FOXO Project
Meal Feedback
Meal Feedback
Making meal logging more meaningful with AI-powered feedback.
An exploration of how I designed a meal analysis experience to help FOXO members better understand their meals and stay engaged with their health routines.
01 / Overview
01 / Overview
Role
Role
Product Design Intern
Product Design Intern
Timeline
Timeline
4 weeks
4 weeks
Team
Team
Solo Product Designer
Solo Product Designer
Platform
Platform
Mobile
Mobile
FOXO members logged their meals so nutritionists could track their eating habits and guide them according to their individual health protocols.
However, feedback largely came through nutritionist calls, leaving members with limited visibility into how their meals aligned with their goals.
I worked on integrating AI-powered meal feedback into the existing logging flow, making feedback available directly in the app.
FOXO members logged their meals so nutritionists could track their eating habits and guide them according to their individual health protocols.
However, feedback largely came through nutritionist calls, leaving members with limited visibility into how their meals aligned with their goals.
I worked on integrating AI-powered meal feedback into the existing logging flow, making feedback available directly in the app.
02 / Problem
02 / Problem
Members could log their meals, but they couldn't easily understand what those meals meant for their progress.
MEMBER CHALLENGE:
Feedback was largely dependent on nutritionist calls. Members lacked timely, meal-specific guidance to understand what their meals were doing well and where they could improve.
BUSINESS CHALLENGE:
Consistent meal logs were important for nutritionists to assess members' habits and track progress, but motivation to log meals was low.
WHAT THE DATA SHOWED:
Of 50 members observed, only 15 were logging their meals consistently.
Members could log their meals, but they couldn't easily understand what those meals meant for their progress.
MEMBER CHALLENGE:
Feedback was largely dependent on nutritionist calls. Members lacked timely, meal-specific guidance to understand what their meals were doing well and where they could improve.
BUSINESS CHALLENGE:
Consistent meal logs were important for nutritionists to assess members' habits and track progress, but motivation to log meals was low.
WHAT THE DATA SHOWED:
Of 50 members observed, only 15 were logging their meals consistently.
Members could log their meals, but they couldn't easily understand what those meals meant for their progress.
MEMBER CHALLENGE:
Feedback was largely dependent on nutritionist calls. Members lacked timely, meal-specific guidance to understand what their meals were doing well and where they could improve.
BUSINESS CHALLENGE:
Consistent meal logs were important for nutritionists to assess members' habits and track progress, but motivation to log meals was low.
WHAT THE DATA SHOWED:
Of 50 members observed, only 15 were logging their meals consistently.
03 / Research & Key Insights
03 / Research & Key Insights
We reviewed the meal-logging challenge to understand why regular logging wasn't translating into a better understanding of their meals.
01 — Logging lacked immediate value
Members were contributing meal information but couldn't easily see what they were learning from it.
02 — Meal-specific guidance was missing
Without clear feedback on individual meals, members had limited visibility into what was working and what needed attention.
03 — The reasoning behind nutrition feedback wasn't visible
Members needed a clearer understanding of how their meals were being assessed against their nutritional goals.
We reviewed the meal-logging challenge to understand why regular logging wasn't translating into a better understanding of their meals.
01 — Logging lacked immediate value
Members were contributing meal information but couldn't easily see what they were learning from it.
02 — Meal-specific guidance was missing
Without clear feedback on individual meals, members had limited visibility into what was working and what needed attention.
03 — The reasoning behind nutrition feedback wasn't visible
Members needed a clearer understanding of how their meals were being assessed against their nutritional goals.
We reviewed the meal-logging challenge to understand why regular logging wasn't translating into a better understanding of their meals.
01 — Logging lacked immediate value
Members were contributing meal information but couldn't easily see what they were learning from it.
02 — Meal-specific guidance was missing
Without clear feedback on individual meals, members had limited visibility into what was working and what needed attention.
03 — The reasoning behind nutrition feedback wasn't visible
Members needed a clearer understanding of how their meals were being assessed against their nutritional goals.
04 / Design Goals
04 / Design Goals
01 — Make meal feedback easy to understand
Help members quickly grasp how their meal aligns with the nutritional parameters being assessed.
02 — Make the feedback actionable
Show which nutritional parameters were detected and where the meal may need improvement.
03 — Fit naturally into the existing logging flow
Introduce feedback without making meal logging feel like a longer or more complicated task.
04 — Communicate feedback clearly across different states
Make the experience understandable when analysis succeeds, detects no parameters, or cannot be completed.
01 — Make meal feedback easy to understand
Help members quickly grasp how their meal aligns with the nutritional parameters being assessed.
02 — Make the feedback actionable
Show which nutritional parameters were detected and where the meal may need improvement.
03 — Fit naturally into the existing logging flow
Introduce feedback without making meal logging feel like a longer or more complicated task.
04 — Communicate feedback clearly across different states
Make the experience understandable when analysis succeeds, detects no parameters, or cannot be completed.
05 / current flow
01 — Start a meal log
Members tap the add button to log a meal.
02 — Add meal photos
Members can capture a photo or upload one from their gallery.
03 — Add context
Members can include a meal description to give the clinical team more context.
04 — Save the meal
Members submit the entry to their meal log.
Gap identified
The existing flow captured meal information, but didn't provide immediate, meal-specific feedback. The opportunity was to make logging more valuable by helping members understand their meals within the same experience.
06 / Design Exploration
Deciding the plate design
1
How I went from here…
To here…
Using plate to show
analysis
2
Finalising gestures
3
Showing macro-breakdown
4
Thumbnail gestures
5
1
From meal photos to a familiar visual
Seeing the meal photos members uploaded inspired me to explore a plate as the central visual for feedback. It connected the analysis to the meal itself, rather than presenting the results as an abstract score.
Actual meal logs inspired the plate design
2
Beyond a single score
I explored different ways to represent meal quality, including percentages, macro portions, motivational messages and gestures.
The challenge was to make the feedback informative without reducing a complex meal to a single score. I worked through these options with product and business stakeholders to find a suitable balance.
Using color gradients ranging from red-to-green to convey feedback
Using same color to demarcate macro's presence
Meal score in %
Text appreciation
3
Making feedback scannable
I explored several gesture options before settling on three hand gestures to indicate how many of the assessed nutritional parameters were detected.
The goal was to give members a quick visual summary, supported by more detailed information on the screen
One parameter detected
Two parameters detected
Three parameters detected
Conceptualization through sketches
4
Adding detail behind the summary
The gestures provided a quick overview, but members also needed to understand what contributed to that feedback.
I explored how to show protein, fibre and fibre diversity, including the minimum thresholds used to determine whether each parameter was detected.
Iteration 1
Iteration 2
Iteration 3
5
Extending feedback beyond the analysis screen
I explored how the same gesture system could appear on meal thumbnails, allowing members to recognise the feedback when revisiting their logged meals.
This kept the visual language consistent between the detailed analysis and the meal log.
Iteration 1
Iteration 2
Iteration 3
Iteration 4
07 / Final Solution
05 / current flow
I integrated AI-powered meal analysis into FOXO's existing meal-logging flow.
After members upload meal photos, the experience presents a visual summary of the analysis, followed by an overall AI-generated explanation and a breakdown of the nutritional parameters assessed: protein, fibre and fibre diversity.
A three-gesture system provides a quick overview, while the detailed breakdown explains which parameters met their minimum thresholds. Members can review their meal inputs alongside the feedback, and the result can also appear on meal thumbnails.
The design also accounts for cases where analysis cannot be completed or no parameters are detected.
01 — Start a meal log
Members tap the add button to log a meal.
02 — Add meal photos
Members can capture a photo or upload one from their gallery.
03 — Add context
Members can include a meal description to give the clinical team more context.
04 — Save the meal
Members submit the entry to their meal log.
06 / Design Exploration
1
From meal photos to a familiar visual
Seeing the meal photos members uploaded inspired me to explore a plate as the central visual for feedback. It connected the analysis to the meal itself, rather than presenting the results as an abstract score.
Actual meal logs inspired the plate design
2
Beyond a single score
I explored different ways to represent meal quality, including percentages, macro portions, motivational messages and gestures.
The challenge was to make the feedback informative without reducing a complex meal to a single score. I worked through these options with product and business stakeholders to find a suitable balance.
Using color gradients ranging from red-to-green to convey feedback
Using same color to demarcate macro's presence
Meal score in %
Text appreciation
3
Making feedback scannable
I explored several gesture options before settling on three hand gestures to indicate how many of the assessed nutritional parameters were detected.
The goal was to give members a quick visual summary, supported by more detailed information on the screen
Conceptualization through sketches
4
Adding detail behind the summary
The gestures provided a quick overview, but members also needed to understand what contributed to that feedback.
I explored how to show protein, fibre and fibre diversity, including the minimum thresholds used to determine whether each parameter was detected.
Iteration 1
Iteration 2
Iteration 3
5
Extending feedback beyond the analysis screen
I explored how the same gesture system could appear on meal thumbnails, allowing members to recognise the feedback when revisiting their logged meals.
This kept the visual language consistent between the detailed analysis and the meal log.
Iteration 1
Iteration 2
Iteration 3
Iteration 3
07 / Final Solution
I integrated AI-powered meal analysis into FOXO's existing meal-logging flow.
After members upload meal photos, the experience presents a visual summary of the analysis, followed by an overall AI-generated explanation and a breakdown of the nutritional parameters assessed: protein, fibre and fibre diversity.
A three-gesture system provides a quick overview, while the detailed breakdown explains which parameters met their minimum thresholds. Members can review their meal inputs alongside the feedback, and the result can also appear on meal thumbnails.
The design also accounts for cases where analysis cannot be completed or no parameters are detected.
08 / Final Screens & Design Decisions
08 / Final Screens & Design Decisions
A quick summary, with the meal still in context
The screen separates the AI analysis from the member's original meal inputs. The plate, fork and spoon provide a familiar visual summary, while the uploaded photos and optional description remain available for reference.
Why this design: Members can review the feedback without losing sight of the meal that was analysed.
A quick summary, with the meal still in context
The screen separates the AI analysis from the member's original meal inputs. The plate, fork and spoon provide a familiar visual summary, while the uploaded photos and optional description remain available for reference.
Why this design: Members can review the feedback without losing sight of the meal that was analysed.
A quick summary, with the meal still in context
The screen separates the AI analysis from the member's original meal inputs. The plate, fork and spoon provide a familiar visual summary, while the uploaded photos and optional description remain available for reference.
Why this design: Members can review the feedback without losing sight of the meal that was analysed.
Meal Feedback Screen
Meal Feedback Screen
Analysis layer
Analysis layer
Member
input layer
Member
input layer
A first-time-use bottom sheet explains what the hand gestures and progress ring represent before members encounter the feedback.
Why this design: The visual system needs context. Introducing the meaning upfront helps members interpret the feedback rather than guess what each gesture signifies.
The overall AI feedback gives members a short summary, followed by a breakdown of protein, fibre and fibre diversity.
Each parameter is assessed against its defined minimum threshold, with dedicated feedback providing more detail.
Keeping AI feedback open to improvement: Members can share feedback on the generated analysis.
Member input layer has the uploaded meal images and description (if any) to refer to, while viewing the meal.
Final Flow
Final Flow
Final Flow
After ideating on each iteration, I consolidated them and drew the final flows and cases.
After ideating on each iteration, I consolidated them and drew the final flows and cases.
After ideating on each iteration, I consolidated them and drew the final flows and cases.
01 — Start a meal log
Members tap Add to begin logging a meal.
02 — Upload meal photos
Members capture a photo or select images from their gallery.
03 — Review meal log
Members review the images and text before viewing the analysis
04 — Start AI analysis
Analysis begins after the photos are uploaded.
05— Review the results
After tapping Done, the meal appears in the log with its corresponding gesture indicator.
06— Designed for flexibility
Members can save the meal without waiting for the analysis to finish and receive a notification when the results are ready.
01 — Start a meal log
Members tap Add to begin logging a meal.
02 — Upload meal photos
Members capture a photo or select images from their gallery.
03— Review meal log
Members review the images and text before viewing the analysis
04— Start AI analysis
Analysis begins after the photos are uploaded.
05— Review the results
After tapping Done, the meal appears in the log with its corresponding gesture indicator.
06— Designed for flexibility
Members can save the meal without waiting for the analysis to finish
Designing Beyond the Happy Path
Designing Beyond the Happy Path
Designing Beyond the Happy Path
The same visual system needed to communicate different outcomes without confusing members. I explored how the plate should respond when analysis couldn't start, failed to complete, or detected none of the assessed nutritional parameters.
The same visual system needed to communicate different outcomes without confusing members. I explored how the plate should respond when analysis couldn't start, failed to complete, or detected none of the assessed nutritional parameters.
The same visual system needed to communicate different outcomes without confusing members. I explored how the plate should respond when analysis couldn't start, failed to complete, or detected none of the assessed nutritional parameters.
01 — No meal photo provided
AI analysis cannot begin without a meal photo.
02 — Analysis unsuccessful
The system couldn't complete the analysis, so the usual feedback wasn't available.
03 — No parameters detected
The analysis completed, but none of the assessed parameters met their defined thresholds.
01 — No meal photo given
AI analysis cannot begin without a meal photo.
02— Analysis unsuccessful
The system couldn't complete the analysis, so the usual feedback wasn't available.
03— No parameters detected
Analysis completed, but none of the assessed parameters met their defined thresholds.
09 / Prototype
09 / Prototype
10 / Reflection
10 / Reflection
This design opened all portals of misconceptions i had about product design. There was no right answer but the viable diable option on which product and business agreed on. i had to think of all cases, scenarios and states that widened my knowledge. a bit challenging as an intern but very fun when completed.
This design opened all portals of misconceptions i had about product design. There was no right answer but the viable diable option on which product and business agreed on. i had to think of all cases, scenarios and states that widened my knowledge. a bit challenging as an intern but very fun when completed.