Why Health Data Exports Need Units and Calculation Methods

Keep the number attached to its meaning

An exported number is not self-explanatory. It needs a unit, a definition, a time window and, when relevant, a calculation method. If one of those details is missing, a later reader may place it beside a similar-looking number that describes something else.

This matters even when the field name is familiar. “Heart rate” could refer to one sample, a resting estimate, an overnight average or a minimum selected from a period. “Sleep” could mean a duration, a stage estimate or a derived score. The visible label alone does not resolve those differences.

The structured observation standard keeps the observation type, value, timing and related details as separate fields. A personal export benefits from the same discipline, even when the file format is simple.

Store the unit with every numeric value

Include the unit beside each numeric field, not only in a chart legend or a note that may be separated from the rows. A heart rate might be expressed as beats per minute. A duration might use seconds, minutes or hours. A distance may be recorded in meters or kilometers.

Conversions should preserve the original unit and note the normalized unit. Rounding can also matter. If the exported value is rounded to a whole number, do not imply that the original measurement had that precision.

A standard vocabulary for units can reduce ambiguity across systems. The UCUM measurement-unit reference defines machine-readable unit expressions. In a personal archive, a plain-language unit is often enough, provided it is explicit and consistently applied.

Define the metric, not just its label

A metric definition explains what was measured or calculated. Include the source sensor where known, the signal or event, and the rule used to summarize it. For derived values, document whether the number is a mean, median, minimum, maximum, total, percentage or model output.

Do not assume that two rows with the same label share the same definition. A “daily average” can use different sampling windows. A “resting” value may be selected by different criteria. If a definition is unavailable, record that limitation rather than inferring it from the label.

The review of wearable HRV measurement discusses the range of measurement choices and the difficulty of interpreting long-term values when methods differ. A data export should keep the method visible beside the metric.

Preserve the time window

The unit and definition still leave out when the number applies. Store the observation timestamp or interval, timezone, and aggregation window. A nightly average and a morning sample may share units but describe different periods.

For a summary, note both its start and end time where possible. Keep the original timestamp separate from the time the file was exported or imported. If a record crosses midnight, describe how the date was assigned.

A chart can make separate windows appear continuous. The export should preserve the boundaries that the chart may hide. If only a calendar date is known, mark the finer time as unavailable.

Record the calculation method

For a derived metric, include a concise method identifier or description. It may name the inputs, aggregation rule, baseline window, exclusions and version where those details are available. Not every system reveals proprietary logic, but the export can still mark the method as documented, partially known or unknown.

A score should not be presented as a raw measurement. Keep the underlying observations in separate fields and say whether the score was imported from another source or recalculated in the destination.

The wearable-data quality review identifies algorithm and data-standard differences as sources of heterogeneity. When a calculation method is missing, comparisons should be limited accordingly.

Separate measurement from estimate

A measured signal, a device-derived estimate and a manually entered value have different origins. Add a status field such as measured, estimated, calculated, imported, manual or unknown. Do not use one generic “value” field to imply that each record was acquired the same way.

If an estimate is based on other signals, document its source inputs where possible. If a value was filled to bridge a gap, mark the interval and method. A filled row should not look identical to an observation that the sensor actually recorded.

A status label does not determine whether a value is useful. It gives a later reader the context needed to decide how to use it.

Keep source and method changes visible

A change in device, sensor position, sampling schedule or calculation method can create a discontinuity. Record when the change occurred and which records belong to each method. Keep old and new series separate until their definitions and coverage are understood.

A field can retain the same name while its calculation changes. For that reason, note the source software or algorithm version if the source provides one. If the version is not available, identify the boundary date and record that uncertainty.

The provenance model offers a useful way to describe an original record, a transformation and the agent or system responsible. A small method note beside a file can carry those relationships without a complex database.

Do not merge values by label alone

Before combining two exports, compare the unit, definition, time window, source, status and method. If any field differs, decide whether a valid conversion or alignment exists. A matching label is not evidence that the values are interchangeable.

For example, two values both labeled “overnight HRV” may differ in sampling window, artifact handling or statistic. Preserve them as separate measures until the methods can be compared. Avoid averaging them just because their units match.

An existing guide on why a data category name does not make metrics equivalent explains this point in more detail. Category labels group values; they do not guarantee common calculation rules.

Include missingness and coverage

A data export should state which intervals were observed and which were absent. Include coverage or valid-sample information when the source provides it. If no observation exists, keep it distinct from a zero value.

Do not let a daily summary obscure an incomplete day. Add a coverage note or completeness status so a later user can see whether the statistic represents a full period. If a system fills missing values, mark the filled segment and preserve the method.

This matters because gaps can affect derived results. A number that appears precise may summarize only a fraction of its expected window.

Make conversions traceable

When converting units or timestamps, preserve the source value and record the conversion rule. A converted value should be possible to reproduce. State any rounding, aggregation or filtering applied during export.

Do not overwrite the source field if a destination system uses a different representation. Store both values, or keep an explicit mapping table. This lets a future reviewer distinguish a conversion from a new measurement.

If a field cannot be converted safely, retain the original and label the destination field unsupported or unresolved. A visible limitation is more useful than an unmarked transformation.

Keep the export usable without the original app

A file should remain understandable if the original interface is unavailable. Include a header row, a short dictionary, timestamps with timezone rules, units, definitions and source information. Add a readme if the format uses coded values or opaque field names.

The exchange overview from health information technology guidance explains why structured fields support safer transfer between systems. A personal file can use plain text and still preserve the same essential context.

Keep the metadata concise. The goal is not to copy an entire software manual. It is to retain enough information to interpret each exported value and identify what has changed.

Review a sample before a full comparison

Choose several rows from different dates and compare the export with the source display. Confirm units, time window, status and method. Include a record near any source or device change and one with incomplete coverage.

If the values differ, determine whether the export converted, rounded, aggregated or filtered them. Record the mapping before comparing the full history. A small sample check can identify a systematic mismatch early.

Do not assume that a successful file download means the export is semantically complete. It confirms that a file was generated, not that every definition traveled with it.

Keep uncertainty attached to the data

An unknown unit or calculation method should remain visible in the dataset. Do not fill the field with a likely value unless the source confirms it. If a later source resolves the uncertainty, add the evidence and date of the correction while preserving the earlier record.

A data dictionary can use clear states: known, partially known, not provided or not applicable. That is more informative than leaving an empty cell without explanation.

This approach keeps the file useful while limiting claims that the metadata cannot support. It also makes future corrections easier to review.

A practical export checklist

Before moving a health-data file, confirm that every numeric value has a unit, every metric has a definition, and every summary has a time window. Record source, status, method, coverage, missing intervals and transformations. Keep original timestamps and normalized values when conversions are made.

Then test a small group of rows against the source and store the data dictionary with the export. If a detail cannot be confirmed, mark it unknown and separate that series from comparisons that require it.

These checks keep the data interpretable after the original chart or software is no longer available.

FAQ

Why do two health-data values with the same label differ?

They may use different units, source signals, time windows or calculation methods. Check the metadata before treating them as equivalent.

What should I do if an export omits the calculation method?

Mark the method as unknown, preserve the source and limit comparisons that depend on knowing how the metric was produced.

Do I need a formal data standard for a personal export?

No. A clear data dictionary with units, definitions, timestamps, source and method can preserve meaning. Recognized codes can improve exchange when they are available.