By TREVOR VAN MIERLO
Let’s face it: for the past 25 years, digital behavioral health has struggled. Yet, we keep reinventing (and funding) the same models over and over again.
How It All Started
In the beginning (mid-1990s), a handful of developers, researchers, and investors envisioned high reach, lower-cost, highly tailored, anonymous interventions reaching millions of people with limited healthcare access.
The initial focus was never healthcare providers and insurers. These organizations were seen as too slow to adopt new technologies, and there was a general distrust of integrated care and insurers. Many digital health companies feared these organizations (and pharma) would leverage their power to learn from smaller companies, and then redevelop interventions internally.
Instead, the focus was on partnerships and B2C sales. Funding was easier to obtain from granting agencies, and there was ample development support flowing from sources like the tobacco Master Settlement Agreement (MSA). The primary concern was 1) whether the population could access these revolutionary tools and, 2) who would pay for them.
The Digital Divide
Back then, funders were often short-sightedly obsessed with the digital divide – the gap between people who had access to digital technology (mostly educated, higher-income earners in large cities) and everyone else. The argument was, “Why should we fund digital tools that will only benefit those who already have access to healthcare?”
Data was available, so academics armed themselves with ANOVA and relentlessly examined variables such as hardware costs, processing speed, age, gender, race, ethnicity, geography, income, and education. If you check Google Scholar, you can see the prevailing sentiment was that it would take decades for the digital divide to narrow, and new policy was desperately required to fix the problem (see: here, here, here, and here).
No More Excuses
Fast forward to 2024. According to a recent article in Forbes, there are 5.4 billion internet users worldwide (66% of the global population). In the U.S., 94.6% of Americans have internet access. Most US households have multiple devices, and according to Pew Research Center Research, 97% own a cellphone, of which 90% are smartphones.
As a Gen X’er who used a typewriter in college before upgrading to a Compaq Deskpro 286 from Future Shop (for about $400), my adult life has been a witness to the rapid progression of digital. Now, my 9-year-old daughter is teaching me how to play Fortnite (Epic Games), my 11-year-old is the only kid on his hockey team without a smartphone (this won’t last), and STARLINK allows me to chat face-to-face with my parents in rural Northern Ontario.
All aspects of technology are pervasive and accessible – but if you search Google or Bing for immediate, evidence-based behavioral help, you can’t get it. If you can find access it’s behind a paywall: through your employer (contact HR), health plan (call to see if you’re covered), or subscription ($19.99 per month).
That’s not meeting the original vision – and we have the technology. So, what’s the problem?
Four Factors Limiting Growth
1. Sales Models
Evidence-based behavioral health organizations typically follow traditional enterprise sales models (complex sales). This is necessary for behavioral health companies to sell to providers, payers, large corporations, or government health systems. If successful, the process involves long sales cycles (runway), high-value contracts, high customer lifetime value (CLV – which investors love), and a need for a dedicated account management team. Recently, the market has witnessed several well-funded digital health companies go out of business because their existence depended on one or two of these relationships.
Digital health has yet to embrace product-led sales (PLS), a bottom-up strategy that relies on a company’s product as the main tool for client acquisition. This approach often involves offering free trials or limited functionality with fees for upgrades, familiar to users of platforms like HubSpot , Slack , SurveyMonkey , and Zoom.
I believe that model has real potential and a division of my company, Evolution Health Care ApS (Denmark), is currently running experiments in Denmark with freemium on this instance.
2. Marketing & Promotion
In enterprise sales, a common pitfall is acquiring a high-value client (Yay!), with compensation tied to engagement (Uh-oh), and health economic value based on efficacy and the number of users as key contract variables (Oops). But how do you get large numbers of users to engage? A link in the company newsletter won’t cut it, and an email from HR will likely have limited impact. If the client does not actively promote the service, engagement suffers, and deals fall apart.
Our experience shows that while we provide promotional templates (tweets, emails, banners, website copy, hard-copy posters, etc.), marketing departments often prefer to develop their own materials (we know our users best). While it hasn’t yet occurred on a mass scale, digital health clients need to adopt modern promotional efforts. For example, Minnesota Alliance on Problem Gambling has started using TikTok to promote their white-label instance of our product, and Western University has a banner promoting their white-label instance (licensing alcohol and cannabis use) on their student Substance Awareness Guide.
3. Localization
In software development, localization is adapting software to the specific needs of specific regions or countries, including language (translations), vernacular, culture, and age groups. There are very few, if any, digital health assets that offer treatment for multiple indications (depression, problem drinking, stress, weight) in multiple languages with cultural adaptations.
For example, a smoking cessation intervention will contain the same treatment approach, but the delivery of that intervention must be localized – not just translated as the smoking culture in North America is very different than the UK, or countries in Asia. Further, a major concern with people who quit is weight gain – but food consumption is also cultural. How do we localize both topics?
4. Evidence-Based (Hard Work) vs. Inspiration (Fleeting)
A significant distinction exists between evidence-based mental health treatments and digital assets that are merely inspirational. Our self-guided mental health courses are grounded in evidence-based approaches like CBT, structured relapse prevention, motivational interviewing, and social cognitive theory. It takes motivation, dedication, and hard work to progress through material. Engagement and attrition are longstanding challenges.
While we value and support sites like Wondermind, which normalize mental health and substance use issues, they are not evidence-based and do not provide outcome data.
Should we enhance the aesthetic appeal of digital evidence-based treatments to make them more attractive and user-friendly? Should we truncate and streamline content delivery? Absolutely. However, these upgrades must be assessed for safety, efficacy, and effectiveness through randomized controlled trials, which take time.
* * *
Digital health has always promised high-reach, lower-cost, and personalized interventions, but traditional enterprise sales models fall short of supporting these goals, often leaving those who need help without access. To realize the original vision, we must shift to product-led strategies that enhance user engagement, akin to the approaches used by successful large digital platforms. By making evidence-based tools directly accessible to individuals, we can finally unlock the full potential of digital behavioral health and drive meaningful, scalable impact.
Dr. Trevor van Mierlo has built mental health and patient support products for more than two decades and is the CEO of Evolution Health