mHealth Scaling and Interoperability: Why So Slow To Evolve?

Four years have passed since we reached a milestone in the mobile technology space: the number of phones on the planet surpassed the number of people for the first time.

That's quite the stat! Indeed, the smartphone is the fastest growing man-made phenomenon ever - from zero to 7.2 billion in three decades. Following suit, the number of mHealth solutions continues to grow, numbering in the tens, perhaps hundreds, of thousands, including:

·     health-related apps;

·     wearable technology;

·     devices for client/patient communication; and

·     wireless devices for real-time medication monitoring and adherence support.

Commensurate with this exponential growth is the number of business models based on smartphones being proposed and implemented.

Sadly, the endless enthusiasm for mHealth solutions far outpaces demonstrations of efficacy, validity, safety, scalability, and interoperability despite calls for higher standards by healthcare companies, benefit providers, consumers, employers, professional healthcare bodies, and government agencies. In particular, what is lacking includes:

• high-quality efficacy trials (does the intervention work in ideal circumstances);
• high-quality effectiveness trials (does the intervention work in real-world settings);
• dissemination research that demonstrates the intervention can be reliably delivered at scale; and
• articulation of costs of the intervention to inform spread and scale.

The exciting aspect of leveraging smartphones to support health and wellness is the fact that most people have a phone and many interventions can be and are delivered via that medium. However, industry’s increasing role in pushing for mHealth scale up is also a cause for concern. Many of these calls emanated from industry representatives rather than researchers, governments, or care providers and it is likely that private enterprise has a quite different understanding of what scale up means, with growing market share, rather than improved health outcomes, at the core of their mission.

As well, no major investments have been made to create a robust platform for mobile phones that could be used by designers of applications and electronic medical records that will allow cross-fertilization or integrated systems to be utilized. Currently, a client or patient with multiple concerns may need to make use of numerous applications for each condition, each unrelated to the other.

 The bottom line is clear.

At present, anyone can create and publish health and medical apps in the app stores without having to test them, and patients must experiment with apps by trial and error. Companies that leverage the evidence-base, translate what works into a superior user experience, and demonstrate cost-effective and superior outcomes, have the opportunity to seize and grown market-share as they implement at scale.

App Evaluation

There have been numerous efforts around the world to provide quality and efficacy assessments of mHealth apps, each devising and using their own app evaluation framework.

Ideally, like clinical “best practices” guidelines, a recognized national body can decide what framework they want to use to evaluate apps and which apps to deem safe for use (the recently re-opened NHS Apps library is a great example despite the initial hurdles with the data safety of some of their previously recommended apps). However, the challenges of a recognized body providing direction on the efficacy of apps are complex and something I will visit in a future post.

As a consumer, don’t just accept the marketing for any app. Instead, investigate the science behind the product or service, examine the outcomes that are reported, seek out independent user reviews (e.g. Google search, app review sites), and question that business model behind the “free” versus “paid” app (esp. with respect to the functionality with/without “in-App purchases” and the like).

As one guiding app-evaluation model, the American Psychiatric Association (APA) has developed an app rating system to assess and evaluate mental health apps. The system does not report if a particular app is good or bad, or whether it’s right for you. It offers resources to help you make an informed decision about whether to use an app.

The APA app evaluation system involves five steps, each with a series of questions to help you assess the app.

Step 1 General information

Step 1 is gathering general information about the app. For example, consider who developed the app, whether there is a cost or in-app purchases required. Does the app claim to be medical?

Step 2 Privacy and security

This step focuses on potential safety issues and the protection of security and privacy. For example, look at what data are collected and what is shared. Can you opt-out of data collection or delete data? Are data maintained on the device or the web (i.e., “the cloud”)? Both? What security measures are in place? Are data encrypted? The app’s privacy policy should answer these questions. If there is no privacy policy, that raises serious concerns.

Step 3 Evidence

App developers often make claims even though there is currently little research or evidence to support the claims. This does not mean that apps don’t work, just that there is much we do not know. Several questions and information sources can help you consider an app’s function and benefits. For example, what does it claim to do vs. what does it actually do? Is there peer-reviewed, published evidence about tool or science behind it? Is there any feedback from users to support claims ( such as the app store, website, review sites, etc.)?

Step 4 Ease of Use

An app is only as useful if it is practical. Ease of use is subjective – different people will have very different ideas about ease of use. A couple of things to consider: Is it easy to access and would it be easy to use over the long-term? Are features of the app customizable? Does it need an Internet connection to work?

Step 5 Interoperability

Interoperability is the ability of different systems to share data. This may be important or useful with some apps, for example apps that track moods or help manage medication. Consider

• Who “owns” the data? (Do you? Does the app developer?)
• Can you print out or export/download your data?
• Can the app share data with other user data tools (e.g., Apple HealthKit, FitBit?)?

There is no specific minimum criteria necessary for an app to be considered “good” or “useful.” That is an individual decision, but these steps and questions can help you make an informed decision.

Note: I would also recommend you DO NOT skip the “Terms of Use/Policy” that most of us simply click “yes” to on a checkbox and don’t read. These terms will (should) include specifics re: privacy, data collection and sharing, and the like. Additionally, check the minimum age for using the app,

Read the original article on my blog here.

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See a list of workshops, seminars, and educational presentations here.