundy, Q. (2019). How private is your mental health app data? An empirical study of mental health app privacy policies and practices. International Journal of Law and Psychiatry, 64, 198–204. https://doi.org/10.1016/j.ijlp.2019.04.002

Robillard, J. M., Feng, T. L., Sporn, A. B., Lai, J.-A., Lo, C., Ta, M., & Nadler, R. (2019). Availability, readability, and content of privacy policies and terms of agreements of mental health apps. Internet Interventions, 17, Article 100243. https://doi.org/10.1016/j.invent.2019.100243

Sunyaev, A., Dehling, T., Taylor, P. L., & Mandl, K. D. (2015). Availability and quality of mobile health app privacy policies. Journal of the American Medical Informatics Association, 22(e1), e28–e33. https://doi.org/10.1136/amiajnl-2013-002605

Tangari, G., Ikram, M., Ijaz, K., Kaafar, M. A., & Berkovsky, S. (2021). Mobile health and privacy: Cross sectional study. BMJ, 373, Article n1248. https://doi.org/10.1136/bmj.n1248

Zhou, L., Bao, J., Watzlaf, V., & Parmanto, B. (2019). Barriers to and facilitators of the use of mobile health apps from a security perspective: Mixed-methods study. JMIR mHealth and uHealth, 7(4), Article e11223. https://doi.org/10.2196/11223