There are several file‑format families that people commonly use when they want to move health‑related measurements (weight, blood‑pressure, mood, sleep, activity, etc.) from one app or service to another. CSV is the simplest, but many platforms and standards exist that preserve richer metadata (units, timestamps, tags, provenance, etc.) and that are easier for downstream tools to ingest.

Below is a concise overview of the most popular alternatives, grouped by purpose and ecosystem.

1️⃣ Structured, schema‑driven formats
Format	What it looks like	Strengths	Typical tooling
JSON (JavaScript Object Notation)	[{ "timestamp":"2025-08-31T07:30:00Z", "type":"weight", "value":73.2, "unit":"kg", "source":"appX" }, …]	Human‑readable, hierarchical, easy to extend with extra fields (e.g., mood notes, device IDs). Widely supported by web APIs and scripting languages.	JavaScript, Python (json), Node‑RED, most mobile SDKs.
NDJSON / JSON Lines	Same as JSON but one object per line → easier streaming.	Great for large datasets; can be appended incrementally.	Command‑line tools (jq, awk), log processing pipelines.
XML (eXtensible Markup Language)	<record><time>2025‑08‑31T07:30:00Z</time><type>blood_pressure</type><systolic>120</systolic><diastolic>78</diastolic></record>	Mature schema support (XSD), namespaces allow interoperability across health standards.	Legacy health‑IT systems, SOAP APIs, Java libraries.
FHIR Bundle (JSON or XML)	A bundle of Observation resources conforming to HL7 FHIR. Example (JSON): { "resourceType":"Bundle","type":"collection","entry":[{ "resource":{ "resourceType":"Observation","code":{"coding":[{"system":"http://loinc.org","code":"29463-7","display":"Body weight"}]}, "valueQuantity":{"value":73.2,"unit":"kg"}, "effectiveDateTime":"2025‑08‑31T07:30:00Z" }}, …]}	The de‑facto standard for clinical data exchange; includes rich semantics (LOINC codes, units, patient identifiers).	EHRs, health‑data platforms, FHIR servers, open‑source libraries (HAPI‑FHIR, SMART on FHIR).
HL7 v2.x Pipe‑delimited messages	`OBX	1	NM
When to pick one
JSON / NDJSON – Best for developers building custom pipelines, data‑science notebooks, or mobile‑to‑cloud sync where you want flexibility without heavy standards overhead.
FHIR – Ideal if the target system is an electronic health record (EHR), a health‑information exchange, or any platform that already consumes FHIR. It guarantees that units, coding systems (LOINC, SNOMED CT), and patient context are preserved.
XML / HL7 v2 – Mostly for integration with older enterprise health‑IT stacks; choose only if you know the receiving system expects those formats.
2️⃣ Tabular formats with richer typing than plain CSV
Format	Description	Advantages
TSV (Tab‑Separated Values)	Same as CSV but tabs separate columns. Reduces ambiguity when commas appear inside free‑text fields (e.g., mood notes).	Simple, still readable by spreadsheet programs.
Excel XLSX	Binary (or OpenXML) workbook containing sheets, cell types, formulas, and optional metadata.	Users can open directly in Excel/Google Sheets; you can embed data validation, dropdowns for mood categories, and even charts.
Parquet	Columnar storage format (Apache Arrow). Optimized for large datasets and analytics.	Very efficient for big‑data pipelines (Spark, Pandas, Dask). Not human‑readable but excellent for downstream processing.
Google Sheets export (CSV/TSV)	Export a live Google Sheet as CSV/TSV, or share the sheet itself via API.	Enables collaborative editing and immediate consumption by other Google‑based tools.
3️⃣ Time‑series‑focused formats
Format	Typical use case	Why it matters for health data
InfluxDB Line Protocol	weight,device=fitbit value=73.2 1725110400000000000	Perfect for continuous streams (e.g., weight measured daily). Works well with Grafana dashboards.
RRD (Round‑Robin Database)	Used by monitoring tools like Cacti.	Good for long‑term trend storage with fixed resolution, but less common for personal health apps.
Open mHealth JSON	A lightweight schema for vitals, activity, and self‑reported outcomes.	Designed specifically for mobile health; includes fields for provenance and measurement context.
4️⃣ Interoperability bundles used by consumer health platforms
Platform	Export format(s) offered	Notes
Apple HealthKit	XML (Apple Health export) – a zip containing export.xml plus optional metadata/*.xml.	The XML follows a proprietary schema but maps cleanly to FHIR concepts; many third‑party converters exist.
Google Fit	JSON (via Takeout) – FitData.json containing activities, body metrics, etc.	Straightforward to parse; includes timestamps in epoch ms.
Garmin Connect	CSV and TCX (Training Center XML) for workouts; FIT binary for detailed sensor data.	TCX/FIT are sport‑focused but can carry heart‑rate, cadence, and sometimes blood‑pressure if paired.
Withings	CSV and JSON via API.	JSON payload mirrors the Withings data model (weight, fat mass, blood‑pressure).
Fitbit	CSV (via export) and JSON via Web API.	JSON includes logId, date, time, value, unit.
Oura Ring	CSV and JSON (via API).	JSON includes score, temperature_deviation, sleep_phase.
5️⃣ Portable “bundle” formats for personal data vaults
Format	What it is	Why it’s useful
ZIP of mixed files (CSV + JSON + images)	Some apps let you download a ZIP that contains a CSV summary plus a folder of JSON records and any associated photos (e.g., skin‑condition pictures).	Keeps everything together; you can unpack and feed each piece into the appropriate tool.
SQLite database	A single .db file with tables for each metric.	Self‑contained, queryable with SQL, portable across platforms. Many health‑apps (e.g., MyFitnessPal export) use this.
FHIR Bulk Data (NDJSON)	A collection of newline‑delimited FHIR resources (Observations, Conditions, etc.).	Designed for large‑scale export/import between health ecosystems; can be imported into any FHIR server.
6️⃣ Choosing the right format for your users
User scenario	Recommended format(s)
Non‑technical user who wants to view/edit in Excel	XLSX (or CSV/TSV if they prefer plain text).
Developer building a custom dashboard	JSON or NDJSON (easy to fetch via REST).
Clinician or researcher needing clinical semantics	FHIR Bundle (JSON) – ensures proper coding (LOINC, SNOMED).
Data‑scientist handling large histories	Parquet (for analytics) or SQLite (for quick local queries).
User wanting to sync with another consumer health app	Export in the native format of the source (Apple Health XML, Google Fit JSON) and then use a conversion tool to the target’s accepted format.
Continuous monitoring (e.g., weight logged every minute)	InfluxDB line protocol or CSV with a timestamp column; later ingest into a time‑series DB for visualization.
7️⃣ Quick “starter kit” for a generic health‑export feature
If you’re designing an export function in a mobile app, consider offering two or three formats out of the box:

CSV – for the widest compatibility (spreadsheets, quick glance).
JSON – for developers and for easy import into services that accept structured data.
FHIR Bundle (JSON) – optional “advanced” export for users who need clinical‑grade interoperability (e.g., exporting to an EHR or a research repository).
Provide a short description next to each option so users understand the trade‑off (e.g., “CSV – simple table, works in Excel; JSON – retains units and notes; FHIR – includes medical codes, best for clinicians”).

TL;DR
Beyond CSV: JSON/NDJSON, XML, Excel (XLSX), Parquet, SQLite, ZIP bundles, and especially HL7 FHIR (the health‑industry standard).
Choose based on who will consume the data (casual user vs. developer vs. clinician) and on the level of metadata you need to preserve (units, timestamps, coding systems, provenance).
Offering CSV + JSON + optional FHIR covers almost every realistic export scenario while keeping the UI simple.
If you need sample schemas or code snippets for generating any of these formats, just let me know—I can walk you through a quick implementation in Swift, Kotlin, or Python.


