Foundational analytics

Foundation metrics provide essential health measurements that form the data infrastructure for advanced health analytics. These metrics transform wearable data points into standardized, clinically relevant measurements through sophisticated data processing and validation algorithms.

Body Composition Analysis

BMI and Waist Circumference Estimation

Calculates body mass index and estimates waist circumference using validated anthropometric algorithms that incorporate age and gender-specific adjustments.

Input Data

dataDimension	dataTypeId	Name	Required	Fallback
epochdaily	5020	Weight		n.a.
epochdaily	5030	Height		male: 175, female: 165
constant	1	Gender		n.a.
constant	10	Birthyear		1980
constant	11	Birth Month		01
constant	12	Birth Day		01

Output Data

dataDimension	dataTypeId	Name
epoch	5026	BMI
epoch	5027	WaistCircumference

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Daily Activity Intelligence

Processes activity data from multiple sources to generate standardized daily activity summaries for Walk, Run and Bike activities.

Input Data

dataDimension	dataTypeId	Name
epoch	1114	ActiveBinary
epoch	1115	WalkBinary
epoch	1116	RunBinary
epoch	1117	BikeBinary
epoch	1715	CoveredDistanceWalk
epoch	1716	CoveredDistanceRun
epoch	1717	CoveredDistanceBike
epoch	1825	ActiveWalkDuration
epoch	1826	ActiveRunDuration
epoch	1827	ActiveBikeDuration

Processing Logic

1. Overlap Detection: Identifies and removes duplicate data from multiple sources

2. Manual vs. Automatic Classification: Separates user-entered from sensor-detected data for daily data aggregation

3. Daily Aggregation: Sums durations and distances by activity type and data source

Output Data

Sensor-Detected Activity:

dataDimension	dataTypeId	Name
daily	1114	ActiveDuration
daily	1115	WalkDuration
daily	1116	RunDuration
daily	1117	BikeDuration
daily	1715	CoveredDistanceWalk
daily	1716	CoveredDistanceRun
daily	1717	CoveredDistanceBike
daily	1825	ActiveWalkDuration
daily	1826	ActiveRunDuration
daily	1827	ActiveBikeDuration

Manual Activity Entries:

dataDimension	dataTypeId	Name
daily	1814	ActiveDurationManual
daily	1815	WalkDurationManual
daily	1816	RunDurationManual
daily	1817	BikeDurationManual
daily	1725	CoveredDistanceWalkManual
daily	1726	CoveredDistanceRunManual
daily	1727	CoveredDistanceBikeManual
daily	1835	ActiveWalkDurationManual
daily	1836	ActiveRunDurationManual
daily	1837	ActiveBikeDurationManual

Data Quality Features

• Timezone-aware daily boundary calculation

• Multi-source conflict resolution with data prioritization

• Quality indicators for manual vs. automatic data classification

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Metabolic Equivalent (MET) Analysis

Real-Time Energy Expenditure Analysis

Calculates metabolic equivalent values from calorie expenditure and activity data, providing continuous assessment of activity intensity relative to weight as a multiple of resting metabolic rate.

Input Data

dataDimension	dataTypeId	Name	Required	Fallback
epoch	1011	ActiveBurnedCalories		n.a.
epoch	1010	BurnedCalories		n.a.
epoch	1200	ActivityType		n.a.
epochdailyconstant	5020	Weight		75kg

Output Data

dataDimension	dataTypeId	Name
epoch	1012	MetabolicEquivalent

Data Processing Features

• Quality Control: Filters unreasonable MET values

• Data Prioritization: Calorie availability takes precedence over generic activity information

Fallback Mechanisms

• Weight defaults to 75kg when unavailable

• Activity type classification is used when calorie data is incomplete

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Advanced MET Analysis

Daily Physical Activity Assessment

Processes continuous MET data to generate comprehensive daily activity intensity assessments and maximum metabolic capacity indicators.

Input Data

dataDimension	dataTypeId	Name
epoch	1012	MetabolicEquivalent

METmax Analysis

Calculates maximum metabolic equivalent values over specified time windows to assess cardiovascular capacity and exercise tolerance.

Processing Logic

1. Daily Maximum Selection: Peak values identified for each time window

Output Data

dataDimension	dataTypeId	Name	Description
daily	1286	MetabolicEquivalentMax1Min	highest 1-minute rolling average
daily	1287	MetabolicEquivalentMax5Min	highest 5-minute rolling average
daily	1288	MetabolicEquivalentMax10Min	highest 10-minute rolling average
daily	1289	MetabolicEquivalentMax60Min	highest 60-minute rolling average

Activity Intensity Classification

Input Data

dataDimension	dataTypeId	Name
epoch	1012	MetabolicEquivalent

Output Data

dataDimension	dataTypeId	Name
daily	1101	ActivityLowDuration
daily	1102	ActivityMidDuration
daily	1103	ActivityHighDuration

Processing Features:

• Timezone consistency is maintained across daily boundaries

Clinical Applications

METmax values provide insights into cardiovascular fitness and exercise capacity, typically available only through clinical exercise testing. Activity intensity classifications align with established exercise prescription guidelines for healthcare applications.

Standardized Sleep Analysis

Sleep Cycle Identification and Standardization

Addresses the challenge of inconsistent sleep definitions across wearable manufacturers by implementing standardized sleep cycle identification and analysis. Provides consistent sleep metrics regardless of the underlying data source. Enriches data when sleep data sets provided by data source are incomplete

Input Data

dataDimension	dataTypeId	Name
epoch	2000	SleepStateBinary
epoch	2001	SleepInBedBinary
epoch	2002	SleepREMBinary
epoch	2003	SleepDeepBinary
epoch	2005	SleepLightBinary
epoch	2006	SleepAwakeBinary
epoch	4101	SnoringBinary

Processing Logic

1. Data Harmonization: Consolidates sleep data from all connected sources

2. Cycle Identification: Identifies all sleep periods using a 30-minute interruption threshold

3. Main Sleep Selection: Selects the longest cycle as the primary sleep period

4. Day Assignment: Assigns sleep cycles to calendar days based on mid-sleep time

ThryveMainSleep Definition

The longest continuous sleep cycle of each day, where interruptions (wake phases) do not exceed 30 minutes. This standardized definition enables consistent metrics across different devices and longitudinal tracking when users change devices.

Output data

Standard Sleep Metrics

dataDimension	dataTypeId	Name
daily	2000	SleepDuration
daily	2001	SleepInBedDuration
daily	2002	SleepREMDuration
daily	2003	SleepDeepDuration
daily	2005	SleepLightDuration
daily	2006	SleepAwakeDuration
daily	2007	SleepLatency
daily	2008	SleepAwakeAfterWakeup
daily	2100	SleepStartTime
daily	2101	SleepEndTime
daily	2102	SleepInterruptions
daily	2103	SleepMidTime

ThryveMainSleep Standardized Metrics

dataDimension	dataTypeId	Name
daily	2300	ThryveMainSleepDuration
daily	2301	ThryveMainSleepInBedDuration
daily	2302	ThryveMainSleepREMDuration
daily	2303	ThryveMainSleepDeepDuration
daily	2305	ThryveMainSleepLightDuration
daily	2306	ThryveMainSleepAwakeDuration
daily	2307	ThryveMainSleepLatency
daily	2308	ThryveMainSleepAwakeAfterWakeup
daily	2400	ThryveMainSleepStartTime
daily	2401	ThryveMainSleepEndTime
daily	2402	ThryveMainSleepInterruptions
daily	2403	ThryveMainSleepMidTime

Epoch-Level Sleep State Data

dataDimension	dataTypeId	Name
epoch	2300	ThryveMainSleepStateBinary
epoch	2301	ThryveMainSleepInBedBinary
epoch	2302	ThryveMainSleepREMBinary
epoch	2303	ThryveMainSleepDeepBinary
epoch	2305	ThryveMainSleepLightBinary
epoch	2306	ThryveMainSleepAwakeBinary