Ringo
Ringo
Predictive Fleet Health Management platform | iOT
0 to 1 - B2B Product
Senior Product Designer
-60% Critical Downtime
Challenge
As the Lead Product Designer, I designed for DocMe—a two-sided platform bridging the gap between proactive personal tracking and clinical decision-making to encourage early adoption for its launch in the UK.
Working as the 2 team designer in a cross-functional, 15-person remote team, I balanced complex technical and regulatory dependencies to build trust & drive adoption through the user experience.
Overview & Context
Role
End-to-End:
UX Design
UI Design
Company
DocMe - Cambridge University alumni startup backed by seed funding.
Platform
Mobile iOS
Mobile Android
SaaS Web App
Duration
1 year, 2021
Tools
Figma
GitHub
Jira
Miro
Notion
Team
Management (3),
Development (5)
Computer Vision (5)
Marketing (2)
Target users
DocMe is a two-sided platform designed to bridge the gap between proactive personal health tracking and clinical decision-making.
Patients (B2C)
Clinicians (B2B SaaS)
Goals
Launch
Build trust
Grow
Constraints
Designing for trust, engagement & adoption
Small Design, Dev, Computer Vision teams
Technical considerations
I collaborated closely cross-teams,
end-to-end, and addressed challenges across Dev, Computer Vision, Clinical Research and Legal & Compliance teams.
Problem
Patients (B2C) struggled with manual vital sign data entry for doctors, facing high input friction, fragmentation, missing critical windows for preventing health conditions
1/3 of doctors consultations
require vital signs measurements
77% of healthcare sector
is still tech-limited
by outdated software
60% doctors view
vitals & lifestyle tracking
as a missed opportunity
preventing chronic conditions.
Data derived from
Interviews: 6x Health & Fitness App Users,
Surveys: 122x Oxford University Students - different colleges (for Personal app)
Interviews: 6x Private Clinics Doctors Interview, and 3 Health Tech Startups in the UK (for Doctors app)
Solution
AI: Computer Vision / rPPG
People
Core Feature
Unified Preventative Health
I designed an intuitive mobile experience that removes the friction of fragmented tracking, turning vital signs into easily digestible insights shared directly with doctors.
The 3-Step Core Flow
The 3-Step Core Flow
Measure Vitals
Users capture accurate, real-time vital signs effortlessly using our remote camera-based selfie scan.
Assessment
The app instantly processes the data, providing clear metrics regarding heart rate, blood pressure, etc. based on NHS Protocols.
Share Results with Doctor
Users can securely share their continuous health history to their doctor replacing clunky legacy check-ups.
1
2
3
AI: Predictive ML Correlation
From Core Feature -> Product Vision
Correlating Behavior with Biometrics
While camera-based vital scanning is the platform's core technical achievement, the long-term design vision shifts the product from a reactive measurement tool into a proactive habit builder.
Add your Goals
Set health goals based on NHS guidelines across 6 core lifestyle areas to track how daily habits directly impact your vital signs.
Track behaviour
Log daily habits—like workouts—to track real-time progress toward your health goals.
Overview
Visualise daily and weekly progress toward your health goals in a unified overview.
1
2
3
Doctors
Core Feature
AI: Predictive Analytics & Anomaly Detection ML
A Secure, Integrated Clinical Dashboard
I designed the clinician interface in the secure NHS Sandpit environment to give doctors real-time vital results as single source of truth for remote triage. Identified the long term the opportunity to automate historical data and highlight key health trends and risk anomalies across the care journey.
Final Prototype
Beta Platform-NHS Sandbox & IPhone Mobile App Demo | System Interaction
Process
Based on the research opportunities,
I collaborated with stakeholders & dev to prioritise features into core & functional areas
General Product Research
Discovery
Experience architecture
AI Opportunity
Feature Prioritization
Measure Vitals
Onboarding & Sign-up
Doctors Platform
Vitals history, overview & sharing
5. Health assesment & health goals
Product area
Measure Vitals
Defining product area metrics
I took a hypothesis approach and
defined the success metrics in designing
the solutions for this feature & connected it to our long term product goal for growth
Defining adoption, engagement & retention as the top metrics, alongside influencing metrics allowed to design intentionally & track success.
1st Launch
Experimental MVP
2nd Launch
Refined Interaction Loop
3rd Update
Trust Optimisations
Core Need, Persona & early Prototyping
I mapped the initial user flow, explored entry point & design decisions based on the core JTBD identified in the discovery & research phase. Bringing tech input early helped define the task flow & early prototype.
Target User Need: Health-conscious adults (ages 25–45) want an effortless way to track post-workout metrics—like heart rate and respiratory rate—and share them directly with their doctor.
JTBD: "As a new user, I want to measure my heart rate and respiratory rate so I can track and ensure optimal health."
Challenge in Early validation
While initial interactive prototype user testing returned a 100% completion rate, we uncovered through AWS monitoring that our closed TestFlight launch a 12% failure rate across 487 measurement attempts caused by real-world positioning issues.
Combining qualitative & quantitative feedback from both the prototype user testing & monitoring our TestFlight release helped to understand users struggled with face positioning uncertainty which also affected the quality of measured data.
AI Tech constraints
Computer Vision / rPPG
I translated real-time AI model constraints into core Interaction Goals to ensure a seamless vitals scanning experience.
I collaborated with Engineering to break down key AI Computer Vision constraints to eliminate user uncertainty, ensure data accuracy & build clinical trust. This helped me frame the possible design solutions.
Product area
Onboarding & Sign-up
Product area
Doctors Platform
Iterating toward optimal guidance: I weighted intrusive vs. non-disruptive UI patterns to ensure high scan compliance without user drop-off.
I mapped data signal states to UI transitions to guide users through measuring & error recovery and to streamline dev handoff.
I integrated WCAG-compliant design tokens and native iOS accessibility approaches into the measurement feature.
User Testing
I used iterative release validation, scaling from MVP scan feasibility, to user self-correcting vitals scanning, and compliant clinical sharing.
Designed a intuitive scanning experience—moving from a generic loading indicator to user self-correcting guidance & compliant sharing flows.
1st Release
1a. Positioning: Static object-focus border.
1b. Timer: Indeterminate circular loader + basic countdown text.
2. Vitals icons:
measured vitals icons displayed.
3. Results: Modal dialog window.
2nd Release
1a. & 1b. Positioning & Timer
combined Positioning UI Element + a Real-time Progress Circle
2. Info Cards Guide the user to ensure confidence & data quality
3. Results: Expandable full-screen bottom sheet with the Share Option
3rd Release
1. Trust Info Modal: "Validation & Accuracy" badges to biometric summaries.
2. Trust Info Modal: "Email & Text" Contextual trust building expandable Modal
3. Share flow: Doctors’ NHS compliant Share option
Formulated hypotheses to guide the user & build clinical trust
across iterative releases.
1st Release
If we provide users with
🧪
a lightweight selfie scan with basic UI guidance
they will successfully capture accurate vitals without requiring complex real-time camera feedback.
2nd Release
If we provide users with
🧪
real-time visual anchoring - determinate countdown & framing indicator
they will overcome the 15-second scanning anxiety.
3rd Release
If we provide users with
🧪
trust info elements
we optimise trust and adoption.
Ensured a multi-channel validation approach connecting qualitative usability testing with quantitative analytics.
User Testing
Remote
Remote Semi Moderated | Figma Prototype
7-10 health-conscious participants (25-45 yrs)
Test Flight app
Remote
AWS Analytics
App Store/
ProductHunt Feedback
1st Release
Usability
Task completion rate
Time to completion
Error rate
Navigation path
Monitoring
Measurement Attempts & Failures
Adoption - Influencing metrics
Satisfaction
Perceived Performance
Net Promoter Score (NPS)
2nd Release
Usability
Task completion rate
Monitoring
New vs. Returning users
Engagement > Adoption - Influencing metrics
Perceived Performance
Satisfaction
3rd Release
Public ratings & sentiment
Feedback
Rating
Trust > Adoption - Influencing metrics
MAU & DAU
Retention Rate
Trust, Engagement > Adoption - Influencing metrics
Satisfaction score
Net Promoter Score (NPS)
Transformed a confusing scanning process into a trusted experience users love, driving a 68% boost in adoption and stronger user retention.
1st Release
❌
Face framing confusion drop-off
Users abandoned the flow due to face-framing ambiguity.
❌
Poor measuring videos quality
Poor measurement video quality due to lighting
2nd Release
✅
Improved user feedback
Scanning anxiety eliminated; the 15 seconds felt fast and predictable.
✅
Improved Data Quality
Seamless, user self-correction for lighting/posture.
❌
Trust issue
Trust issues regarding data accuracy
3rd Release
Adoption
+68%
✅
High User Trust & Engagement
Improved scan skepticism; users praised the real-time, transparent UI experience.
✅
Public Market Validation
Positive ratings across App Store and ProductHunt feedback channels.
Final Updates
Qualitative & quantitative feedback confirmed the measuring felt like "a sticky experience", driving daily repeat usage while meeting clinical compliance standards.
Balancing avoiding overwhelm for relevant info to help the user stay on task.
Hypothesis (1st Launch)
If we provide
a basic camera selfie scan,
users will successfully record and share their vitals despite minimal UI guidance.
Hypothesis (2nd Launch)
If we provide
real-time visual anchoring (determinate countdowns and framing indicators),
users will overcome the 15-second scanning anxiety.
Hypothesis (3rd Launch)
If we combine
cards on the dashboard, trust info data sheets,
we optimize feature discovery and medical trust.
UX/UI Implementation
• Positioning: Static object-focus border.
• Timer: Indeterminate circular loader + basic countdown text.
• Results: Modal dialog window.
UX/UI Implementation
• Positioning: Real-time, progress circle around face.
• Timer: Determinate linear progress bar with absolute countdown.
• Results: Expandable full-screen bottom sheet.
UX/UI Implementation
• Entry Point: Highly visible CTA Promotion Cards + Grid dashboard.
• Trust: Added "Validation & Accuracy" badges to biometric summaries.
• Data: Specialized card groupings for complex trends.
Testing Outcomes & User Feedback
• High anxiety during seconds 5–10 due to invisible progress status.
• Users dropped off from framing confusion.
• Data shock from dense clinical layout in small modal.
Testing Outcomes & User Feedback
• Scanning anxiety eliminated; the 15 seconds felt fast and predictable.
• Seamless, unspoken user self-correction for lighting/posture.
• High data comprehension and readability.
Testing Outcomes & User Feedback
• Clear discovery for feature
• High user trust
Improvement
• Task Completion: 100%
• Low error rate: 1.5
• Time-to-Completion: 18s vs. 15s target
• The Baseline NPS: 71.43
• Satisfaction: 85.7%
Improvement
• Satisfaction: +25.45%
• Perceived Performance: ~+40%
• Returning Users: +40%
Improvement
1.Micro-Engagement → Macro-Adoption (MAU/DAU)
Result: DAU doubled (+100%, 4 to 8 users) and MAU spiked 50% (40 to 60 users)
2. Eliminating confusion → Trust
3.Retention → B2B monetisation (2 LOIs) product stickiness.
I mapped the real time cross-platform data hand-off logic, to align with technical constraints & ensure the design solutions function seamlessly.
Mapping the telemetry data flow logic was the foundation for clinical trust and compliance. By pairing on-device AI with secure cloud architecture, we met strict NHS security bounds, created alignment, and delivered a real-time system that paved the way for product adoption.
Core Need, Persona & early Prototyping
I mapped the initial user flow, explored entry point & design decisions based on the core JTBD identified in the discovery & research phase. Bringing tech input early helped define the task flow & early prototype.
Target User Need: Health-conscious adults (ages 25–45) want an effortless way to track post-workout metrics—like heart rate and respiratory rate—and share them directly with their doctor.
JTBD: "As a new user, I want to measure my heart rate and respiratory rate so I can track and ensure optimal health."
Challenge in Early validation
While initial interactive prototype user testing returned a 100% completion rate, we uncovered through AWS monitoring that our closed TestFlight launch a 12% failure rate across 487 measurement attempts caused by real-world positioning issues.
Combining qualitative & quantitative feedback from both the prototype user testing & monitoring our TestFlight release helped to understand users struggled with face positioning uncertainty which also affected the quality of measured data.
AI Tech constraints
Computer Vision / rPPG
I translated real-time AI model constraints into core Interaction Goals to ensure a seamless vitals scanning experience.
I collaborated with Engineering to break down key AI Computer Vision constraints to eliminate user uncertainty, ensure data accuracy & build clinical trust. This helped me frame the possible design solutions.
Trade-offs
I integrated key testing data & findings to refine the measuring flow, balancing the product goal, constraints, user & compliance needs.
Design direction: To address the uncovered errors & user frustrations,
I identified 2 directions, for our core feature.
Hybrid Approach: Use upfront guidance, then fallback to real-time error prompt only if active data signals degrade. To minimizes friction while securing high data quality.
✅
Pro
Prevents scan failures early; clean camera view; lower tech complexity.
❌
Con
Static during mid-scan lighting shifts; adds pre-scan step friction.
✅
Pro
Immediate in-flight correction; zero onboarding friction; pinpoints exact error area.
❌
Con
Risk visual clutter & mid-scan movements to reset measurement, high tech complexity
Iterating toward optimal guidance: I weighted intrusive vs. non-disruptive UI patterns to ensure high scan compliance without user drop-off.
I mapped data signal states to UI transitions to guide users through measuring & error recovery and to streamline dev handoff.
I integrated WCAG-compliant design tokens and native iOS accessibility approaches into the measurement feature.
User Testing
I used iterative release validation, scaling from MVP scan feasibility, to user self-correcting vitals scanning, and compliant clinical sharing.
Designed a intuitive scanning experience—moving from a generic loading indicator to user self-correcting guidance & compliant sharing flows.
1st Release
1a. Positioning: Static object-focus border.
1b. Timer: Indeterminate circular loader + basic countdown text.
2. Vitals icons:
measured vitals icons displayed.
3. Results: Modal dialog window.
2nd Release
1a. & 1b. Positioning & Timer
combined Positioning UI Element + a Real-time Progress Circle
2. Info Cards Guide the user to ensure confidence & data quality
3. Results: Expandable full-screen bottom sheet with the Share Option
3rd Release
1. Trust Info Modal: "Validation & Accuracy" badges to biometric summaries.
2. Trust Info Modal: "Email & Text" Contextual trust building expandable Modal
3. Share flow: Doctors’ NHS compliant Share option
Formulated hypotheses to guide the user & build clinical trust
across iterative releases.
1st Release
If we provide users with
🧪
a lightweight selfie scan with basic UI guidance
they will successfully capture accurate vitals without requiring complex real-time camera feedback.
2nd Release
If we provide users with
🧪
real-time visual anchoring - determinate countdown & framing indicator
they will overcome the 15-second scanning anxiety.
3rd Release
If we provide users with
🧪
trust info elements
we optimise trust and adoption.
Ensured a multi-channel validation approach connecting qualitative usability testing with quantitative analytics.
User Testing
Remote
Remote Semi Moderated | Figma Prototype
7-10 health-conscious participants (25-45 yrs)
Test Flight app
Remote
AWS Analytics
App Store/
ProductHunt Feedback
1st Release
Usability
Task completion rate
Time to completion
Error rate
Navigation path
Monitoring
Measurement Attempts & Failures
Adoption - Influencing metrics
Satisfaction
Perceived Performance
Net Promoter Score (NPS)
2nd Release
Usability
Task completion rate
Monitoring
New vs. Returning users
Engagement > Adoption - Influencing metrics
Perceived Performance
Satisfaction
3rd Release
Public ratings & sentiment
Feedback
Rating
Trust > Adoption - Influencing metrics
MAU & DAU
Retention Rate
Trust, Engagement > Adoption - Influencing metrics
Satisfaction score
Net Promoter Score (NPS)
Transformed a confusing scanning process into a trusted experience users love, driving a 68% boost in adoption and stronger user retention.
1st Release
❌
Face framing confusion drop-off
Users abandoned the flow due to face-framing ambiguity.
❌
Poor measuring videos quality
Poor measurement video quality due to lighting
2nd Release
✅
Improved user feedback
Scanning anxiety eliminated; the 15 seconds felt fast and predictable.
✅
Improved Data Quality
Seamless, user self-correction for lighting/posture.
❌
Trust issue
Trust issues regarding data accuracy
3rd Release
Adoption
+68%
✅
High User Trust & Engagement
Improved scan skepticism; users praised the real-time, transparent UI experience.
✅
Public Market Validation
Positive ratings across App Store and ProductHunt feedback channels.
Final Updates
Learnings & next steps
As DocMe pivoted into 360Me, the boosted trust scores & stabilized +68% adoption provided the critical proof needed to reposition our core camera telemetry into a premium consumer market focused on BioAge.
Stabilizing the early MVP through improved user trust and adoption gave our pre-revenue team the insights and confidence needed to shift strategy. While the new value proposition aligns with our initial vision of correlating behavior with vitals, it strategically bypasses complex, rigid NHS regulatory dependencies to pivot directly into the premium consumer longevity market.
What worked well
Including stakeholders & tech early in the design process
Forming an understanding through multiple research approaches
What I could have done better
Define a clear scope & problem statement earlier
Better structured user testing sessions
Challenges & Opportunities
Small number of users
Small team & lack of processes
Lack of design maturity
Small early stage funding
1 full time, 1 part time Product Designers
Challenge
End-to-End
5+ Features
AI
As the Lead Product Designer, I designed for DocMe—a two-sided platform bridging the gap between proactive personal tracking and clinical decision-making to encourage early adoption for its launch in the UK.
Working as the 2 team designer in a cross-functional, 15-person remote team, I balanced complex technical and regulatory dependencies to build trust & drive adoption through the user experience.
Impact
Product Adoption
+68%
Indicator metrics
Business Traction
Accepted into 3+ Accelerators (including Cambridge Judge Business School & Panacea Accelerator UK/US).
Secured 2 Letters of Intent (LOIs) for B2B SDK integrations with Health Insurance and Fitness Tech companies
Overview & Context
Role
End-to-End:
UX Design
UI Design
Company
DocMe - Cambridge University alumni startup backed by seed funding.
Platform
Mobile iOS
Mobile Android
SaaS Web App
Duration
1 year, 2021
Tools
Figma
GitHub
Jira
Miro
Notion
Team
Management (3),
Development (5)
Computer Vision (5)
Marketing (2)
Target users
DocMe is a two-sided platform designed to bridge the gap between proactive personal health tracking and clinical decision-making.
Patients (B2C)
Clinicians (B2B SaaS)
Goals
Launch
Build trust
Grow
Constraints
Designing for trust, engagement & adoption
Small Design, Dev, Computer Vision teams
Technical considerations
I collaborated closely cross-teams,
end-to-end, and addressed challenges across Dev, Computer Vision, Clinical Research and Legal & Compliance teams.
Problem
Patients (B2C) struggled with manual vital sign data entry for doctors, facing high input friction, fragmentation, missing critical windows for preventing health conditions
1/3 of doctors consultations require vital signs data
to support their patients
77% of healthcare sector
is still tech-limited
by outdated software
60% doctors view
vitals & lifestyle tracking
as a missed opportunity
preventing chronic conditions.
Data derived from
Interviews: 6x Health & Fitness App Users,
Surveys: 122x Oxford University Students - different colleges (for Personal app)
Interviews: 6x Private Clinics Doctors Interview, and 3 Health Tech Startups in the UK (for Doctors app)
Solution
AI: Computer Vision / rPPG
People
Core Feature
Unified Preventative Health
I designed an intuitive mobile experience that removes the friction of fragmented tracking, turning vital signs into easily digestible insights shared directly with doctors.
The 3-Step Core Flow
Measure Vitals
Users capture accurate, real-time vital signs effortlessly using our remote camera-based selfie scan.
Assessment
The app instantly processes the data, providing clear metrics regarding heart rate, blood pressure, etc. based on NHS Protocols.
Share Results with Doctor
Users can securely share their continuous health history to their doctor replacing clunky legacy check-ups.
1
2
3
AI: Predictive ML Correlation
From Core Feature -> Product Vision
Correlating Behavior with Biometrics
While camera-based vital scanning is the platform's core technical achievement, the long-term design vision shifts the product from a reactive measurement tool into a proactive habit builder.
The 3-Step Core Flow
Add your Goals
Set health goals based on NHS guidelines across 6 core lifestyle areas to track how daily habits directly impact your vital signs.
Track behaviour
Log daily habits—like workouts—to track real-time progress toward your health goals.
Overview
Visualise daily and weekly progress toward your health goals in a unified overview.
1
2
3
Doctors
Core Feature
AI: Predictive Analytics & Anomaly Detection ML
A Secure, Integrated Clinical Dashboard
I designed the clinician interface in the secure NHS Sandpit environment to give doctors real-time vital results as single source of truth for remote triage. Identified the long term the opportunity to automate historical data and highlight key health trends and risk anomalies across the care journey.
Final Prototype
Beta Platform-NHS Sandbox & IPhone Mobile App Demo | System Interaction
Process
Based on the research opportunities,
I collaborated with stakeholders & dev to prioritise features into core & functional areas and isolated the vitals measuring as the core feature.
Throughout the product I customised my design strategy for each product area to tackle its specific challenges, aligning every solution with our core product vision and research.
Research : Product-Level
Discovery
Experience architecture
AI Opportunity
I mapped the real time cross-platform data hand-off logic, to align with technical constraints & ensure the design solutions function seamlessly.
Mapping the telemetry data flow logic was the foundation for clinical trust and compliance. By pairing on-device AI with secure cloud architecture, we met strict NHS security bounds, created alignment, and delivered a real-time system that paved the way for product adoption.
Feature Prioritization
Measure Vitals
Onboarding & Sign-up
Doctors Platform
Vitals history, overview & sharing
5. Health assesment & health goals
Product area
Measure Vitals
Defining product area metrics
I took a hypothesis approach and
defined the success metrics in designing
the solutions for this feature & connected it to our long term product goal for growth
Defining adoption, engagement & retention as the top metrics, alongside influencing metrics allowed to design intentionally & track success.
Core Need, Persona & early Prototyping
I mapped the initial user flow, explored entry point & design decisions based on the core JTBD identified in the discovery & research phase. Bringing tech input early helped define the task flow & early prototype.
Target User Need: Health-conscious adults (ages 25–45) want an effortless way to track post-workout metrics—like heart rate and respiratory rate—and share them directly with their doctor.
JTBD: "As a new user, I want to measure my heart rate and respiratory rate so I can track and ensure optimal health."
Challenge in Early validation
While initial interactive prototype user testing returned a 100% completion rate, we uncovered through AWS monitoring that our closed TestFlight launch a 12% failure rate across 487 measurement attempts caused by real-world positioning issues.
Combining qualitative & quantitative feedback from both the prototype user testing & monitoring our TestFlight release helped to understand users struggled with face positioning uncertainty which also affected the quality of measured data.
AI Tech constraints
Computer Vision / rPPG
I translated real-time AI model constraints into core Interaction Goals to ensure a seamless vitals scanning experience.
I collaborated with Engineering to break down key AI Computer Vision constraints to eliminate user uncertainty, ensure data accuracy & build clinical trust. This helped me frame the possible design solutions.
Trade-offs
I integrated key testing data & findings to refine the measuring flow, balancing the product goal, constraints, user & compliance needs.
Design direction: To address the uncovered errors & user frustrations,
I identified 2 directions, for our core feature.
Hybrid Approach: Use upfront guidance, then fallback to real-time error prompt only if active data signals degrade. To minimizes friction while securing high data quality.
✅
Pro
Prevents scan failures early; clean camera view; lower tech complexity.
❌
Con
Static during mid-scan lighting shifts; adds pre-scan step friction.
✅
Pro
Immediate in-flight correction; zero onboarding friction; pinpoints exact error area.
❌
Con
Risk visual clutter & mid-scan movements to reset measurement, high tech complexity
Iterating toward optimal guidance: I weighted intrusive vs. non-disruptive UI patterns to ensure high scan compliance without user drop-off.
I mapped data signal states to UI transitions to guide users through measuring & error recovery and to streamline dev handoff.
I integrated WCAG-compliant design tokens and native iOS accessibility approaches into the measurement feature.
User Testing
I used iterative release validation, scaling from MVP scan feasibility, to user self-correcting vitals scanning, and compliant clinical sharing.
1st Launch
Experimental MVP
2nd Launch
Refined interaction loop
3rd Update
Trust optimisations
Designed a intuitive scanning experience—moving from a generic loading indicator to user self-correcting guidance & compliant sharing flows.
1st Release
1a. Positioning: Static object-focus border.
1b. Timer: Indeterminate circular loader + basic countdown text.
2. Vitals icons:
measured vitals icons displayed.
3. Results: Modal dialog window.
2nd Release
1a. & 1b. Positioning & Timer
combined Positioning UI Element + a Real-time Progress Circle
2. Info Cards Guide the user to ensure confidence & data quality
3. Results: Expandable full-screen bottom sheet with the Share Option
3rd Release
1. Trust Info Modal: "Validation & Accuracy" badges to biometric summaries.
2. Trust Info Modal: "Email & Text" Contextual trust building expandable Modal
3. Share flow: Doctors’ NHS compliant Share option
Formulated hypotheses to guide the user & build clinical trust
across iterative releases.
1st Release
If we provide users with
🧪
a lightweight selfie scan with basic UI guidance
they will successfully capture accurate vitals without requiring complex real-time camera feedback.
2nd Release
If we provide users with
🧪
real-time visual anchoring - determinate countdown & framing indicator
they will overcome the 15-second scanning anxiety.
3rd Release
If we provide users with
🧪
trust info elements
we optimise trust and adoption.
Ensured a multi-channel validation approach connecting qualitative usability testing with quantitative analytics.
User Testing
Remote
Remote Semi Moderated | Figma Prototype
7-10 health-conscious participants (25-45 yrs)
Test Flight app
Remote
AWS Analytics
App Store/
ProductHunt Feedback
1st Release
Usability
Task completion rate
Time to completion
Error rate
Navigation path
Monitoring
Measurement Attempts & Failures
Adoption - Influencing metrics
Satisfaction
Perceived Performance
Net Promoter Score (NPS)
2nd Release
Usability
Task completion rate
Monitoring
New vs. Returning users
Engagement > Adoption - Influencing metrics
Perceived Performance
Satisfaction
3rd Release
Public ratings & sentiment
Feedback
Rating
Trust > Adoption - Influencing metrics
MAU & DAU
Retention Rate
Trust, Engagement > Adoption - Influencing metrics
Satisfaction score
Net Promoter Score (NPS)
Transformed a confusing scanning process into a trusted experience users love, driving a 68% boost in adoption and stronger user retention.
1st Release
❌
Face framing confusion drop-off
Users abandoned the flow due to face-framing ambiguity.
❌
Poor measuring videos quality
Poor measurement video quality due to lighting
2nd Release
✅
Improved user feedback
Scanning anxiety eliminated; the 15 seconds felt fast and predictable.
✅
Improved Data Quality
Seamless, user self-correction for lighting/posture.
❌
Trust issue
Trust issues regarding data accuracy
3rd Release
Adoption
+68%
✅
High User Trust & Engagement
Improved scan skepticism; users praised the real-time, transparent UI experience.
✅
Public Market Validation
Positive ratings across App Store and ProductHunt feedback channels.
Final Updates
Qualitative & quantitative feedback confirmed the measuring felt like "a sticky experience", driving daily repeat usage while meeting clinical compliance standards.
Balancing avoiding overwhelm for relevant info to help the user stay on task.
I collaborated with the Clinical Research Team to define the sharing of vitals with the doctor.
And updated the flow based on NHS policies.
Some of the design principles I applied.
Product area
Onboarding & Sign-up
Product area
Doctors Platform
Design Decisions
From Core Feature -> refining the Workflow
Guiding the Doctor through
the Patient-Doctor Synch workflow
While testing data reflection within a secure NHS environment solved our core engineering and regulatory dependencies, the design refinements I focused further prioritized optimizing the workflow such as reducing unclear process expectations.
Learnings & next steps
As DocMe pivoted into 360Me, the boosted trust scores & stabilized +68% adoption. This provided the critical proof needed to reposition the core camera telemetry further into a premium consumer market focused on BioAge.
Stabilizing the early MVP through improved user trust and adoption gave our pre-revenue team the insights and confidence needed to shift strategy. While the new value proposition aligns with our initial vision of correlating behavior with vitals, it strategically bypasses complex, rigid NHS regulatory dependencies to pivot directly into the premium consumer longevity market.
What worked well
Including stakeholders & tech early in the design process
Forming an understanding through multiple research approaches
What I could have done better
Define a clear scope & problem statement earlier
Better structured user testing sessions
Challenges & Opportunities
Small number of users
Small team & lack of processes
Lack of design maturity
Small early stage funding
1 full time, 1 part time Product Designers
Product Adoption
+68%
Indicator metrics
Business Traction
Accepted into 3+ Accelerators (including Cambridge Judge Business School & Panacea Accelerator UK/US).
Secured 2 Letters of Intent (LOIs) for B2B SDK integrations with Health Insurance and Fitness Tech companies