Formetrix exposes two distinct kinds of indexes: Composition Metrics and Interpretation Indexes. They serve different purposes by design and are handled differently by the system.
Composition vs Interpretation (Key Distinction)
At a Glance
Aspect Composition Metrics Interpretation Indexes Purpose Describe physical body components Contextualize, normalize, or compare What they represent Fat, lean mass, muscle, water, bone Size, proportion, symmetry, risk, efficiency Primary role Core body state Meaning and interpretation How values are obtained Input directly or estimated Automatically calculated Update behavior Updated per measurement session Recomputed whenever inputs change
The distinction between the two is intentional:
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Composition metrics describe what the body is composed of
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Interpretation indexes explain what those values mean in context
In practice, composition metrics act as the bridge between:
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raw anthropometric inputs (measurements), and
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higher-level interpretation indexes (risk, proportion, efficiency)
They anchor trends, enable continuity, and provide the foundation upon which interpretation is built — without imposing conclusions on their own.
Interpretation Indexes
Interpretation indexes are calculated values that contextualize raw measurements.
They do not describe body composition directly, but instead interpret size, proportion, symmetry, risk, or efficiency using anthropometric inputs and profile attributes.
In Formetrix, interpretation indexes:
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Are calculated automatically when required inputs are available
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Do not rely on device-only composition metrics (with one explicit exception)
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Exist to add meaning, not raw data density
They answer questions such as:
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Is this proportionally healthy for my height?
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Is fat distribution changing even if weight is stable?
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Are left and right limbs developing symmetrically?
Important Clarifications
- Interpretation indexes are not medical diagnoses
- They are contextual tools, designed to be read together rather than in isolation
- Different indexes respond at different speeds:
- some react quickly to change (e.g. waist-based indexes)
- others reflect slower, structural shifts
Categories of Interpretation Indexes
Interpretation indexes in Formetrix are grouped by what they normalize, compare, or contextualize.
Each category answers a different question about the body, using the same underlying measurements.
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Size & Scaling — normalize body mass against height or lean mass to enable fair comparison
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Proportion & Distribution — describe how mass is distributed, not how much exists
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Energy & Efficiency — estimate physiological demand rather than structure
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Symmetry & Balance — compare left vs right measurements to reveal imbalance
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Advanced / Guarded — calculated only under strict conditions and interpreted cautiously
Together, these indexes complement raw measurements by adding context, comparability, and meaning over time.
The current version of Formetrix includes the following interpretation indexes:
| Index | Category | Required Inputs | What It Adds / Why It Matters |
|---|---|---|---|
| BMI | Size & Scaling | Weight, Height | Baseline size normalization. Useful for population comparison but insensitive to body composition or distribution. |
| Ponderal Index | Size & Scaling | Weight, Height | Alternative to BMI with stronger height normalization. More stable across extreme statures. |
| Fat-Free Mass Index (FFMI) | Size & Scaling | Weight, Height, Body Fat % | Separates lean mass from fat mass. Useful for tracking muscular development independent of fat changes. |
| Waist-to-Height Ratio (WHtR) | Proportion & Distribution | Waist, Height | Sensitive indicator of central fat accumulation. Often changes before weight does. |
| Waist-to-Hip Ratio (WHR) | Proportion & Distribution | Waist, Hip | Describes fat distribution pattern rather than quantity. Useful for longitudinal shape analysis. |
| Relative Fat Mass (RFM) | Proportion & Distribution | Waist, Height, Sex | Shape-based fat estimation without requiring body fat devices. Responds quickly to waist changes. |
| ABSI | Proportion & Risk | Waist, Height, BMI | Body shape index designed to be independent of BMI. Highlights risk not visible in weight-based metrics. |
| ABSI-Z | Proportion & Risk | ABSI, Age, Sex | Standardized ABSI for age and sex. Improves comparability across individuals and time. |
| Basal Metabolic Rate (BMR) | Energy & Efficiency | Weight, Height, Age, Sex | Estimates baseline energy needs. Provides context for nutrition, fatigue, and stalled progress. |
| Arm Symmetry Index | Symmetry & Balance | Left & Right Arm Circumference | Detects upper-body asymmetry. Useful for rehabilitation, injury prevention, and performance balance. |
| Leg Symmetry Index | Symmetry & Balance | Left & Right Leg Length | Detects lower-body asymmetry. Often reveals compensation patterns not visible in totals. |
| Visceral Fat Area | Advanced / Guarded | Waist, Thigh, BMI, Age, Sex, DoB | High-constraint estimate. Provides additional context when all inputs are reliable. Should be interpreted cautiously. |
**Composition Metrics
Composition metrics describe the physical makeup of the body.
They represent quantities or proportions such as fat mass, lean mass, muscle, water, or bone — either measured directly or estimated when direct measurement is unavailable.
In Formetrix, composition metrics are treated as core state variables.
What They Represent
Composition metrics model the body as components, not comparisons.
They answer questions such as:
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How much of my body mass is fat versus lean tissue?
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Is weight change driven by fat, muscle, or fluid?
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Is composition shifting even when scale weight is stable?
These metrics capture physical substance, not interpretation.
Sources: Measurement First, Estimation When Needed
Formetrix is designed to work with the level of measurement access you actually have.
If you use a body composition device or clinical scan, you can import those values directly during a measurement session and continue tracking without disruption.
If you do not, Formetrix does not force gaps or incomplete profiles. Instead, it uses a structured estimation system, built on validated anthropometric inputs, to keep your body model complete, interpretable, and comparable over time — without requiring specialized equipment.
Important
Estimation in Formetrix is not a guess. It is a rule-driven process grounded in anthropometric relationships and explicit input requirements.
Each composition metric can be supported by multiple estimation methods, evaluated against available inputs and applied only when their requirements are met. When better data becomes available, it integrates cleanly without breaking historical continuity.
To Consider
Body composition devices themselves are not noise-free. Readings can vary across machines, protocols, hydration state, and time of day.
Formetrix is designed to absorb that variability rather than amplify it.
This approach ensures that:
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composition metrics remain usable across different measurement contexts
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dependent insights and trends remain intact
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users are never blocked by missing equipment or incomplete sessions
Bottom Line
When device-derived values are available, they are used directly. When they are not, Formetrix provides a deliberate, comprehensive alternative — so your body model remains actionable.
Available Composition Metrics
| Metric | What It Represents / Why It Matters | Available Estimation Methods (Formetrix) |
|---|---|---|
| Body Fat % | Proportion of body weight composed of fat. Central reference point for composition analysis and downstream interpretation. | Skinfolds (JP7, DW4, JP3), US Navy, BMI-based |
| Fat Mass (kg) | Absolute fat quantity. Often more stable and interpretable than percentages for fat-loss tracking. | Derived from Body Fat % |
| Lean Body Mass (kg) | Total non-fat mass. Anchors muscle, water, and metabolic context. | From Body Fat %, Boer formula |
| Muscle Mass % | Estimated skeletal muscle proportion. Best used for directional trends rather than absolute values. | Derived from Lean Body Mass |
| Body Water % | Hydration relative to body weight. Highly sensitive to short-term fluctuations. | Derived from Lean Body Mass |
| Bone Mass (kg) | Skeletal mass estimate. Changes slowly; primarily provides structural context. | Not estimated (device-only) |
| Visceral Fat | Internal fat estimate (device-derived). Interpretation depends strongly on measurement method. | Not estimated (device-only) |
| Weight-to-Bone Ratio | Relates total mass to skeletal support. Structural context indicator. | Derived from Weight + Bone Mass |
| Metabolic Age | Device-derived comparative metric. Informational only, not a physiological age measure. | Not estimated (device-only) |
Not all composition metrics carry the same level of certainty
Precision depends on:
Input quality
Measurement method
Estimation assumptions
Consistency of technique and cadence
Putting Indexes Into Practice
These indexes are used to:
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build longitudinal trends
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support visual comparison and progress views
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power estimation chains (e.g. fat → lean → water)
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add context to raw measurement inputs
They are most meaningful when:
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compared over time
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interpreted together, not in isolation
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collected under consistent conditions
Success
What matters most is consistency — using comparable inputs, methods, and conditions over time.
For deeper context, see: