Behavioral Archetypes

Archetypes categorize users based on their behavioral patterns, lifestyle, and wellness states. These classifications help understand long-term trends rather than short-term fluctuations. Unlike numerical health scores, archetypes provide an intuitive, high-level interpretation of an individual's health. Each archetype is derived from multiple biomarkers and health scores, aggregated over weeks or months. Archetypes enable personalized UX, tailored recommendations, and effective cohort-based analysis.

Key Features

Simplified

Converts complex health data into simplified intuitive labels, that can act as profile attributes or tags

Personalization

Helps applications to hyper-personalize their recommendations, interventions, and rewards

Segmentation

Enables grouping of users based on shared health behaviors, helping identify opportunities for targeted engagement

Device Flexibility

Works seamlessly with both smartphone-only and wearable users, ensuring accessibility and inclusivity of different user types

How It Works

Health data is gathered from wearables, smartphone sensors, and health scores over time. The data is analyzed over weeks or months, smoothing out short-term fluctuations. Based on defined thresholds, users are assigned a qualitative label for each archetype. Archetypes are refreshed weekly and monthly, allowing tracking of behavioral shifts over time. 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_duration
- very_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".
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.

Archetype Sleep Radar

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	Ordinality	Periodicity	Description
overall_wellness	Ordinal	`poor_wellness`, `fair_wellness`, `good_wellness`, `optimal_wellness`	0-3	Weekly, Monthly	Reflects general well-being across physical and mental health.
mental_wellness	Ordinal	`poor_mental_wellness`, `fair_mental_wellness`, `good_mental_wellness`, `optimal_mental_wellness`	0-3	Weekly, Monthly	Assesses mental well-being based on stress and other indicators.
activity_level	Ordinal	`sedentary`, `lightly_active`, `moderately_active`, `highly_active`	0-3	Weekly, Monthly	Indicates the user’s overall physical activity level.
exercise_frequency	Ordinal	`rare_exerciser`, `occasional_exerciser`, `regular_exerciser`, `frequent_exerciser`	0-3	Weekly, Monthly	Measures how often the user exercises.
primary_exercise_type	Categorical	`strength_oriented`, `cardio_oriented`, `mind_body_oriented`, `hybrid_oriented`, `sport_oriented`, `outdoor_oriented`	N/A	Weekly, Monthly	Categorizes the user's main type of exercise.
primary_exercise	Categorical	Most frequent exercise (e.g., running, weightlifting, yoga)	N/A	Weekly, Monthly	Identifies the most commonly performed exercise.
secondary_exercise	Categorical	Second most frequent exercise (e.g., swimming, cycling, hiking)	N/A	Weekly, Monthly	Identifies the second most frequent exercise.
sleep_pattern	Categorical	`consistent_early_riser`, `inconsistent_early_riser`, `consistent_late_sleeper`, `inconsistent_late_sleeper`, `early_morning_sleeper`, `chronic_short_sleeper`, `inconsistent_short_sleeper`	N/A	Weekly, Monthly	Identifies habitual sleep timing and consistency.
sleep_duration	Ordinal	`very_short_sleeper`, `short_sleeper`, `average_sleeper`, `long_sleeper`	0-3	Weekly, Monthly	Measures average sleep duration relative to recommended norms.
sleep_regularity	Ordinal	`highly_irregular_sleeper`, `irregular_sleeper`, `regular_sleeper`, `highly_regular_sleeper`	0-3	Weekly, Monthly	Measures consistency in sleep patterns.
sleep_quality	Ordinal	`poor_sleep_quality`, `fair_sleep_quality`, `good_sleep_quality`, `optimal_sleep_quality`	0-3	Weekly, Monthly	Assesses sleep quality based on depth and disturbances.
sleep_efficiency	Ordinal	`highly_inefficient_sleeper`, `inefficient_sleeper`, `efficient_sleeper`, `highly_efficient_sleeper`	0-3	Weekly, Monthly	Evaluates how effectively the user maintains uninterrupted sleep.
bed_schedule	Ordinal	`very_early_sleeper`, `early_sleeper`, `late_sleeper`, `very_late_sleeper`	0-3	Weekly, Monthly	Categorizes typical bedtime habits.
wake_schedule	Ordinal	`very_early_riser`, `early_riser`, `late_riser`, `very_late_riser`	0-3	Weekly, Monthly	Categorizes usual wake-up time.

Output Schema

Archetypes are delivered as a JSON object with the following fields:

Key	Type	Description
`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`	ISO 8601 DateTime	Start of the time period for this archetype.
`endDateTime`	ISO 8601 DateTime	End of the time period for this archetype.
`createdAtUtc`	ISO 8601 DateTime	Timestamp of when the archetype was last updated.

Example:

{ 
    "id": "91ced284-5355-57f0-b162-1ac920a42371",
    "profileId": "6be989eb-813c-4380-be85-a6a7d787da70",
    "accountId": "a17fd912-46a2-48aa-be3e-1146ee2cd258",
    "externalId": "edd9afa-7012-4c30-8121-53fc3a9be461",
    "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" 
}

FAQs

Q: How are Archetypes different from Health Scores?
A: 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.

Q: How does ordinality work?
A: Ordinality represents the ranking of each category, with lower values indicating less value alongt that dimension and higher values representing more value.

Example for sleep_duration:
- 0 = very_short_sleeper
- 1 = short_sleeper
- 2 = average_sleeper
- 3 = long_sleeper

Q: How frequently are Archetypes updated?
A: Archetypes are generated at the end of each week, month, or quarter, depending on the appropriate periodicity.

Getting Started

To integrate Archetypes into your product, subscribe to the archetypes Webhook, or use the Sahha API to fetch the archetype assignments for profiles.

Support

For additional assistance or more detailed information on Archetypes, please contact support@sahha.ai or reach out in the slack community .