SENSE
Empowering women with personalized workout circuits aligned to their menstrual cycle.
Introduction
Closing the Women’s Health Gap: Biometric Fitness Data
Millions of women struggle to align their fitness routines with the natural fluctuations of their menstrual cycle, leading to frustration, exhaustion, and unmet fitness goals.
Recent research shows that biometric data from wearables can identify menstrual cycle phases with up to 87% accuracy (e.g., differences in heart rate, temperature, and sleep) to enable fitness experiences that adapt to a woman’s body in real time rather than relying on static workout programs (Kilungeja et al., 2025).
SENSE ****generates personalized workout circuits based on the data provided by wearable devices to tailor fitness experiences for each phase of the menstrual cycle. This mobile application experience empowers women to work with their bodies instead of against them by simplifying cycle tracking with seamless data syncing. SENSE ****helps women feel stronger, more confident, and in control of their wellness journey.
Problem Statement
How might we help women align their fitness routines with their menstrual cycle and improve consistency with biometric data and intuitive design?
Opportunities
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Generate personalized workout circuits for each menstrual cycle phase
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Integrate seamlessly with wearable tech such as the Oura Ring
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Simplify class booking through platforms like ClassPass*
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Supports users' emotional and physical wellbeing
ClassPass is a monthly subscription service and app that provides access to a vast network of fitness studios, gyms, and so on.
Goals
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Understand how target users’ cycle impacts fitness
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Optimize workouts that match energy levels
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Building self-compassion into their routines
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Help users stay consistent and motivated in the long term
User Research
What Do Roommate Seekers Value the Most?
I conducted 7 user interviews with a group of participants who were roommate seekers and property owners, looking to share a home. My goal was to uncover their pain points and explore how they navigated the housing market and evaluated potential roommates.
Overall, I identified the universal desire for compatibility in lifestyle habits, financial reliability, and social compatibility.
Competitive Analysis
Decoding the Competition: Uncovering Insights
Looking at the competitive space was quite challenging as I had to analyze 2 different types of applications for workouts and period tracking. The following are mobile apps I performed a competitive analysis:
Fitness
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Apple Fitness
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Sweat
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Nike Train Club (NTC)
Period Tracking
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Flo
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Clue
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Lively
Overall Competitive Analysis Findings
With a clear understanding of user pain points, I moved to the ideation phase. The user interviews highlighted the following:
Strengths
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Ability to schedule and customize workouts
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Varying levels of workouts to appeal to different levels
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Personalized period tracking
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Predictive technology for periods
Weaknesses
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Subscription fatigue
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Not beginner friendly for workouts
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Data privacy concerns
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Predictive technology reduces users as an algorithm— it is not always accurate
User Interviews
A Mission to Understand
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I divided the user interview questions into understanding users’ fitness habits and how their menstrual cycles impact their fitness.
Target Audience
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Cis women who experience menstrual cycles
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Ages 22-35
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Women who exercise for 2+ hours/week
Methods
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6 user interviews
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8 participants for user surveys
User Quotes
Assumptions
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Most women experience menstrual cycles
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Most users are not aware of their menstrual cycle phases.
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Women are negatively affected by their MC emotionally, mentally, and physically which affect their workout routine.
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Women are frustrated on their period and generally avoid working out at this time.
Risks
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Collecting data would raise user concerns and risk privacy
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Finding participants who are transparent and comfortable about talking about their menstrual cycle will be difficult.
After Conducting User Interviews on 30 Women, We Found…
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The real pain point for users working out was time management issues as time management was one of their biggest blockers.
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Majority of users preferred guided workouts or a workout class as it reduces cognitive overload for them.
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Users felt differences on their menstrual cycle, but they never considered how it would impact their physical performance which led to a declining mental health.
Other Key themes
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Participants pushed themselves to workout during their period even when they knew they should be resting
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They began to have a lot of negative thoughts when they would rest and would push themselves to be active
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Even though they’ve been having periods, they never thought about how it could impact their motivation
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Participants wanted to learn more about their periods after the interview
Affinity Map
User Story
Conceptualizing: Laying the Foundation
User Flow
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The main architecture of SENSE splits between Health (cycle tracking) and Fitness so users are not overwhelmed by too much data.
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Fitness & Training
I designed 3 distinct paths based on the user’s current needs:
Workout data overview which includes general data of users physical activity.
Personalized workouts — The primary feature for SENSE. Workouts are generated accordingly to the user’s logged symptoms and current cycle phase and ends with automatic data recording.
External integration with ClassPass* — I included a flow for booking local classes that still syncs back into the SENSE system for users who want to workout with instructor led classes
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Menstrual Cycle Health
Data input and validation — Users can manually or automatically input period data through the calendar screen which the system will reference when generating workouts.
Data analysis — Users will be able to view and analyze the MC records as they will be provided with educational tips on why certain workouts were generated/suggested.
The overall structure of the product ultimately consists of the Workout Overview, Period Calendar, Workout Exploration/Generation, and User Profile.
01 Cycle Calendar
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The calendar screen includes the current month as well with the current date marked with a circle. Each week is color coordinated by the phase of the cycle as recorded with the user’s biometric data such as her temperature. Users can also manually record the start date of the menstrual cycle.
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Tapping a date on the screen will show information regarding the cycle associated with that day, possible symptoms, and recommended exercises. Users can also edit the date ranges of those phases as well as choose a color to display in the calendar for it.
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The area where users can tap to change the color palette was too small, so I designed it in the mid-fidelity design where users can easily access the color palette. Instead of allowing users to pick a color for each phase, I thought to have pre set color palettes for users to choose from to reduce cognitive load.
First Iteration
User Feedback
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60% of participants stated that although the ability to choose a color palette for each cycle phase as it allowed a personalized touch, it was not necessary and it may be confusing to keep up with too many colors.
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Solution
I moved the color customization feature out of the calendar screen to reduce the visual and cognitive complexity to keep the primary calendar workflow focused on tracking and planning. While participants did enjoy the customization option, user testing revealed that it was not essential in this context. As a result, I relocated it to the settings screen to simplify the overall experience. This change reinforced the importance of separating core tasks from secondary personalization features to maintain focus and reduce friction.
Final Iteration
02 Workout Generation
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The workout generation screen allows users to explore workouts, view their favorite workouts, generate a workout, and view their upcoming workout class booked through ClassPass.
Low-fidelity Wireframes
First Iteration
User Feedback
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70% of participants stated that they did not think the workout generation portion of the application had enough customizations as they wanted to be able to have the option to set the following:
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How intense the workout would be
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The type of workout they wanted to do
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Targeting certain muscle group(s)
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Workout duration
This feedback reinforced that menstrual experiences and fitness preferences are not universal. It was a good reminder as a designer that data-driven personalization must still be balanced with user autonomy. After all, I wanted to design a mobile experience that allows for users to feel more in sync with their biology instead of forcing them into rigid cycle-based rules and data.
Final Iteration
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The final iteration gives users full control over generating their workout by allowing them to define the duration, difficulty, intensity, workout type, and muscle group. By shifting personalization in to the user’s control, the experience became more flexible and responsive to individual needs. In follow up testing, 100% participants described the workout generation flow as highly personalized and aligned with how they personally preferred to train.
The Final Design
Reflection
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Designing SENSE helped me realize that while biometric insights provided powerful opportunities for personalization, user research consistently showed that flexibility, clarity, and choice ultimately mattered to users. This project challenged me to design systems that guide rather than dictate users by prioritizing trust in every step. I also learned how to translate complex physiological data into intuitive experiences while respecting individual preferences.
Final thought
Designing SENSE taught that wellness is not one-size-fits-all. Empowerment starts when we give users tools to understand and honor their own rhythms.
If I could go back in time and redo this project, I would…
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Run a longer term usability study to observe how users interact with cycle-based recommendations over multiple cycles. This way, I could uncover patterns around trust.
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Investigate edge cases more deeply such as exploring users with irregular cycles, on hormonal birth control, or recovering from illness or injury.
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Place greater emphasis on task completion rates during user interviews.
Future ideas for SENSE
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Implementing goal based workouts such as strength building, isometrics, and fat loss.
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Implementing a feature for adding friends and allowing them to have friendly competitions with one another.
Citation
Kilungeja, G., Graham, K., Liu, X., & Nasseri, M. (2025). Machine learning-based menstrual phase identification using wearable device data. Npj Women’s Health, 3(1). https://doi.org/10.1038/s44294-025-00078-8