AI meditation usage report: data from StillMind users

Part of our research cluster on meditation data and behaviour. See the curated external research roundup for academic and clinical context, or our AI meditation guide for product-level overview.

AI-guided meditation is a new category. Five years ago it barely existed. Two years ago it was a curiosity. Today it’s a real and growing share of how people use meditation apps. And yet, nobody has published the data on what AI meditation users actually do. Not Calm. Not Headspace. Not the academic literature, which is still catching up to a category evolving faster than IRB approval cycles.

This is the first one. The report draws on anonymised, aggregated usage data from roughly 2,500 StillMind users. It covers how people pick guidance tones, how they choose between AI and self-guided practice, when they actually meditate vs when they say they will, and what they ask the AI to generate for them. It is descriptive, not prescriptive. It doesn’t make clinical claims. It does what AI meditation needs right now: it puts the actual usage patterns on the table so that anyone studying this space has a credible source to cite.

AI meditation produces patterns nobody has documented yet. Here are four of them, side by side. What users say vs what they do, across guidance, frequency, time of day, and tone.

Why we published this

AI meditation is new. The category is moving fast, the user base is growing, and no first-party data has been published on how people actually use it. Clinical journals measure whether meditation works under controlled conditions. Analyst decks measure what users will pay for. Neither tells you what someone does when they open an AI meditation app on a Tuesday morning.

This report is the missing piece. It’s a snapshot of how roughly 2,500 StillMind users behave across AI-guided practice, self-directed timer use, tone selection, timing, and the goals they brought with them. It’s the first descriptive map of a category that has, until now, been described only by marketing copy.

We’re publishing it for three audiences. Journalists covering AI meditation who need a credible data point to cite. Researchers shaping the next wave of meditation studies who want to know which questions are worth asking. And product teams building in this space who need to know what real usage looks like before assuming what it should look like. The numbers below are a starting reference. We expect to update them.

Key findings

Murphy, J., & StillMind. (2026). AI Meditation Usage Report. Retrieved May 19, 2026, from https://getstillmind.com/blog/ai-meditation-usage-report/

@techreport{stillmind2026aimeditation,
  author      = {Murphy, Jamie and {StillMind}},
  title       = {AI Meditation Usage Report},
  year        = {2026},
  institution = {StillMind},
  url         = {https://getstillmind.com/blog/ai-meditation-usage-report/}
}

Last updated: 19 May 2026.

How many meditation app users choose AI guidance vs self-guided practice?

Here’s the first piece of data nobody has published before. Inside a meditation app that offers both AI guidance and a silent timer, 24% of completed sessions are AI-guided and 76% are self-directed timer-based practice.

That’s the headline split. AI-guided practice is the fastest-growing share of what these users do, but the silent timer still dominates by a factor of three. Anyone assuming an AI meditation app’s users would mostly use the AI would be wrong. The timer is the workhorse. The AI is the specialist. Until now, nobody outside the apps themselves could say so.

Note one of the patterns this dataset surfaces. 75% of users, when asked during onboarding, say they prefer guided meditation or a mix of guided and self-directed. Only 25% say they want self-guided only. Yet completed sessions skew the other way, 76% self-directed. The gap between what AI meditation users say they want and what they actually do is wide, and it shows up again across this report.

For broader context on the meditation industry’s numbers (60.5 million Americans meditate, clinical trials, market sizing), see our external research roundup. This report sits alongside it as the first-party usage layer.

What self-directed meditators call their practice

Inside an AI meditation app, the people choosing silent practice can label what kind of meditation they’re doing. Until this report, nobody had published what those labels look like in the wild.

85% of self-directed practice is left unlabelled. The user opens a timer, picks a duration, and meditates. No category, no framework. Just time on the cushion.

The remaining 15% reaches for traditional labels. Yoga. Mantra. Body scan. Prayer. Chanting. Walking. Intention-setting. Thirteen distinct sub-types appear across the cohort, with the heads of the distribution being mantra and body scan, and a long tail of less common labels.

The timer experience is identical regardless of label. A 20-minute timer for body scan and a 20-minute timer for prayer do exactly the same thing technically. The label is doing work that the software isn’t.

What the label is doing is framing. The user is telling themselves what kind of practice this is, and that frame shapes what they pay attention to during the silence. It’s not the app’s job to dictate that. The 15% who label their practice are signalling something about their relationship to tradition. The 85% who don’t are saying meditation, for them, is whatever happens between start and stop.

Neither is more correct. They’re just two different ways the same person uses the same tool. The takeaway for anyone studying AI meditation: even when an app makes structured tradition labels easy, the dominant pattern is to ignore them.

Which AI meditation guidance tone do users prefer?

This is the kind of question AI meditation has never been able to answer with real numbers. Among users selecting a guidance tone, scientific (30%), balanced (30%), and spiritual (29%) cluster in a three-way tie. Compassionate (11%) is the clear outlier.

Each tone shapes how the AI talks to you during a session.

• Scientific uses the language of attention, nervous system, and evidence. Calm and technical.
• Balanced sits in the middle. Warm but not flowery. A trusted friend who’s also read the research.
• Spiritual draws on the contemplative traditions. Awareness, presence, the language of the inner life.
• Compassionate is soft and kind. The voice you’d want to hear when things are hard.

Three of those tones are roughly equally popular. The fourth is chosen by only 1 in 10 users. A product optimising purely on preference share would pour resources into the top three and quietly retire compassionate.

But the preference data tells a much less interesting story than the engagement data. Which brings us to the most novel finding in this report.

The compassionate-vs-spiritual completion gap

Here’s something nobody has documented before. Which AI meditation guidance tone produces the highest follow-through, and by how much.

Compassionate-tonal users complete sessions at 70%. Spiritual-tonal users complete at 32%. Same dataset, two tones, more than double the engagement.

Here’s the full breakdown of session completion rate by tone selection.

The smallest preference cohort has the highest engagement of any group. The “most traditionally meditation-coded” tone, the one that sounds most like meditation has historically sounded, has the lowest. That ordering shouldn’t be possible if preference and engagement were measuring the same thing. They aren’t.

Why does this happen? We don’t know yet. We’re going to investigate this further in future reports. For now, three possibilities, and a hard rule against claiming more.

One: the language compassionate users encounter during a session feels more relevant to why they came. If you arrive carrying difficulty, a voice that meets you with softness is more likely to keep you in the chair than a voice that asks you to observe the difficulty from a contemplative distance.

Two: spiritual-tonal expectations are harder for any AI to meet without dedicated training. Spiritual language carries dense connotations, and a phrase that sounds correct in one tradition can sound wrong in another. The bar is high. When it’s missed, the session ends.

Three: a user picking spiritual may already be over-indexed to a specific frame the practice doesn’t deliver. The label is doing some of the work the practice is supposed to do, and the gap between the two becomes the reason the session ends early.

None of those are proven. The gap is. The 70-vs-32 difference is the most useful finding in this report for anyone designing AI meditation experiences. The “obvious” choice for a meditation product (spiritual and contemplative) is the lowest-engagement option in the data. The unobvious one (compassionate, self-directed kindness) is the highest. This is the kind of pattern only first-party AI meditation data can surface.

When do meditation app users actually meditate? And when do they say they will?

Another first for the AI meditation category: published time-of-day distributions for actual practice vs stated intent.

Of completed sessions, 41% happen in the morning, 32% in the evening, 21% in the afternoon, and 6% at night. The morning bucket is the largest by a clear margin. The night bucket is tiny.

Now compare that to what users said during onboarding when asked when they plan to meditate.

Three things jump out.

First: more than half of users (52%) refuse to commit to a time of day during onboarding. The single largest intent cohort is the one that picks flexibility. By a wide margin.

Second: actual morning sessions (41%) almost double intended morning sessions (22%). The gap goes the other direction from what you’d expect. People who didn’t say they were morning meditators end up meditating in the morning anyway.

Third: bedtime is the only category where intent overshoots reality. 11% planned to meditate at bedtime. Only 6% actually do. The wind-down practice people imagine themselves having doesn’t quite materialise.

The “whenever” group deserves a closer look. They’re not random. When you split them by when they actually meditate, they cluster into the same morning-heavy, evening-second, afternoon-third rhythm as the rest of the cohort. The intention to be flexible doesn’t produce flexible behaviour. It produces average behaviour.

What this means for the AI meditation user who says “I’ll just fit it in”: in practice, they do what most users do. Mornings, mostly. Some evenings. The flexibility is mostly an unwillingness to commit at onboarding, not a different relationship to time. Stated preference doesn’t predict behaviour. The day’s rhythm does. This is the kind of pattern AI meditation has been operating on for years without anyone publishing the numbers.

How AI-guided meditators behave differently from self-guided meditators

Two cohorts, two signatures. This is the first published behavioural portrait of who uses AI guidance vs who uses silence inside the same app.

Completion rate. AI-guided sessions complete at 52%. Self-guided sessions complete at 63%. That’s counterintuitive. The “easier” option (the AI tells you what to do, no decision required) finishes less often than the “harder” one (you, a timer, and your own attention).

The explanation is probably about attentional load. A narrated session demands you follow along. If your attention drifts, you’ve drifted from something specific, and the gap between where the voice is and where you are can feel like a reason to stop. A silent timer lets you drift back. The mind wanders, returns, wanders again, and the practice continues uninterrupted. Silence forgives more than instruction does.

This doesn’t mean AI guidance is worse. It means it’s different. A 52% completion rate on a more demanding practice may produce more depth per session than a 63% completion rate on a less demanding one. We can’t measure that from completion data alone.

Journaling proximity. Roughly 2 in 3 (68%) StillMind users who complete a meditation journal within 30 minutes. The post-meditation window is a high-density period for reflection. People are open, attentive, and somatic awareness is up. The journal entries that follow tend to be longer and more specific than entries written cold.

A methodology caveat: this is a temporal proximity proxy. We measure whether a journal entry was created within 30 minutes of a completed session, not whether the entry was explicitly linked to that session by ID. Cleaner instrumentation is on the way, and the next report will be able to make stronger claims here.

Locale. Completed sessions span roughly 30 distinct locales. English variants (en-US, en-GB, en-AU, en-CA, and so on) account for roughly half of completed sessions. Non-English notable spots include Czech, Spanish, Catalan, German, and Brazilian Portuguese. Hungarian users are a smaller but committed cohort. We’re not breaking this down further by AI vs self-guided in this report, because the sub-cells are too small to publish reliably.

One-line characterisation of the two cohorts: AI-guided meditators are heavier engagers per session, with denser post-practice activity. Self-guided meditators are higher-completion, lower-orchestration. Both are valid practices. Different shapes. Anyone modelling AI meditation usage on assumptions about “guided meditation” generally has been missing this distinction.

Custom prompts vs personal presets vs public presets

The question of how AI meditation users actually start a session has, until now, been answered only by guesswork. Here’s the first published distribution. Among AI sessions with full instrumentation, 53% start from a custom prompt the user wrote, 26% from a personalised AI-generated preset, and 22% from a public preset.

More than half of AI-guided sessions begin with the user typing what they need. Not picking from a menu, not selecting a category, but actually composing a one-line description of the kind of practice they want.

That’s unusual for AI products. Most AI tools train users to lean on presets. ChatGPT has system prompts. DALL-E has style menus. Even the most flexible AI products tend to see most usage flow through a small number of common templates because typing is harder than tapping.

Meditators do the opposite. The custom prompt is the most common entry point. The public preset, which is the easiest option (one tap, no typing), is the least common.

Two ways to read this. One is that meditation is more specific than other AI use cases. The user knows roughly what kind of practice they need (something for after a hard meeting, something for falling asleep, something for a sore back), and the public preset library doesn’t quite match. So they describe what they actually want. The other is that meditators have higher willingness-to-invest at the entry point. They’ll spend 20 seconds typing for a 10-minute practice, because the practice itself is worth the setup.

Either way, the design implication is that the personalisation surface matters more than the preset library. The user who’s about to meditate is willing to tell you what they need. The product just has to ask.

The personalised AI-generated preset sits in the middle at 26%. These are presets the AI built for the specific user based on their patterns, then saved for reuse. The user composed once, then returned to a practice the AI remembered. That feels like the right ratio. Custom for the first time. Personal preset for the return. Public preset for the occasional explore. For anyone building or studying AI meditation, this is the shape of how people actually enter a session.

Why people meditate

Onboarding goals data for an AI meditation cohort hasn’t been published before this. When asked why they meditate, users most often select reducing stress, managing emotions, self-discovery, finding calm, and improving sleep. Each of these is chosen by more than half the cohort.

Note that goals is a multi-select field. Users can (and do) pick multiple reasons. The five above are the top five by share. They aren’t mutually exclusive, and most users pick three or four of them.

The pattern is consistent with what the broader meditation literature shows: stress and emotional regulation lead, sleep and self-discovery follow, and “physical health” reasons (lower blood pressure, manage chronic pain) sit lower down the list. People come to meditation primarily for what’s happening in their head, not what’s happening in their body. Even though the body is what the practice trains.

Now look at life circumstances. The top five are work stress, burnout, general wellness, major life change, and low self-esteem.

The interesting cross-finding here is that burnout is #2, just behind work stress. The recovery-from-collapse use case is bigger than people assume. It’s not just stressed-out professionals trying to take the edge off. A significant share of meditators are coming to the practice from a position of nervous-system exhaustion, looking for a way back. That changes what guidance should sound like. Someone in active burnout needs different language from someone managing day-to-day work stress, even if both might pick “work stress” as a top goal.

Major life change at #4 is the under-recognised category. Job change, relationship change, bereavement, parenthood, illness. The points in life when meditation becomes suddenly relevant tend to be the points where someone’s old coping strategies have stopped working. The data shows up at the time the rest of life cracks open. For AI meditation specifically, this means the entry-point user isn’t always casual. A meaningful share are arriving in crisis.

Experience level, practice frequency, and the daily-meditation paradox

Who is the AI meditation user? The assumption is “beginners trying meditation for the first time.” The data says otherwise. 41% identify as intermediate, 38% as beginner, and 22% as experienced. That distribution is more weighted toward intermediate than the conventional wisdom would predict. Most users arrive having already tried meditation, often through other apps, often on and off for years. The “beginner who has never meditated” is a smaller share than the “person who has meditated for years but never made it stick.”

If that’s you, the AI meditation for beginners guide is the right place to start.

On practice frequency, 64% say they want to meditate daily. 14% say a few times a week. 13% say when needed. 8% say when they feel like it.

Two-thirds of users want to meditate daily. But (callback to the timing data above) 52% won’t commit to a time of day to do it. The desire for consistency runs head-on into the reality that consistency requires constraints, and meditators are reluctant to set them. The user who wants to meditate daily is the same user who wants to keep their options open about when. Those two things fight each other.

This is the report’s most actionable finding for anyone trying to actually meditate daily, and a useful one for AI meditation product teams. The desire isn’t the limiting factor. The willingness to pin down a time is. The question isn’t “do I have the motivation” (you do). The question is “what time am I willing to commit to.” Momentum, in StillMind, is what tracks the build of that commitment over time.

Of users who engage with our ADHD focus mode, roughly 1 in 3 opt in. It’s a small but committed cohort. ADHD users get a more structured guidance density by default, with more frequent attentional cues, because that’s what the research and the user feedback both suggest works.

What we’re not measuring (yet)

A first report on a new category should be honest about what’s missing as well as what’s there.

Some features in the app are still early in adoption. Voice notes during meditation, where users speak a thought aloud during a practice and the app captures and transcribes it, is an emerging feature. We can see the shape of it but not the volume yet. Save-for-replay, where users save an AI-generated session to revisit later, is the same. Users are starting to save AI sessions to revisit, and the patterns are interesting, but the cohort sizes are below our reporting floor.

The journal-after-meditation rate (68%) is a temporal proximity proxy. We measure that a journal entry was created within 30 minutes of a completed meditation, not that the entry was explicitly tied to that session by ID. The next report will have cleaner session-linked journaling data, and the number may shift.

Preset adoption velocity, which tracks how quickly users return to personalised presets after first creating them, is excluded entirely from this report. The instrumentation is new, and the dataset isn’t yet stable enough to publish.

This is the first in an ongoing series of first-party reports on AI meditation. We’ll publish updates as the dataset matures and as new patterns emerge. AI meditation is moving faster than academic publishing cycles can track. The goal is to establish a stable, citable reference point now, when no one else has, and keep it current as the category grows.

Methodology

Why this matters. AI meditation is moving faster than academic research. By the time controlled trials catch up, the category will have shifted twice. First-party usage data from real users is the most current source available on what AI meditation actually looks like in practice. This methodology section exists so anyone citing the numbers above can see exactly what they cover and what they don’t.

About this report. Authored by Jamie Murphy, founder of StillMind, May 2026. This report draws on anonymised aggregated event analytics from the StillMind app. It covers tone preference, time-of-day patterns, session completion, prompt source distribution, onboarding goals, life circumstances, experience level, practice frequency, ADHD mode adoption, and journaling proximity. It doesn’t draw on individual-level data, demographic data beyond aggregate locale, or any data that could identify a user.

Sample. Roughly 2,500 StillMind users analysed. Active users only. Data covers recent operating history through May 2026. The sample is self-selected: people who downloaded the app, completed onboarding, and engaged with the practice. It isn’t a representative sample of the general meditation population, the general app-using population, or any clinical cohort.

Privacy and aggregation. No individual-level data was accessed for this report. All percentages are aggregate cohort percentages. The minimum cohort size for any reported percentage is 30 users. Sub-cells below that threshold were either dropped or summarised qualitatively. Locale data is published only in aggregate at the language level. GDPR legitimate-interest basis applies to the underlying analytics.

What this report is. A descriptive snapshot of a self-selected cohort of meditation app users. The first published first-party signal on how AI meditation is used in practice. A reference for journalists, researchers, publishers, and AI product teams that want a citable data point on a category nobody else has measured publicly.

What this report is not. A clinical efficacy claim. A population estimate. A predictor of behaviour in any other meditation app. A causal claim about why tone correlates with completion rate. We report associations and patterns. We don’t claim mechanisms.

Known limitations. Self-selection (users who downloaded and engaged with StillMind specifically). Early-stage sample (the app and its dataset are both still maturing). Journal proximity is a temporal proxy, not a session-linked measurement. Tone selection is at the user level (per preference), while session completion is at the session level (per practice), and the bridge between them assumes users with a stated preference are practicing in that tone, which is true for the great majority but not 100%.

License. This report is published under CC-BY-4.0. You’re welcome to cite, quote, and reproduce the data with attribution.

Cohort sizes appendix. All percentages in this report draw on the cohort of roughly 2,500 analysed users. Sub-cohort sizes (by tone preference, by experience level, by intended time of day) range from approximately 270 to 760 users depending on the slice, all above the 30-user minimum. Session-level percentages draw on the full session log for the analysed cohort. Specific N values are not published at the slice level to preserve the privacy posture of the dataset.

Citing this report? Use the APA or BibTeX citation block near the top. Published under CC BY 4.0; attribution to StillMind required.

For external context on meditation adoption, clinical evidence, and the broader AI therapy landscape, see our companion report on meditation statistics in 2026. For a product-level overview of how AI guidance works in practice, see our AI meditation guide.