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Trends

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Introduction

Trends are part of Sahha Insights . They are designed to detect and communicate directional change in a score, factor, or biomarker over time. Rather than just looking at today's value, a trend reveals how that value has evolved over the last few weeks — is it going up? Down? Staying the same? And by how much?

Category Name Description Unit Value Range isHigherBetter
score sleep Overall sleep quality index [0, 1] true
score activity Physical activity and movement levels index [0, 1] true
score readiness Body's recovery state and preparedness for exertion index [0, 1] true
score wellbeing Holistic health combining sleep and activity index [0, 1] true
score mental_wellbeing Mental wellbeing state based on behavioral patterns index [0, 1] true
factor sleep_duration Total time spent asleep index [0, 1] true
factor sleep_regularity Consistency of sleep schedule index [0, 1] true
factor sleep_continuity Uninterrupted sleep with minimal awakenings index [0, 1] true
factor sleep_debt Accumulated sleep deficit index [0, 1] true
factor circadian_alignment Alignment with natural sleep-wake cycle index [0, 1] true
factor physical_recovery Deep sleep phase duration index [0, 1] true
factor mental_recovery REM sleep phase duration index [0, 1] true
factor steps Daily step count index [0, 1] true
factor active_hours Hours with significant physical activity index [0, 1] true
factor active_calories Calories burned during activity index [0, 1] true
factor intense_activity_duration Time spent in high-intensity activity index [0, 1] true
factor extended_inactivity Prolonged sedentary periods index [0, 1] true
factor floors_climbed Vertical movement measurement index [0, 1] true
factor activity_regularity Consistency of daily activity patterns index [0, 1] true
factor walking_strain_capacity Capacity to do low-intensity activities index [0, 1] null
factor exercise_strain_capacity Capacity to do high-intensity exercises index [0, 1] null
factor resting_heart_rate Heart rate during rest index [0, 1] true
factor heart_rate_variability Variation in time between heartbeats index [0, 1] true

Browse our comprehensive data dictionary to view all available outputs beyond just trends.

How It Works

Trends analyze the last 4 complete weeks of data on a rolling basis. For each eligible metric (like sleep score, activity score, sleep duration, etc.), Sahha computes a weekly average, compares it to previous weeks, and classifies the directional movement as one of:

  • increasing : meaningful upward movement
  • decreasing : meaningful downward movement
  • stable : no significant change

The overall trend state is determined by the percent change in the latest week's data compared to the previous week.

Trends are generated every week, and include:

  • A rolling 4-week time window
  • Percent change calculations between each week
  • A state label (increasing/decreasing/stable)
  • Range and unit metadata

Output Schema

Each trend is scoped to a single profile and metric. It includes the past 4 full weeks of data and is calculated at the end of each week on a rolling basis.

id UUID

Unique identifier for the trend

type string

Always set to trend

category string

One of score, factor, or biomarker

name string

The metric being trended (e.g., sleep)

state string

Directional movement: increasing, decreasing, or stable

isHigherBetter boolean nullable

Whether higher values are generally better

valueRange float[]

Range of expected values, e.g. [0.0, 1.0]

unit string

Unit of the metric (e.g., index, bpm, steps)

periodicity string

Frequency of calculation (currently weekly)

trendStartDateTime datetime

Start of the rolling window

trendEndDateTime datetime

End of the rolling window

data TrendData[]

List of entries with value and change info (4 weekly entries)

createdAtUtc datetime

UTC timestamp when the trend was calculated

version float

Version of trends

Example Response
json 
{
	"type": "trend",
	"category": "score",
	"name": "sleep",
	"state": "increasing",
	"isHigherBetter": true,
	"valueRange": [0.0, 1.0],
	"unit": "index",
	"periodicity": "weekly",
	"trendStartDateTime": "2023-05-01T00:00:00+05:00",
	"trendEndDateTime": "2023-05-29T00:00:00+05:00",
	"data": [
		{ "startDateTime": "2023-05-01", "value": 0.65, "percentChangeFromPrevious": null },
		{ "startDateTime": "2023-05-08", "value": 0.75, "percentChangeFromPrevious": 0.15 },
		{ "startDateTime": "2023-05-15", "value": 0.71, "percentChangeFromPrevious": -0.05 },
		{ "startDateTime": "2023-05-22", "value": 0.78, "percentChangeFromPrevious": 0.1 }
	],
	"createdAtUtc": "2023-05-30T09:30:00Z"
}
Object

TrendData

Each entry in the data array contains:

id UUID

Unique identifier for the data entry

startDateTime datetime

Start of the week for this data entry

endDateTime datetime

End of the week for this data entry

value float nullable

Average value for the metric in this period

percentChangeFromPrevious float nullable

Percent change from the previous entry's value

Getting Started

API

Query trends via GET /api/v1/profile/insight/trend
Webhooks Coming Soon

Receive weekly trend updates automatically

FAQ

How is the trend state (increasing/decreasing/stable) determined?

Sahha uses relative percent change and a metric-specific significance threshold to determine if the movement is meaningful.

What if higher value isn't always better?

That's why we include the isHigherBetter field. It allows consumers to interpret whether a trend is good or bad in the context of the metric.

What if there is missing/no data?

That week is skipped in the 4-week window. At least two valid weeks are needed to calculate a trend.

Can I define my own trends or window size?

Not yet. For now, all trends use a 4-week rolling window.

Support

For assistance with Trends or the Insights product in general, reach out at support@sahha.ai or join the Sahha Slack Community .

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