Case Study
Aira Health
Aira Health
Support for shift workers who work irregular schdules
Support for shift workers who work irregular schdules
Featured Highlights
Health Metric Logging
To provide meaningful AI insights for a shift worker, health metrics must be viewed as an interconnected ecosystem rather than isolated data points. For those with non-traditional schedules, the primary challenge is circadian misalignment, where the body’s internal clock clashes with external work demands.
HEALTH METRIC LOGGING
Sleep
Data identifies accumulated debt and recovery quality across irregular shifts
Hydration
Acts as a metabolic regulator to maintain alertness and core temperature during night-shift dips
Mood
serves as a psychological barometer to detect early signs of burnout or social isolation common in non-traditional schedules.
Stress
supports in distinguishing between temporary stress and chronic plateau of exhaustio
Plans Framework
Users in high-stress or irregular environments require predictability; they need to know what the app is looking at and exactly how it will respond to their changing metrics.
Plan A: Build (Capacity Available)
Focused on awareness and opportunity, this plan is designed to create momentum and measurable progress when the user has the energy to spare.
Plan B: Stabilize (Life is Inconsistent)
Focused on pattern-level regulation and recovery, this plan maintains gains and prevents setbacks during periods of unpredictability.
Plan C: Protect (Compromised Health or Energy)
A protective state for compromised energy, safety, and focusing on rest. Plan C is not a "downgrade"; it is a protective state with its own success criteria, designed to preserve trust and continuity when the user is at their limit.
This is powered by an AI Framework that interprets sleep, stress, and mood based on direction, intensity, and duration to ensure support remains predictable.
Featured Highlights
Health Metric Logging
To provide meaningful AI insights for a shift worker, health metrics must be viewed as an interconnected ecosystem rather than isolated data points. For those with non-traditional schedules, the primary challenge is circadian misalignment, where the body’s internal clock clashes with external work demands.
HEALTH METRIC LOGGING
Sleep
Data identifies accumulated debt and recovery quality across irregular shifts
Hydration
Acts as a metabolic regulator to maintain alertness and core temperature during night-shift dips
Mood
serves as a psychological barometer to detect early signs of burnout or social isolation common in non-traditional schedules.
Stress
supports in distinguishing between temporary stress and chronic plateau of exhaustio
Plans Framework
Users in high-stress or irregular environments require predictability; they need to know what the app is looking at and exactly how it will respond to their changing metrics.
Plan A: Build (Capacity Available)
Focused on awareness and opportunity, this plan is designed to create momentum and measurable progress when the user has the energy to spare.
Plan B: Stabilize (Life is Inconsistent)
Focused on pattern-level regulation and recovery, this plan maintains gains and prevents setbacks during periods of unpredictability.
Plan C: Protect (Compromised Health or Energy)
A protective state for compromised energy, safety, and focusing on rest. Plan C is not a "downgrade"; it is a protective state with its own success criteria, designed to preserve trust and continuity when the user is at their limit.
This is powered by an AI Framework that interprets sleep, stress, and mood based on direction, intensity, and duration to ensure support remains predictable.
Overview
Roles
Researcher, Interaction Design, Visual Design, Wireframing, Prototyping
Timeline
6 months
Tools
Figma, Illustrator
Tuned to your health, not the time of day.
Aira is an AI-powered health and wellbeing app designed for people whose lives do not follow predictable 9-5 routines, meant help make sense of daily health signals without pressure, guilt, or rigid optimization frameworks. It translates fragmented inputs — hydration, stress, mood, and sleep — into adaptive, emotionally intelligent guidance.
The goal was not to build another calorie counter or step tracker, but to reimagine health technology as a flexible companion that supports irregularity rather than punishing it. The core premise is to: translate fragmented health data (hydration, stress, mood, sleep) into humane, supportive guidance that adapts to real life.
Overview
Roles
Researcher, Interaction Design, Visual Design, Wireframing, Prototyping
Timeline
6 months
Tools
Figma, Illustrator
Tuned to your health, not the time of day.
Aira is an AI-powered health and wellbeing app designed for people whose lives do not follow predictable 9-5 routines, meant help make sense of daily health signals without pressure, guilt, or rigid optimization frameworks. It translates fragmented inputs — hydration, stress, mood, and sleep — into adaptive, emotionally intelligent guidance.
The goal was not to build another calorie counter or step tracker, but to reimagine health technology as a flexible companion that supports irregularity rather than punishing it. The core premise is to: translate fragmented health data (hydration, stress, mood, sleep) into humane, supportive guidance that adapts to real life.
Context
Health tracking today is built on the assumption of regularity: eight hours of nightly sleep, meals at consistent times, and activity that neatly fits into daily routines. But for millions of people, this isn’t reality. Nurses rotate shifts, ride share drivers stay up all night, parents wake every few hours, and students live in cycles of exams and deadlines. The consequences of these irregular lifestyles are disrupted circadian rhythms, chronic fatigue, increased risk of cardiovascular disease, and heightened stress.
Yet most health trackers are built around consistency, punishing users with “broken streaks” when life becomes unpredictable. In contrast, Aira prioritizes adaptability over rigidity and empathy over judgment, meeting users where they are rather than where a system expects them to be.
Guilt-Inducing Streaks
Track everything consistently. When users can’t keep up, the product response is often reminders, warnings, or silence, all of which quietly signal failure.
Inaccurate Predictions or Manual Input
Systems fail to account for daytime sleep or fluctuating energy cycles.
The "Failure" Signal
When a user cannot meet a benchmark, the app responds with warnings or silence, signaling failure
Context
Health tracking today is built on the assumption of regularity: eight hours of nightly sleep, meals at consistent times, and activity that neatly fits into daily routines. But for millions of people, this isn’t reality. Nurses rotate shifts, ride share drivers stay up all night, parents wake every few hours, and students live in cycles of exams and deadlines. The consequences of these irregular lifestyles are disrupted circadian rhythms, chronic fatigue, increased risk of cardiovascular disease, and heightened stress.
Yet most health trackers are built around consistency, punishing users with “broken streaks” when life becomes unpredictable. In contrast, Aira prioritizes adaptability over rigidity and empathy over judgment, meeting users where they are rather than where a system expects them to be.
Guilt-Inducing Streaks
Track everything consistently. When users can’t keep up, the product response is often reminders, warnings, or silence, all of which quietly signal failure.
Inaccurate Predictions or Manual Input
Systems fail to account for daytime sleep or fluctuating energy cycles.
The "Failure" Signal
When a user cannot meet a benchmark, the app responds with warnings or silence, signaling failure
Problem Statement
Problem Statement
Standard health technology is built on the "Optimization Trap", the assumption of 8 hours of sleep, regular meals, and steady activity. For shift workers, students, and new parents, these apps become a source of guilt rather than support. The design opportunity is to create a health tracker that uses AI to adapt to unpredictability, providing flexible, personalized guidance that empowers users rather than judging them.
Standard health technology is built on the "Optimization Trap", the assumption of 8 hours of sleep, regular meals, and steady activity. For shift workers, students, and new parents, these apps become a source of guilt rather than support. The design opportunity is to create a health tracker that uses AI to adapt to unpredictability, providing flexible, personalized guidance that empowers users rather than judging them.
Solution
Aira is an AI-powered health tracker built specifically for irregular lives. It uses adaptive pattern recognition to learn each user's unique rhythms, predicting fatigue and delivering empathetic, actionable guidance.
Solution
Aira is an AI-powered health tracker built specifically for irregular lives. It uses adaptive pattern recognition to learn each user's unique rhythms, predicting fatigue and delivering empathetic, actionable guidance.
Research
Research
User Research
User Research
Peer Reviewed
About 1 in 4 shift workers experience clinically significant health impacts linked to their schedule.
Approximately 15–20% of the workforce in industrialized countries participates in shift work, according to research published in the journal Annals of Occupational and Environmental Medicine. Large-scale studies have also linked irregular schedules to increased cardiovascular risk. A meta-analysis involving over 3.3 million participants, found that night shift work is associated with a 13% higher incidence of cardiovascular disease and a 27% higher risk of cardiovascular mortality. Additional findings report that shift workers experience about a 23% higher risk of heart attacks compared with day workers.
These findings illustrate how irregular schedules can contribute to chronic fatigue, sleep disruption, and long-term cardiovascular strain. Despite this, most health tracking systems continue to assume stable routines and predictable sleep cycles, leaving the realities of shift workers largely unaddressed.
Peer Reviewed
About 1 in 4 shift workers experience clinically significant health impacts linked to their schedule.
Approximately 15–20% of the workforce in industrialized countries participates in shift work, according to research published in the journal Annals of Occupational and Environmental Medicine. Large-scale studies have also linked irregular schedules to increased cardiovascular risk. A meta-analysis involving over 3.3 million participants, found that night shift work is associated with a 13% higher incidence of cardiovascular disease and a 27% higher risk of cardiovascular mortality. Additional findings report that shift workers experience about a 23% higher risk of heart attacks compared with day workers.
These findings illustrate how irregular schedules can contribute to chronic fatigue, sleep disruption, and long-term cardiovascular strain. Despite this, most health tracking systems continue to assume stable routines and predictable sleep cycles, leaving the realities of shift workers largely unaddressed.
Personas
I mapped personas across different irregular lifestyles. For each of these users, the core use cases include sleep recovery guidance, fatigue alerts, flexible goal setting, and wellness tracking that aids messy, unpredictable schedules instead of penalizing them.
Personas
I mapped personas across different irregular lifestyles. For each of these users, the core use cases include sleep recovery guidance, fatigue alerts, flexible goal setting, and wellness tracking that aids messy, unpredictable schedules instead of penalizing them.
Personas
I mapped personas across different irregular lifestyles. For each of these users, the core use cases include sleep recovery guidance, fatigue alerts, flexible goal setting, and wellness tracking that aids messy, unpredictable schedules instead of penalizing them.
Competitive Analysis
Mainstream Trackers (Apple Health, Fitbit): Descriptive but assume regularity; they offer irrelevant insights for non-traditional schedules.
High-Performance Tools (Oura, Whoop): Use predictive analytics but target elite athletes and "biohackers" rather than everyday users managing chaos.
Scheduling Apps: Help organize time but offer no guidance on the health impacts of those schedules.
Competitive Analysis
Mainstream Trackers (Apple Health, Fitbit): Descriptive but assume regularity; they offer irrelevant insights for non-traditional schedules.
High-Performance Tools (Oura, Whoop): Use predictive analytics but target elite athletes and "biohackers" rather than everyday users managing chaos.
Scheduling Apps: Help organize time but offer no guidance on the health impacts of those schedules.
Design Process
Design Process
Design Goals & Principles
SYSTEM FRAMEWORK
Adaptation Over Discipline
Plans adapt to the user’s context, energy, and life circumstances with no punishment for deviation, only gentle guidance. The system's adaptive pattern recognition will learn and adjust as the user continues.
Meaning over metrics
Data is interpreted and translated into actionable insights. Users understand why something matters, rather than being overwhelmed by raw numbers.
Normalization of rest
Pausing, taking breaks, or changing plans is expected and encouraged. Rest is framed as a natural part of progress, not failure. Fatigue alerts will provide early warnings before exhaustion sets in.
Supportive anticipation
The system predicts fatigue or stress patterns and responds proactively with personalized recommendations paired with encouragement and guidance, ensuring inclusivity for diverse needs.
Information Architecture
Aira's Taskbar
NAVIGATION
Aira's Taskbar
Given the user's irregular schedule, the hierarchy focuses on surfacing the most critical information with immediate accessibility.
Aira's Taskbar
Information Architecture
Aira's Taskbar
NAVIGATION
Aira's Taskbar
Given the user's irregular schedule, the hierarchy focuses on surfacing the most critical information with immediate accessibility.
Aira's Taskbar
Journey Map
A user journey map is essential because it contextualizes the reality of a shift worker down to the thoughts and emotions that are rarely seen or captured. In a world that never sleeps, it exposes the hidden biological friction of the post-shift cognitive lag and the profound disorientation of waking up in a dark room mid-day. By mapping these raw survival cycles, the journey identifies exact points of improvement, ensuring the app solves for the actual lived experience of the worker rather than trying to fit them into a standard 9-to-5 health framework.
Journey Map
A user journey map is essential because it contextualizes the reality of a shift worker down to the thoughts and emotions that are rarely seen or captured. In a world that never sleeps, it exposes the hidden biological friction of the post-shift cognitive lag and the profound disorientation of waking up in a dark room mid-day. By mapping these raw survival cycles, the journey identifies exact points of improvement, ensuring the app solves for the actual lived experience of the worker rather than trying to fit them into a standard 9-to-5 health framework.
User Flow
This flow illustrates how a user moves from reviewing their health data to selecting a plan within the app. Starting on the Dashboard, the user logs their sleep and navigates to the Insights page to review their weekly insight, helping them understand patterns in their sleep and recovery. From there, the user taps “Adjust Plan,” which directs them to the Plans page where available and recommended plans are presented. The user can browse these options, review plan details, and evaluate which routine best supports their needs before selecting a plan to activate.
Within this process, Aira uses predictive insights generated from the user’s logged data to highlight relevant plan suggestions, guiding the user toward routines that better support their sleep and overall health.
User Flow
This flow illustrates how a user moves from reviewing their health data to selecting a plan within the app. Starting on the Dashboard, the user logs their sleep and navigates to the Insights page to review their weekly insight, helping them understand patterns in their sleep and recovery. From there, the user taps “Adjust Plan,” which directs them to the Plans page where available and recommended plans are presented. The user can browse these options, review plan details, and evaluate which routine best supports their needs before selecting a plan to activate.
Within this process, Aira uses predictive insights generated from the user’s logged data to highlight relevant plan suggestions, guiding the user toward routines that better support their sleep and overall health.
Usability Testing
Initial testing with users, particularly nurses, provided critical insights:
USABILITY TESTING CONCERN #1
Proactive vs Reactive
Users emphasized the need for insights before fatigue set in, not after they were already exhausted.
USABILITY TESTING CONCERN #1
Reduced Cognitive Load
I simplified AI explanations to ensure they felt intuitive during high-stress work blocks.
Usability Testing
Initial testing with users, particularly nurses, provided critical insights:
USABILITY TESTING CONCERN #1
Proactive vs Reactive
Users emphasized the need for insights before fatigue set in, not after they were already exhausted.
USABILITY TESTING CONCERN #1
Reduced Cognitive Load
I simplified AI explanations to ensure they felt intuitive during high-stress work blocks.
Challenges
Challenges
1: Plans Framework
The Overall Health category became a microcosm of the entire project’s complexity. Initially, the volume of content made it difficult to maintain a distinct identity for each plan without them feeling redundant or clinical, this was particularly true for Plan C due to its low pressure approach.
From Categories to Concerns: I shifted the nomenclature from rigid "Health Categories" to user-focused "Health Concerns," making the plans feel more immediate and relevant to daily life.
The Triad Structure: To manage the volume, I established a consistent structure for every category: Steady, Fluctuation, and Strain. This allowed for three distinct plans per category (e.g., Emotional Awareness, Mood Stability, Daily Decompression), ensuring the system could support a user in any state, without overlap
Cohesion without Overlap: Each individual plan was reworked to focus on "rhythms over rules," ensuring they remained cohesive as part of the AIRA ecosystem while maintaining clear boundaries to prevent overlapping guidance.
1: Plans Framework
The Overall Health category became a microcosm of the entire project’s complexity. Initially, the volume of content made it difficult to maintain a distinct identity for each plan without them feeling redundant or clinical, this was particularly true for Plan C due to its low pressure approach.
From Categories to Concerns: I shifted the nomenclature from rigid "Health Categories" to user-focused "Health Concerns," making the plans feel more immediate and relevant to daily life.
The Triad Structure: To manage the volume, I established a consistent structure for every category: Steady, Fluctuation, and Strain. This allowed for three distinct plans per category (e.g., Emotional Awareness, Mood Stability, Daily Decompression), ensuring the system could support a user in any state, without overlap
Cohesion without Overlap: Each individual plan was reworked to focus on "rhythms over rules," ensuring they remained cohesive as part of the AIRA ecosystem while maintaining clear boundaries to prevent overlapping guidance.
2: Choice Fatigue
Problem: Originally, users had to manually navigate a "Masterlist" to find plans. Testing revealed this was overwhelming for tired shift workers.
The Solution: I pivoted to Smart Discovery, where the AI performs the heavy lifting by recommending plans based on real-time health signals. This shifted AIRA from being a "manual tool" to a "responsive companion".
2: Choice Fatigue
Problem: Originally, users had to manually navigate a "Masterlist" to find plans. Testing revealed this was overwhelming for tired shift workers.
The Solution: I pivoted to Smart Discovery, where the AI performs the heavy lifting by recommending plans based on real-time health signals. This shifted AIRA from being a "manual tool" to a "responsive companion".
Final
Final
Outcome
Aira was a very thought provoking and complex project where I integrated AI to better inform health choices and decision making.
Outcome
Aira was a very thought provoking and complex project where I integrated AI to better inform health choices and decision making.
Reflection
Healthcare + Design
Originally, I chose this topic and specified it because of my lived experience as a healthcare worker and being on an irregular schedule compared to my friends and family. I felt that this was a population that was overlooked often because they were essential workers and I wanted to translate my experience into a digital solution to address real-world fatigue and recovery needs. This project allowed me to utilize and combine both fields of study and grow as a professional in both regards.
Less Equals Calm
I learned that "calm is a feature" and that the most difficult part of AI design is knowing when to hold back. Moving away from over-engineered, prescriptive logic, I prioritized providing users with meaning and agency over raw data.
Redefining Success Metrics
In healthcare and design, success is often measured by rigid compliance, but this project taught me to value a system that is adaptable to individual needs. Which is fundamental to holistic design and user experience solutions.
Reflection
Healthcare + Design
Originally, I chose this topic and specified it because of my lived experience as a healthcare worker and being on an irregular schedule compared to my friends and family. I felt that this was a population that was overlooked often because they were essential workers and I wanted to translate my experience into a digital solution to address real-world fatigue and recovery needs. This project allowed me to utilize and combine both fields of study and grow as a professional in both regards.
Less Equals Calm
I learned that "calm is a feature" and that the most difficult part of AI design is knowing when to hold back. Moving away from over-engineered, prescriptive logic, I prioritized providing users with meaning and agency over raw data.
Redefining Success Metrics
In healthcare and design, success is often measured by rigid compliance, but this project taught me to value a system that is adaptable to individual needs. Which is fundamental to holistic design and user experience solutions.
What's Next
Plans Adjustment: Based on the user's interactions with the plans and their retention I would re-evaluate if the plans I created are appropriate. I think it can get quite confusing with the overwhelming amount of plans and text so I intend to do further refinement on which plans are most effective and supportive for shift workers.
Long-term Insight Loops: I will shift the focus from daily performance to long-term trends. By helping users reflect on patterns over weeks and months, the app reinforces self-awareness and effort instead of relying on the pressure of daily consistency.
What's Next
Plans Adjustment: Based on the user's interactions with the plans and their retention I would re-evaluate if the plans I created are appropriate. I think it can get quite confusing with the overwhelming amount of plans and text so I intend to do further refinement on which plans are most effective and supportive for shift workers.
Long-term Insight Loops: I will shift the focus from daily performance to long-term trends. By helping users reflect on patterns over weeks and months, the app reinforces self-awareness and effort instead of relying on the pressure of daily consistency.