Effort as a Measure — Background & Scoping

Lumosity has a strong performance metric (LPI) but no good measure of effort — of how much training someone has actually put in. Our efficacy story is fundamentally about doing enough training, so a simple, cumulative measure of effort gives us three things: a way to create a sense of accomplishment and progress, a shared language for "how much is enough," and a milestone to work toward that's separate from squeezing out more LPI. That's the feature — an effort measure, running in parallel to LPI, that a user can watch grow and aim at a goal.

Goals — what we're trying to achieve

• Give users a parallel measure of training effort (distinct from LPI) that builds a sense of accomplishment, attachment to the product, and a reason to keep coming back.
• Create a shared language and milestones for "you've done enough / here's your progress" — something we can't say consistently today because we don't measure effort this way.
• Serve two kinds of subscribers: those on the LPI-maximization track who want fresh milestones once their score plateaus, and those who never came to chase LPI at all (the need our old courses / 30-day programs used to meet).
• Move more subscribers to the efficacy-linked amount of training — the point at which our research says Lumosity works.
• Lay down a foundational currency we can build on later (program structure, richer rewards, marketing) without having to walk anything back.

Who v1 is for

Primary audience: new subscribers. They have full product access, their engagement pattern isn't set yet, and a goal plus a bit of narrative genuinely shapes how they experience the product early. This is where getting the milestones and the "journey" right actually matters.

v1 scope — the thing to design and prototype

Keep it atomic:

• A visible, cumulative effort measure that ticks up as the user trains.
• Simple milestones along the way (e.g., 100 / 200 / 500 …) with a little delight at each.
• The measure is ~90%+ training. In v1 it's earned by training itself; later we can add a small slice from efficacy-supporting actions (completing an assessment, connecting health data, reading about sleep/stress). It never rewards growth actions (referrals, upgrades) or arbitrary engagement.

Explicitly out of scope for v1

• A full program / journey / branching path (no Duolingo-style pathing). We get most of the "program" benefit just by setting a clear goal and milestones — without building, or even calling it, a program.
• The free → paid conversion / marketing-funnel test (separate, later project).
• Leading with a marketing claim. The claim comes later, as an outcome — "people who reached the goal saw better outcomes" — once we have the data, and only within the Claims Guide.
• The post-goal maintenance system (maintenance dose, "brain grade," zones, rolling windows). Genuinely interesting; explicitly later.
• Any complex, multi-source points economy.
• A headline goal that represents "you've done enough," anchored to the amount of training tied to our efficacy evidence (≈ the Bright Mind dose).

The biggest open question: what's the unit?

This is the central thing we want prototyping to help resolve. Three candidates, each with real tradeoffs:

• Minutes — most intuitive, feels like a "real number," and matches how our papers and health positioning talk ("minutes of training"). Downsides: gameable (idling on screen), needs ceilings, and per our science it isn't a clean efficacy unit — a long game isn't necessarily more effective than a short one.
• Game plays — our current scientific default; all our dose analyses use cumulative game plays. Downsides: a less intuitive user-facing number, and long vs. short games differ.
• Abstract currency (lightbulbs) — most flexible for rewarding varied actions later. Downsides: risks feeling gimmicky and less credible than a "real" number.

Guiding principle: start concrete. We can always convert a concrete unit (minutes or game plays) into an abstract currency later; we can't easily walk back from abstract to concrete. Science may run an analysis on whether minutes or game plays better predicts improvement, which will inform the choice — but design should prototype all three so we can feel how each one reads and behaves.

Calibration to explore alongside the unit:

• Points per game — flat 1/game vs. 2/game; extra credit for longer games; a small bonus for a top-five or high score (pure effort vs. a little performance reward). The current prototype uses 2/game; historically it was 1 for a play, 2 for a top-five, 3 for a high score. Both are reference points, not decisions.
• Anti-gaming ceilings.
• Milestone values and the headline goal number (which shifts with the unit).
• How the measure lives next to LPI without competing with or confusing it.

Principles / guardrails

• Efficacy-tied. If we can't defend a reward as linked to our point of view on what drives improvement, it doesn't earn credit. This is the line that keeps us out of gimmick territory.
• Foundational and convertible. Pick something we can build on, not walk back.
• Simple first. Introduce the currency and milestones; resist the pull toward a complex system.
• Credible over clever. It should feel like a real measure of training, not arcade points.
• Marketing is downstream. The GTM claim is an outcome we earn later, not the v1 lead — and it must clear the Claims Guide when we get there.

How we'll know it's working

The success signal is moving more subscribers to the efficacy-linked thresholds we already believe matter, earlier in their journey — e.g., the training amount tied to efficacy (~900 minutes / game-play equivalent), 30 workouts, 50th-percentile LPI. Illustrative target: if ~50% of subscribers reach the training threshold today, can this push it toward ~65%? Secondary signals: milestone completion rates, engagement, and a sense of attachment to the product and the journey.

The prompt for design exploration

What would help most right now:

1. Prototype the counter + milestones across all three units (minutes / game plays / lightbulbs) so we can compare how each feels and reads side by side.
2. Design the moment a new subscriber first meets the measure — the "here's your goal, here's the journey" framing — without tipping into a full program.
3. Show the earning moment (finish a game → the measure goes up) and the milestone delight beats.
4. Explore how the effort measure coexists with LPI in the core product.
5. Play with calibration, milestone spacing, and the goal number.

Open provocations to hold while exploring: How much should this feel like a "program" versus just a number that grows? What makes the number feel credible rather than made-up? What's the single milestone that would make someone feel they've genuinely accomplished something?

Grounding facts (reference)

• Efficacy anchor (Bright Mind): the studied program was ~15–25 min/day, ≥5 days a week over 12 weeks — roughly 20 hours / ~588 game plays — and improved an overall measure of cognitive performance. This is our anchor for "how much is enough."
• Science note on units: our dose models treat cumulative game plays as the unit; minutes is useful shorthand but not a validated efficacy unit. Bob's analysis will sharpen this.
• Claims guardrail (for the later marketing stage): consumer claims are limited to approved improvement / dose-response language; the study's aging, brain-structure, and brain-age findings are off-limits for marketing copy.