Is Venture Capital-Backed Mental Health Tech Here for Customer Relief or Retention?
An Exploratory Study on the Anatomy of Mental Health App Business and Revenue Models
Anna-Mai Männik 13414844
EBEC Number: 20210420100415 MSc Business Administration: Digital Business
Faculty of Economics and Business (FEB)
Supervisor: Dr Matthew James Dennis June 25th, 2021
Statement of Originality
This document was written by Anna-Mai Männik who takes full responsibility for its contents.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for its contents.
More than 10 000 mental health mobile applications (apps) have become available on app stores, yet very little is known about how these apps monetise their businesses. In this paper, mental health mobile apps’ business and revenue models are being investigated empirically through a multiple- case study focusing on 4 start-ups with 7 apps, with the aim to identify and explore the prevalent business and revenue models, examine the rationale behind these strategies, discuss the main challenges presented by the current business environment, and present guidelines for an alternative revenue model. For this purpose, a theoretical frame of reference was constructed, and 5 semi- structured interviews were carried out. The findings revealed three prevalent business models:
direct-to-consumer, employer-to-consumer, and clinician-to-patient, as well as three revenue models: subscription, pay-per-session, and pay-per-program. The rationales behind the business models are strongly tied to the companies' missions and the key problems they are solving, related to accessibility, affordability, or lack of urgent care. The main challenges are related to the app users’ safety, the criticism towards the apps’ allegedly manipulative practices, and the regulations and complex mechanisms of healthcare reimbursement. As a final contribution, the author proposed an outcome-based revenue model, based on the idea of customers being charged upon a successful outcome.
Keywords: mHealth, digital mental health, digital well-being, business models, revenue models, healthcare reimbursement
Table of Contents
Statement of Originality ... 2
Abstract ... 3
Table of Contents ... 4
Index of Tables and Figures ... 6
1. Introduction ... 7
1.1 Problem Background ... 7
1.2 Research Gap and Objectives ... 8
1.3 Structure of the Thesis ... 9
2. Theoretical Frame of Reference ... 11
2.1 Literature Review ... 11
2.1.1 Key Ethical Challenges ... 11
2.1.2 Key Clinical Challenges ... 12
2.1.3 Key Commercial Challenges ... 13
2.2 Empirical Framework ... 14
2.2.1 Digital Health ... 14
2.2.2 Mobile Health (mHealth) ... 14
2.2.3 Digital Mental Health and Well-Being Market ... 16
2.2.4 Digital Mental Health and Well-Being Landscape ... 16
220.127.116.11 Peer-to-Peer ... 17
18.104.22.168 Digital therapeutics ... 18
22.214.171.124 Telehealth ... 18
126.96.36.199 Mental Health and Well-Being Apps Based on Self-Management ... 19
188.8.131.52 Apps for Measurement and Tracking ... 19
184.108.40.206 Tools for B2B / Mental Health Providers ... 19
2.2.5 Business and Revenue Models ... 20
4. Research Methodology ... 23
4.1 Research Method ... 23
4.2 Data Collection Method ... 24
4.3 Sampling and Case Selection ... 24
4.4 Data Analysis Method ... 25
5. Empirical Findings ... 27
5.1 Case study 1: Mindler ... 27
5.1.1 Foundation of the Business Model ... 27
5.1.2 Revenue Model: Structure, Rationale and Strategic Implications ... 27
5.1.3 Challenges ... 29
5.2 Case study 2: Limbix ... 30
5.2.1 Foundation of the Business Model ... 30
5.2.2 Revenue Model: Structure, Rationale and Strategic Implications ... 31
5.2.3 Challenges ... 34
5.2.4 Alternative Revenue Models ... 34
5.3. Case study 3: Koa Health ... 35
5.3.1 Foundation of the Business Model ... 35
5.3.2 Revenue Model: Structure, Rationale and Strategic Implications ... 36
5.3.3 Challenges ... 37
5.3.4 Alternative Revenue Models ... 38
5.4 Case study 4: Bloom ... 39
5.4.1 Foundation of the Business Model ... 39
5.4.2 Revenue Model: Structure, Rationale and Strategic Implications ... 41
5.4.3 Challenges ... 43
5.4.4 Alternative Revenue Models ... 44
5.5 Interview with Marijn Sax (PhD) ... 45
6. Cross-Case Analysis and Discussion ... 47
6.1 The Foundation of the Business Models ... 47
6.2 The Structure of the Business/Revenue Models ... 48
6.2.1 Direct-to-consumer ... 49
6.2.2 Employer-to-consumer ... 50
6.2.3 Clinician-to-patient ... 51
6.3 Rationale Behind the Business/Revenue Models ... 53
6.4 Challenges and Limitations ... 55
6.5 Guidelines for Alternative Revenue Models ... 56
6.5.1 General Guidelines ... 56
6.5.2 Outcome-Based Model ... 57
7. Conclusion ... 59
7.1 Conclusion ... 59
7.2 Limitations and Suggestions for Future Research ... 60
References ... 62
Appendices ... 68
Appendix 1. Indicative interview questions ... 68
Appendix 2. MAXQDA Coding Results ... 69
Appendix 3. Business Models' Building Blocks Summarised ... 70
Appendix 4. The Mental Health Mobile Apps in the Sample ... 71
Index of Tables and Figures Figure 1 Digital Health Categories. ... 15
Figure 2 D2C Business Models of Mindler France and Bloom ... 50
Figure 4 D2C Business Model of Mindler Sweden ... 50
Figure 5 E2C Business Model of Koa Foundation ... 51
Figure 6 C2P Business Model of Limbix ... 52
Figure 7 C2P Business Model of Mindler Netherlands ... 52
Figure 8 C2P Business Model of Koa Mindset ... 53
Figure 9 Relief / Retention ... 54
Figure 10 Relief / Retention ... 55
Table 1 Revenue Models. ... 21
Table 2 Building Blocks of a Business Model. ... 22
Table 3 The Categorisation of the Apps. ... 47
1.1 Problem Background
The digital mental health and well-being industry is thriving - in 2019, venture capital (VC) firms invested an unprecedented $637 million in more than 60 different mental health–oriented companies - more than 20 times the investment size of 2013 (Berry & Shah, 2020). Moreover, the COVID-19 pandemic has led to an even bigger surge in the use of technology in mental health care, limiting the delivery of traditional in-person care, as well as causing a general spike in mental health needs (CDC, 2021) caused by quarantine measures and a generally volatile situation globally. Mental health is indeed, in crisis: while about 13% of the global population suffers from some kind of mental disorder (GBD, 2017), less than a third of people with mental health disorders receive treatment (Olfson et al., 2016). The situation is a result of a worldwide shortage of mental health professionals: according to the World Health Organization (2017) the median number of mental health workers globally is 9 per 100 000, with an even bigger shortage in low-income countries, with 45% of the world population residing in countries with less than one mental health professional per 100 000 people (Dunne, 2021; WHO, 2017). This economic mismatch of high demand and inadequate supply has not been missed by the private sector, represented by both technology start-ups developing mental health and well-being mobile apps, as well as venture capitalists (VC) seeking lucrative investment opportunities. Backed by major VC investments, mental health apps are exponentially gaining popularity. In 2019, the mindfulness app Calm was valued at $1 billion USD, becoming the first “unicorn” in the digital mental health industry.
However, several concerns arise with this phenomenon which currently exists in what has been called a “a grey zone” by Joshua Gordon (2020), the director of the US National Institute of Mental Health (NIMH), and “the Wild West of healthcare” by John Torous, a psychiatrist at Harvard Medical School, and the chairman of the American Psychiatric Association’s Smartphone App Evaluation Task Force.
Given the premise to provide relief and/or deliver interventions for conditions like depression, anxiety, stress, insomnia, and PTSD among others, these apps can be considered to be a form of healthcare. At the same time, a vast number of the apps are not regulated as medical devices, and therefore require no clinical evidence supporting safety and efficacy in order to be commercially
available to smartphone users. In traditional health care, however, with any treatment, the aim is to achieve the best outcomes for the patient by providing care aligned with six fundamental domains: (1) safety, (2) effectiveness, (3) equity, (4) timeliness, (5) patient-centeredness, and (6) efficiency (Institute of Medicine, 2001). With these principles in mind, several concerns arise in the context of mental health apps: in a widely quoted Harvard Business Review article, Reichheld and Sasser (1990) argue that as the customer’s relationship with the company continues, profits increase. The same basic rule of thumb that businesses benefit more from maintaining long-term than short-term customer relationships in terms of customer profitability is supported by several authors (Morgan and Hunt 1994; Sheth and Parvatiyar 1995; Bendapudi and Berry 1997). Now, considering that all the mental health applications (in focus in this thesis) are exclusively private, profit-oriented companies, their business objectives should not deviate much from these general business practices of customer retention. However, simultaneously operating in the grey area between health and business, healthcare’s fundamental aims of effectiveness and timeliness diametrically oppose businesses’ intentions of achieving high customer retention: why should a customer, or a patient, stay active on an app for as long as possible? Or else, are mental health apps
"designed to be deleted", as, for instance, a leading dating app (Hinge) is claiming to be? These are questions that can be addressed through many different prisms. Based on months of prior research on the topic, the author believes that looking at business and revenue models, as well as the broader business landscape, will provide the best angle answers to these dilemmas.
1.2 Research Gap and Objectives
Operating in a complex industry that combines health and business, the choice of a monetisation strategy in order to generate revenue ultimately defines the company’s market positioning, and its core business objectives: understanding who pays for the service, and how, is crucial. However, in academic literature, little to nothing is known about what business and revenue models prevail in the industry, or the app developers’ rationale and strategic underpinnings for the given business and revenue models. Are these apps a form of healthcare, aligned with the fundamental aims of healthcare, or are these simply harmless digital services (apps) like any other? Numerous academic articles and viewpoints exist on the broader topic of digital health and entrepreneurship in digital health, but no cohesive analysis of the business landscape of digital mental health and well-being yet exists. Hence, in a modest attempt to fill this gap in research, the purpose of this study is to
explore how companies in the digital mental health industry monetise their businesses, examine the rationale behind these strategies, and discuss the main challenges presented by the current business environment. As an additional contribution, and in response to criticism of mental health applications for their manipulative, commercial practices, this study presents guidelines for an alternative revenue model for existing players to implement and future entrants to consider. These research objectives aim to be achieved through answering the following research questions:
RQ1: What are the prevalent business and revenue models in the digital mental health industry, and how are they structured?
RQ2: What are strategic implications and rationales behind the use of the business/revenue models?
RQ3: What is the business environment of digital mental health, and what are the main challenges the companies (apps) are facing on the industry level?
RQ4: How could an alternative revenue model in the digital mental health industry be structured?
1.3 Structure of the Thesis
The thesis begins with an introduction to the research topic, by uncovering the problem background, demonstrating relevant industry data and statistics, explaining the research motivation by defining a current research gap, and laying out research objectives, including the four research questions. The second chapter (“Theoretical frame of reference”) consisting of a literature review and empirical framework aims to provide a theoretical frame of reference. Note that the two chapters are merged into one. As the digital mental health industry is still fairly young, the academic literature on the topic study remains scarce. Thus, the literature review provides an overview of the key challenges (ethical, clinical, commercial) that the industry faces, as these are the topics scholars have been addressing in particular. The second part of the chapter delves into more empirical sources, aiming to present frames for the further analysis of the results, and to provide context for understanding the business environment of digital mental health. The third chapter (“Empirical Findings”) explains the chosen research methodology by describing the research and data collection method, case selection and sampling process, and data analysis method. In the fourth chapter, the empirical findings from 5 in-depth interviews are presented and discussed, meaning that the results are not merely laid out, but already analysed and put into
context provided in chapter two. In the fifth chapter (“Cross-case analysis and discussion”), the results (from both chapter two and four) are synthesised and discussed holistically based on the same matrix presented in chapter five, which derives from the interview themes. In this chapter, the business and revenue models' classifications and guidelines for alternatives revenue models are also presented and discussed. Finally, the sixth chapter (“Conclusion”) summarises the findings and addresses the limitations of the study and gives suggestions for future research.
2. Theoretical Frame of Reference 2.1 Literature Review
2.1.1 Key Ethical Challenges
Since mental health applications operate in an unregulated field still in its infancy, many scholars have drawn attention to the several ethical concerns in the industry. These concerns include user autonomy, privacy, and the apps’ potentially manipulative practices. Widdicks (2020) argues that some digital wellbeing tools, despite their positive agenda, can negatively impact their users' privacy or autonomy and only be “a small step away from being used to manipulate users”. Sax et al. (2018) looked at the users' autonomy of mHealth applications from an ethical and legal perspective, arguing that by merging health and commercial content in ways that are hard to detect, mHealth apps are not only concerned with the user’s health behaviour, but often also with the user’s economic behaviour, e.g., getting the user to buy products or services. Based on an empirically informed ethical analysis of autonomy, Sax et al. (2018) developed a framework that incorporates three different requirements for user autonomy (“independence,” “authenticity,” and
“options”) which are essential for autonomous decision-making. The authors find labelling mHealth apps as being free of charge problematic, as these apps in most cases collect their users’
personal data. The analysis suggests that similarly to informing users about the merging of commercial/advertising and editorial content, the users should be clearly informed about the apps’
Martinez-Martin (2018) finds accountability and liability of direct-to-consumer digital therapy apps to be problematic: although the DTC digital therapy apps act in the role of digital therapist, the professional codes of ethics that apply to psychologists and therapists operating in a traditional way, do not necessarily apply to those apps based on “chatbots, untrained peers, and algorithms”.
Hence, according to Martinez-Martin (2018) the regulatory levels and ethical obligations of D2C digital therapy apps should be created and applied. Last but not least, similarly to Parker et al.
(2019), the author (2018) emphasises the importance of informed consent and argues that the app users need to be presented with an appropriate overview of risks and benefits before using the service. Parker et al. (2019) are also concerned with the lack of regulation in the mental health app industry and argue that in order to improve customer safety, privacy and protection, policy makers from different sectors need to collaborate on establishing an industry-wide regulatory framework.
2.1.2 Key Clinical Challenges
Many scholars are concerned with the lack of clinical validation and the questionable efficiency of mental health applications. Among the main challenges for mental health apps (including assessment, diagnosis and treatment focused clinician-oriented apps, and self-management focused patient-oriented apps) Marley and Farooq (2015) address the lack of medical involvement in the app development process, and the challenges related to healthcare services, such as the lack of evidence-based approaches, clinical risk and patient confidentiality issues. Anthes (2016) draws attention to the lack of testing and regulation, stating that the line between wellness/well-being apps and apps that are considered to be medical devices by the US Food and Drug Administration (FDA) (or equivalent), is unclear, and the outcomes these apps can produce, unpredictable. Anthes (2016) argues that the risks are not perceived properly, as for instance, people with more severe issues might be given harmful advice or be prevented from getting proper treatment. Furthermore, Martinez-Martin (2018) addresses the threats posed by those direct-to-consumer (DTC) digital psychotherapy applications that do not involve a trained mental health professional in the customer journey. According to Martinez-Martin (2018), apps developed by private companies and apps developed by clinical researchers can differ greatly in the requirement for prior testing, which could result in the private apps trying to maximise user engagement rather than “effectiveness”
(by using methods like reward systems and gamification), which can result in the less scientifically trustworthy applications gaining more engagement and popularity among users. Roffarello and
Russis (2019) question the functionality and efficiency of digital well-being apps through an extensive review of 42 apps, a thematic analysis on the apps’ user reviews, and an in-the-wild study on one of those apps. They concluded that although such apps can be useful and appreciated in some cases, they did not help users to achieve what was promised effectively. Firth et al. (2019) argued that despite the benefits that mobile health apps can have in terms of both early evidence on their efficacy, and their potential to extend accessibility to mental health care, results have shown user engagement issues, resulting in both poor uptake and sustained use. Concerned with mental health apps rarely being based on trial-based (clinical) validation, Bakker et al. (2016) formulated a set of evidence-based recommendations for app developers to consider when designing such apps. The most note-worthy recommendations being the employment of cognitive- behavioural-therapy based techniques, responsible utilisation of user data and the formation of multidisciplinary teams consisting of experts (e.g., engineers, experts on health care and clinical psychological interventions, data scientists). Chandrashekar (2018) analyses the efficacy of several evidence-based, smartphone-based treatments for depression, anxiety and schizophrenia, concluding that mental health mobile apps do indeed have potential to offer efficient mental health interventions, and to even bridge the mental health treatment gap if the apps are based on a simple user interface (UI) and experience (UX), the use of self-monitoring features, and high-patient engagement through real-time engagement, usage reminders and gamified interactions.
2.1.3 Key Commercial Challenges
Literature on the commercial elements, e.g., business models and monetisation of digital mental health remains scarce. Powell et al. (2019) evaluated how the monetisation strategies of anxiety management applications differ between anxiety apps in the United States and China, and which monetisation strategies are most associated with commercial success, concluding that it was true for subscription models. With a strong focus on the US market, Firth et al. (2020) explored how mental health apps are being reimbursed, arguing that as mental health apps are increasingly becoming integral parts of healthcare infrastructure, changes have to be made in the reimbursement mechanisms through which the mental health apps, and the interventions and care they deliver, can be compensated. According to Firth et al. (2020), prescription-based reimbursement (e.g., for mental health apps that are certified as digital therapeutics), presents a barrier to both app developers and patients: developers need an FDA approval, and mental health care providers need
prescribing authority, which many of them lack, as mental health care is often delivered by psychologists without the required authority. Furthermore, Firth et al. (2020) argue that many healthcare providers with the authority may hesitate in recommending (prescribing) these apps to patients due to the “lack of guidance on standards for app quality and app-related liability”.
2.2 Empirical Framework
2.2.1 Digital Health
The terms ‘digital health’, ‘eHealth’, ‘mHealth’, ‘telemedicine’ and other similar classifications with subtle yet significant differences in meaning are often used interchangeably, creating even more confusion in this already complex and multi-layered industry. To be able to distinguish the terms and understand the context focus of this study, a comprehensive overview of the different classifications will be given. Although there are many definitions available for the term ‘digital health’, it has most comprehensibly been defined as “use of information and communications technologies to improve human health, healthcare services, and wellness for individuals and across populations” (Kostkova, 2015). The United States Food and Drug Administration (FDA) has established five main categories of digital health, to which a sixth additional category (e.g., digital therapeutics) was recently added by the Digital Therapeutics Alliance (DTA). The categories include: (1) mobile health (mHealth); (2) health information technology (IT); (3) wearable devices, (4) telehealth and telemedicine, (5) personalised medicine, and (6) digital therapeutics.
2.2.2 Mobile Health (mHealth)
The 400 million annual downloads of mobile health (mHealth) apps (Gordon & Doraiswamy, 2020) are a clear indication that consumers are increasingly reaching for mobile devices to take care of their mental health. The global mHealth market size was valued at 11.5 bn USD in 2014 and is expected to grow at a CAGR of 32.5% between 2016 and 2022, reaching a size of 102 bn USD by 2022 (Zion Market research, 2017). mHealth, a component of the umbrella terms ‘digital health’ and ‘eHealth’, has received a variety of definitions in the academic literature. Gee et al.
(2015) see mHealth as a way to optimise the delivery and receipt of health information and services with the use of mobile phones, as well as a broad spectrum of wireless, mobile or wearable technologies (e.g., sensors, devices, or wristbands that monitor physical activity or body metrics),
including the health apps designed for mobile devices. Vasudevan et al. (2018) classify mHealth as the use of mobile phones for delivering health services and information fuelled by rapid innovation in digital communications technologies, and the World Health Organization (2011) has defined mHealth as medical and public health practice supported by mobile and other wireless devices. Moreover, mHealth has multiple sub-domains of its own, categorised and defined differently by various stakeholders and authors. Wang (2020) has distinguished wellness and fitness tracking, and nutrition mobile apps, consumer health information mobile apps, and medication adherence mobile apps. Whereas in another classification (Silicon IT, 2021), the categories have been established as: remote monitoring apps, clinical and diagnostic apps, healthy living apps, clinical reference apps, and productivity apps. Another classification by McVicar (2020) classifies the apps based on the target audience, distinguishing apps for consumers and apps for healthcare professionals:
Figure 1 Digital Health Categories.
2.2.3 Digital Mental Health and Well-Being Market
With more than 300 000 mobile health apps currently on the market worldwide, mental health- related apps represent the biggest segment (IQVIA, 2017), with more than 10 000 applications available for download (Torous & Roberts, 2017). From 2020 to 2027, the global mental health app market is expected to grow at a compound annual growth rate (CAGR) of 24% and reach a market value of over US $ 3,709.2 Mn by 2027 (Acumen, 2021). This steady market growth is driven by several factors: on the one hand, due to the general advocacy and growing awareness for mental health, the effect of health campaigns, the rapid development and launch of new mental health or well-being apps, as well as the growing use of smartphones and connected devices (Acumen, 2021). However, opposed to the array of positive implications, market growth is also supported by an increasing and unsatisfied need for mental health support: even in well-resourced health systems, only one in 10 people in need of mental health-related support benefit from traditional mental health services (Roland et al., 2020). Moreover, globally, less than a third of people with mental health problems can access treatment due to the worldwide shortage of mental health professionals (Olfson, 2016), as well as the affordability issue of generally high-priced mental health services not being affordable to people. Since the emerging mental health apps and platforms in most cases either complement or replace the traditional, offline mental health services, their exponential growth results from both the aforementioned positive and negative trends.
2.2.4 Digital Mental Health and Well-Being Landscape
In both the context of academic literature and in other industry-related texts, reports and sources, the concept of digital mental health is constantly being referenced in different terms, which all have different connotations, but share a very similar core meaning, nevertheless. The most common terms are: "e-Mental health" (Musiat et al., 2012; Goldstone et al. 2014), "mental health tech", "m-Mental Health", "digital well-being", "digital wellness", "mHealth for mental health"
(Ben-Zeev, 2021; Luxton et al., 2011; Singh et al. 2016), "digital mental health" (Roland et al., 2020), "mental health start-ups" (Berry & Shah, 2020). In the context of this thesis, the author has decided to use ‘digital mental health’ as an umbrella term, as it covers all the different sub- categories the different companies operating in the field, and the specific ones included in this study. It is, however, important to distinguish the concepts of "health” and “well-being" or
"wellness”. Giu et al. (2017) define "digital well-being" as a "state where subjective well-being is maintained in an environment characterised by digital communication overabundance" and as the individuals’ ability to "channel digital media usage towards a sense of comfort, safety, satisfaction and fulfilment". According to Roland et al. (2020), digital mental health is much more than digitising offline interventions and can be defined as "the use of internet-connected devices and software for the promotion, prevention, assessment, treatment and management of mental health, either as stand-alone tools or integrated with traditional services", or more generally as a promise of digital, mobile, and connected technologies to advance mental health (Tal & Torous, 2017).
During the last decade, the number of digital tools available for mental health assessment, support, prevention, or treatment, has grown exponentially, with around 100 new digital mental health start- ups launching every year (Roland et al., 2020). What makes developing a cohesive framework of the digital mental health landscape especially complicated, is the complexity of the products and services available on the market, with the companies’ value propositions encompassing different niches, and overlapping in different categories. For example, an app classifiable as a "mental wellness app" which provides educational tools for improving mental health, can at the same time offer features such as tracking or measurement of mood, which then makes the application classifiable as a "measurement and testing" type of digital mental health solution. Furthermore, a telehealth company can offer remote consultations with psychotherapists, but also have a feature that allows the individual to connect with their peers, making it also classifiable as a peer-to-peer solution. As none of the existing categorisations discovered by the author neither cover nor explain the current landscape exhaustively, an overview of the landscape will be given by synthesising the different categorisations provided by the US National Insitute of Mental Health (NIMH) (2019), Hays (2020), Huin (2020), Center for Connected Health Policy (CCHP) (2021) and Roland et al.
(2020), illustrated by examples of companies operating in these niches based on identifying their primary goal of being in business.
Peer-to-peer support-based apps, platforms or self-forming online communities are a form of unsolicited communication among patients and individuals with diverse health concerns (Naslund et al., 2016), addressing either serious mental illnesses or simply facilitating connection, fighting isolation and bringing people together with the aim of an improved mental and physical well-being
(Hays, 2020). Studies have shown that peer-to-peer solutions give people a sense of group belonging, social connectivity, and a skillset to handle the daily challenges of having a mental illness, based on shared insights, experiences and coping mechanisms (Naslund et al., 2016;
Harvey et al. 2007). Peer-to-peer communities can also challenge the stigma around mental illness through mutual empowerment and providing hope (Lawlor & Kirakowski, 2014).
220.127.116.11 Digital therapeutics
Digital therapeutics (DTx) refers to a new and innovative approach to treatment, in which evidence-based software, such as computer or smartphone apps and virtual reality (VR), are used as clinically evaluated, regulatory-approved, prescribed therapeutic interventions to treat, manage and prevent medical conditions (Sverdlov et al., 2018; Digital Theapeutics Alliance, 2021). As Digital Therapeutics have proven to lead to, and impact behavioural changes (Natanson, 2017), the main areas of mental illness which Digital Therapeutics target are psychiatric disorders and chronic diseases, including substance abuse, attention-deficit/hyperactivity disorder, insomnia, panic attacks, anxiety, and depression (Cho & Lee, 2019), that require behavioural changes for treatment. In order to be available on the market, digital therapeutic products are held to the same standards of evidence and regulatory requirements as traditional medical treatments: they must be proven to be effective, safe and credible via rigorous testing through Randomized Control Trials (RCT), deliver clinically meaningful results published in peer-reviewed journals, encompass real- world evidence generation, and analysis of product performance data (Digital Therapeutics Alliance, 2021).
Telehealth, often confused with the term Telemedicine (which is a subset of Telehealth), incorporates a wide range of remote healthcare services, such as treatment, diagnosis and disease management, education, or any engagement or interaction with a clinician from a distance. The National Telehealth Policy Resource Center (2021) distinguishes 4 main applications of Telehealth: (1) synchronous, two-way interaction between a person and a provider, using live videoconferencing, (2) ‘Store-and-forward’, asynchronous transmission of recorded health data to a clinician outside of a real-time interaction, (3) Remote Patient Monitoring (RPM) e.g. medical data collected from the patient in one location being transmitted to a clinician in a different location using electronic devices, (4) Mobile Health (mHealth), e.g. any remote healthcare practice
supported by mobile devices. In the context of mental health, Telehealth appears either in the form of ‘teletherapy apps’, ‘therapy apps’ or ‘tele-mental health apps’ which all essentially connect and/or match users with licensed mental health professionals (including psychologists, psychiatrists, clinical social workers, professional counsellors couples, relationship, and family therapists), based on the user’s specific needs and expectations. The session between the patient and the provider takes place on the mobile app (or online platform), either in the form of a phone or a video call, often additionally supported by a text or a chat option.
18.104.22.168 Mental Health and Well-Being Apps Based on Self-Management
This category encompasses a wide variety of widely known and recognised mass-market mental wellness apps for managing stress, meditation, breathwork and improved sleep (e.g., the industry leaders Calm and Headspace), helping the user to track their progress and gain feedback. More niche apps that address specific conditions, such as depression and anxiety, addictions, autism, attention deficit hyperactivity disorder (ADHD), and post-traumatic stress disorder (PTSD), also fall under this category. In these cases, digitised versions of offline tools, such as cognitive behavioural therapy (CBT), or elements of gamification in their intervention tactics (Roland et al., 2020) are often used, as well as combined solutions with wearables to track biometrics (e.g., heart rate, breathing patterns, blood pressure). Additionally, this category includes educational apps for improving skills such as coping or thinking, and more specific skills like cognitive remediation for patients with more serious mental conditions.
22.214.171.124 Apps for Measurement and Tracking
These are apps for both assessment and passive and active symptom tracking using built-in sensors in smartphones to collect data and notice changes in the user’s behaviour that might indicate the potential occurrence of a mental illness episode. These alerts can be used to notify caregivers about the need for an intervention. Additionally, this category includes other tracking tools of subjective parameters such as mood, as well as tools for screening and remote monitoring (Hays, 2020), and apps for collecting data from users for research purposes.
126.96.36.199 Tools for B2B / Mental Health Providers
This sub-category includes both (1) the applications and tools developed for mental health providers and professionals, such as provider search engines, tools and back office resources,
patient monitoring tools for tracking high-risk cases, progress, checking in and following up with patients, as well as (2) business-to-business (B2B) oriented apps and platforms, like evidence- based workplace mental health platforms for improving employee wellbeing, helping them to better manage their work-life balance, organise doctor visits, etc.
2.2.5 Business and Revenue Models
The distinction between the interchangeably used and often confused terms “business model” and
“revenue model”, in terms of both research objectives and scope, has been a key challenge to the author throughout the thesis-writing process. According to a widely cited definition by Osterwalder and Pigneur (2010), a business model describes the “rationale of how an organisation creates, delivers, and captures value”. A revenue model, which is often labelled as “pricing model”,
“pricing strategy”, “pricing tactic”, “monetisation model” or “monetisation strategy”, represents a sub-unit of a business model, an underlying framework for generating sales, describing the revenue flow or stream coming from the products and services the company is offering (Dempsey &
Kelliher, 2017). Now, as a revenue model determines “which revenue sources to pursue, what value to offer, how to price the offering and who pays for it” (Priem, 2007; Dempsey & Kelliher, 2017) and moreover, each revenue model can have different pricing mechanisms (Osterwalder and Pigneur 2010), the author believes that the term “revenue model” provides enough depth to use it throughout the context of this thesis, instead of the wider term “business model”. In the next section (Table 1), an overview of the most widely employed revenue models in the digital sphere is given.
Revenue model Description Comments
The end-user pays
Subscription Users pay a recurring (weekly, monthly or yearly) fee for access to
the product/service, or premium features Prevalent app business model Freemium Offering the base product/service for free and the premium version
for a fee (often in the form of a subscription, in-app purchases) Prevalent app business model In-app purchases User pays for products offered on the app; consumable (used once,
then deleted, can be purchased again) and non-consumable (e.g., access to premium features, does not expire)
Prevalent app business model
Usage-based (pay- per-use/pay-per- program)
Results-based pricing, user only pays when the product/service is being used
One-time fee/Flat rate/Paid
Users pays a one-time fee and gets access to the product/service Prevalent app business model
Pay-per-user The company (employer) pays for the number of users (employees) using the product/service
Prevalent in the B2B context Free for the end user, third party pays
Revenue generated from a third party paying for their
advertisements displayed (based on impressions, cost-per-click, etc) to the users of the product/service
Prevalent app business model
Marketplace Connecting supply and demand on a platform; the platform operator
is paid a commission upon transaction Prevalent app
business model Affiliate/referral Referring customers or driving web traffic to another company,
referrer receives 5-10% of the price of the transaction Data Mining of user data for revenue value
Table 1 Revenue Models.
Source: Apple (2021); Sax (2021); Dempsey & Kelliher (2017); Board of Innovation (2021).
Although the focus of attention of this thesis is on the revenue generation methods and tactics of mental health start-ups (apps) and not an examination of their overall business models, the most important elements of each case company’s business model will still be covered for the sake of a systematic and clear approach. To explore and illustrate the business models of the mental health start-ups in our sample, we are using and modifying an existing framework presented by Steinberg et al. (2020), consisting of several building blocks relevant to any company entering the digital health and well-being space. The original framework consists of 7 building blocks, of which we specify the “product” category with “product/service”, exclude the “data” section because of both a lack of substantial information and relevance, and add an additional category titled “pricing model” to more specifically summarise the information gathered on the monetisation tactics of each company. According to Steinberg et al. (2020), the main categories are the following:
Product/service What is the product that is being sold, and how does the customer benefit from it?
For example: mobile apps, online communities, therapeutic devices.
Technology What is the technological format the product appears in?
For example: computers, mobile phones or tablets, wearables/devices, sensors.
User/customer Who is the end-user the product is intended and targeted towards?
For example: a “customer” with a milder mental health concern who is seeking help on its own, or a
“patient” with a more serious condition, such as severe depression, or PTSD, whose health concerns are mediated by a doctor.
Delivery How is the product/service delivered or presented to the end-user - directly or through a physician or an employer?
For example: directly via App Store or Google Play, or through a physician or an employer, who is suggesting the use of the product.
Validation How and whether is the product’s medical effect demonstrated?
For example: through an FDA-regulation process or clinical trial, through the involvement of mental health professionals and their personal validation, or by general market/user adoption like any other digitally available product.
How is the business generating revenue? For example: monetisation of data, healthcare reimbursement, collecting payments from employers or end-customers.
Revenue model How is the payment being collected? For example: monthly/yearly subscription, pay- per-service, flat rate.
Table 2 Building Blocks of a Business Model.
Source: Steinberg et al. (2020)
4. Research Methodology
4.1 Research Method
In this chapter, the research methodology of this study is presented in order to clarify why such an approach is most suitable for addressing the research objectives presented earlier. Furthermore, the data collection method, sampling rationale and procedure and data analysis method will be explained. To begin with, there are two fundamentally different approaches to research, namely the quantitative and the qualitative method. While the quantitative research focuses on gathering and quantifying numerical data to explain a particular phenomenon, test theories, find causal relationships and patterns (Bryman, 2012; Babbie, 2010; Muijs, 2010), qualitative research addresses the “how” or “why” questions (Saunders & Lewis, 2012; Yin (2009) and aims aim to explore and assess attitudes and opinions (Kothari, 2004). Additionally, as this study is of an exploratory nature, a qualitative approach allows for identifying elements that the author was not intended to find, which can be beneficial for novel contributions and findings, nonetheless.
The research design of this thesis, which ultimately defines how the research is both conducted and analysed (Van der Velde et al., 2004), is a holistic multiple-case study. The choice of the method is highly dependent on the research objectives: as the goal of this study is to identify and explore the business and revenue models of the mental health apps (e.g., how are they structured, and the rationale behind them, e.g., why such models are employed), a case study is particularly fitting, as case studies are focused on exploring how and why things happen (Anderson, 1994).
According to Yin (2015; 1981), a case study as a research methodology allows an investigation of a contemporary phenomenon in its real-life context, especially when the boundaries between the phenomenon and the context are not obvious, which is true for this thesis. Moreover, a case study is particularly fitting to investigate a specific unit of analysis, rather than a whole organisation (Noor, 2008), based on various sources of evidence. A multiple-case (and not a single-case) design has been chosen, as it allows the author to compare and contrast case findings (Baxter & Jack, 2008) and address multiple research questions (Stake, 2005), which are relevant in the context this thesis. Additionally, as the case-study method has been criticised for lacking scientific rigour and reliability, as the results might not be generalisable (Johnson, 1994), the multiple-case study design can also increase the validity and robustness of the findings (Eisenhardt, 1989). Additionally, a
case study contains either a single or multiple units of analysis. Defining the unit(s) of analysis is what ultimately determines whether the case study has a holistic or an embedded design:
examining more than one unit of analysis is known as the embedded approach, whereas case studies (both single- and multiple-) with one unit of analysis are considered as holistic (Grünbaum, 2007). For this thesis, a holistic approach was chosen, as there is a single unit of analysis: the mental health apps. Secondly, although the cases are presented one by one to provide a rich, in- depth analysis of the studied phenomenon, the goal is to address the research objective in a holistic manner. Out of the three possible approaches to case studies - exploratory, descriptive and explanatory - presented by Yin (1984), the exploratory method was chosen, as rather than describing and explaining a phenomenon or a process, the aim of this study is to specifically explore, and potentially uncover, unexpected factors.
4.2 Data Collection Method
The qualitative data collection consists of obtaining both secondary and primary data. The secondary data analysis was conducted through a literature review and establishment of a theoretical frame of reference, to provide general context for answering all the research questions, but to especially address the first and the third. As a result, the reviewed scholars’ key concerns regarding digital mental health were summarised, and a thorough overview of the market and landscape of digital mental health and well-being was given. As the primary data collection method, in-depth semi-structured interviews with representatives from 4 digital mental health- oriented companies (some of them including several mobile apps with different business models, or apps with varying business models per target market) were conducted. In addition to the interviews with case companies, a relevant researcher in the field (Marijn Sax, PhD) whose insights were especially valuable for answering the third and fourth research questions, was also interviewed. All the interviews were conducted via video calls.
4.3 Sampling and Case Selection
Based on the guidelines by Saunders et al. (2009) for choosing a non-probability sampling technique, as well as the research objectives in mind, a purposive sampling strategy was chosen.
With purposive sampling, the subjects are selected according to their relevance to the research
topic. More specifically, this study contains a maximum variation sample, meaning cases purposefully differ from each other as much as possible. The four case companies (5 mobile apps with 7 different business models altogether) represent a selection of digital mental health companies, each with a unique value proposition and structure. The case companies were found through a relevant start-up database research (e.g., dealroom.co, crunchbase.com, pitchbook.com), with first mapping more than a hundred potential companies successfully operating in the field, then reaching out to the companies via email and/or directly approaching the senior executives on LinkedIn, and finally, based on the answers received, filtering out companies that would represent different business models and value propositions for a diversified, potentially contrasting analysis and comparison of findings. Initially, 6 interviews were conducted: 5 interviews with company representatives and 1 with a researcher. However, during the analysis of the results, it became evident that one company did not fit well into the sample, as it was a telemedicine-oriented company which whilst it did offer mental health related services on its platform, it was not the company’s sole focus (thus, it cannot be categorised as a ‘mental health app’ or a ‘digital mental health start-up’). For the sake of clarity and relevancy, a fifth case company was excluded from the study. Hence, the primary data collection is based on 5 interviews, each 30 to 60 minutes long.
In approaching and choosing the interviewees, the aim was to speak to C-level or senior executives who would be sufficiently informed and experienced to elaborate on the company's business/revenue models and strategy. The interviewees, in a random order, were Remko Vermeulen (VP of Product (Innovation)) from Koa Health, Jessica Lake (Director of Science &
Innovation) from Limbix (Spark), Jolein Hallegraeff (Customer Success Manager) from Mindler, and Leon Mueller, Co-Founder & CEO of Bloom. Additionally, a post-doctoral researcher at the University of Amsterdam, Marijn Sax (PhD), was interviewed.
4.4 Data Analysis Method
The data analysis process started with transcribing the interviews by first producing an exact, word-for-word version of each of the interviews. To be able to better analyse the recorded material at a later stage, the verbatim transcripts were then edited to remove unnecessary filler words as well as incomplete sentences that did not lead to a coherent statement. The same editing technique was used for all of the interviews for consistency. As a next step, the interviews were coded using MAXQDA (2020), a qualitative data analysis tool, to transform the relatively large amount of data
into smaller analytic units and themes (Miles et al., 2020). With coding, a combination of a deductive and inductive approach was used: a few prior codes existed (e.g., “Revenue model”) that derive directly from the research question, but the majority of the codes were developed organically by following the topics discussed during the interview. Strauss and Corbin (1990) distinguish three types of coding processes: open, axial and selective coding, which were all used iteratively. Open coding was used to assign initial themes/labels to longer paragraphs and larger chunks of text. For example, any challenge or problem described by the interviewee was first labelled as “Challenge”, or any sentence related to the concept of a revenue model was labelled as such. Next up, axial coding was used to specify the broader themes (e.g., “Challenges” as industry or revenue model related challenges) and vice versa, by creating more general themes and theme hierarchies. As a result, 4 main core categories were created (“Overview of Product/Service”,
“Revenue Model”, “Industry”, “Alternative revenue models”). The described coding process aimed to be a part of a thematic analysis, which according to Vaismoradi et al. (2013) is suitable for studies of an exploratory nature, and during which themes and patterns are identified within the qualitative dataset regarding the research question (Braun & Clarke, 2006). Thematic analyses are conducted via multiple or cross-case analysis, more specifically by stacking comparable cases (in this case, mobile apps), which allow researchers to analyse each case individually by using a set matrix (in this case, the matrix consisting of 4 main categories developed as a result of the thematic analysis and iterative coding), followed by all gathered data being transferred to a matrix, allowing for presentation of an overview of all cases combined (Miles et al., 2020).
5. Empirical Findings
5.1 Case study 1: Mindler
5.1.1 Foundation of the Business Model
Mindler, according to the definitions provided on their website, is a mobile app-based “digital psychologist clinic”, “digital care provider” or “digital mental health institution” launched in Sweden in 2018. The company’s mission is to offer everyone an equal opportunity to meet a psychologist - despite their location or financial situation, and according to Hallegraeff, "make healthcare super easily accessible, and make the threshold low". Although the company has also expanded to the Netherlands and France and has a different business model in each of these target markets (which will be addressed later), the main value proposition in the form of a mobile app remains the same. According to Hallegraeff, Mindler is "an alternative to a real psychologist" and connects people with mental health concerns with psychologists through a video call on a smartphone or tablet. Therefore, Mindler falls into the telehealth or more specifically, teletherapy category, as it is based on remote yet synchronous, two-way interaction between a person and a provider, using live videoconferencing. The customer journey (a ''digital visit'') consists of first, assessment and investigation and then, treatment of mental illnesses such as anxiety, stress, depression, sleep problems and ADHD/ADD (Mindler, 2021). Once the first assessment is done, the patient undergoes a CBT-based treatment usually lasting for 8-12 sessions ("I would say 8 to 10 sessions is the average right now"). With the face-to-face therapy sessions still being the main value proposition, Mindler also offers a "mixed treatment" solution, which is a combination of teletherapy and self-help programs that can be followed individually or with a psychologist. In addition to the services targeted at individuals, Mindler offers a business-to-business vertical (Mindler at Work) for companies seeking to offer their employees psychological support (Mindler, 2021). However, as the B2B solution was not discussed during the interview and it is not the primary focus of Mindler, it will not be further analysed in this thesis.
5.1.2 Revenue Model: Structure, Rationale and Strategic Implications
As mentioned before, Mindler has a more or less different business model in all the countries in which their services are offered. Mindler Sweden, which employs over 300 psychologists and is
the company’s country of origin, is available to all Swedish residents with a personal identification number. As Mindler Sweden is a part of the country’s primary care system and hence a (virtual) therapy session is equal to a regular visit to any health facility, the costs of using the app are split between the patient and the healthcare reimbursement fund. The patient pays around 10€ (100 SEK) for each session, until reaching the spending limit of 110 € (SEK 1,100). After reaching that limit in a twelve-month period, the person receives a cost exemption/redemption card (“frikort” / Free Card), that allows the person to receive any medical care (incl. the therapy sessions on Mindler) for free until the end of the year. Although the costs are partially covered by national health insurance, Mindler Sweden is still classified as a direct-to-consumer business model, as the app is delivered directly to the end-user, without a referral from a health professional/clinician. In France, Mindler is completely private and not tied to the national healthcare system in any way.
According to Hallegraeff, a “pay-out-of-pocket” model where customers pay 45€ per session, with the first session being free of charge, is being applied there. Since the service is directly available to customers without a GP or mental health professional as a mediator, and the costs are not reimbursed, Mindler France can be classified as a pure direct-to-consumer business model. In the Netherlands, however, Mindler’s business model can be classified as “clinician-to-patient”, as the service is fully reimbursed and available free of charge for the patient if the following conditions are met: the patient needs to be covered by a Dutch health insurance (either ONVZ, VGZ, DSW, EUCare, ASR, CZ, Achmea), and to have received a referral from the General Practitioner (GP), with either a diagnosis or a suspicion of a diagnosis by the GP. If these requirements are met, the therapy sessions are reimbursed by the health insurer. However, similarly to the Swedish “Free card” system, in some cases (depending on the person, their specific health history and insurance plan), patients in the Netherlands might also have to cover their own risk (“Mostly it is 385 euros per year, and that goes for all healthcare you receive ...If you break your leg in hospital, it also counts as your own risk. So, it could be that people already spend their own risk at the hospital then come to us so that the treatment is free”). However, according to Hallegraeff, not all the patients are accepted on the platform: based on referral letter from a GP and depending on the severeness of the illness, Mindler either accepts or rejects the potential patient: “We think that online treatment does have some limitations. /.../ We don't treat very severe problems. /.../ We approve your referral letter if we think Mindler is suited for you.”
Despite the business model (D2C or C2P) employed in the given country, the revenue model of Mindler is the same in every market: the app charges money per session, which makes it a pay- per-session type of revenue model. The pay-per-session model, at least in Sweden and the Netherlands, is rooted in the service being tied to the national healthcare systems of these countries, as reimbursement is based on therapy sessions/visits (and not, for instance, on a monthly subscription). The general rationale behind both the (reimbursed) direct-to-consumer and (reimbursed) clinician-to-patient business model is highly dependent on the company’s aim to make mental health care as accessible (frictionless, fast) and affordable as possible. With these principles in mind, Mindler is adjusts its models depending on the target market’s healthcare system and applies a model that would best meet these criteria (“In those three countries, our business model is different, because of course, every healthcare system is different”). Hallegraeff argues that the company would prefer to set no barriers of access to the app, which for now is the case in the Netherlands (“In France and Sweden, it's way easier... we wish we could do that here as well. /.../ We just want people to access, to be able to book a session the next day and talk to psychologists and prevent people from walking around with complaints for months”) and would ideally see the service being free of charge for users (“In my opinion, healthcare has to be free, or very, very cheap. /.../ I think the Swedish model is already coming close”). Thus, the ideal scenario would be a direct-to-consumer model that is fully reimbursed by health insurance (and/or including the patient’s own risk). In that case, the service would fully meet the accessibility, affordability and urgency criteria, which is in place in Sweden. In France, the pure direct-to-consumer model is motivated by the accessibility and urgency (and not so much affordability) factors, and in the Netherlands, the clinician-to-patient model serves the affordability issue more (and not so much accessibility).
As a key challenge at the industry level, Hallegraeff brings out the limitations presented by governmental rules and regulations, and the complex healthcare systems, which is especially pivotal in the Netherlands (“The Dutch healthcare system is super complicated. /.../ All those rules, and the GP has so much power”). The company’s overall strategy is highly dependent on the regulations of the target country, and regulations are even hindering the company’s growth in the Netherlands (“We are limited in our growth by some rules and regulations. For example, the whole
healthcare system in the Netherlands is going to flip upside down next year. They're going to change the way people get reimbursed”). As a positive change happening on the industry level, Hallegraeff mentions mental health gradually becoming less of a stigma (“It's talked about more.
[For example], it's cool to actually meditate, a few years ago that was super weird”). Additionally, Hallegraeff addresses the potential threats that mental health apps can pose to people with more severe concerns than any of these apps can solve, by both accepting these people on their apps without the involvement and judgment of a mental health professional (“That's a mistake that we made in the beginning that we just accepted almost everyone. Then we noticed that there are limits to online treatments. /.../ I think there are some organisations that just accept whoever wants to come in. /…/ I think that's something that healthcare apps have to be very careful about”), or by presenting the features of the app as a cure to people’s mental health problems, although it might not be enough of an intervention (“People might think that they can fix their issues with an app like Calm or Headspace, while they actually might need a professional to talk to. /.../ So, people seek help in places that aren't suitable for help”). She expresses worry for the mass-market well- being apps becoming a replacement for therapy, while some people might need real therapy (“It could be a supplement to therapy, but it shouldn't be the solution on its own. That's dangerous.”).
Hallegraeff is also concerned with some of these apps not having proper disclaimers.
5.2 Case study 2: Limbix
5.2.1 Foundation of the Business Model
Limbix is a company which develops digital therapeutics for adolescent mental health, with its primary focus being on their lead-product Limbix Spark, a CBT-based mobile app targeted towards teenagers suffering from depression. The team of 27 people, consisting of clinicians, product designers, researchers, and engineers, is based in Palo Alto, United States. What makes Limbix (Spark) an especially interesting case, is the fact that it is not on the market yet: the product is currently in the process of getting FDA clearance as a digital therapeutic, which will presumably take place in early 2022. The journey towards FDA clearance, the “evidence generation pathway”
consists of 3 stages: (1) a feasibility clinical trial, (2) a COVID-19 randomised controlled clinical trial (RCT), and (3) an FDA pivotal RCT. The company has already completed a feasibility study among 30 participants aged 13-21 which showed a clinically meaningful reduction in adolescent
depressive symptoms, and is currently in stage 2, assessing the app among adolescents during the COVID-19 pandemic. The preliminary results of the study, which includes 235 participants aged 13-21, show a clinically significant reduction in depressive symptoms, as well as a statistically significant reduction in depressive symptoms. The third and final stage beginning in 2021 will be a large-scale pivotal trial in partnership with the Duke Clinical Research Institute, the world’s largest academic research organisation and a leader in the advancement of digital health technologies (Limbix, 2021). The Limbix-DCRI pivotal RCT has received a Fast Track Phase I/II Small Business Innovation Research (SBIR) grant from the National Institute of Mental Health (NIMH), providing up to $3.6M of funding to support the clinical validation of Limbix Spark.
Hence, in terms of how the product’s medical effect is demonstrated, Limbix Spark is without a doubt the app reaching the highest clinical bar (and validation) in our case selection.
The app is specifically targeted towards adolescents (“our sweet spot is exclusively focusing on adolescence and building all of our products with adolescents in mind”). According to Lake, the app/treatment is “most impactful for kids who are first identified as having behavioural health needs within primary care”. Since the company sees the access to a mental health professional as the main barrier both in the US and in other countries, the company positions the app as either a
“a stopgap before they're able to access additional support”, or a form of treatment in itself, “under the purview of a primary care provider”. The product itself, Limbix Spark, is a 5-week cognitive- behavioural-therapy-based program that is built on the idea of completion of different “value- based activities that spark feelings of pleasure or mastery” (Limbix, 2021). The app also includes elements of gamification, e.g., a reward system within the app used to drive engagement, as adolescents are particularly motivated by rewards, Lake stated. However, she stresses that the app is not entirely game-based, as “Mental health isn't a game. /.../ Our app isn't a game. So, we pick and choose the elements of gamification that we think are appropriate.”
5.2.2 Revenue Model: Structure, Rationale and Strategic Implications
Once Limbix gets the FDA approval it is currently seeking, the mobile app-based 5-week-program will be a “prescription medical device” prescribed to teenagers by clinicians as a prescription-only digital medicine and reimbursed by insurance (who in the US healthcare system, are often collectively referred to as “payers”), more specifically by either private payers (insurance