Most longevity studies ask one question: did this intervention move these biomarkers, and did participants actually follow it? Capture tracks the intervention, the repeat biomarker panels and the participant-reported and wearable data side by side, with every change recorded.
Free sandbox · No credit card · 21 CFR Part 11 aligned
Participants
40
On protocol
35
Panels due
6
Devices connected
33
78%
83%
91%
At a glance
Native
wearable connection: the participant signs in, data syncs
Field-level
audit trail on every biomarker value
Free
sandbox with every feature, no time limit
Ph 1-3
suitable for early to late-phase studies
The data problem
Longevity and healthspan studies tend to test an intervention, such as a drug, a supplement, an exercise or diet programme, a device or a combination, against changes in biomarkers and function over months. The headline result is a difference in biomarkers between groups or between baseline and follow-up. That result is only as believable as two records: an accurate account of what each participant actually did, and a clean series of measurements taken the same way each time.
These are two separate data problems. The intervention record has to survive dose changes, pauses, switched products and missed weeks. The biomarker record has to survive different laboratories, changed units, repeat draws, out-of-range values and late results. Spreadsheets handle neither well when several people edit them, and most trial EDC systems treat the intervention as a single yes or no on a visit form.
There is also a blurred line in this field between a formal study and personal experimentation. A study that is intended to produce generalisable knowledge needs ethics review and informed consent, and consent should cover the specific data being collected, including wearable data. Our wearable data sharing consent template is a starting point. Nothing on this page is medical advice.
Intervention record
Fields for a repeating intervention table, one row per product or regimen change. Demo structure.
| Field | Type | Why it matters |
|---|---|---|
| Product or regimen | Dropdown or text | Distinguishes arms and any added products |
| Dose and unit | Numeric plus unit | Dose-response and unit consistency |
| Frequency | Single choice | Daily, alternate days, weekly, as needed |
| Start date and stop date | Date | Exposure time per participant |
| Reason for change or stop | Dropdown with other | Separates side effects from convenience |
| Concomitant products | Repeating table | Competing exposures that can move the same biomarker |
| Adherence method | Single choice | Diary, returned count, supplier record or none |
If you use kit management for the study product, dispensing is linked to a visit under e-signature and returns are recorded as used or unused.
Intervention in Capture
Use a repeating table section on the intervention form so each regimen change is its own row with its own audit trail. A participant who halves the dose in week 6 after side effects gives you a dated row with a reason, instead of an overwritten cell. Concomitant medications works the same way for background products.
Between visits, a phone diary captures doses as they happen, with reminders and completion windows, and the medication adherence and response diary is a ready starting point. Compare the diary with the returned-product count at the visit and record the difference; the pill-count formula is covered in medication adherence tracking. Capture does not calculate adherence percentage for you, so derive it in analysis from the recorded dates and counts.
For exercise, sleep or recovery interventions, the diary templates for sleep, recovery and HRV companion cover the participant-reported side, and wearable data can supply the objective side.
Build the repeating intervention table, add a lab panel with units and ranges, and enter a dose change with a reason. Free sandbox, no credit card.
Biomarker panels
A biomarker panel in a longevity study usually repeats at baseline and every few months: metabolic markers, lipids, inflammatory markers, hormones, sometimes an epigenetic age estimate, plus functional tests such as grip strength or walking tests. The scientific caution is that many of these markers are variable, assay-dependent and not validated as surrogate endpoints, so the study should name a primary endpoint in advance and treat the rest as exploratory. The page on epigenetic clock biomarker trials goes into that specific case.
In Capture a lab form is a table with analytes as rows and unit, result and reference-range columns, split by sex and age where the source has them. The AI form builder can draft that table from an uploaded lab report layout, CSV or spreadsheet, and nothing is saved without review. See lab data and reference range management and the laboratory results eCRF. If samples go to different laboratories, record the laboratory as a field, because ranges and assays differ.
Repeat measurements raise a simple discipline: take the same sample under the same conditions each time (fasting state, time of day, days since last dose) and record those conditions on the form. Fields for fasting status and collection time cost seconds at entry and save arguments at analysis. A value outside the plausible range raises an auto-query at entry, so a mistaken unit does not reach the analysis file.
Collection conditions
Fasting at least 8 hours
Collection time
Laboratory
Results
HbA1c
LBORREShs-CRP
LDL cholesterol
Auto-query: value outside plausible range, check unit
Wearables
Capture supports native wearable connection rather than file import. The study team enables a device for the study and can mark it as required. The participant connects their own account by signing in with the device vendor, and data then syncs automatically, with last-sync time, a Sync now button and a way to disconnect. Oura is the device named on the Capture intro page; confirm any other device with us before you build a protocol around it.
For analysis, keep the wearable summary and the clinic biomarkers linked by participant and date. Decide beforehand which wearable metrics are endpoints and which are context, since devices report many measures and their algorithms can change. The guide to the best EDC for wearable data and EDC for wearable device studies cover the design questions, and the FDA digital health technologies guidance page covers the regulatory side.
Setup checklist
One biomarker or function measure; others labelled exploratory.
Product, dose, unit, frequency, dates and reason for change.
Diary, returned count or both; formula written into the analysis plan.
Analytes, units, reference ranges by sex and age, laboratory field.
Fasting, time of day, days since last dose.
Consent names the data; device enabled per study; connection checked at baseline.
Protocol, consent and recruitment materials reviewed before enrolment.
Software that captures the data of an intervention study on ageing or healthspan: consent, the intervention record, repeat biomarker panels, functional tests, participant-reported outcomes and, where used, wearable data, with an audit trail.
That page gives the overview of EDC for longevity trials. This one is about tracking the intervention and the repeat biomarker panels in detail: dose logs, adherence, lab tables and collection conditions.
Yes. The study team enables a device for the study and the participant connects their own account through the vendor's sign-in; data then syncs automatically. Oura is the device named on the intro page. Confirm other devices before planning around them.
Not as a built-in field. Change from baseline and composite scores are best computed in analysis from the recorded values and dates. Capture does calculate a fixed set of clinical formulas such as BMI, eGFR and QTc.
Use a repeating table section on the intervention form. Each change is a row with its dates and reason, and each row carries its own field-level audit trail.
The AI form builder can read an uploaded CSV, XLSX, PDF or DOCX and draft a lab table with units and ranges for you to review. Nothing is saved without human review.
A study intended to produce generalisable knowledge generally does. Consent should name the data collected, including wearable data. This is not legal or medical advice.
Yes. The free sandbox includes every feature with no credit card and no time limit. You pay only when you go live with real participants.
Keep exploring
EDC for longevity clinical trials
The EDC overview.
How to run a longevity clinical trial
Step-by-step set-up guide.
EDC for epigenetic clock trials
Biological age endpoints.
Best EDC for wearable data
Wearable design questions.
Lab data and reference ranges
Panels that stay comparable.
Medication adherence diary
Dose diary template.
Build the dose log, the panel and the diary in the free sandbox. No credit card; pay only when you go live.