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Postgraduate Diploma in Wearable Devices for Clinical Research

800-hour online Postgraduate Diploma in Wearable Devices for Clinical Research. 8 modules covering ISO 14155 (clinical investigation of medical devices), ICH E6 (good clinical practice) and REDCap. Assigned tutor and open enrolment.

Duration800 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Define digital biomarkers and their contexts of use.

02

Select wearable devices suitable for a clinical study.

03

Apply the V3 framework for verification, analytical validation and clinical validation.

04

Design clinical validation studies of digital measures.

05

Integrate wearables into decentralised clinical trials.

06

Process and analyse sensor data using reproducible methods.

07

Meet regulatory, ethical and privacy requirements.

Profile

Who it's for

  • Digital biomarker specialist who wants to specialise in Wearable Devices for Clinical Research.
  • Clinical trial manager who wants to specialise in Wearable Devices for Clinical Research.
  • Clinical data scientist who wants to specialise in Wearable Devices for Clinical Research.
  • Wearables validation engineer who wants to specialise in Wearable Devices for Clinical Research.
  • Digital clinical operations manager who wants to specialise in Wearable Devices for Clinical Research.
  • Biostatistician who wants to specialise in Wearable Devices for Clinical Research.
  • Engineering or science graduates looking to enter the pharmaceutical industry.
Standards & tools

What you'll work with

  • ISO 14155 (clinical investigation of medical devices)
  • ICH E6 (good clinical practice)
  • Regulation (EU) 2017/745 (MDR)
  • 21 CFR Part 11 (electronic records)
  • FDA guidance on digital health technologies for remote data acquisition in clinical investigations (2023)
  • ISO 14971 (risk management)
  • Regulation (EU) 2016/679 (GDPR)
Syllabus

8 modules · 800 h

  1. 1.1Definition of digital biomarker
  2. 1.2Digital outcome measures
  3. 1.3V3 framework for verification and validation
  4. 1.4Context of use
  5. 1.5Heart rate, activity and sleep
  6. 1.6Gait and mobility
  7. 1.7Voice and cognition biomarkers
  8. 1.8Accepted digital endpoints
  9. 1.9DiMe initiatives and consortia
  10. 1.10Case study: gait speed endpoint accepted by the EMA
  1. 2.1Technical selection criteria
  2. 2.2Consumer grade versus medical grade
  3. 2.3Access to raw data
  4. 2.4Battery life and adherence
  5. 2.5Body placement
  6. 2.6Firmware and version control
  7. 2.7Availability and logistics
  8. 2.8Supplier evaluation
  9. 2.9Cost per participant
  10. 2.10Practical: selection matrix for a Parkinson's trial
  1. 3.1Sensor verification
  2. 3.2Analytical validation of algorithms
  3. 3.3Comparison with reference
  4. 3.4Bland-Altman and agreement
  5. 3.5Test-retest reliability
  6. 3.6Validation in the target population
  7. 3.7Bias by skin tone and age
  8. 3.8Real-world conditions
  9. 3.9Technical documentation
  10. 3.10Laboratory: validation of a pedometer against video
  1. 4.1Construct validity
  2. 4.2Responsiveness to change
  3. 4.3Minimal clinically important difference
  4. 4.4Correlation with clinical scales
  5. 4.5Observational studies
  6. 4.6Intra- and inter-subject variability
  7. 4.7Patient-level significance
  8. 4.8Qualification with the EMA
  9. 4.9FDA guidance on digital health technologies
  10. 4.10Workshop: responsiveness-to-change analysis
  1. 5.1Protocol and digital endpoints
  2. 5.2Participant training
  3. 5.3Adherence and wear time
  4. 5.4Device management and shipping
  5. 5.5Data platforms
  6. 5.6Integration with EDC
  7. 5.7Data quality monitoring
  8. 5.8Missing data
  9. 5.9Safety and adverse events
  10. 5.10Case study: operational plan for a wearables trial
  1. 6.1Preprocessing of accelerometry signals
  2. 6.2Wear-time detection
  3. 6.3Activity and sleep algorithms
  4. 6.4Machine learning
  5. 6.5Daily and weekly aggregation
  6. 6.6Longitudinal analysis with mixed models
  7. 6.7Pipeline reproducibility
  8. 6.8Data standards
  9. 6.9Visualisation
  10. 6.10Laboratory: actigraphy pipeline with GGIR
  1. 7.1Wearables as medical devices
  2. 7.2Research data and GDPR
  3. 7.3Consent for continuous data
  4. 7.4International transfers
  5. 7.5Cybersecurity
  6. 7.6Data ownership
  7. 7.7Equity and the digital divide
  8. 7.8Ethics committees
  9. 7.9Return of results
  10. 7.10Practical: data protection impact assessment
  1. 8.1Clinical question
  2. 8.2Selection of the digital biomarker
  3. 8.3Device selection
  4. 8.4Validation plan
  5. 8.5Study design
  6. 8.6Data and analysis plan
  7. 8.7Ethical and regulatory analysis
  8. 8.8Budget
  9. 8.9Report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is REDCap, R, Python (pandas), MATLAB, ActiLife (ActiGraph), in free, academic or evaluation versions. Trials are studied using real analytical validation procedures against a reference device, pilot adherence studies with volunteers and decentralised clinical trial protocols. Assessment is continuous by module, plus the final integrative project.

Accreditable laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with a track record in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as core pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Clinical question

Define digital biomarkers and their contexts of use.

Selection of the digital biomarker

Select wearable devices suitable for a clinical study.

Device selection

Apply the V3 framework for verification, analytical validation and clinical validation.

Validation plan

Design clinical validation studies of digital measures.

Study design

Integrate wearables into decentralised clinical trials.

Career opportunities

Where this takes you

Digital biomarker specialistClinical trial managerClinical data scientistWearables validation engineerDigital clinical operations managerBiostatistician
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish to follow the reading of technical regulations.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 800 hours, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

You will work with REDCap, R, Python (pandas), MATLAB, ActiLife (ActiGraph). Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An own SEIUM diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry of Education.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the higher programme. Check the recognition policy when you enrol.

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