Advocacy
Closing the women's health gap through data, science, and lived experience
Healthcare was not built for women. And the data proves it. Women live longer, but spend more years in poor health, with a measurable gap between lifespan and healthspan.
The evidence
Three data points that define the gap
Women Spend More Years in Poor Health
Across 183 WHO member states, women live longer than men, but spend an average of 2.4 more years in poor health or disability. This healthspan–lifespan gap defines the challenge.
"Women are living more years managing symptoms, chronic conditions, and functional strain, often during their prime working years."
Menstrual Symptoms Reduce Workplace Performance
A 2025 study found that severe menstrual symptoms directly reduce productivity through presenteeism: employees are at work but cognitively impaired, not absent.
"Many women are working through pain, brain fog, and fatigue. It's measurable."
6 in 10 Working Mothers Struggle with Work–Life Balance
60% of working mothers in Southeast Asia struggle to balance work and caregiving, driving sleep issues, anxiety, and cognitive overload, while return-to-work remains a high-risk period with little structured support.
"This isn't an individual failure. It's a systemic support gap."
The consequence
This creates a cycle that affects everyone
When women's health is under-supported, the consequences compound for individuals, organisations, and economies.
Why it matters
Closing the gap is not just ethical. It's economic
Women's health gaps impact workforce productivity, retention, and performance. Employers and insurers are absorbing rising, avoidable costs. Individuals are left navigating fragmented, confusing care pathways.
Yet most systems still treat health as episodic, reactive, one-size-fits-all, and fragmented. Blissmi is built to change that.
Our approach
Building a new evidence base for women's health
Blissmi combines biometric and lifestyle data, life-stage context, behavioural adherence tracking, and clinical and service integration, to shift healthcare from reactive treatment to predictive, personalised prevention.
Research methodology
We don't just collect data. We prove what works
Our research is designed to generate causal insight, not just correlation.
Real-World, Longitudinal Studies
We track users over time (6+ months) to measure symptom improvement, behavioural change, and adherence to personalised plans, capturing real-life health patterns across cycles and life stages.
Focused on the Highest-Impact Health Gaps
We focus on high-impact, under-addressed areas: hormonal transitions (menstrual, postpartum, perimenopause), maternal mental health, and cognitive load and return-to-work performance.
Linking Behaviour to Outcomes
We measure not just outcomes, but why they happen. Do personalised recommendations improve results? Does higher adherence lead to better outcomes? This creates causal insight, not just correlation.
Clinical and Ecosystem Validation
We integrate clinical testing (hormonal panels), physiological markers, and service provider engagement, bridging the gap between digital health and real-world care.
Focus areas
Building a comprehensive women's health dataset
Women's Full Lifespan Health
From menstruation → fertility → postpartum → menopause → longevity
Hormonal and Metabolic Health
Root-cause drivers of long-term disease
Mental and Cognitive Health
Including presenteeism, burnout, and cognitive decline
Behavioural Change and Adherence
What actually drives sustained health improvement
Collaborate
Seeking partners who share this mission
Our work is stronger with the right partners. Reach out to explore how we can build this evidence base together.
Research Institutions and Universities
- Co-develop studies
- Validate methodologies
- Publish findings
Employers and Insurers
- Pilot programmes at scale
- Generate population-level insights
- Quantify ROI and outcomes
Healthcare and Service Providers
- Integrate clinical pathways
- Validate treatment impact
- Build evidence across modalities