Product & Stakeholder FAQ

Sahha helps you collect, analyze, and act on health & lifestyle data from smartphones and wearables—turning raw signals into product-ready outputs (Scores, Biomarkers, Insights, Archetypes) that power personalization and engagement.

Sahha is positioned as “Science-as-a-Service”: it doesn’t only ingest data, it converts it into behavioral intelligence (e.g., scores + explanations) so teams can ship personalization faster than building models and data pipelines in-house.

Most users don’t want a dashboard of numbers—they want a clear answer and a next step. Scores compress complexity into a simple signal and pair it with explainability factors so changes are understandable and actionable.

Sahha provides Scores (point-in-time state), Biomarkers (standardized metrics), Insights (trends + comparisons), Archetypes (long-term segmentation personas), plus Widgets (drop-in UI to ship faster).

Scores tell you where someone is today; Insights tell you what’s changing and how a user compares to baselines/peers; Archetypes tell you what kind of user they are over weeks/months; Biomarkers provide the detailed metrics layer underneath.

Lead with Widgets + Scores + Factors so users and stakeholders immediately see a clear signal and “why it changed” without building custom charts. Expand into Biomarkers/Insights once the narrative is established.

Lead with Archetypes + Insights: Archetypes define stable audience groups, while Insights support timing and messaging based on change (improving/declining) and context.

Lead with Biomarkers + Insights: Biomarkers give clean metrics for dashboards; Insights add interpretation (trend direction, comparisons) so reporting isn’t just charts—it’s meaning.

Insights turn point-in-time metrics into a story over time—surfacing change, consistency, progress, and context built on Scores, Factors, and Biomarkers.

They enable triggers based on patterns, not one-off readings—supporting progress narratives, smarter nudges, benchmarking, and early detection of meaningful change.

Insights are positioned as zero-setup: once data is flowing, Insights can be generated using existing Sahha data and delivered through the same access patterns (API/webhooks).

The core Insight types highlighted are Trends and Comparisons.

A Trend summarizes whether a metric is increasing, decreasing, or stable, turning granular data into a clear directional signal for product logic and user feedback loops.

Trends analyze the last 4 complete weeks on a rolling basis (currently not configurable).

Weeks with missing/no data are skipped, and at least two valid weeks are required to calculate a trend.

Comparisons contextualize a user’s daily scores/biomarkers against reference groups (e.g., global averages, demographic cohorts, and/or personal baselines) so “good/bad” has meaning.

They reduce confusion (“is this typical?”), support benchmarking and progress reporting, and enable targeted journeys when someone falls meaningfully below/above expected ranges.

Archetypes are human-readable personas that categorize users by long-term health/lifestyle patterns (e.g., chronotype, sleep quality, activity level) and are designed to be segmentation-ready for journeys and personalization.

They let teams segment by real behavior (not just demographics or self-report), enabling campaigns, content, and experiences that match how people actually live—and improving relevance and conversion.

Scores are numeric point-in-time values; Archetypes are category labels that smooth short-term noise to represent more stable patterns across weeks/months.

Archetypes refresh on weekly and monthly cycles to reflect change without being noisy.

Yes—Sahha notes you receive two weeks of historical archetypes immediately on integration, supporting faster onboarding and time-to-value.

Many Archetypes are smartphone compatible (wearables can enrich some patterns, but they’re not always required).

Two types are described: Ordinal (ranked progression, e.g., very_short → long sleeper) and Categorical (distinct groups, e.g., early bird vs night owl).

Common examples include: notification timing based on chronotype, challenges based on sleep consistency, or offers/content tailored to long-term activity patterns.

Archetypes enable cohort analysis: you can compare engagement/retention across behavior groups and track whether interventions shift users toward more favorable archetypes over time.

Scores are simple 0–1 measures of key health dimensions designed to be easy to interpret and use in product logic (e.g., Sleep, Activity, Mental Wellbeing, Readiness).

Sahha lists scores including Wellbeing, Sleep, Activity, Mental Wellbeing, and Readiness, each representing a different dimension of user state derived from behavioral/physiological inputs.

Scores convert complex sensor data into a single state signal you can use immediately for personalization (what to show today, when to message, which journey to trigger), rather than forcing product teams to interpret dozens of metrics.

Yes—Scores include explainability factors, helping users and teams understand the key drivers pushing a score up/down, improving trust and actionability.

“Health States” help interpret factors on levels like minimal / low / medium / high, reducing confusion about what “good” looks like and improving clarity in user experiences.

Scores are strongest for moment-to-moment decisions: adapting UX based on current state (timing, content, nudges, gating interventions), including “state-based UX” patterns.

Start with highly intuitive domains like Sleep or Activity, pair the score with factor explanations, and measure lift in engagement/retention vs a control experience.

No—Mental Wellbeing is positioned as informational, not diagnostic, and should be communicated as a wellness/behavioral signal (with appropriate responsible-language in UX).

Biomarkers are a standardized set of health metrics transformed from raw data into clean, consistent outputs across areas like activity, sleep, vitals, and body.

Scores are best for state. Biomarkers provide detail for dashboards, reporting, experiments, and richer personalization logic beyond a single number.

Instead of building your own cleaning/standardization layer, you can use Sahha’s biomarker outputs as canonical metrics aligned to a common dictionary—reducing analytics inconsistency across teams.

The biomarker catalog covers many metrics (with units, periodicity, aggregation, and wearable requirements) so teams can choose what they need for UX, reporting, or segmentation.

Biomarkers are ideal for detailed breakdowns, experiments, coach/admin dashboards, and recommendations that need more nuance than a single state score.

If you need fast user clarity, lead with Scores + Factors first; add Biomarkers once users understand the narrative and you need deeper detail.

Data Logs are the raw, unfiltered stream of health/activity data events—useful for audit/debugging, custom pipelines, or advanced analytics needs.

Typical reasons: (1) debugging permissions/ingestion, (2) building custom analytics/models beyond standard outputs, and/or (3) storing a full raw history for internal ML/BI workflows.

Biomarkers are standardized, processed metrics for product + BI; Data Logs are raw event-level data for maximum flexibility and auditability.

Data logs are delivered via webhooks and described as immediate delivery (not batched the same way as some processed outputs).

Treat logs as a backend stream: receive only what you need via webhook configuration, store/process in your pipeline, and use Biomarkers/Scores for most product experiences.

Use Widgets (drop-in WebView UI components like arcs/charts/factor lists) to accelerate MVPs and stakeholder demos while keeping presentation consistent and explainable.

The resources site includes demo/testing guidance to validate Scores, factors, and data streaming quickly—useful for internal buy-in before deeper product work.

Sahha can deliver data via API or webhooks, enabling you to pipe scores/insights/archetypes into your backend and trigger campaigns/experiences in your existing systems.

Yes—Sahha provides an Event Reference with schemas and example payloads for webhook events (scores, insights, archetypes, biomarkers, data logs).

No—Sahha is designed to work with phones and wearables. Wearables can enrich signals, but they’re not required for value.

Use staged UX: start with phone-only value, then offer wearables as an upgrade for richer factors. Widgets reduce early UX complexity while maintaining immediate usefulness through Scores.

No—scores should be communicated as wellness/behavioral signals, not clinical diagnosis. Avoid diagnostic language in UX and pair explanations with appropriate disclaimers.

Use the Science Behind the Scores series and the score-specific science pages for due diligence and trust-building.

Use “Scores Explained” for plain-English understanding and “Science Behind…” for deeper validation—this supports clarity without turning your UX into a science lecture.

If you present Garmin-derived data, you may need device attribution (e.g., “Garmin [device model]”) per Garmin branding enforcement guidance—build this into your UX where relevant.

Widgets help present scores/biomarkers/factors consistently; pairing them with “explained” resources reduces misinterpretation and improves user trust.

Sahha provides a Security FAQ covering privacy/security commitments, data handling, and governance topics commonly required for vendor review.

Sahha publishes an End User Privacy Policy describing how Sahha protects and uses data; customers should still maintain their own privacy policy for how their app uses the data.

Yes—Sahha provides an API Licence Agreement that defines rights and obligations for API usage.