Harnessing Technology to address the Global Mental Health: An Introductory Brief Harnessing Technology to Address the Global Mental Health Crisis: An Introductory Brief Amirali Batada and Rene Leon Solano May 2019 1 Contents Acknowledgements 4 I. Introduction 5 II. Rise in Global Mental Disorders: Exploring Contributing Factors 6 Defining Mental Health, the Spectrum of Mental Disorders and Psychosocial Support 6 Mental Health and Well-Being 6 Mental Disorders 6 Psychosocial Illness and Support 7 Determining Who is Affected 7 Exploring Contributing Factors 8 Poverty, Inequality and Exclusion 8 Economic Insecurity 9 Violence, War and Mass Displacement 9 Ubiquitous and Addictive Technology 10 Diagnostic Capacity and Mental Literacy 11 III. Challenges and Barriers in the Provision of Scalable Mental Health Services 12 Cultural Norms, Social Perceptions and the Stigma of Mental Illness 12 Recognition of Mental Illness and Funding of Support Services 13 Infrastructure and Capacity for Treatment 14 Intervention Points for Technology 16 IV. Technology for Mental Health Care: No Panacea, but a Partner to Adapt and Scale Response 17 Defining Emerging and Applicable Technologies 17 Exploring the Role of Technology in the Promotion of Mental Health Care 17 2 Addressing Challenges and Barriers of Scalable Mental Health Care 18 Lack of Integrated Interventions to Reduce Risk Factors and Stigma of Mental Illness 18 Employment 19 Education 19 Public Services 20 Social Prescribing 20 Positive Psychology 20 Shortage of Non-Specialist, Non-Medical and Community Based Workers Trained in Mental Health and Psychosocial Interventions 21 Build Capacity of Treatment Providers through Digital and Mobile Learning 21 Limited Access to Mental Health Services for Vulnerable Groups and those Living in Remote Areas 22 Therapeutic / Coaching Bots 23 Virtual Therapists 24 Self-Guided Therapy 24 Virtual Reality (VR) Based Exposure Therapy 25 Telepsychiatry / Video Based Therapy 25 Digital Peer Support Communities 26 Inadequate Data Available to Discern the Scale and Complexity of the Mental Health Challenge, Build Systems Level Efficiencies, and Inform the Design and Delivery of Interventions 27 Strengthening Data Systems Infrastructure to Generate Efficiencies and Service Improvements 27 Leveraging Big Data and AI for Mental Health 28 V. Exploring Pathways Forward 30 VI. Endnotes 31 3 Acknowledgements This document has been prepared by Amirali Batada and Rene Leon Solano from the World Bank. The authors gratefully acknowledge the inputs and comments provided by those inside and outside the World Bank including Dr. Ernest E. Massiah, Zeina Afif, Renos Vakis, James Walsh, Francarlo Roberto Valcarcel Benitez, Athena Robinson, Alison Darcy and Amira Nazarali. The authors are grateful for the guidance and support received from Saroj Kumar Jha and Hana Brixi. The Multi-Donor Trust Fund for the Middle East and North Africa Region financed the study. 4 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF I. Introduction T he mounting challenges of global mental health are affecting millions of individuals throughout industrialized and developing regions of the world. Though the underlying driving forces may vary across contexts, the consequences of mental illnesses are experienced globally, resulting in rising social and economic costs for governments, communities, and families attempting to manage the growing challenge.1 Global estimates illustrate that mental illness contributes to 32.4% of years lived with disability (YLDs) and 13% of disability-adjusted life years (DALYs).2 Presently, in excess of 300 million people globally experience depression, while an estimated 800,000 deaths from suicide are linked to the disease, annually.3 “Depression is the leading cause of disability globally”4, coupled with anxiety disorders, contributes to an estimated US$1.15 trillion of lost economic output and 12 billion days of lost work each year, resulting from diminished productivity.5 Mental, neurological and substance use disorders impact annual economic output, with losses contributing to US$2.5-8.5 trillion globally; this figure is projected to nearly double by 2030.6 For individual households, out of pocket expenditures constitute the primary method of payment for mental health care in 40% of low income countries.7 Aside from having a significant economic impact, mental illness is a cross-cutting issue that affects a breadth of development objectives, including eight of the Millennium Development Goals (MDGs).8 The disease’s prevalence and impact on global development contributed to the inclusion of mental illness in the UN Sustainable Development Goals (SDGs) in 2015.9 One key factor hindering the progress of addressing mental illness lies in the structural imbalance between inadequate capacity to extend support, and the high growth rate of individuals requiring treatment.10 On average, nearly half the global population resides in countries where there is one psychiatrist per 200,000 people.11 This reality is reflected primarily in low and middle income countries (LMICs) where, for example, 76-85% of individuals with a severe mental disorder do not receive treatment for their illness.12 Addressing this gap in a timely and effective manner will warrant innovative approaches that operate in parallel with traditional strategies. This approach will assist in resolving the key challenges and barriers associated with the planning, design and deployment of effective mental health services, and reducing the risk factors associated with mental illness. The application of emerging technologies holds promise in potentially addressing different dimensions of the global mental health challenge. This brief explores how select technologies and tools could potentially garner new insights, build efficiencies, and scale support in responding to the mental health challenges unfolding in a wide array of communities and contexts. 5 RISE IN GLOBAL MENTAL DISORDERS: EXPLORING CONTRIBUTING FACTORS II. Rise in Global Mental Disorders: Exploring Contributing Factors Defining Mental Health, the Spectrum of Mental Disorders and Psychosocial Support 1. Mental Health and Well-Being According to the World Health Organization (WHO), mental health is defined as “a state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community.”13 As such, mental health is not just the absence of mental disorders, but rather a state of being that enables individuals to live active and productive lives. The state of mental health is not only determined by individual attributes including “the ability to manage one’s thoughts, emotions and behaviors”14, but is also linked with socioeconomic, biological, environmental, cultural and political factors. Mental health may be promoted and protected through a range of actions including quality and constructive early childhood experiences, effective education and skill building opportunities, socio-economic empowerment, and psychosocial tools and community networks to manage stress and difficult contexts. 15 This publication will outline a series of challenges associated with maintaining mental health across broad contexts and explore how the application of various technologies can promote mental well-being and facilitate access to strong protective factors against mental illness. 2. Mental Disorders A series of definitions and descriptions have been developed on the spectrum of mental and behavioral disorders. Two leading sources often referenced are the International Statistical Classification of Diseases and Related Health Problems (ICD-10)16 developed by the WHO, and the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)17 published by the American Psychiatric Association (APA). For the purposes of this brief, a broad definition of mental disorders is used, aligning with the guidelines of the WHO. In this regard, mental disorders are “generally characterized by a combination of abnormal thoughts, perceptions, emotions, behavior and relationships with others”; they include “depression, bipolar affective disorder, schizophrenia and other psychoses, dementia, intellectual disabilities and developmental disorders including autism.” 18 Common mental disorders, including anxiety and depression, are more prevalent across broad population segments and are often addressed through standard treatments such as cognitive behavioral therapy (CBT). Severe mental illnesses, including schizophrenia and dementia, may require more complex and intensive interventions, though these illnesses have a lower rate of incidence. This brief will focus on the challenges and treatment barriers associated with common mental disorders, and propose solutions that leverage technology to strengthen treatment capacity and introduce new approaches to alleviate suffering. For severe mental disorders, the proposed technological interventions may have mechanisms embedded to alert trained specialists of high risk cases and enable users to identify appropriate treatment options through referral services. 6 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF 3. Psychosocial Illness and Support The concept of psychosocial illness or support is defined and utilized in different forms, often depending on the domain of research and practice. Within the context of international development and humanitarian relief, psychosocial illness may be interpreted as “the result of a disturbed relationship between psychological and social effects” and as such, understood that “social problems can easily affect the psychological status of the individual” and the corollary, where “psychological problems can affect the individual’s social wellbeing”19, illustrating the interplay and co-dependence between the two elements. Some of the interventions that support psychosocial well-being in humanitarian settings, often termed as Mental Health and Psychosocial Support (MHPSS), may include providing counselling for individuals, groups and families; establishing child-friendly spaces and support for schoolchildren; and extending assistance to cope with and manage mental disorders through nonpharmacological support provided by general healthcare experts and community volunteers. 20 For the most part, this research brief will examine broad themes associated with mental health and well-being, and explore several technology-enabled interventions that intend to address common mental disorders, including through the application of psychosocial support. The review will take a global perspective, covering both developed and developing regions, and discuss considerations pertinent to international development and humanitarian relief practitioners. Determining Who is Affected The emphasis of this brief is to explore the external forces that either initiate or exacerbate mental illness, rather than examine internal factors such as genetic or other biological contributors to mental illness. There are numerous segments of the global population that experience mental illness and endure varying degrees of its effects on their daily lives. Some factors, illustrated in the following section, increase the likelihood of developing a mental disorder, while others which may seem banal, can also give rise to mental illnesses for those living even in the most prosperous and stable settings. In more developed environments, mental illnesses are affecting almost every segment of the population from its youngest members such as children and adolescents, to seniors and the elderly. Among the adult population, there are a spectrum of individuals experiencing mental illnesses, these include: women who have experienced violence;21 veterans returning from war or those exposed to hostile environments;22 employees in highly dynamic and demanding work settings;23 caregivers and frontline health workers providing continuous support whilst facing capacity constraints; 24 young and middle aged adults facing social and financial stressors; 25 , 26 individuals who are poor or unemployed - facing hardship and uncertainty; 27 , 28 , 29 others battling addiction and substance abuse issues;30,31 people living in isolation and experiencing loneliness;32 and many other population segments considered as vulnerable groups comprising of high risk factors. At the other end of the spectrum, within contexts rife with instability and inadequate resources, environments that may be defined as fragile, conflict affected or having a prevalence of violence, there are a multitude of individuals affected by mental illness, often proportionally higher than those living in more stable and developed settings. In these contexts, the individuals who may be suffering include victims of armed conflicts 7 RISE IN GLOBAL MENTAL DISORDERS: EXPLORING CONTRIBUTING FACTORS and violence; survivors of sexual and gender-based violence (SGBV); children and adolescents who have experienced trauma through exposure to extreme violence and conflict; individuals wounded in conflict or those with disabilities; others directly affected by pandemics and emergencies; frontline workers or volunteers responding to emergencies and providing support; and internally displaced populations (IDPs) and refugees.33 Among the population segments affected by mental illness, each require thoughtful, suitable and effective interventions, which in some instances, may warrant tailored solutions that are adapted for the respective context and individual/group circumstances. Arguably, the statistics associated with the respective population segments may not accurately reflect the precise incident levels of mental illness sufferers within the wider population (due to numerous factors including stigma, culture, education, etc.). As such, inaccuracies or exclusions may not reveal important population differences relating to access, labelling or treatment among the different groups affected. The different population groups identified in this brief may be considered a subset of a larger cohort of individuals or groups experiencing mental illnesses across different regions and contexts. Exploring Contributing Factors It is evident, perhaps more so than ever, that mental disorders are having a detrimental impact on broad segments of societies globally.34 Findings from the Global Burden of Disease study undertaken in 2010, illustrate that mental and substance use disorders are the primary cause of years lived with disability (YLDs) worldwide, amounting to 175.3 million YLDs.35 The forces contributing to mental illnesses often vary within and between societies; these can be driven by biological, psychological, social or environmental factors. The emergence of these illnesses may occur through traumatic experiences or sustained exposure to more common risk factors, including poverty or work related stress. Extensive studies on the subject illustrate the importance of social determinants as key risk factors among those affected by mental disorders; this may include groups encountering low socioeconomic conditions, violence, low levels of education attainment, chronic physical ill-health, or conflict.36 For the purposes of this brief, the research and recommendations focus primarily on social and environmental forces that contribute to mental disorders. The following are select examples of social and environmental factors that are contributing to a rise of mental disorders in varied geographic, economic and social contexts. 1. Poverty, Inequality and Exclusion In developing countries, linkages between poverty and mental illness are not often found associated solely with income, 37 but rather through factors such as insecurity, hopelessness, swift and disruptive social changes, and incidents and risks of violence and physical ill-health. 38 In developed country contexts, those from lower or marginalized socio-economic segments are more likely to experience higher levels of common mental disorders. 39 The frequency of mental disorders is increased with low education attainment, material disadvantage and unemployment. 40 Though more common in wealthier societies, higher levels of income inequality are associated with greater prevalence of mental illness.41 Other areas of socioeconomic disadvantage, including low education attainment, unemployment and deprivation, are also found to be linked with 8 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF mental illness. Some population segments, such as those experiencing extreme poverty, homelessness or displacement, may encounter structural exclusion in their respective societies. Other groups including minorities, migrants, or those with disabilities may also experience different forms of discrimination. Despite the circumstances, groups encountering exclusion are more likely to experience broader health challenges, including a range of mental illnesses.42 If not addressed, systemic and prolonged forms of exclusion could engender sentiments of unfairness or injustice, further exacerbating mental disabilities. 2. Economic Insecurity Globalization and rapid technological developments, including increased automation and applications of artificial intelligence, will continue to drive economic growth and productivity in the years ahead. The impact of artificial intelligence is projected to increase global GDP by US$3.5-15.7 trillion, with some forecasting attainment by 2030. 43 , 44 This transformation towards higher productivity will inevitably require a modification and retraining of existing work practices. Some researchers estimate that by 2030, 400 to 800 million individuals may be displaced by automation, and 60 percent of occupations may have at least one-third of work activities automated. 45 As such, investments in human capital and a commitment to lifelong learning will be essential for individuals and countries to remain competitive and reap the economic and social benefits of the transformations.46 The impact of these changes also has and continues to shift many societies towards building a knowledge and innovation based economy to remain competitive. As these developments progress, it is anticipated that more individuals will endure higher levels of economic insecurity 47 and precarious employment,48 which may potentially exacerbate mental illness stemming from rising anxiety and stress levels, already taking form in many societies globally.49 As the pace, complexity and scale of economic transformations accelerate, societies may continue to observe rising anxiety levels50 contributing to a growing sense of frustration and fear of what may lie ahead.51 In some contexts, these changes have contributed to growth in temporary or part-time employment,52 which is becoming more prevalent in many large global cities. 53 In other regions, historical and structural economic factors have contributed to persistent unemployment or underemployment, especially for large youth populations.54 Unemployment is often cited as a strong risk factor for developing mental disorders; 55 initiatives to develop relevant skills that support employment warrant consideration and integration into strategies intending to address mental illness. 3. Violence, War and Mass Displacement In recent years, pockets of regions and countries have encountered increased violence, wars and displacement of citizens at unprecedented levels – in some cases, unobserved since the second World War. 56 The economic impact of violence globally amounts to US$14.3 trillion, nearly 12.6% of the world’s economic activity. 57 Deteriorations in the Global Peace Index were observed in 68 countries, stemming from factors including declining societal safety and security, increased domestic and international conflict, and the degree of militarization. Regions affected by higher instability and conflict include the Middle East and North Africa (MENA) and sub-Saharan Africa. The consequences of violence and conflict, along with other social and economic factors, have contributed to the forced displacement of 65.6 million people globally by the end of 2016, the highest 9 RISE IN GLOBAL MENTAL DISORDERS: EXPLORING CONTRIBUTING FACTORS point in several decades. 58 Another prevalent issue of concern is domestic violence, where it is estimated that 35% of women globally have experienced physical or sexual violence during their lifetime. 59 Catastrophic circumstances or events, at times underpinning these tragic outcomes, are producing conditions and environments conducive to forming mental disorders. Some of these situations stem from observances of armed violence, loss of family and friends, untreated trauma, sexual and gender based violence, discrimination and exclusion, extreme poverty, fear and insecurity, isolation and loss of community, unemployment, and harsh living conditions. 60 The underlying factors are often prolonged, while the interventions to improve the conditions sometimes provide only temporary relief across different dimensions of the challenge. This is concerning as prolonged cases of untreated post-traumatic stress disorder (PTSD), severe depression as well as other related mental disorders risk marginalizing and destabilizing families, communities and countries, leading to further social, economic and political consequences. 4. Ubiquitous and Addictive Technology The authors of this brief recognize that technology is both an enabler, capable of accelerating and scaling initiatives to achieve positive outcomes, but also holds potential to produce unanticipated or undesired consequences for some. The appropriate use of technology could potentially support different facets of the mental health challenge;61 though its misuse or overuse may be linked to mental disorders such as depression and addiction.62 There are now over 2.6 billion social media users globally; this figure is expected to grow to 3.02 billion by 2021. 63 Globally, the average daily time spent on social media has increased by 50% over the past five years, rising from 90 to 135 minutes each day.64 The shift online has initiated wider discussion and research on the notion of smartphone, screen or social media addiction, though only a minority of users have been formally diagnosed with having psychological issues including anxiety, depression, loneliness and attention deficit hyperactivity disorder, stemming from overuse of social media. 65 Despite the small number of confirmed cases of related mental disorders, broader population segments continue to express the perceived negative impact of excessive technology and social media use on their lives.66,67,68 Some potential consequences associated with the overuse of technology have compelled several global institutions and companies, including the WHO and Apple, to employ new measures to raise awareness of the challenge and extend users more control over their usage. Earlier this year, the WHO introduced ‘gaming disorder’ into the revision of its International Classification of Diseases (ICD-11),69 while Apple deployed new anti-addiction control features in the latest release of its mobile operating system, iOS 12, for iPhones.70 One primary challenge is that this field is relatively nascent and that evidence continues to emerge, supporting both sides of the equation. More work and research is required to improve the understanding of linkages between digital technological applications and how their use could potentially affect (or not) the mental health and well-being of its users. In the domain of work, as industries shift towards digital or knowledge-based production (or incorporate elements of related technologies and practices), more work activities are taking place outside the confines of office environments and beyond traditional work hours. As such, individuals are facing growing pressures to always be online – accessible 10 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF and connected to their work. 71 As professional responsibilities occupy more time and become ever-present, societies may experience rising stress levels, 72 potentially contributing to mental disorders and disabilities for some, culminating in a loss of global economic productivity.73 For example, the economic impact of work-related stress in the United States may cost businesses up to US$300 billion annually.74 The evolving role of technology in the workplace, both as an enabler of productive work but also a contributing factor to new forms of stress, makes it difficult to quantify the adverse impact on personal mental health and well-being, as well as the net economic costs to business and society. As for leisure and lifestyle uses of technology, some studies illustrate linkages between the use of digital technology and increased levels of anxiety, depression, loneliness and addiction. 75 Other studies have generated differing conclusions, illustrating minimal impact on the mental well-being of users. 76 Despite some of the evidence based or perceived drawbacks of technology that are emerging, similar technologies have also brought new benefits, knowledge and opportunities to vast population segments. This includes previously ‘unconnected’ groups whom traditionally have had lower social capital or encountered access barriers to essential information and services. 77 One example, perhaps non-intuitive at first, illustrates the dual aspects of technology within the bourgeoning field of digital gaming. The growing prevalence of digital games has been found to increase isolation and addiction levels of some users, while in other instances, the concept of ‘games for good’ has been known to offer vast social benefits contributing to better health, economic and educational outcomes. 78 Essentially, technology has and continues to offer a double-edge sword to health, progress and development. The domain of digital technology, its intersection with society, and their collective impact on individual and societal mental health and well-being is a rapidly evolving field that continues to be explored. New research will continue to emerge as technology develops and its implications on society are explored and understood by a wide body of stakeholders and researchers. As such, the advances and applications of technology should be navigated carefully, along with effective measures to mitigate related risks. 5. Diagnostic Capacity and Mental Literacy Aside from a breadth of external forces that are contributing to a rise of mental disorders worldwide, there are also some practical factors that have supported a rise in the estimated number of individuals affected by mental illness. As more countries adopt legislation on mental health, increase the frequency and scale of related public surveys, enhance the sophistication of diagnostic tools, and strengthen support services and treatment capacities for mental illness, 79 it is inevitable that these efforts would contribute to more individuals self-identifying their challenges of mental illness and coming forward to seek treatment. Additionally, particularly in high income countries (HICs), an emphasis on fostering increased mental health literacy and employing creative strategies to reduce the stigma associated with the disease,80 would likely contribute to a rise in the number of individuals disclosing and/or seeking treatments for mental illnesses. These efforts have likely contributed to a broader awareness of mental disorders, and as such, have likely increased the documented prevalence of mental illness globally. 11 CHALLENGES AND BARRIERS IN THE PROVISION OF SCALABLE MENTAL HEALTH SERVICES III. Challenges and Barriers in the Provision of Scalable Mental Health Services The growth of mental disorders is a shared global challenge affecting a vast array of populations, while mechanisms to address the issue vary across countries and contexts. Findings from the WHO World Mental Health Survey Initiative illustrate varying utilization levels of mental health services across 17 countries; at the low end is Nigeria at 1.6%, and the top end is the United States with 17.9%. 81 The treatment gap for individuals suffering from mental disorders is in excess of 50% for all countries of the world, and nearly 90% in some of the least resourced countries.82 There are numerous factors distinguishing response levels, accessibility of support services, and efficacy of treatments; these may include adequate and relevant mental health policies and laws; sufficient financial resources allocated in health budgets for mental illness treatments; organization and planning of mental health service delivery including robust infrastructure and systems; and capacity for evidence-based interventions and training.83 Additional factors may include treatment coverage guidelines, access to quality treatment centers, social and cultural perceptions of mental illness, level of country development, and other relevant factors. The following are select examples illustrating pertinent challenges and barriers for providing accessible and effective treatments of mental disorders. Cultural Norms, Social Perceptions and the Stigma of Mental Illness Throughout the history of many industrialized and developing countries, the dominant perceptions and cultural understandings of mental illness has and continues to evolve. 84 , 85 The degree of societal-level understanding and acceptance, of both the contributing factors and treatments of mental illness, has been slowly shifting towards scientific and biomedical explanations, rather than cultural or religious reasoning more commonly held in the past. 86 Though, inevitably, there remain differences among population groups within and between countries as healthcare and other development objectives, knowledge, policies, systems and practices take root and improve the quality of life for millions of people. As such, varying historical factors and present day perceptions of mental illness continue to influence the public health response, the extent by which mental health services are included within the broader set of offerings extended by government health ministries, and the service utilization levels by different population segments, where available, across many countries.87,88 Despite efforts in select countries to boost mental health literacy and treatment capacity, some societies have not observed the expected uptake of individuals seeking treatment services. In these cases, the barriers to mental health treatments involve a low perceived need; limited awareness or understanding of treatment services; and transportation or financial barriers that preclude access to essential support.89 In other 12 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF instances, there are significant cultural or social factors that prevent individuals from acknowledging they are coping with mental illnesses, let alone desiring to seek treatment.90,91 Stigma is considered a key deterrent to seeking professional assistance for coping with and resolving mental health problems. Two specific forms of stigma, internalized stigma (e.g., shame or embarrassment) and treatment stigma (e.g., using mental health services or receiving mental health treatment), are identified as being most frequently associated with reduced help-seeking.92 Depending on cultural norms or other contextual factors, some individuals may feel that their struggles are not necessarily mental illnesses, but rather affiliated with their faith or beliefs,93 while others view it as a test or personal struggle that must be surmounted on their own.94,95 The stigma of mental illness in some societies is acute and widespread, bringing with it shame and discrimination for those openly disclosing or seeking treatment for their illness.96 For others, they may be compelled to live in silence or seek alternate coping mechanisms, which at times can be destructive or even self-harming.97 As such, the cultural, social and attitudinal dimensions of mental illness should be addressed in tandem with treatment capacity building efforts. This approach could optimize impact and ensure that more individuals feel comfortable disclosing illnesses and seeking treatment. Recognition of Mental Illness and Funding of Support Services The provision of mental health services, particularly through public resources and institutions, is often predicated on whether and how governments formally recognize and classify mental disorders. In contexts where mental illness is considered a primary health issue, funds may be allocated as part of annual public health budgets. In some instances, publicly funded mental health services may be identified in budgets and channeled through programs that take place outside the Ministry of Health, and as such, may not explicitly appear on the health ministry’s budgets.98,99 These services may take place in the form of school counselling, social support for the elderly, social work support programs and other related offerings. In these cases, the support being extended may not be formally recognized and associated with broader mental health programs or strategies. The following analysis explores the present state, where governments have formally recognized, allocated resources, and put forward laws and policy interventions in support of mental health care. In 2017, 72% of all WHO Member States confirmed their countries had a stand-alone policy or plan for mental health in place, 57% reported to have a stand-alone law for mental health, and 49% had a functioning dedicated authority “to assess compliance of mental health legislation with international human rights.”100 In wealthier countries such as those of the OECD, expenditures for mental disorders are estimated to be 5% to 18% of all health expenditure in select countries.101 In LMICs, the figures are starkly lower where often less than 1% of health budgets may be allocated for mental health. 102 Globally, average annual spending on mental health is less than US$ 2 per capita; HICs allocate the most funding at US$ 50,103 while low income countries spend US$ 0.25 per capita.104 The majority of budget allocations, in excess of 80%, are used to fund mental hospitals. Expenditures for mental health treatments may comprise of counselling, hospitalization, medication and other psychosocial support services. 13 CHALLENGES AND BARRIERS IN THE PROVISION OF SCALABLE MENTAL HEALTH SERVICES In contexts where timely and effective mental health treatments are unavailable through public health services, individuals turn to private providers.105 In this instance, out-of- pocket costs for mental health treatments can serve as an additional barrier to accessing quality services, often the primary source of mental health financing in LMICs. 106 In countries where there is no formal recognition or prioritization of mental disorders, there may be nominal, if any, allocations made in health budgets for the provision of treatments. As such, many individuals in developing countries must rely on private or civil society sectors for receiving mental health treatment, when affordable or available. The ensuing gap in accessing quality treatment contributes to millions of individuals living with mental illnesses globally, unable to access the essential support and services needed.107 Infrastructure and Capacity for Treatment In many LMICs and contexts, mental health infrastructure and capacity constraints contribute to reduced access and delays in obtaining quality treatment. A broad set of infrastructure challenges impact the organization and planning of mental health services; these may include gaps in referral systems, evidence-based treatment guidelines, mental health information systems, and integrated offerings with other relevant sectors in the areas of policy and operational plans. 108 Effective research capabilities and a focus on data quality could be underpinned by robust mental health information systems. A lack of data and evidence-based research practices, primarily in LMICs, compounds the issue determining the scale and complexity of the mental health challenge, and presents impediments to accessing quality treatments and innovative, contextually relevant interventions.109 This may entail outdated or absent information systems, coupled with inadequate data collection, analysis and research practices on critical variables pertinent to the identification of needs and delivery of mental health treatments for those coping with related illnesses.110 The impact of these challenges may contribute to operational inefficiencies, longer waiting times, and gaps in accessing quality and tailored support services. Aside from data and research which inform critical decisions, there are numerous examples of capacity shortages leading to diagnostic and treatment gaps, and access delays for those suffering from mental illness. It is estimated that less than 10% of individuals from LMICs who suffer from mental disorders, end up receiving quality treatment. 111 Globally, there are 9 mental health workers per 100,000 population (median), with a wide spectrum consisting of 72 in HICs and below 1 in LICs.112 There is a significant shortage of specialized mental health practitioners including psychiatrists and psychiatric nurses; in HICs these constitute 8.59 and 29.15 per 100,000 population respectively, while in LICs they account for .05 psychiatrists and .42 psychiatric nurses per 100,000 population. This constitutes a ratio of 1 psychiatrist per 200,000 individuals for nearly half the global population.113 These stark figures were one of the contributing factors compelling the WHO to establish its Mental Health Gap Action Programme (mhGAP), which focuses on “scaling up” services for mental, neurological and substance use disorders, by empowering “non-specialist healthcare providers in resource-poor settings.”114 14 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF Harnessing the potential of non-specialist healthcare providers to deliver mental health support to the millions of individuals suffering from common mental disorders may serve as one of the most effective methods today for addressing the magnitude of the global mental health challenge.115 This concept has been in practice for many years whereby the trust, accessibility and capabilities of key stakeholders including those involved in task-shifting, community based programs, and members of the general population are leveraged to swiftly build capacity and support the organization and delivery of mental health services. This approach may not necessarily be the most effective in treating individual cases, however under the present circumstances, particularly in LMICs which encounter significant capacity shortages of trained mental health specialists and limited publicly funded treatment options, the method is promising.116 Successful examples of this approach have been documented in many countries, select examples include: engaging faith and traditional healers in Ethiopia to reduce stigmatization, facilitate the inclusion of sufferers, and increase access to mental health support; 117 training volunteers as community support officers who provided referrals and case management assistance to mental health sufferers following the 2004 tsunami in Sri Lanka; 118 and leveraging the mhGAP behavioral disorders module to train primary school teachers in North West Nigeria on understanding and supporting children suffering from attention deficit hyperactivity disorder (ADHD).119 Other capacity constraints include inaccessible service providers and institutions due to geographic, linguistic, cultural or financial barriers;120 disproportionately low number of graduates from relevant training programs (e.g., psychiatry, psychology, etc.) to extend treatment in comparison to the size and growth rate of the population requiring mental health services; 121 limited capacity of hospitalization rooms for those requiring specialized treatment; inconsistent availability and limited supply of relevant medications; and a lack of integrated psychotherapeutic interventions and nonpharmacological treatment options and providers among the primary health institutions extending frontline mental health treatment (e.g., for extending PSS services).122 The presence of such constraints may also contribute to increased waiting times for quality support or lead to cases of ineffective or unavailable treatment options. 15 CHALLENGES AND BARRIERS IN THE PROVISION OF SCALABLE MENTAL HEALTH SERVICES Intervention Points for Technology A subset of challenges and barriers associated with the prevention and treatment of mental disorders could potentially be reduced through the application of select technologies across different elements of the mental health care ecosystem. One set of challenges are associated with the promotion of mental health and prevention strategies against mental illness, while another involves support with treatment and related services for those suffering from mental disorders. The following figure illustrates potential intervention points for technology across a spectrum of relevant challenges and barriers in mental health care. 16 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF IV. Technology for Mental Health Care: No Panacea, but a Partner to Adapt and Scale Response Defining Emerging and Applicable Technologies Artificial Intelligence (AI) There are a broad set of definitions that address different dimensions of AI, two prominent being thought processes and reasoning, and the other involving behavior. The field of AI attempts to build and understand intelligent entities using machines.123 Some common definitions of AI include: “1: a branch of computer science dealing with the simulation of intelligent behavior in computers; 2: the capability of a machine to imitate intelligent human behavior”124 Machine Learning A prominent figure specializing in the domain of ‘deep learning’, Yoshua Bengio, defines machine learning as “a way to try and make machines intelligent by allowing computers to learn from examples about the world around us or about some specific aspect of it.”125 Predictive Analytics The topic of predictive analytics is closely tied to the field of data science; data science is understood as “the application of quantitative and qualitative methods to solve relevant problems and predict outcomes.”126 Predictive analytics could be defined as “the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.”127 Exploring the Role of Technology in the Promotion of Mental Health Care As noted in this brief, technology has the potential to support positive outcomes and scale different elements of the mental health challenge, but it may also be a damaging force that initiates or exacerbates mental disorders, based on the manner that technology is used. If leveraged thoughtfully, some types of technologies hold potential to enhance access to mental health treatments;128 provide insights on understanding and addressing related issues;129 and extend targeted and personalized support to essential mental health promoting factors. The impact of these efforts and innovations may contribute to new insights and efficiencies, wider reach and efficacy of services, lower treatment and operating costs, and overall improvements to societal mental health and well-being.130 The following cases illustrate potential applications of varied technology to address different facets of the global mental health challenge. The applicability of these proposed interventions is predicated on numerous factors pertaining to the design, development and deployment of these solutions in different countries and contexts. 17 TECHNOLOGY FOR MENTAL HEALTH CARE: NO PANACEA, BUT A PARTNER TO ADAPT AND SCALE RESPONSE Some factors for consideration may include, adequate financial resources for prototyping, testing and deploying technological solutions at scale; specialized talent from a range of disciplines to develop and manage solutions; sophistication of mobile technology infrastructure, accessibility and adoption in a specific context; education levels, language and digital literacy of target populations; social and cultural norms associated with mental illness; and government investments, priorities and commitments for mental health. For example, if a specific context has a high smartphone penetration rate, wide access to mobile broadband and low data usage costs, these factors may enhance the applicability of some interventions and increase their likelihood of a successful outcome versus other contexts where similar conditions are not present. As such, the proposed interventions outlined below will be applicable and feasible in contexts based on the respective requirements and implementation criteria. Finally, many of the solutions outlined in this section have been developed and deployed in HIC contexts; more research and prototyping is required to assess the applicability and effectiveness in LMICs. Addressing Challenges and Barriers of Scalable Mental Health Care 1. Lack of Integrated Interventions to Reduce Risk Factors and Stigma of Mental Illness Proposed Solution: Scale Access to Mental Health Promoting and Risk Mitigating Interventions It is important to note that the identification of one single causal factor that reduces the risk of developing a mental illness is difficult to determine, and research in this area is still emerging. Additionally, interventions that support the social determinants of mental health, have often not been evaluated against mental health outcomes following the implementation of an intervention. As such, the focus of this section is to illustrate how technology may be leveraged to deploy a range of interventions that reduce the risk factors associated with mental illness, rather than treating the illness directly. Integrated interventions that promote mental health or reduce the risk of mental illness can be deployed across the life-cycle, potentially enabling partnerships with numerous public ministries, private sector or civil society organizations. As such, solutions may be implemented in many areas including, supporting healthy and constructive early development and relationships between young children and their parents, to ensuring that seniors can age gracefully while maintaining their dignity and having access to essential services that support their mental health and well-being. An emerging set of research illustrates that a series of mental health promoting factors could support resilience building and assist those coping with common mental disorders. 131 In some cases, researchers have found that nonpharmacological interventions can be as effective in treating mental illness as those receiving prescribed medication,132 while the demand for nonmedicinal based treatments is also on the rise, particularly among younger patients. 133 For example, there is a growing movement towards addressing the social determinants of mental health through nonpharmacological interventions, such as exercise, volunteering or skills development. 134 Given the magnitude of the mental health challenge, some governments and civil society organizations have sought to strengthen support for these 18 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF services through increased resource allocation, integrated offerings with public health services, and raising societal awareness of the benefits of these approaches through public campaigns. 135 Depending on the context, some countries have made progress while others have not yet started to formally coordinate and integrate these elements into their current mental health strategy. As communication technology becomes more prevalent, even for the most marginalized populations, an integrated digital strategy that promotes and facilitates access to mental health promoting factors may serve instrumental in enhancing mental well-being. This could also involve forging partnerships with public, private and civil society organizations to raise broader awareness of the opportunities and services available, especially for those who may not be connected or lack social capital.136 An integrated digital strategy could take on many forms, based on the requirements of the context being considered. One intervention could take the form of an engaging and informative platform, which centralizes, maintains and disseminates essential information contributing to mental well-being. The collective features of the platform may be tailored according to contextual needs; below are some examples of potential elements that could be incorporated into such a platform. i) Employment The ability to earn a living and engage in purposeful work can be a positive contributor to mental well-being. Though the corollary also holds true where unemployment is often cited as a strong risk factor associated with common mental disorders.137 To support the acquisition of employment, a digital platform can facilitate broader awareness and uptake of skills development programs that hold potential in increasing wages or providing access to new employment. Additionally, the platform could also enable the identification and pursuit of new employment opportunities in real-time, including virtual or microwork where applicable (i.e., contexts where segments of the population are not permitted to engage in formal work such as displaced individuals living in temporary settings). ii) Education Low education attainment is associated with common mental disorders, while the acquisition of education can be a strong protective factor, contributing to the building of emotional resilience and supporting relevant life outcomes that reduce the risk of mental disorders. 138 A trusted platform can be useful in navigating through the expansive landscape of educational offerings to find relevant and effective resources that contribute to the desired outcomes. This may entail identifying and enrolling in formal or informal learning programs, exploring career pathways and relevant course offerings, or accessing convenient and self-paced digital learning resources and tools. Aside from professional development, the educational content can also strengthen personal development which may impart training on resilience, stress management and other coping skills to manage common mental disorders. 19 TECHNOLOGY FOR MENTAL HEALTH CARE: NO PANACEA, BUT A PARTNER TO ADAPT AND SCALE RESPONSE iii) Public Services The inability to access essential services or obtain basic material needs including food/nutrition, water, sanitation or housing may influence or risk the onset of mental disorders.139,140 In some cases, those who are ‘connected’ or have access to social capital may have insights on how to obtain these services more readily than those without the advantage. 141 , 142 Technology may be a potential equalizer in democratizing this information, such that more individuals could identify public and private support services, thereby alleviating some of the burden facing households. Emerging technology can improve public service delivery by either facilitating increased access or in certain cases, providing services directly through digital channels. iv) Social Prescribing Aside from identifying and accessing essential services, the emerging area of ‘social prescribing’ 143 and its potential benefits to enhancing mental well-being could be another service supported by technology. 144 Though still considered to be an experimental approach with limited data supporting outcomes, social prescribing involves referrals, made by health professionals or a link-worker, to a breadth of non- clinical, local and personalized activities intended to improve health and well-being.145 Components of the approach include a focus on physical health, psychological well- being, perceived social isolation and financial stressors. Recommended ‘prescriptions’ involve a spectrum of activities undertaken by community organizations; these may include “volunteering, arts activities, group learning, gardening and cookery.”146 Similar to public services, technology could also facilitate simpler identification and participation of social prescribing programs through an integrated network of patients, health professionals (or link-workers) and civil society organizations, collaborating to achieve common objectives. v) Positive Psychology Technology has fundamentally altered how, when and where people consume content. Undoubtedly, it has enabled society to amplify the amount of information generated and consumed daily. For example, the following occurs in just one minute online each day: 4 million search queries on Google; 2.4 million pieces of content shared on Facebook; 72 hours of new video uploaded on to YouTube; 216,000 photos uploaded on to Instagram; and much more.147 Though, not all of this content is constructive to our mental health and well-being. The sheer volume and varied nature of information encountered holds potential to increase an individual’s anxiety, stress and depression levels.148,149 There are several measures that can potentially be taken to reduce the risk; one approach could be to intentionally disconnect or find quiet offline time to engage in other activities. An alternate approach could be to leverage aspects of positive psychology by prioritizing online content that relays constructive messages, thereby restoring balance in the types of content consumed and contributing to positive mental health and well-being.150,151 In this instance, technology can facilitate the creation and dissemination of positive messages at scale – leveraging literature and other types of narratives and media that promote hope and resilience. The intent would be to enhance the frequency, proportion and depth of positive messaging and engagement, thereby shifting mindsets towards constructive pursuits and pathways. Additionally, complex topics such as stigma 20 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF associated with mental health may also be addressed through this vehicle, serving to educate users and provide insights to alleviate concerns. In this regard, technology may support the reduction of treatment stigma through privacy, autonomy and community education opportunities offered by many technology leveraged solutions. Many online mechanisms intended to enable these objectives are functioning at the early stage of development. Further research on best practice and evidenced based outcomes associated with these interventions will be required to enhance wider adoption and endorsement from public health institutions. 152 In the interim, preliminary findings suggest this form of intervention is promising and warrants further exploration and innovation, particularly given the magnitude of the challenge and persistent resource constraints. 2. Shortage of Non-Specialist, Non-Medical and Community Based Workers Trained in Mental Health and Psychosocial Interventions Proposed Solution: Enhance Access to Mental Health Treatments by Scaling Capacity Building Efforts Build Capacity of Treatment Providers through Digital and Mobile Learning In numerous countries, there is an insufficient supply of mental health professionals to meet the increasing demand for mental health treatment.153 The issue is compounded further by a disproportionately lower rate of trained professionals graduating from quality educational institutions and training programs, when compared to the growing base of individuals requiring mental health treatment. Both factors warrant a new approach to rapidly building the capacity of organizations and large cohorts of individuals (e.g., NGOs, community counsellors, etc.) to offer quality mental health support. In this instance, technology could be leveraged to train and certify community advisors to dispense quality mental health support, while referring priority cases to specialized treatment providers. To facilitate swift and scaled capacity building of community advisors, as well as those already working in the health field, digital or mobile learning platforms could be leveraged to disseminate content widely and strengthen learner/user engagement. The technology could also guide learners, track progress, and award certification for completing relevant training modules. 154 High quality content, including the World Health Organization’s Mental Health Gap Action Programme (mhGAP), 155 could be leveraged and delivered through these tools. Additionally, partnerships to facilitate digital learning and accreditation (e.g., micro-credentials such as micro or nano degrees) with quality degree granting academic institutions in the field of mental health, could be another pathway to quickly build local capacity in meeting the growing demand for treatment. For example, in 2017, a mobile-based blended learning initiative was deployed in rural Afghanistan to improve the knowledge of health providers across four primary mental health illnesses; these include depression, psychosis, PTSD and drug abuse. The results of the initiative were promising, where marked gains in the treatment group were observed; this entailed an increase in the overall knowledge scores from 45% to 63%, evaluated through the pre-and post- intervention tests. 156 Another key intervention is underway, led by the World Health Organization (WHO) through the development of its Program Management Plus (PM+) initiative. The program trains non-specialists in helping to address common mental disorders; a study of its efficacy and cost-effectiveness is presently being examined through a trial in Pakistan. 157 These types of approaches could potentially address 21 TECHNOLOGY FOR MENTAL HEALTH CARE: NO PANACEA, BUT A PARTNER TO ADAPT AND SCALE RESPONSE present capacity constraints, thereby contributing to lower wait times, reduced costs per treatment, and wider access to services, particularly in remote or rural areas. 3. Limited Access to Mental Health Services for Vulnerable Groups and those Living in Remote Areas Proposed Solution: Explore Digital Mental Health Support Tools Despite efforts to allocate more financial resources, improve accessibility and quality of services, and build the capacity of providers to increase mental health treatments, there is a likelihood that some societies, particularly those in developing regions, will fall short in meeting the growing demand for quality and cost-effective mental health services. As such, numerous governments and private providers are exploring investments in technology-based mental health interventions that extend high efficacy while lowering costs, shortening wait times, and widening access, particularly to lower income and remote communities. These approaches not only hold potential in addressing key structural challenges, but also indirectly resolve a notable social issue, the stigma associated with disclosing or seeking treatment for mental disorders, often identified as a key barrier in accessing mental health support in many developing countries.158 Since most of the technology based treatments are inherently virtual, individuals can obtain support discretely without being concerned about the social consequences that may arise from seeking in-person mental health support. Undoubtedly, as with any technology intended to operate in a traditionally human-centric domain, there may need to be a cultural shift to overcome potential barriers that inhibit human-machine interaction. This may begin with technology facilitating more efficient human-to-human interaction, and then gradually shifting to more human-to-machine interaction for more predictable and algorithmic use cases. The following are some examples of new technological approaches that hold promise to enhancing access to quality mental health treatments across broad contexts, in both developed and developing societies, based on the availability of underlying infrastructure and resources required to deploy the services.159 At the present time, not all of these technologies may be accessible to developing and remote regions, or would necessarily be suitable for vulnerable groups as more research into their effectiveness across different population segments may still be required. Additionally, smartphone ownership, technological infrastructure and network reliability, data costs, illiteracy, and cultural norms are some of the added barriers 160 that may preclude poor and rural communities, as well as other population segments from accessing and benefiting from these emerging technologies and mental health services.161,162 However, observing the continuous trend and benefits of increased computing power, declining data and hardware costs including more entry level smartphones, 163 and the growth of local startups and software engineers in developing countries, it can be confidently expected that some of these barriers will be transcended in the near future. For now, the technology-based examples below provide a glimpse of how mental health services are evolving and could be delivered in the years ahead, globally. 22 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF Therapeutic / Coaching Bots As mobile infrastructure and technology become ubiquitous coupled with low cost smartphones, access to essential information and connected services through applications (or apps) and other platforms will grow. Additionally, as artificial intelligence is leveraged more widely and integrated into consumer apps, individuals will have more powerful tools in their hands capable of performing high-level computations across a spectrum of domains. Bots, essentially applications that can perform automated tasks via the internet (or provide pre-programmed responses),164 are being utilized more readily to undertake both simple and complex tasks. One category of application that has been steadily growing is the use of chatbots, also commonly referred to as conversational agents, to provide mental health support.165 These technologies provide a virtual outlet for individuals to express their feelings and concerns about their mental well- being, and then are recommended tools and techniques that could improve their mental health. Some of these applications leverage and integrate common therapies through a virtual format – such as internet based cognitive behavioral therapy (ICBT). ICBT has been found to work well through an online, self-guided format with results comparable to in- person treatments, especially when integrated with in-person therapist support.166 Some applications have been designed with safety measures to ensure that high risk cases are alerted to human professionals that can provide more extensive support. In other instances, users may be able to consult a human specialist by requesting support from a list of partnering mental health professionals. Both features still require further testing and improvements as gaps have been identified among varying user issues, The Woebot is ready for you. segments and contexts. Image credit: Woebot Labs Inc. Though many of these applications are in their infancy, some have demonstrated promise in providing timely and accessible support, as well as positive outcomes in dealing with different forms of mental illness. Woebot, a conversational agent developed by Stanford researchers, that combines CBT, a guided self-help infrastructure, and natural language processing, holds promise in providing effective digital mental health support at scale.167 An initial study, based on a randomized control trial, illustrated that participants who conversed with Woebot via the text message interface “experienced a significant reduction in symptoms of depression” after two weeks, while those in the control group did not. 168 23 TECHNOLOGY FOR MENTAL HEALTH CARE: NO PANACEA, BUT A PARTNER TO ADAPT AND SCALE RESPONSE Virtual Therapists Similar to bots, the growth of virtual reality fused with artificial intelligence has enabled shifts beyond automated text-based support (conversational agents) to a new class of engaging, empathetic and life- like virtual therapists. Though smaller in number given the extent of resources required and technological complexity involved, this new category of therapist is demonstrating positive results in helping mental health sufferers access quality, effective treatments in a resource Meet Ellie, a virtual therapist constrained environment.169 In some cases, user sentiments suggest preference for virtual therapists over their human counterparts, as some Image credit: Teresa Dey/The patients have expressed feeling more comfortable disclosing their issues Guardian/USC institute for without having concerns of judgement that may stem from human Creative Technologies therapists.170 This discovery has been observed through a virtual therapist named, Ellie, designed by the University of Southern California’s Institute for Creative Technologies.171 Ellie has been demonstrating astonishing results by detecting symptoms of depression and supporting veterans coping with PTSD.172 Self-Guided Therapy Another form of digital therapy, slightly different from guided sessions facilitated by bots or digital therapists, are self-guided individually paced therapy tools. These approaches provide individuals with added control and permit users to undertake a structured and observable path of healing. This occurs through a sequenced set of guidance and insights, tools and techniques, and recommended interventions that are provided along a user’s individualized journey. Some individuals who may be reluctant to utilize traditional treatments due to their length or limited visibility of a healing pathway, may find this option appealing. In many instances, cognitive behavioral therapy (CBT) is often leveraged, Leveraging CBT for Mobile as it lends itself well to this type of digital treatment format, consistent Therapy Apps with face to face treatments.173 Self-guided therapies, underpinned on the principles of CBT and delivered through digital formats are gaining Image credit: Pacifica Labs momentum among young people coping with common mental illnesses (e.g., stress, anxiety, etc.).174,175 Some examples of popular apps leveraging CBT include, Pacifica, Moodnotes, and Happify. The incentives driving uptake in enrollment include individualized control versus offline sessions driven by therapists; anonymity, particularly in contexts where stigma persists; ease of use and the convenience of seeking treatment from home; and the low cost of enrollment or subscription fees, in comparison to private therapists or counsellors. 176 24 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF Virtual Reality (VR) Based Exposure Therapy As technology advances, individuals could explore new forms of therapy outside of traditional physical environments or even hybrid physical- virtual contexts, and shift towards more immersive spaces in virtual worlds. For individuals dealing with mental illnesses relating to trauma or various phobia, virtual reality is demonstrating progress in extending support by affording patients with new forms of exposure therapy that provide a safe and controlled space to seek treatment.177 178 A virtual world can be reconstructed, enabling patients to heal from a challenging situation or environment that was encountered in the past. Gradually, VR based immersive patients find comfort and a constructive way forward to resolve anxieties, Therapy. develop coping mechanisms, and obtain greater mental well-being as Image credit: USC institute they move past the experience and manage their lives more effectively. for Creative Technologies There are excellent examples of this work, including the Bravemind project at the University of Southern California’s Institute for Creative Technologies, which designs therapy tools to help veterans recover from PTSD using VR based exposure therapy tools.179 Telepsychiatry / Video Based Therapy Developments in the quality and accessibility of video technology are prompting some therapists to offer telepsychiatry or video-based therapy sessions. Although the format is not significantly different from in-person sessions, the accessibility, convenience, cost savings and discretion extended to patients has made this new approach grow in popularity.180 Additionally, in countries where there is a shortage of qualified mental health therapists, this new format facilitates increased access and balanced distribution of global resources (i.e., globally trained professionals that can reach more patients, in cases where there are Delivering real-time, limited language or cultural barriers). As with other digital therapies, there video-based therapy. are some concerns pertaining to patient privacy and quality of service Image credit: Talkspace delivered through this channel; though these are likely to be addressed as the technology evolves and digital therapies become more prevalent. Some notable providers of video-based therapy in the United States and Western Europe include BetterHelp, Talkspace, Babylon and PlusGuidance. The growth and popularity of this delivery format for mental health therapy has prompted the UK government to allocate further resources in building the digital infrastructure and pilot models for expanding this form of treatment.181 25 TECHNOLOGY FOR MENTAL HEALTH CARE: NO PANACEA, BUT A PARTNER TO ADAPT AND SCALE RESPONSE Digital Peer Support Communities Aside from connecting with mental health professionals, individuals coping with mental illnesses are also opting to engage with each other to share experiences and foster meaningful connections through utilizing different online platforms. The digital spaces that are created in response to this need permit mental health sufferers to express their feelings and Virtual Peer Support for concerns anonymously and safely, while also seeking comfort and support Mental Health from those that can understand and empathize with their situation. Emerging platforms, such as Big White Wall and PatientsLikeMe, are Image credit: Big White examples of the type of support and potential benefits that may be offered Wall through this service. Alternatively, traditional social media platforms such as Facebook, also have dedicated groups for mental health sufferers, though these environments may be more challenging to maintain patient safety, anonymity and trust. Other platforms focus on different segments of the population coping with mental illness, such as young adults. A study undertaken in the UK involved the review of a sizeable dataset from a popular youth focused online mental health peer support forum, Kooth. The findings illustrated that youth are benefitting both from informational and emotional support delivered through these digital spaces and communities.182 Though, more research needs to be undertaken, particularly on how to mitigate risks associated with users extending directive support, and ensuring that global experiences shared can be contextualized to local health care systems and practices.183 26 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF 4. Inadequate Data Available to Discern the Scale and Complexity of the Mental Health Challenge, Build Systems Level Efficiencies, and Inform the Design and Delivery of Interventions Proposed Solution: Harness Data to Derive Insights i. Strengthening Data Systems Infrastructure to Generate Efficiencies and Service Improvements Investments made by mental health promoting institutions in the areas of data systems, processes and specialists, hold potential in offering insights to enhance the accessibility and quality of mental health treatments. If essential mental health related data is captured consistently, maintained securely, and harnessed responsibly, mental health providers may be able to make more informed decisions and investments, leading to quality treatments and improved outcomes for more patients.184 Demand Side Though this varies by context, some accessibility barriers noted by patients deterring their access or pursuit of treatments involve the following information gaps: location and proximity of services; timing and availability of providers and services; service fees (if applicable); background of providers (e.g., gender and language fluency for cultural and comprehension purposes); wait times and duration of service; knowledge gaps pertaining to treatment process, expected outcomes and the types of mental health issues that can be supported or treated; and how discretion is maintained to avoid stigma or manage social perceptions. 185 These are just some examples that individuals coping with mental illnesses identify as impediments for either accessing or pursuing mental health treatments.186 If relevant data on each of these points was regularly captured and presented in a manner that is easily accessible, interpretable and trusted by those requiring treatment, there is strong likelihood that more individuals would become aware and possibly seek mental health services, thereby enhancing the accessibility and demand for treatments. Supply Side The example above illustrates data improvements to enhance the awareness and uptake of mental health treatments – focusing on the demand side of the equation. In some contexts, the demand for mental health treatment is already high, though capacity constraints impede individuals from obtaining treatment in a timely manner, resulting in increased waiting times. In this instance, supply side improvements could potentially enhance service quality and efficiency by better understanding and anticipating the demand, delivery and coordination of treatments. Some of the challenges treatment providers identify as impediments to providing quality service include, inadequate tools for patient demand forecasting; limited data capture, management and accessibility of patient records (i.e., history, tracking, services, outcomes, etc.); unavailability of essential resources when needed (e.g., medicines); misallocation of provider capacity (i.e., mismatch between patient demand and treatment supply across different 27 TECHNOLOGY FOR MENTAL HEALTH CARE: NO PANACEA, BUT A PARTNER TO ADAPT AND SCALE RESPONSE geographical areas); performance targets and tracking systems to measure the impact of treatments; unstructured and inefficient diagnostic and referral systems for mental disorders; and limited integration between mental health providers and institutions providing psychosocial support – when social prescribing is recommended (e.g., volunteering, group learning, employment programs, etc.).187 Addressing some of these challenges through enhanced data capture and utilization could bring about operational improvements leading to new efficiencies and insights, thereby contributing to the overall enhancement in the quality and delivery of treatment services. In some ways, this is an optimization challenge that requires providers to leverage data more effectively. The resulting outcome would potentially enable treatment providers to better anticipate, plan and respond to patient demand, thereby offering quality services more efficiently, despite having capacity constraints (i.e., shorter waiting times, increased number of patients served, improved outcomes, etc.). ii. Leveraging Big Data and AI for Mental Health As the acquisition, management and utilization of data becomes more entrenched in the planning and delivery of mental health services, more sophisticated uses of data can be pursued to garner new insights, enhance treatment services, and improve patient outcomes, such as those promised in the emerging field of Computational Psychiatry.188 This may entail leveraging and bridging large datasets across multiple domains (e.g., medical, census, social media, economic, etc.), and then harnessing supercomputing and analytical technology - including artificial intelligence, machine learning and predictive analytics, to extract useful patterns and insights that can inform different dimensions of the mental health challenge.189 Predictive Psychiatry An emerging field operating at the intersection of psychiatry and big data analytics, is ‘predictive psychiatry’ or ‘predictive analytics in mental health’. 190 The objectives and aspirations tied to this field are, in some ways, aligned to the emerging arena of personalized and precision medicine. The intent is to utilize vast amounts of aggregated data, pertaining to patient backgrounds, mental health issues, preferences, treatments, outcomes and numerous other factors, to determine the most suitable therapy option that may likely produce a positive outcome for patients. 191 The technology would incorporate large sets of variables to determine the types of compositional and correlating factors that best align to different treatment options, thereby producing desired results for different patients.192 The success of this approach is predicated on numerous factors, one particularly essential, is having access to very large data sets (i.e., patients, variables, etc.) to perform deep analytics. The field is in its infancy and clouded with many unknowns, yet holds promise along with broader areas of precision medicine to provide individualized diagnosis and treatments for addressing mental health issues. 28 HARNESSING TECHNOLOGY TO ADDRESS THE GLOBAL MENTAL HEALTH CRISIS: AN INTRODUCTORY BRIEF Social Media Mining Though often crowded with noise, social media platforms house vast amounts of data that could provide insights on user sentiments and issues relating to mental health and well-being.193,194 This information could be tremendously useful in anticipating the onset of potential mental health issues amongst certain user segments (i.e., based on socioeconomic, ethnic, gender, geographical or other factors), and then thoughtfully formulating strategies and responses, both online and offline, to extend support and treatment services.195 For example, an online intervention may be to develop and deploy a targeted marketing campaign promoting volunteering, skills building and vocational employment training programs for certain segments of high-risk or vulnerable youth. This type of intervention can be supported through stronger partnerships with private sector companies managing social media platforms and other digital tools and environments. Data Analytics and Visualizations Many public institutions today are facing resource constraints and thus require more targeted and efficient interventions to address issues such as mental health. The effective allocation of limited resources can be enabled through harnessing vast amounts of data which can be analyzed and visualized to generate and communicate insights more effectively. 196 , 197 For example, a geographical visualization may provide new perspectives on how different regions or municipalities may be faring on important mental health indicators (predictive and responsive). The visualized map can also draw in other data sets, such as those associated with the social determinants of mental health, to provide a more nuanced understanding of contributing factors and inform regional intervention strategies accordingly. 198 If relevant data is captured regularly, accurately and comprehensively, visualizations can be constructed to better understand specific mental health pain-points and priority areas, enabling effective interventions and resource allocations to resolve issues immediately. 29 EXPLORING PATHWAYS FORWARD V. Exploring Pathways Forward Governments across the globe, spanning industrialized and developing nations, are raising awareness on the importance of understanding and addressing mental disorders. Though progress is being made, the underlying forces associated with the growth of mental illnesses prevail, placing increased burdens on individuals, families and governments to resolve the formidable challenge. Resources allotted to build the capacities of public health providers coupled with new regulations to enhance the accessibility of mental health services have been welcomed, yet in many countries, extensive waiting times for receiving treatments persist. Traditional approaches to building treatment capacity and delivering mental health services, in many cases, have not been able to keep pace with treatment demands and requirements. There is an essential need to provide quality interventions, particularly to the most vulnerable populations that require not only treatments, but support to address the social determinants that contribute to the onset and persistence of mental disorders. Given the magnitude of the mental health challenge, innovative approaches to accelerate the deployment of feasible, effective and scalable solutions will be imperative to underwrite structural and sustainable transformation. A thoughtful and targeted approach that leverages emerging technology may be immensely useful in addressing different elements of the problem. As more countries and contexts increase their adoption of and reliance on mobile and broadband communication technology, many more individuals may benefit from enhanced access to mental health treatments through traditional and digitally enabled services. Innovative technologies may also enable the development of new insights and operational efficiencies, leading to improvements in the quality and accessibility of treatment services. Exploring beyond treatments, technology also holds potential to enhance health promoting factors that address the underlying forces associated with common mental illnesses. Notwithstanding its potential risks and related factors concerning the applicability of interventions, technology may still be a powerful tool to drive and scale impact across different dimensions of the mental health challenge, and contribute to the improvement of societal mental health and well-being. 30 ENDNOTES VI. Endnotes 1 Trautmann, S., Rehm, J., Wittchen, H.-U., & Rehm, J. (January 01, 2016). The economic costs of mental disorders: Do our societies react appropriately to the burden of mental disorders? The economic costs of mental disorders: Do our societies react appropriately to the burden of mental disorders? Sebastian Trautmann et al. Embo Reports. 2 Vigo, Daniel & Thornicroft, Graham & Atun, Rifat. (2016). Estimating the true global burden of mental illness. The Lancet Psychiatry. 3. 171-178. 10.1016/S2215-0366(15)00505-2. 3 World Health Organization. (2017). Depression and other common mental disorders: global health estimates. World Health Organization, 1–24. https://doi.org/CC BY-NC-SA 3.0 IGO 4 World Health Organization (2018, March). Depression. Retrieved from http://www.who.int/news-room/fact-sheets/detail/depression 5 Chisholm, D., Sweeny, K., Sheehan, P., Rasmussen, B., Smit, F., Cuijpers, P., & Saxena, S. (May 01, 2016). Scaling-up treatment of depression and anxiety: a global return on investment analysis. The Lancet Psychiatry, 3, 5, 415-424. 6 Ibid. 7 Anna, D., David, M. D., Martin, K., & Claire, C. (May 01, 2006). Financing mental health services in low- and middle-income countries. Health Policy and Planning, 21, 3, 171-182. 8 Votruba, N., Thornicroft, G., & FundaMentalSDG Steering Group. (January 01, 2016). Sustainable development goals and mental health: learnings from the contribution of the FundaMentalSDG global initiative. Global Mental Health (cambridge, England), 3. World Health Organization (2015, September). Mental health included in the UN Sustainable 9 Development Goals. Retrieved from http://www.who.int/mental_health/SDGs/en/ 10 Vikram Patel (2012) Global Mental Health: From Science to Action, Harvard Review of Psychiatry, 20:1, 6-12 11 World Health Organization. (2011)Mental health atlas 2011 .. World Health Organization. World Health Organization. (2013). Mental Health Action Plan 2013-2020. WHO Library 12 Cataloguing-in-Publication Data Library Cataloguing-in-Publication Data, 1–44. 31 ENDNOTES https://doi.org/ISBN 978 92 4 150602 1 13 World Health Organization (2014, August). Mental health: a state of well-being. Retrieved from http://www.who.int/features/factfiles/mental_health/en/ 14 World Health Organization. (2013). Mental Health Action Plan 2013-2020. WHO Library Cataloguing-in-Publication Data Library Cataloguing-in-Publication Data, 1–44. https://doi.org/ISBN 978 92 4 150602 1 15 World Health Organization (2018, March). Mental health: strengthening our response. Retrieved from http://www.who.int/en/news-room/fact-sheets/detail/mental-health-strengthening- our-response World Health Organization. (2009). The ICD-10 classification of mental and behavioural 16 disorders: Clinical descriptions and diagnostic guidelines. Geneva: World Health Organization. American Psychiatric Association., & American Psychiatric Association. (2013). Diagnostic and 17 Statistical Manual of Mental Disorders: DSM-5. 18 World Health Organization (2018, April). Mental disorders. Retrieved from http://www.who.int/en/news-room/fact-sheets/detail/mental-disorders de Jong, K., & Médecins sans frontières (Association). (2011). Psychosocial and Mental Health 19 Interventions in Areas of Mass Violence: A Community Based Approach. Amsterdam: Médecins sans Frontières. 20 Tol, W. A., Barbui, C., Galappatti, A., Silove, D., Betancourt, T. S., Souza, R., Golaz, A., ... Van, O. M. (October 29, 2011). Mental health and psychosocial support in humanitarian settings: Linking practice and research. The Lancet, 378, 9802, 1581-1591. 21 Coker, A. L., Davis, K. E., Arias, I., Desai, S., Sanderson, M., Brandt, H. M., & Smith, P. H. (November 01, 2002). Physical and mental health effects of intimate partner violence for men and women. American Journal of Preventive Medicine, 23, 4, 260-268. 22 Seal, K. H., Bertenthal, D., Miner, C., Sen, S., Marmar, C. (January 01, 2007). Bringing the war back home: mental health disorders among 103.788 US veterans returning from Iraq en Afghanistan seen at Department of Veterans Affairs facilities. Archives of Internal Medicine, 167, 5, 476-482 23 Harvey, S. B., Modini, M., Joyce, S., Milligan-Saville, J. S., Tan, L., Mykletun, A., Bryant, R. A., ... Mitchell, P. B. (January 01, 2017). Can work make you mentally ill? A systematic meta-review of work-related risk factors for common mental health problems. Occupational and Environmental Medicine, 74, 4, 301-310. 32 ENDNOTES 24 Khamisa, N., Oldenburg, B., Peltzer, K., & Ilic, D. (January 01, 2015). Work related stress, burnout, job satisfaction and general health of nurses. International Journal of Environmental Research and Public Health, 12, 1, 652-66. 25 Bovier, P. A., Chamot, E., & Perneger, T. V. (February 01, 2004). Perceived stress, internal resources, and social support as determinants of mental health among young adults. Quality of Life Research, 13, 1, 161-170. 26 Moos, R. H., Schutte, K. K., Brennan, P. L., & Moos, B. S. (July 01, 2005). The Interplay Between Life Stressors and Depressive Symptoms Among Older Adults. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60, 4.) Belle, D. (January 01, 1990). Poverty and women's mental health. American Psychologist, 385- 27 389. 28 Linn, M. W., Sandifer, R., & Stein, S. (May 01, 1985). Effects of unemployment on mental and physical health. American Journal of Public Health, 75, 5, 502-506. 29 Marrone, J., & Golowka, E. (January 01, 1999). If work makes people with mental illness sick, what do unemployment, poverty, and social isolation cause?. Psychiatric Rehabilitation Journal, 23, 2, 187-193. 30 Unger, J. B., Kipke, M. D., Simon, T. R., Montgomery, S. B., & Johnson, C. J. (June 01, 1997). Homeless Youths and Young Adults in Los Angeles: Prevalence of Mental Health Problems and the Relationship Between Mental Health and Substance Abuse Disorders. American Journal of Community Psychology, 25, 3, 371-394. 31 Najavits, L. M., Weiss, R. D., & Shaw, S. R. (January 01, 1997). The Link Between Substance Abuse and Posttraumatic Stress Disorder in Women: A Research Review. American Journal on Addictions, 6, 4, 273-283. 32 Cornwell, E. Y., & Waite, L. J. (March 01, 2009). Social Disconnectedness, Perceived Isolation, and Health among Older Adults. Journal of Health and Social Behavior, 50, 1, 31-48. 33 World Bank Group. Mental Health and Psychosocial Support in Fragile, Conflict, and Violence (FCV) Situations. FCV Health Knowledge Notes. Washington, Health, Nutrition & Population, World Bank Group, 2014. 34 Mnookin, Seth. (2016). Out of the Shadows : Making mental health a global development priority (English). Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/270131468187759113/Out-of-the-shadows-making- mental-health-a-global-development-priority 33 ENDNOTES 35 Whiteford, H. A., Degenhardt, L., Rehm, J. ., Baxter, A. J., Ferrari, A. J., Erskine, H. E., Charlson, F. J., ... Johns, N. (January 01, 2013). Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The Lancet, 382, 9904, 1575-1586. Organization, W. H. (2010). Equity, Social Determinants and Public Health Programmes. 36 Geneva: World Health Organization. 37 Patel, V., & Kleinman, A. (2003). Poverty and common mental disorders in developing countries. Bulletin of the World Health Organization, 81, 8, 609-615. 38 World Health Organization and Calouste Gulbenkian Foundation. Social determinants of mental health. Geneva, World Health Organization, 2014. 39 Weissman, J. S., National Center for Health Statistics (U.S.), & National Health Interview Survey (U.S.). (2015). Serious psychological distress among adults, United States, 2009-2013. Fryers, Tom, Melzer, David, Jenkins, Rachel, & Brugha, Traolach. (2005). The distribution of 40 the common mental disorders: social inequalities in Europe. (BioMed Central Ltd.) BioMed Central Ltd. 41 Pickett, K. E., & Wilkinson, R. G. (2010). Inequality: an underacknowledged source of mental illness and distress. The British Journal of Psychiatry, 197, 6, 426-428. 42 World Health Organization and Calouste Gulbenkian Foundation. Social determinants of mental health. Geneva, World Health Organization, 2014. 43 McKinsey Global Institute. (2018). Notes from the AI frontier: Insights from hundreds of use cases. Global. 44 PwC. (2017). Sizing the Prize: What’s the real value of AI for your business and how can you capitalize? Global. 45 McKinsey Global Institute. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. Global. 46 World Bank. 2019. World Development Report 2019: The Changing Nature of Work. Washington, DC: World Bank. doi:10.1596/978-1-4648-1328-3. License: Creative Commons Attribution
CC BY 3.0 IGO Brynjolfsson, E., & McAfee, A. (2016). The second machine age: Work, progress, and 47 prosperity in a time of brilliant technologies. New York: Norton. Standing, G. (2011). The precariat: The new dangerous class. London: Bloomsbury Academic. 48 34 ENDNOTES 49 Rohde, N., Tang, K. K., Osberg, L., & Rao, P. (2016). The effect of economic insecurity on mental health: Recent evidence from Australian panel data. Social Science & Medicine, 151, 250-258. 50 Patel, P. C., Devaraj, S., Hicks, M. J., & Wornell, E. J. (January 01, 2018). County-level job automation risk and health: Evidence from the United States. Social Science & Medicine, 202, 54-60. Gallie, D., Felstead, A., Green, F., & Inanc, H. (2017). The hidden face of job insecurity. Work, 51 Employment and Society, 31, 1, 36-53. International Labour Organisation,. (2016). Non-standard employment around the world: 52 Understanding challenges, shaping prospects. 53 Schonfeld, I. S., & Mazzola, J. J. (2015). A qualitative study of stress in individuals self- employed in solo businesses. Journal of Occupational Health Psychology, 20, 4, 501-513. World Employment and Social Outlook 2016: Trends for youth. International Labour Office – 54 Geneva: ILO, 2016 55 World Health Organization and Calouste Gulbenkian Foundation. Social determinants of mental health. Geneva, World Health Organization, 2014. Office of the United Nations High Commissioner for Refugees (2017). Global trends: Forced 56 displacement in 2016. 57 Institute for Economics & Peace. (2017). Global Peace Index 2017: Measuring peace in a complex world. Sydney, Australia. Office of the United Nations High Commissioner for Refugees. (2017). Global trends: Forced 58 displacement in 2016. Organization, W. H. (2013). Global and Regional Estimates of Violence against Women: 59 Prevalence and Health Effects of Intimate Partner Violence and non-partner Sexual Violence. Geneva: World Health Organization. 60 Save the Children (2017). Invisible Wounds: The impact of six years of war on the mental health of Syria’s children. 61 Fairburn, C. G., & Patel, V. (January 01, 2017). The impact of digital technology on psychological treatments and their dissemination. Behaviour Research and Therapy, 88, 19-25. 35 ENDNOTES 62 Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (November 14, 2017). Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates Among U.S. Adolescents After 2010 and Links to Increased New Media Screen Time. Clinical Psychological Science, 3.) 63 Statista (2018). Number of social media users worldwide from 2010 to 2021. Retrieved from https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/ 64 Statista (2018). Daily time spent on social networking by internet users worldwide from 2012 to 2017. Retrieved from https://www.statista.com/statistics/433871/daily-social-media-usage- worldwide/ 65 Kuss, D. J., & Griffiths, M. D. (January 01, 2017). Social Networking Sites and Addiction: Ten Lessons Learned. International Journal of Environmental Research and Public Health, 14, 3.) 66 Royal Society for Public Health and Young Health Movement. (2017). #Status of Mind: Social media and young people’s mental health and wellbeing. Royal Society for Public Health. Retrieved from https://www.rsph.org.uk/our-work/campaigns/status-of-mind.html 67 Vogel, E. A., Rose, J. P., Roberts, L. R., & Eckles, K. (January 01, 2014). Social comparison, social media, and self-esteem. Psychology of Popular Media Culture, 3, 4, 206-222. 68 Common Sense Media. (2015). Children, Teens, Media, and Body Image. Common Sense Media. Retrieved from https://www.commonsensemedia.org/research/children-teens-media- and-body-image 69 World Health Organization (2018, January). Gaming disorder. Retrieved from http://www.who.int/features/qa/gaming-disorder/en/ 70 Apple Inc. (2018, June). iOS 12 introduces new features to reduce interruptions and manage Screen Time. Retrieved from https://www.apple.com/newsroom/2018/06/ios-12-introduces-new- features-to-reduce-interruptions-and-manage-screen-time/ Tarafdar, M. (2015). The dark side of information technology. (MIT Sloan Management Review, 71 56, 2. 72 Barley, S. R., Meyerson, D. E., & Grodal, S. (July 01, 2011). E-mail as a source and symbol of stress. Organization Science, 22, 4, 887-906. 73 Trautmann, S., Rehm, J., Wittchen, H.-U., & Rehm, J. (January 01, 2016). The economic costs of mental disorders: Do our societies react appropriately to the burden of mental disorders? The economic costs of mental disorders: Do our societies react appropriately to the burden of mental disorders? Sebastian Trautmann et al. Embo Reports. 36 ENDNOTES 74 Newman, R. (2007, March 5). Top Companies Show Investing in Employee Health and Well- Being Leads to Business Success. American Psychological Association. Retrieved from http://www.apa.org/news/press/releases/2007/03/phwa.aspx 75 Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (January 01, 2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30, 2, 252-262. 76 Przybylski, A. K., & Weinstein, N. (February 01, 2017). A Large-Scale Test of the Goldilocks Hypothesis: Quantifying the Relations Between Digital-Screen Use and the Mental Well-Being of Adolescents. Psychological Science, 28, 2, 204-215. 77 Verhulst, S. (May 9, 2017). How social media data can improve people’s lives – if used responsibly. [Blog post]. Retrieved from https://blogs.worldbank.org/publicsphere/how-social- media-data-can-improve-people-s-lives-if-used-responsibly 78 Kato, P. M., Cole, S. W., Bradlyn, A. S., & Pollock, B. H. (August 01, 2008). A video game improves behavioral outcomes in adolescents and young adults with cancer: A randomized trial. Pediatrics, 122, 2.) 79 Tarun, D., Corrado, B., Nicolas, C., Alexandra, F., Vladimir, P., Mark, . O., M, T. Y., ... Shekhar, S. (November 15, 2011). Evidence-Based Guidelines for Mental, Neurological, and Substance Use Disorders in Low- and Middle-Income Countries: Summary of WHO Recommendations. Plos Medicine, 8, 11.) Stuart, H. (January 01, 2016). Reducing the stigma of mental illness. Global Mental Health, 3. 80 81 Wang, P. S., Aguilar-Gaxiola, S., Alonso, J., Angermeyer, M. C., Borges, G., Bromet, E. J., Bruffaerts, R., ... Wells, J. E. (September 08, 2007). Use of mental health services for anxiety, mood, and substance disorders in 17 countries in the WHO world mental health surveys. The Lancet, 370, 9590, 841-850. 82 Patel, V., Maj, M., Flisher, A. J., Pregelj, P., & WPA Zonal and Member Society Representatives. (January 01, 2010). Reducing the treatment gap for mental disorders: A WPA survey. World Psychiatry, 9, 169-176. 83 Rathod, S., Pinninti, N., Irfan, M., Gorczynski, P., Rathod, P., Gega, L., & Naeem, F. (January 01, 2017). Mental Health Service Provision in Low- and Middle-Income Countries. Health Services Insights, 10. Fernando, S. (2010). Mental health, race, and culture. Basingstoke, Hampshire: Palgrave 84 Macmillan. 37 ENDNOTES Kleinman, A., Cohen, A., & Scientific American, inc. (1997). Psychiatry's global challenge. New 85 York: Scientific American, Inc. 86 Schomerus, G., Schwahn, C., Holzinger, A., Corrigan, P. W., Grabe, H. J., Carta, M. G., & Angermeyer, M. C. (June 01, 2012). Evolution of public attitudes about mental illness: a systematic review and meta-analysis: Evolution of public attitudes. Acta Psychiatrica Scandinavica, 125, 6, 440-452. 87 Gureje, O., Nortje, G., Makanjuola, V., Oladeji, B., Seedat, S., & Jenkins, R. (January 01, 2015). The role of global traditional and complementary systems of medicine in treating mental health problems. The Lancet. Psychiatry, 2, 2, 168-177. World Health Organization. (2013). WHO traditional medicine strategy, 2014-2023. Geneva: 88 World Health Organization. 89 Andrade, L. H., Alonso, J., Mneimneh, Z., Wells, J. E., Al-Hamzawi, A., Borges, G., … Kessler, R. C. (2014). Barriers to Mental Health Treatment: Results from the WHO World Mental Health (WMH) Surveys. Psychological Medicine, 44(6), 1303–1317. http://doi.org/10.1017/S0033291713001943 90 Patel, V., Chowdhary, N., Rahman, A., & Verdeli, H. (September 01, 2011). Improving access to psychological treatments: Lessons from developing countries. Behaviour Research and Therapy, 49, 9, 523-528. 91 Acharya, B., Maru, D., Schwarz, R., Citrin, D., Tenpa, J., Hirachan, S., Basnet, M., ... Ekstrand, M. (January 01, 2017). Partnerships in mental healthcare service delivery in low-resource settings: developing an innovative network in rural Nepal. Globalization and Health, 13, 1.) 92 Clement, S., Schauman, O., Graham, T., Maggioni, F., Evans-Lacko, S., Bezborodovs, N., Morgan, C., ... Thornicroft, G. (January 01, 2015). What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies. Psychological Medicine, 45, 1, 11-27. 93 Kishore, J., Gupta, A., Jiloha, R. C., & Bantman, P. (2011). Myths, beliefs and perceptions about mental disorders and health-seeking behavior in Delhi, India. Indian Journal of Psychiatry, 53(4), 324–329. http://doi.org/10.4103/0019-5545.91906 94 Laverne, W., Robyn, G., & Sidney, H. (April 01, 2014). Implementing a Mental Health Ministry Committee in Faith-Based Organizations: The Promoting Emotional Wellness and Spirituality Program. Social Work in Health Care, 53, 4, 414-434. 95 Bell, R. A., Franks, P., Duberstein, P. R., Epstein, R. M., Feldman, M. D., Fernandez, . G. E., & Kravitz, R. L. (January 01, 2011). Suffering in silence: reasons for not disclosing depression in primary care. Annals of Family Medicine, 9, 5.) 38 ENDNOTES 96 Corrigan, P. W., Druss, B. G., & Perlick, D. A. (January 01, 2014). The Impact of Mental Illness Stigma on Seeking and Participating in Mental Health Care. Psychological Science in the Public Interest : a Journal of the American Psychological Society, 15, 2, 37-70. 97 Laye-Gindhu, A., & Schonert-Reichl, K. A. (October 01, 2005). Nonsuicidal Self-Harm among Community Adolescents: Understanding the "Whats" and "Whys" of Self-Harm. Journal of Youth and Adolescence, 34, 5, 447-457. Raja, Shoba, Wood, Sarah K, de Menil, Victoria, & Mannarath, Saju C. (2010). Mapping mental 98 health finances in Ghana, Uganda, Sri Lanka, India and Lao PDR. (BioMed Central Ltd.) BioMed Central Ltd. 99 Bhana, A., Petersen, I., Baillie, K. L., Flisher, A. J., & The, M. H. P. P. R. P. C. (December 01, 2010). Implementing the World Health Report 2001 recommendations for integrating mental health into primary health care: A situation analysis of three African countries: Ghana, South Africa and Uganda. International Review of Psychiatry, 22, 6, 599-610. 100 World Health Organization. (2018)Mental health atlas 2017 .. World Health Organization. 101 Hewlett, E., Moran, V., & Organisation for Economic Co-operation and Development. (2014). Making mental health count: The social and economic costs of neglecting mental health care. 102 Rathod, S., Pinninti, N., Irfan, M., Gorczynski, P., Rathod, P., Gega, L., & Naeem, F. (January 01, 2017). Mental Health Service Provision in Low- and Middle-Income Countries. Health Services Insights, 10. 103 World Health Organization. ((2015 Mental health atlas 2014. World Health . .Organizationhttp://www.who.int/iris/handle/10665/178879 104 World Health Organization. (2013). Mental Health Action Plan 2013-2020. WHO Library Cataloguing-in-Publication Data Library Cataloguing-in-Publication Data, 1–44. https://doi.org/ISBN 978 92 4 150602 1 105 Patel, V., & Cohen, A. (2003). Mental health services in primary care in “developing” countries. World Psychiatry, 2(3), 163–164. 106 Rathod, S., Pinninti, N., Irfan, M., Gorczynski, P., Rathod, P., Gega, L., & Naeem, F. (January 01, 2017). Mental Health Service Provision in Low- and Middle-Income Countries. Health Services Insights, 10. 107 World Health Organization. ((2003 Investing in mental health. Geneva : World Health . .Organizationhttp://www.who.int/iris/handle/10665/42823 39 ENDNOTES 108 Rathod, S., Pinninti, N., Irfan, M., Gorczynski, P., Rathod, P., Gega, L., & Naeem, F. (January 01, 2017). Mental Health Service Provision in Low- and Middle-Income Countries. Health Services Insights, 10. 109 Sweetland, A. C., Oquendo, M. A., Sidat, M., Santos, P. F., Vermund, S. H., Duarte, C. S., … Wainberg, M. L. (2014). Closing the mental health gap in low-income settings by building research capacity: Perspectives from Mozambique. Annals of Global Health, 80(2), 126–133. http://doi.org/10.1016/j.aogh.2014.04.014 110 Upadhaya, N., Jordans, M. J. D., Abdulmalik, J., Ahuja, S., Alem, A., Hanlon, C., … Gureje, O. (2016). Information systems for mental health in six low and middle income countries: cross country situation analysis. International Journal of Mental Health Systems, 10, 60. http://doi.org/10.1186/s13033-016-0094-2 111 Patel, V., Maj, M., Flisher, A. J., Pregelj, P., & WPA Zonal and Member Society Representatives. (January 01, 2010). Reducing the treatment gap for mental disorders: A WPA survey. World Psychiatry, 9, 169-176. 112 World Health Organization. (2018)Mental health atlas 2017 .. World Health Organization. 113 World Health Organization. (2011)Mental health atlas 2011 .. World Health Organization. 114 World Health Organization (2017). WHO Mental Health Gap Action Programme (mhGAP). Retrieved from http://www.who.int/mental_health/mhgap/en/ 115 Becker, A. E., & Kleinman, A. (January 01, 2012). Introduction An Agenda for Closing Resource Gaps in Global Mental Health: Innovation, Capacity Building, and Partnerships. Harvard Review of Psychiatry, 20, 1, 3-5. Becker, A. E., & Kleinman, A. (January 01, 2013). Mental health and the global agenda. The 116 New England Journal of Medicine, 369, 14, 1380-1. 117 Fekadu, A., Hanlon, C., Medhin, G., Alem, A., Selamu, M., Giorgis, T. W., Shibre, T., ... Lund, C. (January 01, 2016). Development of a scalable mental healthcare plan for a rural district in Ethiopia. The British Journal of Psychiatry, 208. 118 Kakuma, R., Minas, H., van, G. N., Dal, P. M. R., Desiraju, K., Morris, J. E., Saxena, S., ... Scheffler, R. M. (January 01, 2011). Human resources for mental health care: current situation and strategies for action. The Lancet, 378, 9803, 1654-1663. 119 Lasisi, D., Ani, C., Lasebikan, V., Sheikh, L., & Omigbodun, O. (December 01, 2017). Effect of attention-deficit–hyperactivity-disorder training program on the knowledge and attitudes of 40 ENDNOTES primary school teachers in Kaduna, North West Nigeria. Child and Adolescent Psychiatry and Mental Health, 11, 1.) 120 Kakuma, R., Minas, H., van, G. N., Dal, P. M. R., Desiraju, K., Morris, J. E., Saxena, S., ... Scheffler, R. M. (January 01, 2011). Human resources for mental health care: current situation and strategies for action. The Lancet, 378, 9803, 1654-1663. 121 Fricchione, G. L., Borba, C. P. C., Alem, A., Shibre, T., Carney, J. R., & Henderson, D. C. (2012). Capacity Building in Global Mental Health: Professional Training. Harvard Review of Psychiatry, 20(1), 47–57. http://doi.org/10.3109/10673229.2012.655211 122 Rathod, S., Pinninti, N., Irfan, M., Gorczynski, P., Rathod, P., Gega, L., & Naeem, F. (January 01, 2017). Mental Health Service Provision in Low- and Middle-Income Countries. Health Services Insights, 10. Russell, S. J., & Norvig, P. (2018). Artificial intelligence: A modern approach. 123 124 Merriam-Webster (2018). Definition of Artificial Intelligence. Retrieved from https://www.merriam-webster.com/dictionary/artificial%20intelligence 125 Bengio, Y. (Arpil 6, 2017). A conversation with AI pioneer Yoshua Bengio. Microsoft: The AI Blog. Retrieved from https://blogs.microsoft.com/ai/a-conversation-ai-pioneer-yoshua-bengio/ 126 Waller, M. A., & Fawcett, S. E. (January 01, 2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34, 2, 77-84. 127 SAS. (2018). Predictive Analytics. SAS. Retrieved from https://www.sas.com/en_us/insights/analytics/predictive-analytics.html 128 Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M.-R., & Christensen, H. (November 15, 2013). Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review. Journal of Medical Internet Research, 15, 11. 129 Massachusetts, I. O. T. (July 21, 2017). The Emerging Science of Computational Psychiatry. Emerging Technology from the Arxiv | Mit Technology Review, 2017-7. 130 Olff, M. (December 01, 2015). Mobile mental health: a challenging research agenda. European Journal of Psychotraumatology, 6, 1, 27882. 131 World Health Organization and Calouste Gulbenkian Foundation. Social determinants of mental health. Geneva, World Health Organization, 2014. 41 ENDNOTES 132 Cottraux, J. (January 01, 2002). Nonpharmacological treatments for anxiety disorders. Dialogues in Clinical Neuroscience, 4, 3, 305-319. 133 McHugh, R. K., Whitton, S. W., Peckham, A. D., Welge, J. A., & Otto, M. W. (June 01, 2013). Patient preference for psychological vs pharmacologic treatment of psychiatric disorders: A meta- analytic review. Journal of Clinical Psychiatry, 74, 6, 595-602. 134 Moffatt, S., Steer, M., Lawson, S., Penn, L., & O'Brien, N. (January 01, 2017). Link Worker social prescribing to improve health and well-being for people with long-term conditions: qualitative study of service user perceptions. Bmj Open, 7, 7.) 135 Mayor of London (2017, August). Better health for all Londoners: Consultation on the London health inequalities strategy. Greater London Authority. Retrieved from https://www.london.gov.uk/sites/default/files/draft_health_inequalities_strategy_2017.pdf 136 Pescheny, J. V., Pappas, Y., & Randhawa, G. (December 07, 2018). Facilitators and barriers of implementing and delivering social prescribing services: a systematic review. Bmc Health Services Research, 18, 1.) Linn, M W, Sandifer, R, & Stein, S. (n.d.). Effects of unemployment on mental and physical 137 health. 138 Erickson, J., El-Gabalawy, R., Palitsky, D., Patten, S., Mackenzie, C. S., Stein, M. B., & Sareen, J. (April 20, 2016). Educational attainment as a protective factor for psychiatric disorders: findings from a nationally representative longitudinal study. Depression and Anxiety. Marmot, M. G., & Wilkinson, R. G. (2003). Social determinants of health: The solid facts. 139 Copenhagen: World Health Organization. 140 Krieger, J. (January 01, 2002). Housing and health: Time again for public health action. American Journal of Public Health, 92, 5.) 141 Pinxten, W., & Lievens, J. (September 01, 2014). The importance of economic, social and cultural capital in understanding health inequalities: using a Bourdieu-based approach in research on physical and mental health perceptions. Sociology of Health & Illness, 36, 7, 1095- 1110. Ramady, M. A. (2016). The Political Economy of Wasta: Use and Abuse of Social Capital 142 Networking. (Political economy of wasta.) Cham: Springer. Polley, M.J., Fleming, J., Anfilogoff, T. and Carpenter, A. (2017) Making Sense of Social 143 Prescribing. Technical Report. University of Westminster, London. 42 ENDNOTES 144 Moffatt, S., Steer, M., Lawson, S., Penn, L., & O'Brien, N. (January 01, 2017). Link Worker social prescribing to improve health and well-being for people with long-term conditions: qualitative study of service user perceptions. Bmj Open, 7, 7.) 145 Bickerdike, L., Booth, A., Wilson, P. M., Farley, K., & Wright, K. (January 01, 2017). Social prescribing: less rhetoric and more reality. A systematic review of the evidence. Bmj Open, 7, 4.) 146 Donnelly, L. (2017, December 26). NHS should prescribe tango dancing and book clubs, not 'a pill for every ill'. The Telegraph. Retrieved from https://www.telegraph.co.uk/news/2017/12/26/nhs-should-prescribe-tango-dancing-book-clubs- not-pill-every/ Data Never Sleeps 2.0. Domo Inc. 147 DOMO. (2015). Retrieved from https://www.domo.com/learn/data-never-sleeps-2 148 Royal Society for Public Health. (May 19, 2017). Status of Mind: Social media and young people’s mental health and wellbeing. UK: Royal Society for Public Health. 149 O'Keeffe, G. S., & Clarke-Pearson, K. (April 01, 2011). The Impact of Social Media on Children, Adolescents, and Families. Pediatrics, 127, 4, 800-804. 150 Rathbone, A. L., & Prescott, J. (January 01, 2017). The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review. Journal of Medical Internet Research, 19, 8.) 151 Kobau, R., Diener, E., Chapman, D., Thompson, W., Seligman, M. E. P., Peterson, C., & Zack, M. M. (August 01, 2011). Mental health promotion in public health: Perspectives and strategies from positive psychology. American Journal of Public Health, 101, 8.) 152 Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (March 01, 2016). Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments. Jmir Mental Health, 3, 1.) 153 Wainberg, M. L., Scorza, P., Shultz, J. M., Helpman, L., Mootz, J. J., Johnson, K. A., Neria, Y., ... Arbuckle, M. R. (January 01, 2017). Challenges and Opportunities in Global Mental Health: a Research-to-Practice Perspective. Current Psychiatry Reports, 19, 5.) 154 Petersen, I., Marais, D., Abdulmalik, J., Ahuja, S., Alem, A., Chisholm, D., Egbe, C., ... Thornicroft, G. (January 01, 2017). Strengthening mental health system governance in six low- and middle-income countries in Africa and South Asia: challenges, needs and potential strategies. Health Policy and Planning, 32, 5, 699-709. 43 ENDNOTES World Health Organization., & World Health Organization,. (2016). mhGAP intervention guide 155 for mental, neurological and substance use disorders in non-Specialized health settings: Mental health gap action programme (mhGAP). 156 Tirmizi, S. N., Khoja, S., Patten, S., Yousafzai, A. W., Scott, R. E., Durrani, H., Khoja, W., ... Husyin, N. (January 01, 2017). Mobile-based blended learning for capacity building of health providers in rural Afghanistan. Mhealth, 3. 157 Hamdani, S. U., Ahmed, Z., Sijbrandij, M., Nazir, H., Masood, A., Akhtar, P., Amin, H., ... Minhas, F. A. (January 01, 2017). Problem Management Plus (PM+) in the management of common mental disorders in a specialized mental healthcare facility in Pakistan; study protocol for a randomized controlled trial. International Journal of Mental Health Systems, 11. 158 Mascayano, F., Armijo, J. E., & Yang, L. H. (January 01, 2015). Addressing stigma relating to mental illness in low- and middle-income countries. Frontiers in Psychiatry, 6. 159 Firth, J., Cotter, J., Torous, J., Bucci, S., Firth, J. A., & Yung, A. R. (January 01, 2016). Mobile Phone Ownership and Endorsement of "mHealth" Among People With Psychosis: A Meta- analysis of Cross-sectional Studies. Schizophrenia Bulletin, 42, 2, 448-55 160 Latif, S., Rana, R., Qadir, J., Ali, A., Imran, M. A., & Younis, M. S. (January 01, 2017). Mobile Health in the Developing World: Review of Literature and Lessons From a Case Study. Ieee Access, 5, 11540-11556. 161 Norris, L., Swartz, L., & Tomlinson, M. (September 01, 2013). Mobile phone technology for improved mental health care in South Africa: possibilities and challenges. South African Journal of Psychology, 43, 3, 379-388. Mechael, P. (2010). Barriers and gaps affecting mHealth in low and middle income countries: 162 Policy white paper. New York, NY etc.: Columbia university. Earth institute. Center for global health and economic development (CGHED) : with mHealth alliance. Gallaher, Mike. 2005. A technology white paper on improving the efficiency of social safety 163 net program delivery in low income countries an introduction to available and emerging mobile technologies (English). Social Protection discussion paper series no. 522. Washington, DC: World Bank. 164 Temperton, J. (2016, November). Bots: Apps are dying. Long live the subservient bots ready to fulfil your every desire. WIRED. Retrieved from http://www.wired.co.uk/article/here-come-the- bots 165 Molteni, M. (2017, June). The chatbot therapist will see you now. WIRED. Retrieved from https://www.wired.com/2017/06/facebook-messenger-woebot-chatbot-therapist/ 44 ENDNOTES 166 Gratzer, D., & Khalid-Khan, F. (March 01, 2016). Internet-delivered cognitive behavioural therapy in the treatment of psychiatric illness. Cmaj, 188, 4, 263-272. 167 Knight, W. (2017, October). Intelligent Machines: Andrew Ng Has a Chatbot That Can Help with Depression. MIT Technology Review. Retrieved from https://www.technologyreview.com/s/609142/andrew-ng-has-a-chatbot-that-can-help-with- depression/ 168 Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (June 06, 2017). Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. Jmir Mental Health, 4, 2.) 169 Robinson, A. (2015, September). Meet Ellie, the machine that can detect depression. The Guardian. Retrieved from https://www.theguardian.com/sustainable-business/2015/sep/17/ellie- machine-that-can-detect-depression 170 Lucas GM, Rizzo A, Gratch J, Scherer S, Stratou G, Boberg J and Morency L-P (2017) Reporting Mental Health Symptoms: Breaking Down Barriers to Care with Virtual Human Interviewers. Front. Robot. AI 4:51. doi: 10.3389/frobt.2017.00051 171 Rizzo, A., Morency, L. (2018, May 16). SimSensei. Retrieved from http://ict.usc.edu/prototypes/simsensei/ 172 Gonzalez, R. (2017, October). Virtual therapists help veterans open up about PTSD. WIRED. Retrieved from https://www.wired.com/story/virtual-therapists-help-veterans-open-up-about- ptsd/ 173 Andersson, G., Cuijpers, P., Carlbring, P., Riper, H., & Hedman, E. (October 01, 2014). Guided Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: a systematic review and meta-analysis. World Psychiatry, 13, 3, 288-295. 174 Silva, C. (2017, June). The Millennial Obsession with Self-Care. NPR. Retrieved from https://www.npr.org/2017/06/04/531051473/the-millennial-obsession-with-self-care 175 Perez, S. (2018, April). Self-care apps are booming. TechCrunch. Retrieved from https://techcrunch.com/2018/04/02/self-care-apps-are-booming/ 176 Ebert, D. D., Zarski, A.-C., Christensen, H., Stikkelbroek, Y., Cuijpers, P., Berking, M., Riper, H., ... Stikkelbroek, Y. (January 01, 2015). Internet and computer-based cognitive behavioral therapy for anxiety and depression in youth : a meta-analysis of randomized controlled outcome trials. Plos One, 10, 3.) 45 ENDNOTES 177 Gerardi, M., Rothbaum, B. O., Ressler, K., Heekin, M., & Rizzo, A. (January 01, 2008). Virtual reality exposure therapy using a virtual Iraq: Case report. Journal of Traumatic Stress, 21, 2, 209- 213. 178 Rothbaum, B. O., Price, M., Jovanovic, T., Norrholm, S. D., Gerardi, M., Dunlop, B., Davis, M., ... Ressler, K. J. (June 01, 2014). A randomized, double-blind evaluation of D-cycloserine or alprazolam combined with virtual reality exposure therapy for posttraumatic stress disorder in Iraq and Afghanistan war veterans. American Journal of Psychiatry, 171, 6, 640-648. 179 Rizzo, A. (2018, May 18). Bravemind. Retrieved from http://medvr.ict.usc.edu/projects/bravemind/ 180 Kraemer, D. (2017, March 20). Huge surge in online mental health appointments attacked by specialists. Independent. Retrieved from https://www.independent.co.uk/life-style/health-and- families/health-news/online-therapy-mental-health-help-services-webcam-appointments- increase-messenger-nhs-depression-a7626496.html 181 Government of the United Kingdom (2017, January 9). Prime Minister unveils plans to transform mental health support. Prime Minister’s Office: Press Release. Retrieved from https://www.gov.uk/government/news/prime-minister-unveils-plans-to-transform-mental-health- support 182 Prescott, J., Hanley, T., & Ujhelyi, K. (August 02, 2017). Peer Communication in Online Mental Health Forums for Young People: Directional and Nondirectional Support. Jmir Mental Health, 4, 3.) 183 Harding, C., & Chung, H. (January 01, 2016). Behavioral health support and online peer communities: international experiences. Mhealth, 2. World Health Organization. (2005). Mental health information systems. Geneva: World Health 184 Organization. World Health Organization. (2003). Investing in mental health. Geneva: World Health 185 Organization. 186 Gulliver, A., Griffiths, K. M., & Christensen, H. (December 30, 2010). Perceived barriers and facilitators to mental health help-seeking in young people: A systematic review. Bmc Psychiatry, 10. World Health Organization. (2005). Mental health information systems. Geneva: World Health 187 Organization. 46 ENDNOTES 188 Huys, Q. J. M., Huys, Q. J. M., Maia, T. V., & Frank, M. J. (February 23, 2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19, 3, 404-413. 189 Massachusetts, I. O. T. (July 21, 2017). The Emerging Science of Computational Psychiatry. Emerging Technology from the Arxiv | Mit Technology Review, 2017-7. 190 Hahn, T., Nierenberg, A. A., & Whitfield-Gabrieli, S. (January 15, 2017). Predictive analytics in mental health: applications, guidelines, challenges and perspectives. Molecular Psychiatry, 22, 1, 37-43. 191 Monteith, S., Glenn, T., Geddes, J., & Bauer, M. (December 01, 2015). Big data are coming to psychiatry: a general introduction. International Journal of Bipolar Disorders, 3, 1, 1-11. 192 Gillan, C. M., & Whelan, R. (December 01, 2017). What big data can do for treatment in psychiatry. Current Opinion in Behavioral Sciences, 18, 34-42. 193 Conway, M., & O'Connor, D. (January 01, 2016). Social Media, Big Data, and Mental Health: Current Advances and Ethical Implications. Current Opinion in Psychology, 9, 77-82. 194 Toulis, A., & Golab, L. (January 01, 2017). Social Media Mining to Understand Public Mental Health. 195 Schwartz, H. A., Sap, M., Kern, M. L., Eichstaedt, J. C., Kapelner, A., Agrawal, M., Blanco, E., ... Ungar, L. H. (January 01, 2016). PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 21, 516-27. 196 Kruse, C. S., Goswamy, R., Raval, Y., & Marawi, S. (November 21, 2016). Challenges and Opportunities of Big Data in Health Care: A Systematic Review. Jmir Medical Informatics, 4, 4.) 197 Ola, O., & Sedig, K. (January 01, 2016). Beyond simple charts: Design of visualizations for big health data. Online Journal of Public Health Informatics, 8, 3.) 198 Zakkar, M., & Sedig, K. (January 01, 2017). Interactive visualization of public health indicators to support policymaking: An exploratory study. Online Journal of Public Health Informatics, 9, 2.) 47