Quick context: I'm an actuary/pension-modelling background, founder of Life & Health Metrics. We've built a model that estimates biological age and aging speed from passive smartphone step data (Apple Health / Google Fit) — validated against epigenetic clocks and blood-test panels, methodology published in Lancet Healthy Longevity. The idea I want to pressure-test with this group: two clients, same chronological age, can have very different retirement horizons — one biologically 60, the other 70. That gap is measurable and trackable over time, including inflection points when a lifestyle change shifts the trajectory. Where I think it could fit into your workflow: as an additional, individually data-driven input alongside the standard longevity-risk conversation — not a replacement for population-based tables, but a personalization layer on top of them. A few specific questions I keep getting split answers on from advisors I've already talked to, and would love more perspectives on: - Does showing a client their real BioAge (say, 10 years older than chronological) increase stress short-term but build trust long-term — or is it just an unwelcome number with no behavioral payoff? - Does this kind of signal strengthen fiduciary defensibility (documented, personalized diligence), or is it "algorithmic speculation" that doesn't hold up as a basis for advice? - Where would it actually change a recommendation — spending rate, LTC timing, annuitization — versus just being an interesting chart the client never revisits? We're in closed beta (Android/iOS) with real user data flowing. I'd be glad to give access to anyone in the group who wants to try it hands-on and tell me honestly whether the output is useful or noise — no commitment, just candid feedback. And if it turns out to be genuinely useful, I'm open to talking about a pilot with integration into your existing tools. Happy to answer questions in the comments, or reach out directly: 📧
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