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EnaResearch
Use case

Spot missing data early in months-long follow-up

In chronic condition follow-up studies, the hard part is keeping participants sending data for months and noticing missing data in time. EnaResearch gives participants access without accounts, shows completeness for every metric and flags participants who stop sending data.

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Completeness

The share of days in the period with data, per participant and metric; suspicious values are flagged, never changed.

Warnings

Warnings for active participants with no connected data source or no data for more than 7 days.

Access without accounts

Participant code, activation card and PIN; a daily summary of the last 7 days in the participant area.

Common challenges

  • Over a long study participants change phones, switch off permissions or stop sending data, and it is noticed months later.
  • How much of a metric is missing for which participant only becomes visible at analysis time.
  • Apps that require an account and password make participation harder.
  • What happens to the data of participants who are lost to follow-up or withdraw is handled inconsistently.

Data kept together

  • Resting heart rate, heart rate variability, SpO₂
  • Steps and active energy (5-minute buckets)
  • Sleep stages
  • Weight and blood pressure (if a device or health app writes them)
  • Outpatient visit and follow-up assessment records
  • Manually entered fatigue and lab values

Seeing missing data before analysis

The study’s data quality page shows completeness (the share of days in the period that have data) and quality flags for every participant and metric: out of range, sudden jump, source conflict, low wear time. Suspicious values are flagged, never changed, and arrive in exports as quality.csv.

Active participants get “Not connected” (no data source) and “No data” (nothing for more than 7 days) warnings. Sync status shows the last measurement and last receipt per source and metric.

Participant access over a long study

Participants do not sign up with an email and password. They activate with the card from the researcher, sign in to the participant area on the web with their code and PIN, and see their studies, connected data sources, last sync time and a daily summary of the last 7 days.

When a participant changes phones, the researcher issues a new activation key; for a lost card the code is rotated or revoked. The phone apps are built and await store release.

Follow-up visits and participation statuses

Outpatient visits and follow-up assessments are recorded as clinical events, and datasets can also be windowed around them. Manual measurements must be inside the participation period and within the catalog range.

Participation statuses are active, completed, withdrawn and lost to follow-up. When recording a withdrawal, the researcher chooses whether to keep or delete the data, and the choice is audited; deletion covers only that participation’s data window.

Chronic condition follow-up

Access without accounts, completeness, warnings for missing data and follow-up visits.

  1. 01

    Pick metrics

    Select the metrics to follow in the protocol.

  2. 02

    Activate participants

    Hand over the activation card; the participant sets a PIN.

  3. 03

    Watch completeness

    See missing and suspicious data on the data quality page.

  4. 04

    Record visits

    Add follow-up visits and manual measurements.

Related pages

Related use cases

Frequently asked questions

Do participants get reminder notifications?
No, participants do not receive automatic notifications. Participants not sending data are shown to the research team as warnings; the team contacts them through its own channels.
How long is raw device data kept?
Raw device payloads are kept for 90 days. Standardized measurements are kept for the term of the license by default; the organization can set a shorter period counted from the study’s final closure.
What happens to the data of a participant lost to follow-up?
The participant is marked “lost to follow-up” and the data is kept. On withdrawal, the researcher chooses whether to keep or delete it.

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