Data ScienceDigital HealthMobile Health

The Peril and Promise of mHealth

By Edmondo Robinson, CDO, Moffitt Cancer Center

Mobile health, or “mHealth,” has received an increasing amount of attention recently in connection with approaches to combating the COVID pandemic. mHealth, as defined by the National Institutes of Health (NIH), is a relatively simple concept: the delivery of healthcare services via mobile communication devices. In reality, this simple definition may mask a rather complex interplay between patients, providers, payers, policy, and the underlying infrastructures that support the health of populations.

To address equity, mHealth solutions should be designed by diverse product teams that focused on an expanded customer experience discovery process.

mHealth includes a relatively broad set of solution types. These solutions range from mobile applications focused on health care engagement or intervention to wearables that track specified data points to connected devices that are purpose-built to extend data collection beyond traditional healthcare settings. This set of mobile solutions has generated a market that is projected to grow rapidly. Currently estimated to be a global market of almost $55B, mHealth is expected to grow 20-30% annually over the next five years. This accelerated growth will likely shed a spotlight on critical challenges that the mHealth market will need to address.


Design is a fundamental component of mHealth. Well-designed mHealth solutions can lead to impressive outcomes. However, poor design is not only problematic itself but leads to exacerbation of the other challenges of mHealth, including quality, privacy, data management, and access. Three of the more important considerations for mHealth design include:

  1. Consumer focus – mHealth solutions should be designed with user interfaces and user experiences (UI/UX) that mirror design features in non-health mobile applications. The UI/UX for mHealth applications should not reflect the underlying complexity of healthcare delivery but should aim to simplify experiences.

  2. Improve health – mHealth solutions should be relentlessly focused on the end goal of improving health. Features that are not proven to ultimately improve health should be reassessed. It is also critical to avoid the “killer app” syndrome where there is a solution looking for a problem.

  3. Integration – mHealth solutions are often deployed within complex healthcare environments and well-designed solutions will not expose that complexity to the end-user. However, it is still important that these solutions are designed to integrate into the larger health ecosystem.
Data management

The volume of data generated from mHealth solutions is staggering and consistent with the idea of “big data.” Wearable devices are capable of sharing continuous data over expended periods, while connected devices are designed to provide multiple data points in settings much more diverse than the typical medical office or hospital. A core challenge is navigating the signal-to-noise ratio of mHealth-generated data. Solution architects may look to design features that improve the signal-to-noise ratio and/or develop underlying machine learning algorithms that manage large mHealth data sets to provide actionable information.

Privacy and security

The generation of significant amounts of data through mHealth solutions is promising but also poses a privacy risk. The devices themselves can be targeted as well as the flow of data from source to destination. Potentially more challenging to address is the ability of seemingly anonymized mHealth-generated data to be traced back to individual contributors. Leadership at the Digital Medicine Society recently noted that just six days of step counts are enough to uniquely identify an individual from amongst 100 million other people. mHealth data generation and distribution should be treated with the privacy and security afforded to research-grade health data and will likely require additional protections unique to mHealth.


mHealth solutions have a unique role amongst other mobile applications in that their goal should be to promote and improve health. Therefore, mHealth solutions must consider the added dimension of healthcare quality to their design and implementation. There is increasing concern that poor quality mHealth solutions are being deployed and used. Both the quality of the content as well as software functionality have been cited as ongoing concerns. These issues can lead to poor outcomes, including sharing misleading educational content or incorrect diagnosis. To address these challenges, mHealth solutions should be evidence-based, validated, and engage experts in the development and designs of the solutions.


Like other digital health tools, mHealth has the potential to exacerbate current health inequities or help address disparities. Unintuitive and complex mHealth solutions that do not consider the increasing diversity of mHealth users may differentially harm patients who are already disadvantaged in the current healthcare ecosystem. To address equity, mHealth solutions should be designed by diverse product teams that focused on an expanded customer experience discovery process. The design should also consider diverse environments of use, including device types, bandwidth, disabilities, and digital health literacy, among other considerations. Additionally, underlying algorithms should be explicitly designed to address and overcome bias. The growth of digital health solutions, including mHealth, has accelerated recently. It is now clear that mHealth will increasingly become a core component of healthcare engagement and delivery. In order for the growth of mHealth to coincide with a parallel increase in health outcomes, the challenges of design, data management, privacy and security, quality, and equity will need to be addressed.

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