Digital Therapeutics for Mental Health and Addiction: The State of the Science and Vision for the Future

Digital Therapeutics for Mental Health and Addiction: The State of the Science and Vision for the Future presents the foundations of digital therapeutics with a broad audience in mind, ranging from bioengineers and computer scientists to those in psychology, psychiatry and social work. Sections cover cutting-edge advancements in the field, offering advice on how to successfully implement digital therapeutics. Readers will find sections on evidence for direct-to-consumer standalone digital therapeutics, the efficacy of integrating digital treatments within traditional healthcare settings, and recent innovations currently transforming the field of digital therapeutics towards experiences which are more personalized, adaptable and engaging.

This book gives a view on current limitations of the technology, ideas for problem-solving the challenges of designing this technology, and a perspective on future research directions. For all readers, the content on cultural, legal and ethical dimensions of digital mental health will be useful.

• Gives a comprehensive overview of the field of digital therapeutics and research on their efficacy, effectiveness, scalability and cost-effectiveness
• Introduces novel directions in which digital therapeutics are currently being extended, including personalized interventions delivered in real-time
• Reviews important considerations surrounding digital therapeutics, including how they can be monetized and scaled, ethical issues, cultural adaptations, privacy and security concerns, and potential pitfalls

• Language: English
• Publisher: Academic Press
• Release date: Sep 27, 2022
• ISBN: 9780323885614

Book preview

Chapter 1

Introduction: A vision for the field of digital therapeutics

Nicholas C. Jacobsona,b,c,d, Tobias Kowatsche,f,g and Lisa A. Marscha,b,c

aCenter for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, United States

bDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, United States

cDepartment of Psychiatry, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, United States

dDepartment of Computer Science, Dartmouth College, Hanover, New Hampshire, United States

eCentre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zürich, Switzerland

fCentre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland

gFuture Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, Singapore, Singapore

1.1 Why read this book?

The purpose of this book is to introduce the topic of digital therapeutics to a broad audience. Traditional treatments for mental health and addiction have not been capable of scaling to meet the large number of persons who need or seek treatment. Digital therapeutics—software designed to prevent, treat, or manage pathology—may help to close a longstanding access to treatment barrier, providing scalable, low-cost interventions to a broad audience. Within the past decade, there has been a massive growth of digital therapeutics, but there is limited integration of the literature on the current state of the science. Mental health clinicians and researchers alike have difficulty in keep abreast of the advancement of these topics, including evidence for direct-to-consumer standalone digital therapeutics, the effectiveness of integrating digital treatments within traditional healthcare settings, as well as recent innovations which are currently and will continue to transform the field of digital therapeutics toward therapeutic models which are more personalized, adaptable, and engaging. The goal of this book is to introduce these topics, discuss cutting-edge advancements, and offer important considerations for implementation of digital therapeutics. The editors believe that this combination of features has not been offered in any other books to date.

1.2 Who is this book for?

The intended audience of this book includes: (1) researchers and graduate students in the fields of biomedical engineering, health informatics, biomedical data science, software engineering, and computer science, (2) persons in industry within the field of digital health, as well as in the business of healthcare, and (3) policy makers and persons who study digital mental health from the legal and/or humanities fields. We have designed this book as a seminal text introducing persons without a background in mental health and substance use to the problems faced within the fields of mental health and addiction, and how digital therapeutics may be leveraged to help treat these persons and fill gaps within the traditional care system. Although the book is tailored for audiences with broad technical backgrounds, this book could also be useful for readers with more mental health backgrounds (e.g., clinical psychology, counseling psychology, counseling, psychiatry, substance use counseling, and social work programs).

1.3 What topics are covered in this book?

First, we introduce a comprehensive overview of the field of digital therapeutics and research on their efficacy, effectiveness, scalability, and cost-effectiveness. Second, we introduce the novel directions in which digital therapeutics are currently being extended, including personalized interventions delivered in real-time. Third, we review important considerations surrounding digital therapeutics, including how they can be monetized and scaled, ethical issues, cultural adaptations, privacy and security concerns, and potential pitfalls.

1.3.1 First section: Introduction to digital therapeutics

The next chapter of this book (Chapter 2: Using digital therapeutics to target gaps and failures in traditional mental health and addiction treatments) provides an overview of the current gaps and failings of the traditional mental health and addiction care system, including how and why most persons needing mental health treatment do not receive it. The chapter then introduces and defines digital therapeutics, and discusses how digital therapeutics can be used to help remedy gaps in the traditional care system.

After introducing the problems faced within the traditional care systems, the subsequent chapter discusses the first wave of scalable digital therapeutics (Chapter 3: First wave of scalable digital therapeutics: internet-based programs for direct-to-consumer standalone care for mental health and addiction). This chapter introduces the first major wave of evidence-based scalable treatments for the masses, focusing on the use of internet-based treatments delivered directly to consumers (outside of healthcare systems). This chapter both introduces the seminal advances made by pioneers within digital therapeutics, as well as reviews the current evidence base.

After discussing the first wave of digital therapeutics, the following chapter (Chapter 4: Second wave of scalable digital therapeutics: mental health and addiction treatment apps for direct-to-consumer standalone care) discusses the emergence of smartphone applications that are used for symptom monitoring and standalone interventions and made available direct-to-consumers, highlighting how these tools are differentiated from web-based counterparts, and reviews their current evidence base.

With Chapters 3 and 4 most principally devoted to standalone care, the next chapter (Chapter 5: Blending digital therapeutics with traditional treatments within the healthcare system) reviews the integration of digital therapeutics and traditional treatments in order to extend and augment traditional treatments for mental health and addiction.

1.3.2 Second section: The new frontier

The second section of this book will discuss how digital therapeutics can leverage recent advancements in technology for more effective interventions. The first chapter of this section (Chapter 6: Receptivity to mobile health interventions) will introduce the importance of engagement in and receptivity toward mobile health interventions. Relevant receptivity studies are reviewed after describing an ideal mobile health intervention and the specific processes involved in receptivity, that is, receiving, processing, and using support. Then, pertinent factors that seem helpful for detecting and predicting receptive states are summarized. The chapter concludes with various technical challenges intervention authors face when developing receptive-capable mobile health interventions.

The following chapter (Chapter 7: Analytics for adapting interventions to context targeting vulnerability and receptivity) will discuss the design of effective just-in-time adaptive interventions (JITAIs). JITAIs leverage intensive longitudinal data from a target person and combine it with context information to deliver the most appropriate support at opportune moments. The authors clarify the definition and operationalization of vulnerability and receptivity in JITAIs, and offer specific research questions to support intervention authors in identifying the most suitable study designs such as observational studies or microrandomized trials.

Therapeutic alliance is an important relationship quality between patients and health care providers that is robustly linked to treatment success. Chapter 8 (Digital therapeutic alliance) will explain how this therapeutic relationship can also be developed between an individual and a given digital therapeutic. In particular, the authors discuss therapeutic alliance in digital mental health interventions that are either supported by humans or unsupported, that is, in purely technological interventions. The chapter also discusses how to measure digital therapeutic alliance, its impact on outcomes in digital mental health interventions, and what intervention authors can do to foster therapeutic alliance with digital therapeutics.

Chapter 9 (Conversational agents on smartphones and the web) will then introduce conversational agents and how these computer programs that imitate communication with human beings can be used for health service delivery. In particular, the authors discuss conversational agents deployed over the web and on smartphones that offer scalable interventions for mental health and addiction. Affordances of web- and smartphone-based conversational agents, such as low cost, reach or availability, as well as safety concerns, or benefits of conversational agents, are described, too. For example, individuals may feel less stigma when conversing about mental health or addiction with conversational agents, in contrast to human health coaches.

In complementing Chapter 9, the authors of Chapter 10 (Voice-based conversational agents for sensing and support: examples from academia and industry) will detail the rise of voice assistants as potential outlets for care delivery in ways that are both intuitive and engaging. The chapter will also discuss the potential of acoustic data streams that can be used by such technology for the detection of vulnerable states. Moreover, the authors present various examples of voice assistants from both academia and industry, and outline potentials for mental health and addiction treatment. The chapter concludes with a discussion on various challenges developers of voice assistants face, such as using vocal biomarkers via automated speech analysis or concerns around data and conversation privacy.

1.3.3 Third section: Structural considerations

Chapter 11 starts the section of this book focused on the broad array of structural considerations in the field of digital therapeutics. This chapter (Chapter 11: Design considerations for preparation, optimization, evaluation, and maintaining digital therapeutics) introduces a framework designed to guide developers of digital therapeutics across their lifespan, including their formative development, optimization, evaluation, implementation, and maintenance. This framework may support the development of maximally effective, engaging, implementable, and sustainable digital therapeutics.

Chapter 12 (Chapter 12: Cultural adaptations of digital therapeutics) then embraces the important topic of adapting digital therapeutics for diverse cultures, languages, and contexts. The chapter provides an overview of frameworks for guiding cultural adaptation of evidence-based interventions, the evidence supporting such as adaptation, and recommendations for advancing this field.

The following chapter (Chapter 13: Implementation, business models, and regulation) provides an overview of the progression of the digital therapeutics industry. This chapter reviews business models of digital therapeutics, including how they are being commercialized, scaled, and regulated across the world.

Next, Chapter 14 (Chapter 14: Lessons learned and potential pitfalls) reviews the major lessons that have emerged from the past decade of research and development of digital therapeutics as applied to mental health and substance use. It additionally reviews challenges and suggests strategies to increase utilization, engagement, and accessibility of clinically validated digital therapeutics worldwide.

The following chapter (Chapter 15: Privacy and security) discusses how the growth in digital therapeutics presents an unprecedented and important need to concurrently address the privacy and security vulnerabilities inherent to some of these interventions. This chapter reviews common risks related to patient data in digital therapeutics, suggests strategies for navigating the relevant regulatory landscape, and discusses methods for evaluating and addressing security and privacy considerations with digital therapeutics.

Chapter 16 (Ethical considerations of digital therapeutics for mental health) introduces the ethical challenges faced by researchers and clinicians utilizing digital therapeutics, including issues related to transparency, autonomy, fairness, and quality. The chapter provides an overview of the role health care providers can play in navigating these ethical considerations.

The final chapter of this book (Chapter 17: A look forward to digital therapeutics in 2040 and how clinicians and institutions get there) presents a vision for how digital therapeutics may evolve and transform over the next 10–20 years, discussing how the current tools will adjust as new technological innovations mature and advance.

1.4 What will readers learn?

After reading this book, readers will have a strong foundation to understand both the current utility of digital therapeutics in addressing systemic inadequacies in the current care system for mental health and substance use. Additionally, readers will understand the current evidence base of digital therapeutics, both as standalone interventions as well as interventions that are integrated within the care system. Readers will also be introduced to recent and ongoing advances in the field of digital therapeutics, including how therapeutics can be dynamically adapted to place and time to provide care in the moment when persons are most likely to benefit and in their times of greatest need, and the connections formed between persons and their digital therapeutics. Readers will learn about digital therapeutics that are developed through linguistic and verbal interactions mimicking conversational patterns across a series of devices. Readers will understand study designs used to test and optimize digital therapeutics, tailoring digital therapeutics to integrate cultural differences, and potential areas of concern and pitfalls including privacy, security, and ethical considerations. Lastly, readers will be introduced to the marked growth within the field of digital therapeutics and how they are projected to evolve over time and fundamentally improve models of mental health and substance use care worldwide.

Chapter 2

Using digital therapeutics to target gaps and failures in traditional mental health and addiction treatments

Nicholas C. Jacobsona,b,c,d, Rachel E. Quista, Camilla M. Leea and Lisa A. Marscha,b,c

aCenter for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, United States

bDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, United States

cDepartment of Psychiatry, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, United States

dDepartment of Computer Science, Dartmouth College, Hanover, New Hampshire, United States

2.1 Prevalence and impact of mental health and substance use disorders

Globally, one in five people suffer from a mood, anxiety, or substance abuse disorder (Steel et al., 2014) and one in seven children meet criteria for a mental disorder (Ferrari et al., 2022). These rates are higher in certain populations: lesbian, gay, bisexual, and transgender (LGBT) people are more than twice as likely to have a mental health disorder in their lifetime (Semlyen, King, Varney, & Hagger-Johnson, 2016) and about a third LGBT youth meet criteria for a disorder (Mustanski, Garofalo, & Emerson, 2010). Income inequality also factors into mental health: the likelihood of having a mental health issue is twice as high among low-income people as high-income people (Patel, Araya, de Lima, Ludermir, & Todd, 1999). The first onset for any mental health disorder begins in early childhood or adolescence; half of all lifetime cases start by age 14 and three quarters by age 24 (Kessler et al., 2005). Up to 79% of people experiencing chronic illness will experience a mental health disorder in their lifetime (Solano, Gomes, & Higginson, 2006). Furthermore, an estimated 8.9 million people in the United States have a comorbid mental health and substance use disorder (Substance Abuse and Mental Health Services Administration [SAMHSA], 2016). Therefore, mental health disorders are a significant and pertinent problem worldwide and some groups have even higher rates of mental health issues.

The impact of these disorders worldwide is significant in many ways. In the United States, estimates for national spending on outpatient treatment for depression alone are US$ 17 billion (Hockenberry, Joski, Yarbrough, & Druss, 2019), with an estimated 200 million days lost from work each year (Conti & Burton, 1994). Lost productive time among US workers costs US$44 billion to employers every year, in contrast to US$13 billion lost from peers without depression (Stewart et al., 2003). In 2010, mental health and substance use disorders accounted for 183.9 million Disability Adjusted Life Years (DALYs), or 7.4% of the worldwide rate (Whiteford et al., 2013). This measure calculates the global burden of disease by combining years of life lost due to premature mortality with years of healthy life lost due to disability. However, the actual number may be closer to 13% of all DALYs worldwide; on average, mental health disorders contributed to a 6.8-day decrease in healthy days per month (Vigo, Thornicroft, & Atun, 2016).

In line with loss of healthy life, those with mental health disorders ranked their health-related quality of life (HRQoL) lower than those without mental health disorders (Spitzer et al., 1995). The HRQoL for those with mental health disorders was also significantly lower than those with other chronic conditions, such as back pain, diabetes, or hypertension (Cook & Harman, 2008). Additionally, chronic mental health disorders such as social anxiety and dysthymia have been associated with worse HRQoL than episodic disorders (Saarni et al., 2007). Mortality rates in general are higher among people with mental health disorders, resulting in a median 10-year decrease in life expectancy (Walker, McGee, & Druss, 2015). Worldwide, substance use accounts for 4% of all deaths and 5.4% of the global burden of disease (World Health Organization, 2010). Therefore, the global prevalence of mental health disorders, including substance use disorders, is an economic and global health burden.

2.2 Lack of treatment receipt in the traditional care system

Despite the high prevalence of these disorders and the related human and economic outcomes, only an estimated 41% of adults and 45% of adolescents worldwide have received any form of care for their mental health disorders, and a substantial subset of these people do not receive minimally adequate care for their disorders, where minimally adequate mental health care is defined as at least 2 months of treatment with an appropriate medication as well as at least four visits with any physician or, without medication, at least eight visits with a mental health professional. Lack of access to treatment is associated with demographic factors such as race, gender, and socioeconomic status (Wang, Demler, & Kessler, 2002). These demographic factors can interfere with protective factors against developing mental health issues, such as having autonomy when responding to severe life events, having access to resources that allow choices when confronting severe life events, and having support from family, friends, and health providers when confronting severe life events (World Health Organization, 2004).

One study in California found that men, Latinos, Asians, young people, older adults, people with less education, uninsured adults, and individuals with limited English proficiency were significantly more likely to have unmet need (Tran & Ponce, 2017). In the United States, Latino and African-American parents are more likely to seek familial or community-based help than professional help for children with mental health issues (McMiller & Weisz, 1996). People living in rural populations are less likely to seek out treatment than nonrural populations (Crumb, Mingo, & Crowe, 2019) despite the fact that rural populations experience mental illness at equal or greater rates than nonrural populations, especially when it comes to women (Hauenstein, Boyd, Submission, & H., 1994). LGBT individuals are more likely than their non-LGBT counterparts to have unmet need (Whaibeh, Mahmoud, & Vogt, 2020). Worldwide, both attitudinal and structural factors are barriers to receiving care for mental health issues, though some research has indicated that treatment for mild or moderate mental illness can be more impacted by attitudinal behaviors while serious mental illness may be more impacted by structural barriers (Andrade et al., 2014). Low perceived need for treatment was listed as a common reason for respondents in the National Comorbidity Survey Replication study for not seeking treatment, with attitudinal reasons being more common than structural barriers among those with perceived need (Mojtabai et al., 2011). Serious mental illness is defined as a mental illness diagnosis that typically leads to extensive inpatient and outpatient treatment and results in significant disability in one or more major life domains (Parabiaghi, Bonetto, Ruggeri, Lasalvia, & Leese, 2006).

2.3 Barriers to traditional treatment: stigma, personal beliefs, and cultural competence

Despite the fact that up to 57% of individuals around the world report that they are in daily contact with an individual who they think suffers from a mental illness, including addiction (Seeman, Tang, Brown, & Ing, 2016), one of the biggest attitudinal barriers to care is the stigma surrounding treatment for mental health issues. Cultures across the world have relatively stable negative prejudices toward people with mental illness over the past 20 years despite efforts to publicize a model of mental illness as a biological, medical disease (Schomerus et al., 2012). In LGBT populations, stigma surrounding mental illness can intersect with stigma surrounding sexual or gender identity, which leads to discrimination and internalized shame, making access to care even more difficult (Wainberg et al., 2017). Men and non-White individuals are more likely to report stigmatized attitudes toward people with mental illness or substance use disorders; however, increased reported stigma by people of color may be confounded by socioeconomic status and discrimination due to identity (Corrigan & Watson, 2007).

Individuals with substance use disorders face higher levels of stigma and discrimination than those with other psychiatric disorders. Only 11% of people with substance use disorders received professional help in the past year (Substance Abuse and Mental Health Services Administration [SAMHSA], 2019). In the public eye, individuals with substance use disorders are likely to be seen as dangerous and unpredictable, unable to make decisions about treatment or finances, and to be blamed for their own condition (Yang, Wong, Grivel, & Hasin, 2017). Even healthcare professionals have a more negative attitude toward individuals with substance use disorders than individuals with other mental health issues (van Boekel, Brouwers, van Weeghel, & Garretsen, 2013).

In addition to stigma from family, friends, and society, stigma can also stem from personal beliefs of an individual with mental illness. Despite the finding that religious participation can be a protective factor against mental illness (Levin & Chatters, 1998), mental illness can be seen by both the individual and their family and friends as a lack of religious connection (Crumb et al., 2019). Worldwide, low perceived need is the most common reason for not initiating treatment among those with mild or moderate cases. A desire to handle the problem on one's own was the most common reason for not seeking treatment among those who perceived a need for treatment (Andrade et al., 2014). Another reason is due to perceived inadequacy of treatment. Of individuals who received treatment for their serious mental illness, only 38.9% received treatment that was minimally adequate as defined earlier in this chapter (Wang et al., 2002). Similarly, a large number of individuals with depression received inadequate treatment for their depression; those who had recently attempted suicide received equally inadequate care as those who had not (Oquendo, Malone, Ellis, Sackeim, & Mann, 1999). Worldwide, the most common reason for dropping out of mental health treatment is perceived ineffectiveness of care (Andrade et al., 2014).

2.4 Barriers to traditional treatment: cultural competence

The perceived cultural competency of a mental health provider is also a barrier to treatment for some groups. Cultural competency is defined as providing health care that meets the needs of a population diverse in gender, race, ethnicity, sexual orientation, age, religion, [dis]ability, language, national origin, immigration status and socioeconomic status (Bassey & Melluish, 2013). Cross-cultural attitudinal barriers to mental health treatment include cognitive barriers, affective barriers, and value orientation barriers, and effective practitioners must be able to understand how these barriers relate to their patient (Leong & Kalibatseva, 2011). A lack of understanding in these areas can lead to underutilization or low retention for mental health services (Whaibeh et al., 2020).

Even when an individual with mental illness overcomes attitudinal barriers to seek out treatment, there are many structural barriers that can limit access to care. One of the most frequently cited structural barriers to care is ability to pay (Andrade et al., 2014). Like most areas of medical care, assessment and treatment for mental illness is expensive. In 2006, inpatient treatment—often required for more severe mental illness cases and for substance use disorders—ranged from $766 per day to over $1000 per day ($1060 and $1833 respectively when adjusted for inflation) (Stensland, Watson, & Grazier, 2012) while average household income in 2006 was $48,450 (Statistical Abstract of the United States, 2006). In 2013, the average annual cost for managing depression in the United States ranged from $8662 to $16,375 depending on the severity of symptoms (Chow et al., 2019) and the average annual cost for managing schizophrenia hovered around $21,672 (Fitch, Iwasaki, & Villa, 2014). For comparison, the average household income in 2013 was $52,250 (Noss, 2012). This means that simply managing the care for mental illness could cost over 40% of the average household's income without insurance.

2.5 Barriers to traditional treatment: high cost and lack of insurance coverage

Because of the financial costs, in most developed or developing countries insurance coverage is one of the most significant deciding factors for whether or not an individual can receive care (Mechanic, 2002). In the United States, 28 million people did not have any insurance and millions more were uninsured for part of the year (Keisler-Starkey & Bunch, 2021). A 2010 comparison of physicians and psychiatrists revealed that about 90% of physicians accept private insurance in comparison to only 55% of psychiatrists (Bishop, Press, Keyhani, & Pincus, 2014). This is largely due to how managed care determines reimbursement rates; practitioners are underpaid when they accept insurance as payment in comparison to accepting out-of-pocket fees. Therapists are also disincentivized by the limitations imposed by managed care, which include decreased flexibility for clinical judgment, restrictions on who can receive treatment, and increased pressure to make referrals for prescriptions/medication (Cohen, Marecek, & Gillham, 2006). Additionally, adults with severe mental illness have the lowest rates of receiving care, while adults with public insurance have the highest rates (Rowan, McAlpine, & Blewett, 2013). Thirty-seven percent of adults with severe mental illness lack insurance for at least part of the year, compared to 28% of adults without a severe mental illness. Out-of-pocket expenses and premiums exceed 5% of income for at least 2 years for 40% of adults with at least 3 chronic mental health conditions and 20% of adults with one chronic condition (Cunningham, 2009).

Even when individuals are willing to seek out care and have insurance to pay for it, government-sponsored health insurance plans are often lacking in their mental health coverage. For example, in the United States, Medicare recipients' restriction of access to care depends on availability of providers who will accept Medicare. About 48.6% of rural counselors referred an existing client to a different counselor due to Medicare ineligibility. These mid-treatment referrals disrupt the therapeutic alliance and contribute to the problem of limited access and long waitlists for treatment (Fullen, Brossoie, Dolbin-MacNab, Lawson, & Wiley, 2020). Finding in-network practitioners is easier with private insurance, but still challenging. Some private insurance plans have limits on the amount of inpatient days they will cover, which can be detrimental to those with severe mental or substance use disorders that need inpatient care (Cohen et al., 2006).

Lack of health insurance coverage is not specific to the United States; while nearly 50 countries have attained universal coverage, many other countries have insurance rates close to 0. In Africa, out-of-pocket health expenses account for nearly 45% of total health care expenditures. In Asia, countries like Korea and China have nearly universal coverage, while India's coverage level is under 10% of the population. More well off Latin American countries like Chile and Costa Rica have almost universal coverage, while poorer countries like Colombia have coverage rates around 30%. Europe boasts the highest number of countries with universal coverage, but effective access to the system remains an issue for many countries in the region (International Labour Office, 2008).

2.6 Barriers to traditional treatment: transportation and appointment time availability

Another structural barrier to accessing mental health treatment is whether or not the individual seeking care has the ability to access the provider. Lack of available transportation has long been established as a significant barrier to mental health and substance use care (Sommers, 1989). This lack of access is exacerbated by rural living situations; Australian people living in rural regions were nearly 20 times more likely to delay seeking treatment by over 1 year in comparison to those living in urban locations (Green, Hunt, & Stain, 2012). When transportation is provided free of charge, distance to care does not decrease utilization of mental health treatment programs (Whetten et al., 2006).

Frequently, the lack of public transportation in rural areas couples with the fact that driving distances tend to be much longer. Patients might not be able to drive themselves to receive mental health care as there is often a comorbidity between mental health conditions and physical health conditions that would interfere with and could even prevent a patient's ability to drive. The prevalence of comorbid mental and physical conditions has increased significantly within the past decades (Sartorious, 2013). Comorbid diseases are also rapidly increasing at younger ages. Although comorbid diseases are often overlooked, it is recommended that psychiatrists and other clinicians promote the idea that all conditions have both psychological and somatic components (Sartorious, 2013). Mental health disorders are a risk factor for developing a chronic condition and vice versa. The combination is associated with higher costs of treatment, decreased quality of life, increased symptom burden (Sartorius, 2018).

Additionally, the location of appointments and the times appointments are available may interfere with the patient's responsibilities. Many mental health care workers have hours that fall within the working day, which may require clients to take off work, find childcare, or sacrifice family time (Harvey & Gumport, 2015). The inconvenience and inability to get an appointment are additional structural barriers that prevent access (Sareen et al., 2007). A study that surveyed respondents in the National Comorbidity Survey Replication with common 1-year DSM-IV disorders revealed that those that are married or cohabiting with a partner report higher levels of structural barriers, but only among milder mental health cases. This finding may reflect how marriage correlates with increased family responsibilities and consequently, higher demands on time and financial resources (Mojtabai et al., 2011). The barrier to seeking treatment might only be overcome when cases are more serious.

2.7 Barriers to traditional treatment: inadequate number of mental health providers

One in five people means that of the 5.2 billion adults currently alive in the world, approximately 1.3 billion are suffering from a mental health disorder, and 762 million have not received any form of care. The previously discussed attitudinal and structural barriers to receiving care are responsible in many ways for the number of people with untreated mental illness. However, even if all the problems surrounding insurance, access, and stigma were solved, there would still be millions of people without adequate mental health care because the number of providers of mental health care is too low. On average across the globe, there are 10.7 mental health workers per 100,000 people (Morris, Lora, McBain, & Saxena, 2012). If 20,000 (or one in five) of those people have a mental health disorder, each mental health worker would need to treat 1869 patients each year. Although this is clearly not plausible, a few calculations will determine just how implausible it is.

Let's say a psychologist works a typical 40-hour week and spends all of their time in-session with clients for 45 minutes at a time. At the end of 1 week, this person would be able to help about 53 people. It is important to note that this number is not plausible for most providers. In fact, most psychotherapists average around 25–30 clients in their caseload because they are not solely practicing therapy; psychotherapists also manage clinical notes, treatment reports, telephone calls, and billing among other responsibilities. Utilizing a large dataset (N = 18,322) from a counseling center, the average client caseload (referring to a rolling estimate of unique individual clients seen by a clinician in the past 30 days) in 2016 was determined to be 26.45, the highest the average client caseload seen between 2009 and 2017 (Bailey, Erekson, Goates-Jones, Andes, & Snell, 2021). The average number of hours of direct client interaction was found to be 22 hours in 2008 (Michalski, Mulvey, & Kohout, 2008).

However, let's imagine a scenario involving the maximum client caseload of a therapist at 53 for now. Let's assume that this person is incredibly good at their job, and therefore each client requires only ten sessions before they no longer need treatment. This psychologist then helps 53 individuals every 10 weeks. In 1 year, a single professional working as hard as they can for 50 out of the 52 weeks of the year can help approximately 265 patients—or just under 12% of the estimated number of patients they would need to serve in the current system in order to meet the deficit of care. These statistics are all estimates, and therefore the numbers derived here are not exact. However, the case presented here is a sobering example of the challenge of meeting the demands of global mental health care.

Both the length of treatment and suggested caseload for the mental health practitioner mentioned above are quite unrealistic. For instance, the National Institute for Health and Care Excellence (NICE) guidelines recommend 16–20 cognitive-behavioral therapy (CBT) sessions for those struggling with depression. Additionally, reviews of the length of therapy approximate that half of patients recover after an average of 15–20 sessions. There are a specific number of treatments of 12–16 weeks in duration that lead to clinically significant improvements. Therapists and patients sometimes prefer to extend sessions over a 6-month period to maintain goals or increase remission. Comorbid conditions and personality disorders may require 12–18 months of treatment (American Psychological Association, Division 12, 2017). As far as caseload, even meeting with 20–35 individual clients in a week may be too much for a single mental health practitioner. In one survey of 29 directors of community mental health centers in a rural area, two thirds reported feelings of high emotional exhaustion and low personal accomplishment (Rohland, 2000) and 54% of community mental health workers in northern California reported feelings of high emotional exhaustion (Webster & Hackett, 1999). These feelings of exhaustion and low accomplishment correlate with worse interactions between practitioners and patients, including negative attitudes toward patients in mental health treatment facilities (Holmqvist & Jeanneau, 2006). Altogether, this research suggests that despite the sobering lack of providers to handle the need we present above, the estimates are likely to be far worse as they present unrealistic overoptimistic estimates about both caseload, therapeutic efficacy, and therapeutic efficiency.

Even for those who are eventually able to access care, help often comes after a significant delay from providers. On average, the average wait time to an initial assessment can be up to 110 days, depending on the presenting problem (Kowalewski, McLennan, & McGrath, 2011). The extensive waiting times for care leads to an increased no-show rate and an increase in client symptom severity, including an increased risk of suicide (DiMino & Blau, 2012; Williams, Latta, & Conversano, 2008). Waiting times are also linked to a reduced likelihood of responding to treatment once it is eventually started. Clinically significant and reliable patient outcome deterioration is amplified by increased waiting times, and effects are significant between 3 and 12 months of waiting time (Reichert & Jacobs,