Archetypes turn weeks of health and lifestyle signals into stable, human-readable labels for segmentation and personalization. Learn how Sahha archetypes work, the science behind the behaviors they represent, and how to use them in your product.
Archetypes convert weeks of health, lifestyle, and behavioral data into human-readable labels (e.g., night_owl, short_sleeper, highly_active). Unlike daily scores, archetypes are designed to be stable traits you can use for segmentation, targeting, personalization, and analytics — without overreacting to one-off days like travel, illness, or deadlines.
Key Takeaways
- What they are: long-term behavioral labels derived from weeks of data.
- Why they matter: archetypes are stable, segmentable, and easy to use in product logic.
- What makes them credible: archetypes summarize validated behavioral dimensions (sleep, activity, sedentary behavior, recovery patterns) that have strong scientific links to health and wellbeing.
- Best practice: treat archetypes as “profile traits,” and use daily scores/biomarkers for real-time nudges.
Metric Spec
| Item | Value |
|---|---|
| Output type | Archetype assignment (label + metadata) |
| Typical cadence | Weekly and monthly refresh (some schemas may support longer cycles) |
| Best used for | Segmentation, targeting, personalization, analytics/BI |
| Data requirements | Varies by archetype; most are smartphone compatible |
| Delivery methods | API (pull) or Webhooks (push) |
| Retroactive support | New integrations can receive recent historical archetypes depending on availability |
How Archetypes Work
Sahha analyzes weeks of data and assigns users to behavioral categories. Compared to day-level metrics, this “smoothing” reveals stable patterns and reduces noise.
There are two archetype types:
Ordinal archetypes (ranked progression)
Ordinal archetypes represent a ranked scale where categories move from lower → higher states in a meaningful order.
Example concept:
sleep_duration:very_short_sleeper→short_sleeper→average_sleeper→long_sleeper
These are useful for:
- cohort comparisons
- “improving vs declining” narratives
- simple threshold-based journeys
Categorical archetypes (distinct groups)
Categorical archetypes group users into distinct categories without implying “better” or “worse”.
Examples in Sahha include:
primary_exercise_type(e.g.,strength_oriented,cardio_oriented)sleep_pattern(distinct timing/consistency patterns)
These are useful for:
- content routing
- preferences and persona-style segmentation
- personalization that shouldn’t be framed as a performance ranking
The Science Behind Archetypes
Archetypes are credible when they represent constructs that are (1) measurable from passive data, (2) behaviorally meaningful over time, and (3) supported by evidence linking them to outcomes.
Sahha archetypes draw their meaning from the same evidence-backed dimensions used in Sahha’s score models:
1) Sleep archetypes map to validated sleep health dimensions
Modern sleep science treats sleep health as multi-dimensional. Beyond duration, dimensions like regularity, continuity, circadian alignment, sleep debt, and restorative stages each independently relate to health and functioning. Sahha’s Sleep Score science outlines these dimensions and why they matter, and archetypes summarize these same dimensions as stable patterns over weeks.
How this builds credibility: archetypes like sleep_duration, sleep_regularity, sleep_quality, bed_schedule, and wake_schedule are long-term labels for sleep timing and consistency — the same dimensions used in the Sleep Score scientific model.
2) Activity archetypes map to validated dimensions of physical activity behavior
Physical activity cannot be captured by a single metric like step count. Evidence supports multiple independent dimensions: volume, intensity, frequency, energy expenditure, and sedentary behavior. Sahha’s Activity Score science lays out this multi-dimensional model and its links to outcomes.
How this builds credibility: archetypes like activity_level and exercise_frequency summarize movement behavior over time using these validated dimensions.
3) Mental and overall wellness archetypes reflect behavioral signals linked to wellbeing trends
Behavioral patterns in sleep regularity, circadian alignment, activity, and sedentary behavior are each associated with emotional balance and resilience. Sahha’s Mental Wellbeing Score science describes a model built from these behavioral dimensions (steps, active hours, extended inactivity, activity regularity, sleep regularity, circadian alignment). Archetypes provide stable “trait-like” versions of these behavioral patterns for segmentation.
Similarly, Sahha’s Wellbeing Score science describes a holistic approach combining sleep and activity factors. Archetypes like overall_wellness provide a stable label aligned to holistic, multi-factor wellbeing.
4) Exercise preference archetypes have direct internal validation for recommendation use
Sahha archetypes include primary_exercise, secondary_exercise, and primary_exercise_type. Sahha’s research on archetype-based class matching shows statistically significant evidence that a user’s preferred sports (captured in archetypes) can help predict other sports they may engage with — supporting recommendation experiences built on these archetypes.
5) Scientific references for compliance and transparency
Sahha provides dedicated scientific reference pages for scores (often used for app store submission transparency). These are useful to link in your product for credibility when you explain “why” health signals matter.
Science Links by Archetype Family
| Archetype family | What it summarizes | Closest Sahha science references |
|---|---|---|
| Activity | Volume, frequency, intensity, energy expenditure, sedentary behavior | Activity Score science; Activity Score scientific reference |
| Sleep | Duration, timing, regularity, quality/continuity patterns | Sleep Score science; Sleep Score scientific reference |
| Mental wellness | Behavioral signals linked to emotional balance/resilience (sleep + activity rhythms) | Mental Wellbeing Score science |
| Overall wellness | Combined sleep + activity lifestyle patterns | Wellbeing Score science |
| Exercise preference | Stable preference signals from exercise logs | Archetype-based class recommendation research; Exercise types taxonomy |
List of Archetypes
Below is the current set of archetypes and their possible values.
| Archetype | Type | Possible Values | Periodicity | Description | Requires Wearable |
|---|---|---|---|---|---|
activity_level | Ordinal | sedentary, lightly_active, moderately_active, highly_active | Weekly, Monthly | Overall level of physical activity including movement and exercise | No |
exercise_frequency | Ordinal | rare_exerciser, occasional_exerciser, regular_exerciser, frequent_exerciser | Weekly, Monthly | How often the individual exercises | No |
mental_wellness | Ordinal | poor_mental_wellness, fair_mental_wellness, good_mental_wellness, optimal_mental_wellness | Weekly, Monthly | Mental wellness and resiliency based on physical activity, sleep, and stress indicators | No |
overall_wellness | Ordinal | poor_wellness, fair_wellness, good_wellness, optimal_wellness | Weekly, Monthly | Overall wellbeing across all aspects of health | No |
primary_exercise | Categorical | Most frequent exercise (e.g., running, weightlifting, yoga) | Weekly, Monthly | Most commonly performed exercise | No |
primary_exercise_type | Categorical | strength_oriented, cardio_oriented, mind_body_oriented, hybrid_oriented, sport_oriented, outdoor_oriented | Weekly, Monthly | Categorizes the primary exercise into strength, cardio, sports, etc. | No |
secondary_exercise | Categorical | Second most frequent exercise (e.g., swimming, cycling, hiking) | Weekly, Monthly | Second most commonly performed exercise | No |
sleep_duration | Ordinal | very_short_sleeper, short_sleeper, average_sleeper, long_sleeper | Weekly, Monthly | Typical sleep duration relative to recommended norms | No |
sleep_efficiency | Ordinal | highly_inefficient_sleeper, inefficient_sleeper, efficient_sleeper, highly_efficient_sleeper | Weekly, Monthly | How effectively the individual maintains uninterrupted sleep | Yes |
sleep_pattern | Categorical | consistent_early_riser, inconsistent_early_riser, consistent_late_sleeper, inconsistent_late_sleeper, early_morning_sleeper, chronic_short_sleeper, inconsistent_short_sleeper | Weekly, Monthly | Overall sleep behavior based on timing and consistency | No |
sleep_quality | Ordinal | poor_sleep_quality, fair_sleep_quality, good_sleep_quality, optimal_sleep_quality | Weekly, Monthly | Long-term quality of sleep based on duration, regularity, recovery, and debt | No |
sleep_regularity | Ordinal | highly_irregular_sleeper, irregular_sleeper, regular_sleeper, highly_regular_sleeper | Weekly, Monthly | Consistency in sleep timings | No |
bed_schedule | Ordinal | very_early_sleeper, early_sleeper, late_sleeper, very_late_sleeper | Weekly, Monthly | Typical bedtime | No |
wake_schedule | Ordinal | very_early_riser, early_riser, late_riser, very_late_riser | Weekly, Monthly | Typical wake-up time | No |