Detecting Perimenopause with Smart Watch Data

The Challenge

Over 34 symptoms are associated with perimenopause, impacting women from their late thirties through their forties. Research revealed that 1 in 3 women knew nothing about these symptoms before experiencing them, whilst 1 in 2 felt completely unprepared when symptoms began. The most impactful symptoms included brain fog, tiredness, and anxiety - all occurring during women’s busiest life stages whilst juggling careers and caring responsibilities.

Dotti’s vision was to create a solution that would automatically detect and track perimenopause symptoms using smart watch data, then provide expert advice and community support. However, they faced fundamental technical questions: Could sleep patterns, temperature fluctuations, heart rate variability, and activity data from wearables actually identify perimenopause onset? What technical infrastructure would this require? How could they prove the concept’s viability?

The startup needed a comprehensive technical and data strategy to assess feasibility, design the architecture, and create a roadmap that would support their funding applications. This required navigating health data integration complexities, addressing privacy concerns, and designing analytics approaches that could distinguish perimenopause symptoms from other health factors.

The Solution

We partnered with Clare Montgomery and the Dotti team to develop a comprehensive technical and data strategy over a three-month engagement, establishing the foundation for their proof-of-concept.

Health Data Integration Strategy

The solution evaluated multiple approaches for connecting to diverse health platforms including Apple Health, Fitbit, Garmin, and other major wearable ecosystems. The strategy identified optimal integration paths that would minimise development complexity whilst ensuring comprehensive device coverage - critical given research showing significant smart watch adoption among the target demographic.

Study Design Framework

A multi-cohort participant framework was developed to validate whether health data could meaningfully detect perimenopause. This included designing protocols for baseline data collection, symptom correlation analysis, and differentiating perimenopause patterns from other health variations. The framework specified participant qualification criteria, data collection periods, and analysis methodologies.

Technical Architecture Design

The comprehensive technical strategy covered:

• Data acquisition workflows for collecting historical and real-time health metrics
• Security and privacy frameworks ensuring GDPR compliance for sensitive health data
• Storage and processing architectures capable of handling large-scale health data
• Analytics pipeline designs for correlating health metrics with self-reported symptoms
• Integration approaches supporting multiple device manufacturers and health platforms

AI Enhancement Strategy

Beyond core data collection, the strategy specified how generative AI could transform raw health insights into personalised support, including chatbot architectures and recommendation systems. This vision helped position Dotti as more than just a tracking tool, but as a comprehensive support platform.

Results and Benefits

The technical and data strategy provided Dotti with the clarity and credibility needed to advance their vision and secure stakeholder support.

Clear Technical Direction

The comprehensive strategy enabled Dotti to:

• Understand the technical feasibility of their concept
• Identify key technical challenges and mitigation approaches
• Make informed decisions about development priorities
• Articulate their technical vision to stakeholders and investors

Chris worked with myself and the team to create a high-level data strategy that would enable us to implement a Proof of Concept… he provided knowledgeable insight in an area I had little knowledge and enabled me to build a technical scope to seek additional funding.

The deliverables included:

• Detailed technical architecture documentation
• Resource and cost projections for proof-of-concept development
• Risk assessment and mitigation strategies
• Clear roadmap for technical development phases

Validated Approach

The strategy demonstrated that:

• Health data from wearables could potentially identify perimenopause patterns
• Technical integration across multiple device ecosystems was achievable
• Privacy and security requirements could be met within the proposed architecture
• The concept had genuine potential for meaningful impact

Looking Forward

The technical foundation established for Dotti demonstrates how thoughtful technical strategy can transform an ambitious vision into an actionable plan. By providing clear technical direction and validating the approach’s feasibility, the project created a solid foundation for seeking investment and moving toward proof-of-concept development.

This work showcases how AI and smart device data could potentially transform women’s health support, providing a blueprint for other FemTech innovations seeking to leverage health data for meaningful impact.

Chris challenged our ideas with practical insight and added technical credibility to our nascent project. I would thoroughly recommend working with Chris where you need insight on data and AI, particularly when building a business case for further investment.