Building the case for actionable ethics in digital health research supported by artificial intelligence

The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research

Digital tools for youth mental health.

No abstract available

Recent advances in understanding schizophrenia

Schizophrenia is a highly disabling disorder whose causes remain to be better understood, and treatments have to be improved. However, several recent advances have been made in diagnosis, etiopathology, and treatment. Whereas reliability of diagnosis has improved with operational criteria, including Diagnostic and Statistical Manual of Mental Disorders, (DSM) Fifth Edition, validity of the disease boundaries remains unclear because of substantive overlaps with other psychotic disorders. Recent emphasis on dimensional approaches and translational bio-behavioral research domain criteria may eventually help move toward a neuroscience-based definition of schizophrenia. The etiology of schizophrenia is now thought to be multifactorial, with multiple small-effect and fewer large-effect susceptibility genes interacting with several environmental factors. These factors may lead to developmentally mediated alterations in neuroplasticity, manifesting in a cascade of neurotransmitter and circuit dysfunctions and impaired connectivity with an onset around early adolescence. Such etiopathological understanding has motivated a renewed search for novel pharmacological as well as psychotherapeutic targets. Addressing the core features of the illness, such as cognitive deficits and negative symptoms, and developing hypothesis-driven early interventions and preventive strategies are high-priority goals for the field. Schizophrenia is a severe, chronic mental disorder and is among the most disabling disorders in all of medicine. It is estimated by the National Institute of Mental Health (NIMH) that 2.4 million people over the age of 18 in the US suffer from schizophrenia. This illness typically begins in adolescence and derails the formative goals of school, family, and work, leading to considerable suffering and disability and reduced life expectancy by about 20 years. Treatment outcomes are variable, and some people are successfully treated and reintegrated (i.e. go back to work). Despite the effort of many experts in the field, however, schizophrenia remains a chronic relapsing and remitting disorder associated with significant impairments in social and vocational functioning and a shortened lifespan. Comprehensive treatment entails a multi-modal approach, including psychopharmacology, psychosocial interventions, and assistance with housing and financial sustenance. Research to date suggests a network of genetic, neural, behavioral, and environmental factors to be responsible for its development and course. This article aims to summarize and explain recent advancements in research on schizophrenia, to suggest how these recent discoveries may lead to a better understanding and possible further development of effective therapies, and to highlight the paradigm shifts that have taken place in our understanding of the diagnosis, etiopathology, and treatment

Use of smartphones, mobile apps and wearables for health promotion by people with anxiety or depression:An analysis of a nationally representative survey data

People with mental illness have increased cardiovascular risk factors, which contributes significantly to mortality in this population. Digital interventions have emerged as promising models to promote physical health, although their potential for use in mental health populations is relatively unexplored. We examined the potential for using digital tools for health promotion by people with common mental disorders like anxiety or depression. Using data from the 2019 edition of the Health Information National Trends Survey (HINTS 5), we evaluated differences between individuals with self-reported history of diagnosed depression/anxiety and the general population with respect to ownership, usage, and perceived usefulness of digital tools for managing their health. Overall, individuals with anxiety or depression were as likely as the general population to use digital devices for their care. Those with anxiety or depression who had health apps were more likely to report intentions to lose weight than those without health apps. Significant sociodemographic predictors of digital tools usage included gender, age, income, and education level. People with anxiety or depression own and use digital health tools at similarly high rates to the general population, suggesting that these tools present a novel opportunity for health promotion among people with these disorders

Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders

Purpose of ReviewSleep is an important feature in mental illness. Smartphones can be used to assess and monitor sleep, yet there is little prior application of this approach in depressive, anxiety, or psychotic disorders. We review uses of smartphones and wearable devices for sleep research in patients with these conditions.Recent FindingsTo date, most studies consist of pilot evaluations demonstrating feasibility and acceptability of monitoring sleep using smartphones and wearable devices among individuals with psychiatric disorders. Promising findings show early associations between behaviors and sleep parameters and agreement between clinic-based assessments, active smartphone data capture, and passively collected data. Few studies report improvement in sleep or mental health outcomes.SummarySuccess of smartphone-based sleep assessments and interventions requires emphasis on promoting long-term adherence, exploring possibilities of adaptive and personalized systems to predict risk/relapse, and determining impact of sleep monitoring on improving patients' quality of life and clinically meaningful outcomes.Peer reviewe

Bipolar disorder in the digital age: new tools for the same illness

“Nothing is more difficult than to ascertain the length of time that a maniacal patient can exist without sleep.”—Dr. Sutherland (Br J Psychiatry 7(37):1–19, 1861). Dr. Sutherland’s patient was suffering from an acute manic episode, which today is called bipolar illness. 150 years later, we continue to struggle with the same challenges in ascertaining accurate symptoms from patients. In era of new digital tools, the quantified self-movement, and precision medicine, we can ask the question: Can we advance understanding and treatment for bipolar illness beyond asking the same questions as in 1861

ReMindCare App for Early Psychosis: Pragmatic Real World Intervention and Usability Study

[EN] Background: eHealth interventions are widely used in clinical trials and increasingly in care settings as well; however, their efficacy in real-world contexts remains unknown. ReMindCare is a smartphone app that has been systematically implemented in a first episode of psychosis program (FEPP) for patients with early psychosis since 2018. Objective: The objective of this study was to assess the efficacy of ReMindCare after 19 months of use in the clinic and varying use by individual patients. Methods: The integration of the ReMindCare app into the FEPP started in October 2018. Patients with early psychosis self-selected to the app (ReMindCare group) or treatment as usual (TAU group). The outcome variables considered were adherence to the intervention and number of relapses, hospital admissions, and visits to urgent care units. Data from 90 patients with early psychosis were analyzed: 59 in the ReMindCare group and 31 in the TAU group. The mean age of the sample was 32.8 (SD 9.4) years, 73% (66/90) were males, 91% (83/90) were White, and 81% (74/90) were single. Results: Significant differences between the ReMindCare and TAU groups were found in the number of relapses, hospitalizations, and visits to urgent care units, with each showing benefits for the app. Only 20% (12/59) of patients from the ReMindCare group had a relapse, while 58% (18/31) of the TAU patients had one or more relapses (chi(2) =13.7, P=.001). Moreover, ReMindCare patients had fewer visits to urgent care units (chi(2) =7.4, P=.006) and fewer hospitalizations than TAU patients (chi(2) =4.6, P=.03). The mean of days using the app was 352.2 (SD 191.2; min/max: 18-594), and the mean of engagement was 84.5 (SD 16.04). Conclusions: To our knowledge, this is the first eHealth intervention that has preliminarily proven its benefits in the real-world treatment of patients with early psychosis.This study was supported by the Sanitary Research Institute of the University Clinic Hospital and the Mental Health Networking Biomedical Centre. It was also supported by the Generalitat Valenciana and the Program for Scientific Research, Technological Development, and Innovation in the Generalitat Valenciana of the European Union (2017/9830). It was also supported by grants from the Generalitat Valenciana (PROMETEO/2016/082, PROMETEO/2020/024), Carlos III Health Institute (PI13/00447; PI17/00402), and European Union through the European Regional Development Fund.Bonet, L.; Torous, J.; Arce Grilo, AD.; Blanquer Espert, I.; Sanjuan, J. (2020). ReMindCare App for Early Psychosis: Pragmatic Real World Intervention and Usability Study. JMIR mHealth and uHealth. 8(11):1-13. https://doi.org/10.2196/22997S113811Firth, J., Cotter, J., Torous, J., Bucci, S., Firth, J. A., & Yung, A. R. (2015). Mobile Phone Ownership and Endorsement of «mHealth» Among People With Psychosis: A Meta-analysis of Cross-sectional Studies. Schizophrenia Bulletin, 42(2), 448-455. doi:10.1093/schbul/sbv132Bonet, L., Izquierdo, C., Escartí, M. J., Sancho, J. V., Arce, D., Blanquer, I., & Sanjuan, J. (2017). Utilización de tecnologías móviles en pacientes con psicosis: una revisión sistemática. Revista de Psiquiatría y Salud Mental, 10(3), 168-178. doi:10.1016/j.rpsm.2017.01.003Kannarkat, J. T., Smith, N. N., & McLeod-Bryant, S. A. (2020). Mobilization of Telepsychiatry in Response to COVID-19—Moving Toward 21st Century Access to Care. Administration and Policy in Mental Health and Mental Health Services Research, 47(4), 489-491. doi:10.1007/s10488-020-01044-zTorous, J., & Keshavan, M. (2020). COVID-19, mobile health and serious mental illness. Schizophrenia Research, 218, 36-37. doi:10.1016/j.schres.2020.04.013Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., McIntyre, R. S., … Ho, C. (2020). A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain, Behavior, and Immunity, 87, 40-48. doi:10.1016/j.bbi.2020.04.028Trefflich, F., Kalckreuth, S., Mergl, R., & Rummel-Kluge, C. (2015). Psychiatric patients׳ internet use corresponds to the internet use of the general public. Psychiatry Research, 226(1), 136-141. doi:10.1016/j.psychres.2014.12.037Gay, K., Torous, J., Joseph, A., Pandya, A., & Duckworth, K. (2016). Digital Technology Use Among Individuals with Schizophrenia: Results of an Online Survey. JMIR Mental Health, 3(2), e15. doi:10.2196/mental.5379Bonet, L., Llácer, B., Hernandez-Viadel, M., Arce, D., Blanquer, I., Cañete, C., … Sanjuán, J. (2018). Differences in the Use and Opinions About New eHealth Technologies Among Patients With Psychosis: Structured Questionnaire. JMIR Mental Health, 5(3), e51. doi:10.2196/mental.9950Hau, Y. S., Kim, J. K., Hur, J., & Chang, M. C. (2020). How about actively using telemedicine during the COVID-19 pandemic? Journal of Medical Systems, 44(6). doi:10.1007/s10916-020-01580-zBucci, S., Berry, N., Morris, R., Berry, K., Haddock, G., Lewis, S., & Edge, D. (2019). «They Are Not Hard-to-Reach Clients. We Have Just Got Hard-to-Reach Services.» Staff Views of Digital Health Tools in Specialist Mental Health Services. Frontiers in Psychiatry, 10. doi:10.3389/fpsyt.2019.00344Arango, C., Bernardo, M., Bonet, P., Cabrera, A., Crespo-Facorro, B., Cuesta, M. J., … Melau, M. (2017). Cuando la asistencia no sigue a la evidencia: el caso de la falta de programas de intervención temprana en psicosis en España. Revista de Psiquiatría y Salud Mental, 10(2), 78-86. doi:10.1016/j.rpsm.2017.01.001Camacho, E., Levin, L., & Torous, J. (2019). Smartphone Apps to Support Coordinated Specialty Care for Prodromal and Early Course Schizophrenia Disorders: Systematic Review. Journal of Medical Internet Research, 21(11), e16393. doi:10.2196/16393Correll, C. U., Galling, B., Pawar, A., Krivko, A., Bonetto, C., Ruggeri, M., … Kane, J. M. (2018). Comparison of Early Intervention Services vs Treatment as Usual for Early-Phase Psychosis. JAMA Psychiatry, 75(6), 555. doi:10.1001/jamapsychiatry.2018.0623Bucci, S., Barrowclough, C., Ainsworth, J., Machin, M., Morris, R., Berry, K., … Haddock, G. (2018). Actissist: Proof-of-Concept Trial of a Theory-Driven Digital Intervention for Psychosis. Schizophrenia Bulletin, 44(5), 1070-1080. doi:10.1093/schbul/sby032Eisner, E., Drake, R. J., Berry, N., Barrowclough, C., Emsley, R., Machin, M., & Bucci, S. (2019). Development and Long-Term Acceptability of ExPRESS, a Mobile Phone App to Monitor Basic Symptoms and Early Signs of Psychosis Relapse. JMIR mHealth and uHealth, 7(3), e11568. doi:10.2196/11568Ben-Zeev, D., Brian, R., Wang, R., Wang, W., Campbell, A. T., Aung, M. S. H., … Scherer, E. A. (2017). CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse. Psychiatric Rehabilitation Journal, 40(3), 266-275. doi:10.1037/prj0000243Torous, J., Woodyatt, J., Keshavan, M., & Tully, L. M. (2019). A new hope for early psychosis care: the evolving landscape of digital care tools. The British Journal of Psychiatry, 214(5), 269-272. doi:10.1192/bjp.2019.8Torous, J., Lipschitz, J., Ng, M., & Firth, J. (2020). Dropout rates in clinical trials of smartphone apps for depressive symptoms: A systematic review and meta-analysis. Journal of Affective Disorders, 263, 413-419. doi:10.1016/j.jad.2019.11.167Killikelly, C., He, Z., Reeder, C., & Wykes, T. (2017). Improving Adherence to Web-Based and Mobile Technologies for People With Psychosis: Systematic Review of New Potential Predictors of Adherence. JMIR mHealth and uHealth, 5(7), e94. doi:10.2196/mhealth.7088Krzystanek, M., Krysta, K., & Skałacka, K. (2017). Treatment Compliance in the Long-Term Paranoid Schizophrenia Telemedicine Study. Journal of Technology in Behavioral Science, 2(2), 84-87. doi:10.1007/s41347-017-0016-4Arnold, C., Villagonzalo, K.-A., Meyer, D., Farhall, J., Foley, F., Kyrios, M., & Thomas, N. (2019). Predicting engagement with an online psychosocial intervention for psychosis: Exploring individual- and intervention-level predictors. Internet Interventions, 18, 100266. doi:10.1016/j.invent.2019.100266Ross, J., Stevenson, F., Lau, R., & Murray, E. (2016). Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update). Implementation Science, 11(1). doi:10.1186/s13012-016-0510-7Allan, S., Bradstreet, S., Mcleod, H., Farhall, J., Lambrou, M., … Gleeson, J. (2019). Developing a Hypothetical Implementation Framework of Expectations for Monitoring Early Signs of Psychosis Relapse Using a Mobile App: Qualitative Study. Journal of Medical Internet Research, 21(10), e14366. doi:10.2196/14366Palmier-Claus, J. E., Rogers, A., Ainsworth, J., Machin, M., Barrowclough, C., Laverty, L., … Lewis, S. W. (2013). Integrating mobile-phone based assessment for psychosis into people’s everyday lives and clinical care: a qualitative study. BMC Psychiatry, 13(1). doi:10.1186/1471-244x-13-34Kannisto, K. A., Adams, C. E., Koivunen, M., Katajisto, J., & Va lima ki, M. (2015). Feedback on SMS reminders to encourage adherence among patients taking antipsychotic medication: a cross-sectional survey nested within a randomised trial. BMJ Open, 5(11), e008574-e008574. doi:10.1136/bmjopen-2015-008574Torous, J. B. (2018). Focusing on the Future of Mobile Mental Health and Smartphone Interventions. Psychiatric Services, 69(9), 945-945. doi:10.1176/appi.ps.201800308Bonet, L., Torous, J., Arce, D., Blanquer, I., & Sanjuán, J. (2020). ReMindCare , an app for daily clinical practice in patients with first episode psychosis: A pragmatic real‐world study protocol. Early Intervention in Psychiatry, 15(1), 183-192. doi:10.1111/eip.12960Endicott, J. (1976). The Global Assessment Scale. Archives of General Psychiatry, 33(6), 766. doi:10.1001/archpsyc.1976.01770060086012Cannon-Spoor, H. E., Potkin, S. G., & Wyatt, R. J. (1982). Measurement of Premorbid Adjustment in Chronic Schizophrenia. Schizophrenia Bulletin, 8(3), 470-484. doi:10.1093/schbul/8.3.470Greer, B., Robotham, D., Simblett, S., Curtis, H., Griffiths, H., & Wykes, T. (2019). Digital Exclusion Among Mental Health Service Users: Qualitative Investigation. 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ReMindCare, an app for daily clinical practice in patients with first episode psychosis: A pragmatic real-world study protocol

[EN] Aim Despite the potential benefits of e-health interventions for patients with psychosis, the integration of these applications into the clinical workflow and analysis of their long-term effects still face significant challenges. To address these issues, we developed the ReMindCare app. This app aims to improve the treatment quality for patients with psychosis. We chose to study the app in real world and pragmatic manner to ensure results will be generalizable. Methods This is a naturalistic empirical study of patients in a first episode of psychosis programme. The app was purpose-designed based on two previous studies, and it offers the following assessments: (a) three daily questions regarding anxiety, sadness and irritability; and (b) 18 weekly questions about medication adherence, medication side effects, medication attitudes and prodromal symptoms. The app offers preset alerts, reminders and the ability for patients to reach out to their clinicians. Data captured by the app are linked to the electronic medical record of the patient. Patients will use the app as part of their ongoing care for a maximum period of 5 years, and assessments will occur at baseline and at the end of the first, second and fifth years of app use. Results Recruitment started in October 2018 and is still ongoing. Conclusions The ReMindCare app represents early real-world use of digital mental health tools that offer direct integration into clinical care. High retention and compliance rates are expected, and this will in turn lead to improved quality of assessments and communication between patients and clinicians.Centro de Investigacion Biomedica en Red de Salud Mental; European Social Fund, Grant/Award Number: 2017/9830; Generalitat Valenciana, Grant/Award Number: PROMETEO/2016/082; Instituto de Salud Carlos III, Grant/Award Numbers: PI01399, PI13/00447, PI17/00402Bonet, L.; Torous, J.; Arce Grilo, AD.; Blanquer Espert, I.; Sanjuán, J. (2021). ReMindCare, an app for daily clinical practice in patients with first episode psychosis: A pragmatic real-world study protocol. Early Intervention in Psychiatry. 15(1):183-192. https://doi.org/10.1111/eip.12960S183192151Abbott, P. A., Foster, J., Marin, H. de F., & Dykes, P. C. (2014). Complexity and the science of implementation in health IT—Knowledge gaps and future visions. International Journal of Medical Informatics, 83(7), e12-e22. doi:10.1016/j.ijmedinf.2013.10.009American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders. doi:10.1176/appi.books.9780890425596Appelbaum, S. H., & Wohl, L. (2000). Transformation or change: some prescriptions for health care organizations. Managing Service Quality: An International Journal, 10(5), 279-298. doi:10.1108/09604520010345768Arango, C., Bernardo, M., Bonet, P., Cabrera, A., Crespo-Facorro, B., Cuesta, M. J., … Melau, M. (2017). Cuando la asistencia no sigue a la evidencia: el caso de la falta de programas de intervención temprana en psicosis en España. Revista de Psiquiatría y Salud Mental, 10(2), 78-86. doi:10.1016/j.rpsm.2017.01.001Arango C. Crespo‐Facorro B. Cuesta M. J. González‐Pinto A. Gutiérrez J. R. Lalucat L. &Sanjuán J. Libro blanco de la atención temprana en psicosis.2018. Available at:www.sepsiq.org/file/Enlaces/Libro%20blanco%20de%20la%20Intervenci%C3%B3n%20Temprana%20en%20Espa%C3%B1a%20(2018).pdfAref-Adib, G., O’Hanlon, P., Fullarton, K., Morant, N., Sommerlad, A., Johnson, S., & Osborn, D. (2016). A qualitative study of online mental health information seeking behaviour by those with psychosis. BMC Psychiatry, 16(1). doi:10.1186/s12888-016-0952-0Barnett, I., Torous, J., Staples, P., Sandoval, L., Keshavan, M., & Onnela, J.-P. (2018). Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology, 43(8), 1660-1666. doi:10.1038/s41386-018-0030-zBaumel, A., Faber, K., Mathur, N., Kane, J. M., & Muench, F. (2017). Enlight: A Comprehensive Quality and Therapeutic Potential Evaluation Tool for Mobile and Web-Based eHealth Interventions. Journal of Medical Internet Research, 19(3), e82. doi:10.2196/jmir.7270Beck, A. (2004). A new instrument for measuring insight: the Beck Cognitive Insight Scale. Schizophrenia Research, 68(2-3), 319-329. doi:10.1016/s0920-9964(03)00189-0Ben-Zeev, D., Brenner, C. J., Begale, M., Duffecy, J., Mohr, D. C., & Mueser, K. T. (2014). Feasibility, Acceptability, and Preliminary Efficacy of a Smartphone Intervention for Schizophrenia. Schizophrenia Bulletin, 40(6), 1244-1253. doi:10.1093/schbul/sbu033Ben-Zeev, D., Kaiser, S. M., & Krzos, I. (2014). Remote «Hovering» With Individuals With Psychotic Disorders and Substance Use: Feasibility, Engagement, and Therapeutic Alliance With a Text-Messaging Mobile Interventionist. Journal of Dual Diagnosis, 10(4), 197-203. doi:10.1080/15504263.2014.962336Berry, N., Lobban, F., Emsley, R., & Bucci, S. (2016). Acceptability of Interventions Delivered Online and Through Mobile Phones for People Who Experience Severe Mental Health Problems: A Systematic Review. Journal of Medical Internet Research, 18(5), e121. doi:10.2196/jmir.5250Bonet, L., Izquierdo, C., Escartí, M. J., Sancho, J. V., Arce, D., Blanquer, I., & Sanjuan, J. (2017). Utilización de tecnologías móviles en pacientes con psicosis: una revisión sistemática. Revista de Psiquiatría y Salud Mental, 10(3), 168-178. doi:10.1016/j.rpsm.2017.01.003Bonet, L., Llácer, B., Hernandez-Viadel, M., Arce, D., Blanquer, I., Cañete, C., … Sanjuán, J. (2018). Differences in the Use and Opinions About New eHealth Technologies Among Patients With Psychosis: Structured Questionnaire. JMIR Mental Health, 5(3), e51. doi:10.2196/mental.9950Borzekowski, D. L. G., Leith, J., Medoff, D. R., Potts, W., Dixon, L. B., Balis, T., … Himelhoch, S. (2009). Use of the Internet and Other Media for Health Information Among Clinic Outpatients With Serious Mental Illness. Psychiatric Services, 60(9), 1265-1268. doi:10.1176/ps.2009.60.9.1265Brenner, C. J., & Ben-Zeev, D. (2014). Affective forecasting in schizophrenia: Comparing predictions to real-time Ecological Momentary Assessment (EMA) ratings. Psychiatric Rehabilitation Journal, 37(4), 316-320. doi:10.1037/prj0000105Bucci, S., Barrowclough, C., Ainsworth, J., Machin, M., Morris, R., Berry, K., … Haddock, G. (2018). Actissist: Proof-of-Concept Trial of a Theory-Driven Digital Intervention for Psychosis. Schizophrenia Bulletin, 44(5), 1070-1080. doi:10.1093/schbul/sby032Camacho, E., Levin, L., & Torous, J. (2019). Smartphone Apps to Support Coordinated Specialty Care for Prodromal and Early Course Schizophrenia Disorders: Systematic Review. Journal of Medical Internet Research, 21(11), e16393. doi:10.2196/16393Cannon-Spoor, H. E., Potkin, S. G., & Wyatt, R. J. (1982). Measurement of Premorbid Adjustment in Chronic Schizophrenia. Schizophrenia Bulletin, 8(3), 470-484. doi:10.1093/schbul/8.3.470Cresswell, K., & Sheikh, A. (2013). Organizational issues in the implementation and adoption of health information technology innovations: An interpretative review. International Journal of Medical Informatics, 82(5), e73-e86. doi:10.1016/j.ijmedinf.2012.10.007DiFilippo, K. N., Huang, W., & Chapman-Novakofski, K. M. (2017). A New Tool for Nutrition App Quality Evaluation (AQEL): Development, Validation, and Reliability Testing. JMIR mHealth and uHealth, 5(10), e163. doi:10.2196/mhealth.7441Dixon, L. (2017). What It Will Take to Make Coordinated Specialty Care Available to Anyone Experiencing Early Schizophrenia. JAMA Psychiatry, 74(1), 7. doi:10.1001/jamapsychiatry.2016.2665Endicott, J. (1976). The Global Assessment Scale. Archives of General Psychiatry, 33(6), 766. doi:10.1001/archpsyc.1976.01770060086012Firth, J., Cotter, J., Torous, J., Bucci, S., Firth, J. A., & Yung, A. R. (2015). Mobile Phone Ownership and Endorsement of «mHealth» Among People With Psychosis: A Meta-analysis of Cross-sectional Studies. Schizophrenia Bulletin, 42(2), 448-455. doi:10.1093/schbul/sbv132García, S., Martínez-Cengotitabengoa, M., López-Zurbano, S., Zorrilla, I., López, P., Vieta, E., & González-Pinto, A. (2016). Adherence to Antipsychotic Medication in Bipolar Disorder and Schizophrenic Patients. Journal of Clinical Psychopharmacology, 36(4), 355-371. doi:10.1097/jcp.0000000000000523Gay, K., Torous, J., Joseph, A., Pandya, A., & Duckworth, K. (2016). Digital Technology Use Among Individuals with Schizophrenia: Results of an Online Survey. JMIR Mental Health, 3(2), e15. doi:10.2196/mental.5379Gitlow, L., Abdelaal, F., Etienne, A., Hensley, J., Krukowski, E., & Toner, M. (2017). 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