A

I

-

e

n

a

b

l

e

d

A

I

-

e

n

a

b

l

e

d

Wellbeing Companion

Wellbeing Companion

B2C App

User Research & Analysis

UX + UI

0 → 1 Design

Product Strategy

User Testing and A/B tests

Design Iterations

This app is designed to be ‘Longevity Companion’ for people with chronic conditions, supporting lifestyle change and long-term risk reduction. Forming healthy habits with tools and insights that feels achievable and supportive for different physical abilities was imperative to this solution. This solution is developed with the guidance of trusted NHS health care experts to keep it grounded in clinical workflows. The health log, habit stacking tools and AI insights are designed using behaviour-change principles to improve outcomes at a personalised pace.

Timeframe

Timeframe

2024 / 5 months

2024 / 5 months

Client/Industry

Client/Industry

NDA /
Health & Wellbeing

NDA /
Health & Wellbeing

My Role

My Role

Product Designer and Project Lead

Product Designer and Project Lead

Team

Team

UK National Innovation Centre for Ageing

2 Researchers, 1 Product Owner,

1 AI engineer, multiple Healthcare Experts.

🔒 Confidentiality notice: Please note, this project is pre-launch and subject to confidentiality agreement. Some project details including company name, AI data and full UX journey cannot be publicly shared until the app is launched. Case study contents (designs and findings) have been generalised or modified to maintain confidentiality.

👉🏻The product team approached us with a bold requirement:

How might we design a low-friction digital product that empowers people with chronic conditions to manage their wellbeing and build healthy habits?

The Real Problem

The correlation between UK's rising chronic illnesses and lack of motivation to implement healthy lifestyle changes.

The correlation between UK's rising chronic illnesses and lack of motivation to implement healthy lifestyle changes.

The correlation between UK's rising chronic illnesses and lack of motivation to implement healthy lifestyle changes.

People are living longer, but most approaches in healthcare still focus on treating illness after it appears rather than preventing it. With chronic diseases on the rise, reactive care is becoming increasingly difficult to sustain in the UK. This creates a strong opportunity for proactive wellbeing solutions that help people stay healthy before issues develop.

As part of the NHS’s long-term shift towards preventive care, early-risk identification has grown using predictive AI and lifestyle data to identify early signs of potential chronic conditions. Research evidence suggested low adherence in existing digital health programmes due to lack of time and motivation for big health and lifestyle overhauls.

Using behaviour-change principles, this solution will focus on enabling users to take consistent steps at a personalised pace to improve their lifestyle and reduce long-term health risks.

40%

of emergency hospital admissions for chronic conditions like asthma, diabetes, and heart disease could be prevented with effective preventive care.

20%, or 1 in 5

GP appointments are for non-clinical reasons.

My Approach

👉🏻 As project lead, I led UX/UI design, facilitated research and guided product strategy in collaboration with the product owner, researchers.

👉🏻 To ground design in clinical realities and real user needs, I also included collaborative "co-discovery" consultations with NHS healthcare professionals (subject experts) and relevant chronic-condition patients to gain insights on professional and lived experiences.

👉🏻 Reframing user needs -> people's aspirations

👉🏻 Refining AI tone with more empathy and trust.

👉🏻 Discovery -> Rapid concepts -> Design -> Test -> Iterate

👉🏻 As project lead, I led UX/UI design, facilitated research and guided product strategy in collaboration with the product owner, researchers.

👉🏻 To ground design in clinical realities and real user needs, I also included collaborative "co-discovery" consultations with NHS healthcare professionals (subject experts) and relevant chronic-condition patients to gain insights on professional and lived experiences.

👉🏻 Reframing user needs -> people's aspirations

👉🏻 Refining AI tone with more empathy and trust.

👉🏻 Discovery -> Rapid concepts -> Design -> Test -> Iterate

We embedded ourselves with NHS healthcare practitioners (GPs, social prescribers, health coaches, sport trainers) and patients in the the North East of England, to understand people’s experiences with existing health & wellbeing apps. This helped us to identify what is missing, which data insights are most helpful to encourage healthy behaviours and how it can manage chronic illnesses.

Product Strategy

Turning barriers into opportunities

Alongside researchers, I helped shape the Product Strategy. I approached these problems through the longevity lens, aiming to enable longer, healthier lives and improve wellbeing outcomes. Since evidence suggested lack of motivation to big health and lifestyle "overhauls" and poor digital health literacy as key barriers to adherence in existing solutions, reducing onboarding friction and providing micro doses of health milestones became a core UX goal.

Alongside researchers, I helped shape the Product Strategy. I approached these problems through the longevity lens, aiming to enable longer, healthier lives and improve wellbeing outcomes. Since evidence suggested lack of motivation to big health and lifestyle "overhauls" and poor digital health literacy as key barriers to adherence in existing solutions, reducing onboarding friction and providing micro doses of health milestones became a core UX goal.
I helped shape the Product Strategy alongside my team at UK National Innovation Centre for Ageing, approaching these problems through longevity lens, aiming for a solution to "enable longer, healthier lives" with improved wellbeing outcomes. Early research shows low adherence to existing digital health programs, so increasing engagement was a core goal.

Barriers

Barriers

Opportunities

Opportunities

! Reactive care

! Reactive care

! Reactive care

✓ "Preventative" interventions that promote healthy and proactive behaviour change

✓ "Preventative" interventions that promote healthy and proactive behaviour change

! Poor digital health literacy

! Poor digital health literacy

✓ Guided, easy-to-understand insights and adjustable goals that support informed progress

✓ Guided, easy-to-understand insights and adjustable goals that support informed progress

✓ Guided, easy-to-understand insights and adjustable goals that support informed progress

! Big health & lifestyle overhauls

! Big health & lifestyle overhauls

✓ Informative AI insights and planner that generate sustainable, personalised milestones

✓ Informative AI insights and planner that generate sustainable, personalised milestones

Product Goals

Patient-facing

Enable people to take a more proactive role in improving their health and wellbeing

Healthcare worker-facing

Support in reducing low risk, non-medical appointments through a preventative digital solution that can be tracked and integrated in clinical workflow.

Workflow

Survey

Survey

Published a survey (qualitative) to understand people's opinions and experience with existing health & wellbeing apps

Focus Group 1

Facilitated a workshop with 20 participants to understand 'patient perspective' on AI-enabled health solution

Focus Group 2

Facilitated a session with health professionals to understand opportunities and risks of digital solutions

Design Scoping

Identified 5 key user groups and produced actionable insights to flesh out core app features

Design Phase 1

Design rapid concepts and designs for onboarding and core flows for first-time and returning users

Design Phase 2

Refining UI and UX with high-fidelity prototyping of core features and implementing gamification

Usability Testing

Moderated 1:1 usability testing with ~9 users to evaluate UX, efficiency and desirability of the app

Design Phase 3

Based on analysis and prioritised insights, refined some flows and iterated designs

Design Handover

Final prototypes and style guide documentation handed over for development

User Research & Analysis

Participatory Research

Participatory Research

Participatory Research

Survey
Solving the right problem and user needs from the get go

Survey
Solving the right problem and user needs from the get go

Survey
Solving the right problem and user needs from the get go

108 people (ages 31–85) across UK completed a qualitative survey through Voice® platform to share their opinions, motivations and frustrations with existing digital health and wellbeing solutions. Through analysis, we identified the key topics to probe into more deeply in focus groups and challenge unfounded assumptions:

High digital familiarity

Many respondents were wearable users who use health or wellbeing apps regularly.

Many respondents were wearable users who use health or wellbeing apps regularly.

High digital familiarity

Adoption barrier

Adoption barrier

Both wearable and non-wearable users cited lack of personalisation, meaningful insights and real outcomes as the main barrier.

Both wearable and non-wearable users cited lack of personalisation, meaningful insights and real outcomes as the main barrier.

Age related challenges

Age related challenges

Isolation and changing physical needs impacting how they maintain their wellbeing over time.

Isolation and changing physical needs impacting how they maintain their wellbeing over time.

Health awareness priority

Health awareness priority

High percentage of users rated “understanding my own wellbeing” as very important

High percentage of users rated “understanding my own wellbeing” as very important

Focus Groups
Balancing patient needs with those of healthcare professionals

  1. Discovery Workshop with Patients

    Participants
    Diverse patient representatives, ranging from wearable savvy to limited digitally exposed users, recruited via Voice® - citizen research & engagement platform.

    Key Insights (Generalised due to confidentiality)
    Despite differences in lived experiences, participants shared some goals, including following personal goals, social engagement to overcome isolation and access to professional advice.

  1. Roundtable Consultation with HCPs


    Participants

    Experienced NHS general practitioners, social prescriber, care coordinators, and health coaches.


    Key Insights
    (Generalised due to confidentiality)
    Healthcare professionals see value in an app that tracks wearables data and provides low risk, non-medical AI insights. The key goal being patient independence and preventative guidance. Together, we built a strategy to integrate this app in the healthcare workflow.

Healthcare Professional Perspective (HCPs)


Experience Range: GPs, social prescribers, care coordinators, health coaches

Key Insight:
HCPs see value in a non-medical app that empowers patients to stay engaged through non-medical assessment and goal setting, aligning with broader preventative care and patient independence.

Identified where this app can be fit into the current system.

The feedback analysis from both focus groups highlighted key considerations for the app features including:

Non-medical diagnosis

Habit stacking suggestions

Personalised AI nudges

Community Engagement

User Segments and Targeting

Armed with these insights, three key user segments were identified with varying lifestyle, abilities, and expectations. The app MVP focuses to onboard all these user segments.

Dissatisfied Wearable Users

*Limited information shown due to confidentiality

One key group are the 'Dissatisfied Wearable Users' who feel dissatisfied with health metrics provided by current devices and are looking for more useful data insights, interpretations and guidance. They are seeking clarity and confidence in understanding their health insights and support in making lasting lifestyle changes through structured, goal-driven tracker style solution.

UX + UI Design

The Solution

The Solution

The Solution

Habit stacking toolkit co-created with trusted clinicians

This app is designed to not feel like tracker, but as a digital wellbeing companion. The toolkit features goals and themed chapters such as building energy, restoring balance, strengthening mind. These are grounded in evidence-based techniques to reinforce healthy habits and behaviour change. Developed with healthcare experts, the progression and nudges focus on small, sustainable wins instead of drastic overhauls, driving engagement, retention and long-term motivation.

Logs that reflect progress and real wellbeing outcomes

Over time, AI integrates activity logs + emotional wellbeing check-ins to help users make sense of how their daily habits are improving their wellbeing journey. Empathic AI nudges are designed to empower users with greater self awareness and help in taking informed steps.

Reduce isolation with access to community groups

Social Wellbeing is a great way to stay well and active. The app signposts to group activity providers such as such as nature walks, workouts, mindful exercises, etc. to connect people with shared interests.

User Testing

User Testing

User Testing

User Testing Summary

User Testing Summary

User Testing Summary

The goal of user testing was to validate UX, effectiveness and desirability with high-fidelity prototypes

Recruitment
A mixed cohort of new and few returning (participants from focus group) users were recruited for comparative insights.


Methodology
• In-person moderated testing sessions

• Task-based scenarios (setting a goal, completing wellbeing assessment, interpreting AI insight)

• Think aloud protocol to capture reasoning and frustrations

• Post test surveys to measure user satisfaction, desirability and perceived usefulness.


Key Metrics
Task completion rate, customer satisfaction, system usability scale (SUS) and desirability.


The goal of user testing was to validate UX, effectiveness and desirability with high-fidelity prototypes

Recruitment
A mixed cohort of new and few returning (participants from focus group) users were recruited for comparative insights.


Methodology
• In-person moderated testing sessions

• Task-based scenarios (setting a goal, completing wellbeing assessment, interpreting AI insight)

• Think aloud protocol to capture reasoning and frustrations

• Post test surveys to measure user satisfaction, desirability and perceived usefulness.


Key Metrics
Task completion rate, customer satisfaction, system usability scale (SUS) and desirability.


Key Findings (Generalised due to confidentiality)

Key Findings (Generalised due to confidentiality)

⚠️ Desire for AI analysis to be more transparent.

💡Insight: Trust is earned through transparency.


👉🏻 Feature enhancement: While the AI insights already showed the analysis with user wellbeing data, it was buried 3 clicks deep. 68% of users interviewed were unaware of this capability. So, simple causal labels such as [hidden due to NDA] were added next to AI insights for quick understanding of the reasoning. Also, introduced a "was this helpful?" feedback loop to enhance future iterations.

⚠️ Desire for AI analysis to be more transparent.

💡Insight: Trust is earned through transparency.


👉🏻 Feature enhancement: While the AI insights already showed the analysis with user wellbeing data, it was buried 3 clicks deep. 68% of users interviewed were unaware of this capability. So, simple causal labels such as [hidden due to NDA] were added next to AI insights for quick understanding of the reasoning. Also, introduced a "was this helpful?" feedback loop to enhance future iterations.

⚠️ Risk of early dropouts due to high number of daily goals

💡Insight: Habit building must feel easy, not burdensome.


👉🏻 Feature enhancement: Implemented a progression flow that gradually adapts as users build motivation and consistency. [Details hidden due to NDA]

⚠️ Risk of early dropouts due to high number of daily goals

💡Insight: Habit building must feel easy, not burdensome.


👉🏻 Feature enhancement: Implemented a progression flow that gradually adapts as users build motivation and consistency. [Details hidden due to NDA]

Task Completion Rate

Task Completion Rate

~89%

~89%

~89%

System Usability Scale (SUS)

System Usability Scale (SUS)

78 (good)

78 (good)

Customer Satisfaction (Likert scale)

Customer Satisfaction (Likert scale)

Design Round 1

Design Round 1

3.9%

3.9%

Design Round 2

Design Round 2

4.6%

4.6%

Desirability

Desirability

Common words: Supportive, helpful, reassuring, intuitive

Common words: Supportive, helpful, reassuring, intuitive

Conclusion

Conclusion

Following two rounds of user testing, the design iterations produced improvements in usability and perception metrics. User satisfaction increased, and transparency around AI insights reduced confusion and increased trust. Desirability feedback was strongly positive with users describing the experience as supportive, helpful, reassuring and intuitive.

Although MVP was focused only on B2C patient-facing intervention, gathering feedback from both patients and trusted healthcare experts early in the design process helped with strong product positioning to ensure the app remained clinically grounded and can be connected with clinical workflows. This approach enabled us to deliver meaningful value with strong adoption potential.

Following two rounds of user testing, the redesign produced clear improvements in usability and perception metrics. User satisfaction increased, and transparency around AI insights reduced confusion and increased trust. Desirability feedback was strongly positive with users describing the experience as supportive, helpful, reassuring and intuitive.

Although MVP was focused only on B2C patient-facing intervention, gathering feedback from both patients and trusted healthcare experts early in the design process helped with product positioning, This ensured the app remained clinically grounded and aligned with real-world workflows. This approach enabled us deliver meaningful value with strong adoption potential.

Following two rounds of user testing, the redesign produced clear improvements in usability and perception metrics. User satisfaction increased, and transparency around AI insights reduced confusion and increased trust. Desirability feedback was strongly positive with users describing the experience as supportive, helpful, reassuring and intuitive.

Although MVP was focused only on B2C patient-facing intervention, gathering feedback from both patients and trusted healthcare experts early in the design process helped with product positioning, This ensured the app remained clinically grounded and aligned with real-world workflows. This approach enabled us deliver meaningful value with strong adoption potential.

Next Project