Despite the promise of personalized digital health, many tools — from fitness trackers to health apps and wearables — remain blind to the needs of women and gender-diverse users. Addressing this oversight requires a shift toward inclusive design, evidence-based validation, and meaningful user engagement that accounts for gendered experiences across the healthcare continuum.
The Personalization Paradox
Digital health technologies promise a revolution in care — offering personalized, real-time data to optimize everything from daily wellness to chronic disease management. Wearables, health apps, remote monitoring platforms, and artificial intelligence (AI)-powered diagnostics are designed to empower users and enable precision medicine at scale. But for too many people, that promise remains unfulfilled.
Despite their rapid growth, most digital health products continue to reflect a narrow, male-centered design paradigm. From algorithmic baselines trained predominantly on male data to wearables that fail to account for menstrual cycles or body morphology, these tools often overlook the physiological, behavioral, and healthcare needs of women, transgender, and non-binary users. Gendered experiences of health — ranging from hormonal rhythms to caregiving roles — are rarely considered in product development, let alone embedded into device logic or data interpretation.
The result is a fundamental mismatch between who these technologies claim to serve and how they are actually designed. Instead of delivering truly personalized care, many tools reinforce existing inequities and erode user trust, particularly among those already marginalized in traditional healthcare systems. For digital health to fulfill its transformative potential, gender inclusivity must move from a secondary consideration to a core design principle — beginning not with the user as a data point, but as a complex and diverse individual.
Historical Bias Meets Emerging Tech
The gender bias embedded in today’s digital health tools is not a new problem — it is the digital inheritance of longstanding exclusion in both medical research and technology design. Historically, clinical studies have disproportionately focused on male subjects, both human and animal, under the assumption that male bodies offer a simpler or more “standard” baseline. This bias has carried over into the algorithms, hardware, and data frameworks that underpin digital health technologies today.1,2
Wearables and biometric tracking systems often rely on male-centric baselines that fail to account for the anatomical and hormonal variability that defines many users' experiences. Devices may misread or underperform when worn on breasts, misinterpret hormonal fluctuations during the menstrual cycle or menopause, or entirely overlook gender-affirming physiological changes among trans and non-binary individuals. Despite purporting to offer precision and personalization, these tools frequently default to a “one-size-fits-men” model, rendering them less useful — or even misleading — for those outside that norm.3,4
These oversights are rarely accidental. Innovation cultures in tech and digital health have historically been male-dominated, both in terms of leadership and funding flows. Products are often developed and tested in contexts that fail to meaningfully include women or gender-diverse individuals, resulting in design decisions that privilege efficiency, aesthetics, or performance metrics that align with male users’ needs and experiences. Even before the complexity of health enters the conversation, we can observe countless examples of design that fails to acknowledge the female experience, from cell phones that can be better manipulated in a larger, male hand than in a smaller, female one, to phone-based step tracking apps that assume that the phone is carried in a pants pocket –– an assumption that overwhelmingly favors male users. At the same time, commercial incentives have historically prioritized generalized scalability over inclusive usability, favoring mass-market tools over solutions tailored to underrepresented groups.5
As digital health becomes increasingly central to preventive care, diagnostics, and therapeutic support, this structural bias is no longer tenable. Emerging technologies must break from these historical patterns — not replicate them — and that begins with rethinking the assumptions that shape how devices are conceived, tested, and brought to market.
What Exclusion Looks Like in Practice
The consequences of gender-blind design in digital health are not abstract — they show up every day in the way users experience, abandon, or misinterpret the very tools meant to support their well-being. Across the landscape of consumer health technology, patterns of exclusion persist in product functionality, data interpretation, and even basic user onboarding.
Many fitness trackers and wellness platforms, for instance, fail to account for menstruation or hormonal fluctuations. Some devices exclude menstruation tracking altogether, while others misclassify symptoms such as cramps, fatigue, or mood changes as anomalies or signs of stress.1,6 This not only limits the value of the tool for people with menstrual cycles but can lead to inappropriate health recommendations or misaligned alerts.
Physiological events like pregnancy or menopause further complicate the picture. Metrics such as heart rate variability or sleep quality are frequently misinterpreted by devices that lack adaptive algorithms sensitive to these natural changes. The result can be a barrage of misleading notifications, diminished data accuracy, or outright exclusion from platform features.1
Transgender and non-binary users often encounter even more foundational barriers. Many digital health platforms offer only binary gender options or tie biometric assumptions to sex assigned at birth, preventing users from logging transition-related data or accurately representing their identities in the system. This mismatch undermines both the usability and the trustworthiness of the technology.4
Even within the growing FemTech sector — ostensibly dedicated to women’s health — too few tools have undergone rigorous clinical validation. Many products marketed for fertility, hormone tracking, or sexual health operate without peer-reviewed studies, real-world evidence, or regulatory oversight — in some cases without even minimal female input into their design. This lack of validation raises concerns about safety, efficacy, and long-term health impacts, particularly when these tools are used for self-diagnosis or treatment decisions.7,8
Together, these design failures contribute to significant data gaps, drive user disengagement, and erode the credibility of digital health solutions. The promise of personalized, data-driven care is undermined when technologies cannot accommodate or accurately interpret the full spectrum of gendered experiences. True personalization starts with representation — both in the data that train algorithms and the people for whom these tools are built.
Consequences of Gender-Blind Design
Designing digital health technologies without accounting for gender-specific needs and experiences carries measurable consequences — ones that ripple far beyond user dissatisfaction. When devices and platforms fail to recognize or accommodate gender differences in health, the result is often compromised care. This includes missed diagnoses, delayed interventions, and poor management of chronic and reproductive conditions, particularly for users whose symptoms fall outside a male-centered norm.2,9 Even seemingly superficial elements, such as sizing and comfort, can have a huge impact on adherence, disproportionally favoring male patients with their benefits.
Such oversights are especially damaging in conditions like polycystic ovary syndrome, perimenopause, or endometriosis, where diagnostic delays are already common. When digital health tools lack the capacity to collect or interpret sex-specific biomarkers and symptoms, they exacerbate a system already prone to gender disparities in care. Users may dismiss the technology as unreliable — or worse, rely on inaccurate feedback that leads to harm.
These design flaws do not affect all users equally. Women of color, individuals with lower socioeconomic status, and LGBTQ+ populations are often further marginalized by digital health systems that fail to reflect their lived realities. Language barriers, limited access to connected devices, and culturally mismatched interfaces can all hinder adoption. At the same time, an intersectional failure to consider gender, race, and class in algorithm development can lead to outputs that reproduce the very health disparities technology claims to reduce.8,10
The result is a widening digital divide, both in terms of who uses health technology and who benefits from it. When people do not see themselves reflected in the design, they are less likely to engage meaningfully with the product. This underuse leads to data gaps that further bias machine learning models, creating a feedback loop in which exclusion becomes self-reinforcing. Models trained on incomplete or skewed data sets perform poorly for underrepresented users, reinforcing mistrust and discouraging further participation.11,12
Ultimately, the failure to center gender in digital health design weakens the foundations of personalized medicine. Trust, accuracy, and equity are not by-products — they are prerequisites. Without inclusive design, the very technologies intended to expand access and improve outcomes may end up perpetuating the inequalities they were meant to solve.
Building Better: Principles for Gender-Inclusive Design
Creating truly inclusive digital health tools begins with a fundamental shift in how technologies are conceived, developed, and validated. Gender-inclusive design is not simply about retrofitting existing platforms — it requires rethinking the entire innovation process from the ground up. This starts with who is at the table when these tools are designed.
Co-design and participatory research approaches offer a powerful corrective to the traditional, top-down model of technology development. By involving users — particularly women, trans, and non-binary individuals — from the earliest stages of ideation, developers gain access to lived experiences that reveal gaps and assumptions invisible to homogeneous teams. These approaches support the creation of products that reflect real-world needs rather than imagined user profiles.13,14
Beyond engagement, inclusive design requires systems for representing gender in more flexible and meaningful ways. The use of gender-aware ontologies in data structures — how gender is defined, categorized, and linked to physiological metrics — is essential for building models that avoid misclassification and error. These frameworks enable nuanced data collection and interpretation, recognizing that gender is not binary and that biological sex is only one part of the health equation.12
Inclusive defaults also matter. Rather than forcing users into rigid gender categories or assuming static baselines, platforms should accommodate physiological diversity — offering options to input hormone therapies, pregnancy status, surgical history, or transition-related information. Health metrics must be customizable, allowing users to define what’s relevant and meaningful for their own bodies and identities.3,4
Equally critical is transparency. Developers should clearly communicate the design intentions behind their platforms, the populations represented in their training data sets, and the clinical limitations of their outputs. This not only fosters user trust but also signals a commitment to continuous improvement. As validation studies become more robust and inclusive, companies should publish results to ensure that health claims are supported by evidence, not just marketing.7,15
These design principles are not just best practices — they are prerequisites for building tools that can deliver on the promise of digital health for all. Inclusion, when done intentionally and structurally, is a force multiplier for usability, safety, and adoption. It transforms digital health from a high-tech novelty into a trusted, effective, and equitable part of everyday care.
From Use to Impact: Clinical and Commercial Relevance
While the case for gender-inclusive digital health is often framed in terms of ethics and equity, it also holds significant clinical and commercial advantages. Inclusive design improves not only who is reached by a product but how well the product performs. Tools that reflect a wider range of user experiences, physiological profiles, and health needs generate more reliable outputs, enhance user engagement, and are better positioned for long-term adoption.
Products that incorporate sex and gender considerations from the earliest stages of development benefit from richer, more representative data. This leads to AI models that are more accurate across diverse populations — reducing error rates, improving personalization, and avoiding the legal and reputational risks associated with biased outputs. Early integration of sex and gender data also enables more robust validation strategies, strengthening the clinical evidence base that regulators and payers increasingly expect.1,8
Regulatory agencies, such as the U.S. Food and Drug Administration (FDA), are becoming more attuned to issues of bias in digital health submissions. Tools that fail to demonstrate adequate representation or performance across different sexes and gender identities may face greater scrutiny, slower approval, or requirements for additional post-market data. Conversely, platforms that proactively address these issues and provide transparent validation data are more likely to earn regulatory trust, facilitating faster market entry and broader clinical use.2
From a commercial standpoint, inclusive products also tap into a larger and more diverse consumer base. Women are among the most active users and purchasers of digital health technologies, and LGBTQ+ individuals represent a growing and underserved segment of the market. By designing for these users, companies not only meet a moral imperative, they unlock new opportunities for innovation, growth, and differentiation in a crowded marketplace.
Inclusion, in this context, is not a cost — it’s an asset. It enhances product quality, improves health outcomes, and strengthens the alignment between commercial success and clinical relevance. The companies that lead in this space will not be those that treat inclusion as a checkbox, but those that recognize it as a cornerstone of smart, scalable, and future-ready design.
Policy, Industry, and the Path Forward
Achieving gender inclusivity in digital health requires more than better design — it demands structural accountability across the entire innovation ecosystem. Regulators, funding bodies, public health agencies, startups, and major tech firms each have a critical role to play in setting expectations, enforcing standards, and incentivizing inclusive development.
Regulators can lead by establishing clear guidance on gender representation and performance benchmarks in digital health submissions. This includes requiring sex- and gender-disaggregated data in clinical validation studies, ensuring that labeling reflects intended populations, and mandating transparency around algorithmic training data. Such standards would help formalize practices that are currently applied inconsistently across the industry.5,11
At the same time, there is a growing need for a dedicated framework — akin to the NIH’s Sex as a Biological Variable (SABV) policy — that explicitly defines expectations for gender considerations in digital health. This would give companies a roadmap for compliance and encourage researchers to build inclusive design into early-stage R&D. By adopting a structured approach to gender-aware development, digital health innovators can better align their work with both clinical realities and public health priorities.9
Public and private funders also have levers to drive change. Grant programs, venture capital portfolios, and procurement contracts can prioritize platforms that demonstrate inclusive practices. This includes requirements for participatory design, diverse user testing, and transparent reporting on sex and gender variables. These mechanisms not only de-risk innovation by promoting usability and safety; they also help direct capital toward solutions that meet the needs of a broader, more representative population.1
Startups and tech incumbents alike must recognize that inclusion is no longer a niche concern — it is rapidly becoming a market and regulatory expectation. By embedding inclusive principles into corporate strategy, development pipelines, and go-to-market plans, companies can avoid costly redesigns and build credibility with users, providers, and regulators from the outset.
Moving forward, the path to truly inclusive digital health will be shaped by policy frameworks that reward intentionality, business models that prioritize impact, and collaborations that center underrepresented voices. The transformation won’t come from any one actor. It will require collective alignment across sectors. But the outcome is clear: a more just, responsive, and effective digital health landscape for everyone.
Conclusion: Designing for the Real World
The promise of digital health lies in its potential to deliver personalized, data-driven care — empowering individuals to understand and manage their health in ways that are precise, proactive, and tailored to their unique needs. But without gender inclusivity, that promise falls short. When digital tools are built on datasets and design choices that ignore or misrepresent the realities of half the population — and further marginalize trans and non-binary individuals — personalization becomes illusion, and precision turns into bias.
As wearables, mobile health apps, and AI-driven diagnostics become increasingly embedded in everyday healthcare, inclusivity is no longer a nice-to-have. It must be the baseline. Technologies that aim to improve health must begin by recognizing the full spectrum of human experience — accounting for physiological differences, gender identities, and the social contexts that shape health outcomes.
Designing for inclusion does not dilute innovation; it strengthens it. Tools built with equity and user diversity in mind perform better, serve more people, and earn greater trust. They are better prepared to meet regulatory expectations, expand market reach, and demonstrate real-world clinical value. In short, inclusive design is not only better science — it’s better business, and above all, better healthcare. If digital health is to live up to its transformative potential, it must start by designing for the world as it is — not as it used to be.
References
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