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Archetypes

Labels that capture traits and persona

Archetypes Hero

Segmentation ·Targeting ·Product Recommendations ·Analytics/BI

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:

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.

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

Example Response
json 
{
	"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

API

Query archetype assignments for any profile
Webhooks

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 .

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