Narcolepsy · Type IIWakefulnessSleep attacks + difficult mornings
Predicta · accessibility UX case study
Predicting better days by designing for the hardest ones.
Predicta is a symptom-planning concept co-designed with a graduate student living with chronic illness. It explores how passive signals and low-effort check-ins could help people prepare for difficult days.
Role
Course
Type
Output
Predicta
Tuesday · 7:40 AMHighrisk today
Today’s forecast
Plan for a gentler day.
Sleep disruption, weather change, and a full calendar are raising your symptom risk.
Report how I feelRemind me later
Sleep continuity42%
Weather shift+18%
Calendar loadHeavy
Recent symptoms2 days
Built to explain risk before asking for effort.
Overview
What if a difficult day could be predicted before it begins?
Sam lives with Type 2 narcolepsy, chronic migraines, and an autoimmune condition. Energy, pain, and wakefulness can shift without warning, so the design challenge was direct: create something that still works on the day it is needed most.
Co-design with lived experience
Sam’s routines, workarounds, and feedback shaped the product from the first conversation.
Predict with passive signals
Wearable, sleep, calendar, and weather data reduce the burden of constant manual logging.
Validate with the person affected
Screen-by-screen reviews turned Sam’s feedback into concrete interaction decisions.
The accessibility test
The hardest day cannot require the most effort.
01
Predict before symptoms
02
Ask for one tap
03
Save the record
Predicta shifts the burden from the person to the system.
02 / Problem
The tools Sam tried asked for effort when energy was already low.
Mornings require medication and a wall of alarms. Afternoons can bring sudden sleep attacks. Migraine episodes make light, noise, typing, and complex navigation painful or impossible. The tracking tools meant to help become another burden when symptoms peak.
Wakefulness
Unpredictable
Narcolepsy makes waking up - and staying awake - unreliable.
Migraines
Sensory overload
Bright screens and interaction-heavy tools can make logging impossible mid-episode.
Tracking
Too much effort
Existing apps demand sustained input precisely when Sam has the least energy to give.
Lived system · interacting conditionsComplex disability is not a list. It is a system.
Autoimmune disorderEnergyFatigue + joint pain
MigrainesSensory loadLight + noise sensitivity
InflammationPainReduced movement + dexterity
One person · one dayComplex disabilityWakefulness · energy · pain · sensory load
Old modelRemember → type → make sense → repeat
Sam has to remember and explain everything during a difficult moment.
Predicta modelSense → predict → explain → ask once
Predicta reads the signals first, then asks for one simple response.
The design question
How might we help Sam anticipate a difficult day and log symptoms without demanding energy, typing, or attention?
03 / Outcome preview
Predicting risk before the day begins.
Before unpacking the process, here is the experience the research led to: a low-effort forecast that explains risk, supports one-tap reporting, and lets Sam defer logging until energy returns.
9:41
PredictaToday
Tuesday’s outlookHigh risk
Prepare before symptoms peak.
Why this forecast
Interrupted sleep
Weather change
Heavy afternoon
A forecast that respects fluctuating capacity
Support arrives before the difficult moment.
NightPassive signals
Sleep and wearable data update without asking Sam to log.
MorningExplain risk
A clear forecast shows what changed and why it matters.
EpisodeOne-tap response
Capture the moment without typing through pain or fatigue.
LaterComplete when ready
A gentle reminder preserves context after energy returns.
04 / Evidence
Six pain points became non-negotiable design requirements.
A one-hour semi-structured interview covering daily routines, assistive technology, workarounds, and frustrations showed how much infrastructure Sam had already built alone - from math alarms to weather tracking.
1:1primary co-designer
“The logging tool cannot become another symptom.”Research synthesis
Starting the day takes medication, several alarms, and repeated effort.
Fatigue and sleep attacks can interrupt plans without warning.
Typing is hardest when the information matters most.
During an episode, explaining what is happening can take too much energy.
Light, noise, and unfamiliar environments can trigger symptoms.
Sam often learns the day is difficult only after it has already started.
Research translated into requirements
Ask lessPredict earlierExplain the forecastLet Sam pauseReduce sensory strain
05 / Market gap
The tools reviewed still relied on manual effort.
We reviewed Bearable, Flo, Migraine Buddy, and Alarmy alongside passive signals already available through Apple Watch, weather data, and Sam’s TENS unit. None combined low-effort logging with meaningful prediction.
Competitive audit · four productsUseful in pieces. Exhausting as a system.
Each product solved one part of the experience. None combined passive context, meaningful prediction, and low-effort follow-through.
01
Bearable
Broad trackingPowerful tracking, but sustained logging turns the tool into another daily burden.
High input demand02
Flo
Focused interfaceA clean, low-effort experience reduces friction, but sensitive data still asks for trust.
Privacy concerns03
Migraine Buddy
Shareable historyUseful for clinical conversations, but built around detailed input during difficult moments.
Manual tracking04
Alarmy
Effective wake-upTasks can break through sleep, but puzzles punish a brain already dealing with fatigue and fog.
Cognitive loadPassive context already available
Apple Watch health streams · weather data · Sam’s TENS unit
Predicta opportunityMove effort from Sam to the system.
Predict earlier, explain clearly, and ask once.
06 / Flow decision
Ask the system to predict before asking Sam to log.
Affinity mapping grouped the research into three territories and identified unpredictability as the upstream problem. We then explored five directions before choosing a prediction-first flow: passive data before active input, one decision per screen, and no typing at the worst moment.
Affinity synthesisCo-design brainstorming
Constraints → ideas → one low-effort systemOverall app ideas
Note-taking / reminder system
Low-effort prediction system★
Show how the day may go and what precautions could help.
Wake-up system
App feature ideas
Quick note / reminder creation
Avoid nested notifications and repeated prompts
One-tap symptom log by time of day★
Prediction from symptom history
Activity tracking across the day★
Calendar + energy context
Weather + symptom correlation★
Apple Watch + health signals
TENS unit integration
Delayed reminder when feeling better
Home-screen reporting shortcut
Simple morning + evening summaries
Custom alarm system
Movement task to break sleep inertia
Light, sound or haptic stimulation
Selected systemPredicta
Use passive signals to forecast difficult periods, explain what changed, and make symptom reporting possible with one tap.
Reduce unpredictability first01Daily readiness
02Smart wake-up
03Zero-effort log
04Environment layer
05Invisible support
Selected directionPredicta
Prediction + one-tap logging + deferred follow-through
01Passive before active
02One decision per screen
03No typing at the worst moment
Sleep, weather, calendar, and wearable signals update quietly.
Predicta identifies elevated risk before Sam begins the day.
The interface shows which signals changed and what they mean.
Sam reports with one tap or chooses to be reminded later.
The system builds a useful pattern history without daily burden.
Accessibility guardrail
Every step must remain understandable when attention, dexterity, light tolerance, or wakefulness is reduced.
07 / Validation
Co-design turned feedback into the next iteration.
After an initial design critique, we walked Sam through the prototype screen by screen. This was not usability testing with a stranger; it was a design review with the person the product exists for.
Co-design review
“If I’m not good five hours from now, can I do a second remind?”Sam · primary co-designer
BeforeOne reminder
One reminder assumed Sam would feel better at a set time.
AfterStackable reminders
If Sam is not ready, one tap can postpone the reminder again.
01Less effort
02More control
03Easy to return
08 / Accessibility-first
Dark-first is an accessibility decision, not an aesthetic one.
Bright screens trigger Sam’s migraines, so the visual system prioritizes low stimulation, clear contrast, static surfaces, gentle language, and large, forgiving controls.
#0E1713Primary surface
#7BAE8AAction + status
#DDEFE3Low-stimulus wash
#F7F8F5Quiet background
Plan for a gentler day.
Clear hierarchy · sentence case · plain language44px+minimum target
4.5:1contrast goal
0required typing during an episode
1primary decision per screen
09 / Final solution
A concept for anticipating difficult days.
The prototype uses sample sleep, weather, energy, calendar, and wearable signals to explore a simple risk forecast. It shows why risk may be high, supports one-tap symptom reporting, and lets people choose “remind me later.” Forecast accuracy has not been clinically validated.
Final experience · interactive prototype
Try Predicta for yourself.
This is a working prototype. Tap through the app to check a forecast, see what is behind it, and record how you feel with less effort.
Tap inside the phone to explore the flow.
Open full-screen ↗
01
Prediction first
Today’s risk level leads the screen so Sam can plan before getting out of bed.
02
Explainable
Predicta shows why risk is elevated and offers gentle, actionable guidance.
03
Effortless
Report now and log later - without typing through pain or fatigue.
10 / Impact
A concept project, measured honestly.
Predicta has not shipped, so there are no product metrics to claim. The prototype established a validated direction; these are the measures I would use next to evaluate effort, comprehension, follow-through, and trust.
Effort
Taps and timeCan Sam log during a difficult day?
Understanding
Reason shown clearlyDoes Sam understand why the risk is higher?
Follow-through
Completed laterDoes ‘remind me later’ lead to a completed check-in?
Trust
Forecast vs. real lifeDoes the forecast match Sam’s day over time?
Prototype outcome
This project defines what to test next; it does not claim the product has launched.
11 / Reflection
Accessibility means designing for the worst day, not the average one.
I used to think accessibility meant contrast, readability, and simpler navigation - a checklist. Sam taught me it means designing for someone’s worst day, not their average one. The tasks I’d normally call easy can be impossible during a migraine.
The key lesson was to design for low-energy moments, not just ideal conditions. Next, I would test with a broader group of people with fluctuating symptoms and accessibility needs. Clinical validation of the forecasting model would be a separate step.
Built with Ruby, Clara, and David - and most importantly, with Sam.