Introduction
Turn complex health data into human-readable labels. Instead of numbers, get categories like "Night Owl", "Short Sleeper", or "Highly Active"—derived from weeks of behavioral data. Perfect for segmentation, personalization, and understanding your user base at a glance.
Key Features
Human-Readable
Complex data becomes intuitive labels like 'Night Owl' or 'Highly Active'
Segmentation-Ready
Group users by shared behaviors for targeted campaigns and offers
Personalization Fuel
Power recommendations and UX based on behavioral profiles
Adaptive
Updates weekly and monthly to reflect changing behavior
Retroactive
Get 2 weeks of historical archetypes immediately on integration
Smartphone Compatible
Most archetypes work without wearables—phone data is enough
How It Works
Sahha analyzes weeks of health data and assigns users to behavioral categories. Unlike daily scores, archetypes smooth out short-term fluctuations to reveal stable patterns. They're refreshed weekly and monthly. There are two types of archetypes:
Ordinal Archetypes (Ranked Progression)
Ordinal archetypes represent a ranked scale, where values move from lower to higher states in a meaningful order. Each level in the archetype reflects an increase in the measured behavior.
- Example:
sleep_durationvery_short_sleeper→short_sleeper→average_sleeper→long_sleeper- What it means: As users get more sleep, they progress toward higher sleep duration categories.
- Why This Matters:
- Enables comparisons between different user groups and population benchmarks.
- Helps identify trends and improvements over time.
Categorical Archetypes (Distinct Groups)
Categorical archetypes group users into distinct categories without any hierarchical ranking. Unlike ordinal archetypes, these categories are independent of each other and do not imply a progression.
- Example:
sleep_chronotype- Categories:
early_bird,night_owl,intermediate - What it means: These categories describe natural sleep-wake tendencies but are not ordered from "worse" to "better" or "less" to "more".
- Categories:
- Why This Matters:
- Useful for segmentation and personalization (e.g., optimizing app notifications based on chronotype).
- Provides insights into natural inclinations rather than performance-based measures.
By having both ordinal and categorical archetypes, Sahha provides a comprehensive view of measurable behaviors (e.g., activity level, sleep efficiency) to inherent traits (e.g., chronotype). This approach enables more actionable information for a variety of use cases.
List of Archetypes
| 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. See possible exercise types | 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. See possible exercise types | 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 |
Browse our comprehensive data dictionary to view all available outputs beyond just archetypes.
Output Schema
Archetypes are delivered as a JSON object with the following fields:
id UUID Unique identifier for the archetype assignment
profileId UUID Unique identifier for the user profile
accountId UUID Identifier for the account linked to the profile
externalId UUID External profile identifier for integration
name string Name of the archetype (e.g., sleep_duration)
value string Assigned category name (e.g., short_sleeper)
dataType string Type of archetype: ordinal or categorical
ordinality integer Numerical representation of the category
periodicity string Frequency: weekly, monthly, or quarterly
startDateTime datetime Start of the time period for this archetype (ISO 8601)
endDateTime datetime End of the time period for this archetype (ISO 8601)
createdAtUtc datetime Timestamp of when the archetype was created
{
"id": "91ced284-5355-57f0-b162-1ac920a42371",
"name": "sleep_duration",
"value": "short_sleeper",
"dataType": "ordinal",
"ordinality": 1,
"periodicity": "monthly",
"startDateTime": "2025-01-01T00:00:00+13:00",
"endDateTime": "2025-01-31T00:00:00+13:00",
"createdAtUtc": "2025-02-01T13:08:53.322886Z"
}FAQ
How are Archetypes different from Health Scores?
Health Scores provide numerical values (e.g., 0.0-1.0), while Archetypes classify users into predefined categories. Archetypes offer a more intuitive interpretation of long-term behavior.
How does ordinality work?
Ordinality represents the ranking of each category, with lower values indicating less and higher values representing more. For example, sleep_duration: 0 = very_short_sleeper, 1 = short_sleeper, 2 = average_sleeper, 3 = long_sleeper.
How frequently are Archetypes updated?
Archetypes are generated at the end of each week, month, or quarter, depending on the appropriate periodicity.
Getting Started
Query archetype assignments for any profile
Receive archetypes as they're assigned weekly or monthly
Support
For additional assistance or more detailed information on Archetypes, please contact support@sahha.ai or reach out in the slack community.