Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study

Antecedentes: el trastorno depresivo mayor (TDM) y el trastorno de ansiedad generalizada (TAG) son muy prevalentes entre los estudiantes universitarios y predicen un deterioro del rendimiento universitario y del funcionamiento posterior de la vida. Sin embargo, la mayoría de los estudiantes no recib...

Full description

Autores:
UCC
Benjet, Corina
Carrasco , Nayib
Kessler, Ronald
Alan , Kazdin
Pim , Cuijpers
Yesica , Albor
Carlos , Contreras-Ibáñez
Ma Socorro , Durán González
Sarah , Gildea
Noé , González
José Benjamín , Guerrero López
Alex , Luedtke
Maria Elena, Medina-Mora5
Jorge , Palacios
Derek , Richards
Alicia , Salamanca-Sanabria
Nancy , Sampson
Tipo de recurso:
Article of investigation
Fecha de publicación:
2022
Institución:
Universidad Cooperativa de Colombia
Repositorio:
Repositorio UCC
Idioma:
OAI Identifier:
oai:repository.ucc.edu.co:20.500.12494/52855
Acceso en línea:
https://doi.org/10.1186/s13063-022-06255-3
https://hdl.handle.net/20.500.12494/52855
Palabra clave:
Depresión
Ansiedad
Terapia Cognitivo Conductual
Estudiantes Universitarios
Depression
Anxiety
Cognitive Behavioral Therapy
University Students
Rights
openAccess
License
http://purl.org/coar/access_right/c_abf2
id COOPER2_f483e05fdfcf01e436a8eb2cfabf568e
oai_identifier_str oai:repository.ucc.edu.co:20.500.12494/52855
network_acronym_str COOPER2
network_name_str Repositorio UCC
repository_id_str
dc.title.none.fl_str_mv Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study
title Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study
spellingShingle Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study
Depresión
Ansiedad
Terapia Cognitivo Conductual
Estudiantes Universitarios
Depression
Anxiety
Cognitive Behavioral Therapy
University Students
title_short Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study
title_full Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study
title_fullStr Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study
title_full_unstemmed Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study
title_sort Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study
dc.creator.fl_str_mv UCC
Benjet, Corina
Carrasco , Nayib
Kessler, Ronald
Alan , Kazdin
Pim , Cuijpers
Yesica , Albor
Carlos , Contreras-Ibáñez
Ma Socorro , Durán González
Sarah , Gildea
Noé , González
José Benjamín , Guerrero López
Alex , Luedtke
Maria Elena, Medina-Mora5
Jorge , Palacios
Derek , Richards
Alicia , Salamanca-Sanabria
Nancy , Sampson
dc.contributor.author.none.fl_str_mv UCC
Benjet, Corina
Carrasco , Nayib
Kessler, Ronald
Alan , Kazdin
Pim , Cuijpers
Yesica , Albor
Carlos , Contreras-Ibáñez
Ma Socorro , Durán González
Sarah , Gildea
Noé , González
José Benjamín , Guerrero López
Alex , Luedtke
Maria Elena, Medina-Mora5
Jorge , Palacios
Derek , Richards
Alicia , Salamanca-Sanabria
Nancy , Sampson
dc.subject.none.fl_str_mv Depresión
Ansiedad
Terapia Cognitivo Conductual
Estudiantes Universitarios
topic Depresión
Ansiedad
Terapia Cognitivo Conductual
Estudiantes Universitarios
Depression
Anxiety
Cognitive Behavioral Therapy
University Students
dc.subject.other.none.fl_str_mv Depression
Anxiety
Cognitive Behavioral Therapy
University Students
description Antecedentes: el trastorno depresivo mayor (TDM) y el trastorno de ansiedad generalizada (TAG) son muy prevalentes entre los estudiantes universitarios y predicen un deterioro del rendimiento universitario y del funcionamiento posterior de la vida. Sin embargo, la mayoría de los estudiantes no reciben tratamiento, especialmente en los países de ingresos medianos bajos (PIBM). Nuestro objetivo es evaluar los efectos de ampliar el tratamiento utilizando terapia cognitivo-conductual transdiagnóstico (iCBT) escalable y económica a través de Internet entre estudiantes universitarios con síntomas de TDM y/o TAG en dos países de ingresos bajos y medios de América Latina (Colombia y México) e investigar la viabilidad. de crear una regla de tratamiento de precisión (PTR) para predecir para quién iCBT es más efectiva. Métodos: Primero llevaremos a cabo un ensayo clínico pragmático aleatorio en múltiples sitios (N = 1500) de estudiantes que buscan tratamiento en clínicas de salud mental para estudiantes en universidades participantes o respondiendo a un correo electrónico ofreciendo servicios. Los estudiantes en listas de espera para servicios clínicos serán asignados al azar a iCBT no guiada (33%), iCBT guiada (33%) y tratamiento habitual (TAU) (33%). iCBT se proporcionará inmediatamente, mientras que TAU se proporcionará siempre que haya una cita clínica disponible. Los efectos agregados a corto plazo se evaluarán a los 90 días y los efectos a más largo plazo a los 12 meses después de la aleatorización. Usaremos el aprendizaje automático conjunto para predecir la heterogeneidad de los efectos del tratamiento de iCBT guiada versus no guiada versus TAU y desarrollaremos una regla de tratamiento de precisión (PTR) para optimizar el resultado individual del estudiante. Luego realizaremos un segundo y un tercer ensayo con muestras separadas (n = 500 por brazo), pero con una asignación desigual entre dos brazos: el 25 % se asignará al tratamiento que se determine que produzca resultados óptimos según el PTR desarrollado en el primer ensayo. (PTR para resultados óptimos a corto plazo para el ensayo 2 y resultados a 12 meses para el ensayo 3), mientras que al 75 % restante se le asignará la misma asignación en los tres brazos de tratamiento. Discusión: Al recopilar características de referencia integrales para evaluar la heterogeneidad de los efectos del tratamiento, proporcionaremos información valiosa e innovadora para optimizar los efectos del tratamiento y guiar la planificación universitaria del tratamiento de salud mental. Un esfuerzo de este tipo podría tener enormes implicaciones para la salud pública de la región al aumentar el alcance del tratamiento, disminuir las necesidades insatisfechas y los tiempos de espera en las clínicas, y servir como modelo de planificación e implementación de intervenciones basadas en evidencia.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022-06-02
dc.date.accessioned.none.fl_str_mv 2023-10-07T19:22:31Z
dc.date.available.none.fl_str_mv 2023-10-07T19:22:31Z
dc.type.none.fl_str_mv Artículos Científicos
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.none.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
format http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.issn.none.fl_str_mv 1745-6215
dc.identifier.uri.none.fl_str_mv https://doi.org/10.1186/s13063-022-06255-3
https://hdl.handle.net/20.500.12494/52855
dc.identifier.bibliographicCitation.none.fl_str_mv Benjet, C., et. al. (2022). Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study. Trials, 23 (450), 2-19.
identifier_str_mv 1745-6215
Benjet, C., et. al. (2022). Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study. Trials, 23 (450), 2-19.
url https://doi.org/10.1186/s13063-022-06255-3
https://hdl.handle.net/20.500.12494/52855
dc.relation.isversionof.none.fl_str_mv https://pubmed.ncbi.nlm.nih.gov/35658942/
dc.relation.ispartofjournal.none.fl_str_mv TRIALS
dc.relation.references.none.fl_str_mv Auerbach RP, Alonso J, Axinn WG, Cuijpers P, Ebert DD, Green JG, et al. Mental disorders among college students in the World Health Organization World Mental Health Surveys. Psychol Med. 2016;46(14):2955–70. https://doi. org/10.1017/S0033291716001665.
Cuijpers P, Auerbach RP, Benjet C, Bruffaerts R, Ebert D, Karyotaki E, et al. Introduction to the special issue: The WHO World Mental Health International College Student (WMH-ICS) initiative. Int J Methods Psychiatr Res. 2019;28(2):e1762. https://doi.org/10.1002/mpr.1762.
Auerbach RP, Mortier P, Bruffaerts R, Alonso J, Benjet C, Cuijpers P, et al. Mental disorder comorbidity and suicidal thoughts and behaviors in the World Health Organization World Mental Health Surveys International College Student initiative. Int J Methods Psychiatr Res. 2019;28(2):e1752. https://doi.org/10.1002/mpr.1752.
Alonso J, Vilagut G, Mortier P, Auerbach RP, Bruffaerts R, Cuijpers P, et al. The role impairment associated with mental disorder risk profiles in the WHO World Mental Health International College Student Initiative. Int J Methods Psychiatr Res. 2019;28(2):e1750. https://doi.org/10.1002/mpr.1750.
Bruffaerts R, Mortier P, Auerbach RP, Alonso J, Hermosillo De la Torre AE, Cuijpers P, et al. Lifetime and 12-month treatment for mental disorders and suicidal thoughts and behaviors among first year college students. Int J Methods Psychiatr Res. 2019;28(2):e1764. https://doi.org/10.1002/mpr.1764.
Alonso J, Liu Z, Evans-Lacko S, Sadikova E, Sampson N, Chatterji S, et al. Treatment gap for anxiety disorders is global: Results of the World Mental Health Surveys in 21 countries. Depress Anxiety. 2018;35(3):195–208. https:// doi.org/10.1002/da.22711.
Degenhardt L, Glantz M, Evans-Lacko S, Sadikova E, Sampson N, Thornicroft G, et al. Estimating treatment coverage for people with substance use disorders: an analysis of data from the World Mental Health Surveys. World Psychiatry. 2017;16(3):299–307. https://doi.org/10.1002/wps.20457.
ThornicroftG,ChatterjiS,Evans-LackoS,GruberM,SampsonN,Aguilar-GaxiolaS, etal.Undertreatmentofpeoplewithmajor depressivedisorderin21countries.BrJ Psychiatry.2017;210(2):119–24. https://doi.org/10.1192/bjp.bp.116.188078.
Evans-Lacko S, Thornicroft G. Viewpoint: WHO World Mental Health Surveys International College Student initiative: Implementation issues in low- and middle-income countries. Int J Methods Psychiatr Res. 2019;28(2):e17566. https://doi.org/10.1002/mpr.1756.
Mullan F, Frehywot S, Omaswa F, Buch E, Chen C, Greysen SR, et al. Medical schools in sub-Saharan Africa. Lancet. 2011;377(9771):1113–21. https://doi. org/10.1016/s0140-6736(10)61961-7.
Schendel R, McCowan T. Expanding higher education systems in low- and middle-income countries: the challenges of equity and quality. High Educ. 2016;72(4):407–11. https://doi.org/10.1007/s10734-016-0028-6.
ShamsuddinK,FadzilF,IsmailWSW,ShahSA,OmarK,MuhammadNA,etal. Correlatesofdepression,anxietyandstressamongMalaysianuniversitystudents. AsianJPsychiatr.2013;6(4):318–23. https://doi.org/10.1016/j.ajp.2013.01.014.
Ibrahim AK, Kelly SJ, Adams CE, Glazebrook C. A systematic review of studies of depression prevalence in university students. J Psychiatr Res. 2013;47(3):391–400. https://doi.org/10.1016/j.jpsychires.2012.11.015.
Brosnan C, Southgate E, Outram S, Lempp H, Wright S, Saxby T, et al. Experiences of medical students who are first in family to attend university. Med Educ. 2016;50(8):842–51. https://doi.org/10.1111/medu.12995.
Southgate E, Brosnan C, Lempp H, Kelly B, Wright S, Outram S, et al. Travels in extreme social mobility: how first-in-family students find their way into and through medical education. Crit Stud Educ. 2017;58(2):242–60. https:// doi.org/10.1080/17508487.2016.1263223.
Stebleton MJ, Soria KM, Huesman RL Jr. First-generation students’ sense of belonging, mental health, and use of counseling services at public research universities. J Coll Couns. 2014;17(1):6–20. https://doi.org/10.1002/j.2161-1 882.2014.00044.x.
Covarrubias R, Romero A, Trivelli M. Family achievement guilt and mental well-being of college students. J Child Fam Stud. 2015;24(7):2031–7. https:// doi.org/10.1007/s10826-014-0003-8.
Hakim JG, Chidzonga MM, Borok MZ, Nathoo KJ, Matenga J, Havranek E, et al. Medical education partnership initiative (MEPI) in Zimbabwe: outcomes and challenges. Glob Health Sci Pract. 2018;6(1):82–92. https://doi. org/10.9745/ghsp-d-17-00052.
Palacios JE, Richards D, Palmer R, Coudray C, Hofmann SG, Palmieri PA, et al. Supported internet-delivered cognitive behavioral therapy programs for depression, anxiety, and stress in university students: Open, non-randomised trial of acceptability, effectiveness, and satisfaction. JMIR Ment Health. 2018; 5(4):e11467. https://doi.org/10.2196/11467.
ArjadiR,NautaMH,ChowdharyN,BocktingCLH.Asystematicreviewofonline interventionsformentalhealthinlowandmiddleincomecountries:aneglected field.GlobMentHealth.2015;2:e12. https://doi.org/10.1017/gmh.2015.10.
Fu Z, Burger H, Arjadi R, Bockting CL. Effectiveness of digital psychological interventions for mental health problems in low-income and middleincome countries: a systematic review and meta-analysis. Lancet Psychiatry. 2020;7(10):851–64. https://doi.org/10.1016/S2215-0366(20)30256-X.
Jiménez-Molina Á, Franco P, Martínez V, Martínez P, Rojas G, Araya R. Internet-based interventions for the prevention and treatment of mental disorders in Latin America: a scoping review. Front Psychiatry. 2019;10:664. https://doi.org/10.3389/fpsyt.2019.00664.
Harrer M, Adam SH, Baumeister H, Cuijpers P, Karyotaki E, Auerbach RP, et al. Internet interventions for mental health in university students: a systematic review and meta-analysis. Int J Methods Psychiatr Res. 2019;28(2): e1759. https://doi.org/10.1002/mpr.1759.
Harrer M, Adam SH, Fleischmann RJ, Baumeister H, Auerbach R, Bruffaerts R, et al. Effectiveness of an internet- and app-based intervention for college students with elevated stress: randomized controlled trial. J Med Internet Res. 2018;20(4):e136. https://doi.org/10.2196/jmir.9293.
Salamanca-Sanabria A, Richards D, Timulak L, Connell S, Mojica-Perilla M, Parra-Villa Y, et al. A culturally adapted cognitive behavioral internetdelivered intervention for depressive symptoms: randomized controlled trial. JMIR Ment Health. 2020;6(12):1–20. https://doi.org/10.2196/13392.
Norton PJ, Roberge P. Transdiagnostic therapy. Psychiatr Clin North Am. 2017;40(4):675–87. https://doi.org/10.1016/j.psc.2017.08.003.
Richards D, Enrique A, Eilert N, Franklin M, Palacios J, Duffy D, et al. A pragmatic randomized waitlist-controlled effectiveness and costeffectiveness trial of digital interventions for depression and anxiety. Digital Med. 2020;3:85. https://doi.org/10.1038/s41746-020-0293-8.
NIH National Institute of Mental Health. Strategic Objective 3. 2008. https:// www.nimh.nih.gov/about/strategic-planning-reports/strategic-objective-3. shtml. Accessed 16 Sept. 2019.
Maj M, Stein DJ, Parker G, Zimmerman M, Fava GA, De Hert M, et al. The clinical characterization of the adult patient with depression aimed at personalization of management. World Psychiatry. 2020;19(3):269–93. https://doi.org/10.1002/wps.20771.
Pescosolido BA. Stigma as a mental health policy controversy: positions, options, and strategies for change. In: Goldman H, Frank R, Morrissey J, editors. The Palgrave Handbook of American Mental Health Policy. Cham: Palgrave Macmillan; 2020. p. 543–72. https://doi.org/10.1007/978-3-030-11 908-9_19.
Luedtke AR, van der Laan MJ. Evaluating the impact of treating the optimal subgroup. Stat Methods Med Res. 2017;26(4):1630–40. https://doi.org/10.11 77/0962280217708664.
Kessler RC. The potential of predictive analytics to provide clinical decision support in depression treatment planning. Curr Opin Psychiatry. 2018;31(1): 32–9. https://doi.org/10.1097/yco.0000000000000377.
Cohen ZD, DeRubeis RJ. Treatment selection in depression. Annu Rev Clin Psychol. 2018;14(1):209–36. https://doi.org/10.1146/annurev-clinpsy-050817084746.
Karyotaki E,Efthimiou O,Miguel C, Bermpohl FMG,Furukawa TA, Cuijpers P, et al. Internet-basedcognitivebehavioral therapy for depression:a systematic review and individual patient data network meta-analysis.JAMAPsychiatry. 2021;78(4):361–71.https://doi.org/10.1001/jamapsychiatry.2020.4364.
Huguet A, Rao S, McGrath PJ, Wozney L, Wheaton M, Conrod J, et al. A systematic review of cognitive behavioral therapy and behavioral activation apps for depression. PLoS One. 2016;11(5):e0154248. https://doi.org/10.1371/ journal.pone.0154248.
Coull G, Morris PG. The clinical effectiveness of CBT-based guided self-help interventions for anxiety and depressive disorders: a systematic review. Psychol Med. 2011;41(11):2239–52. https://doi.org/10.1017/s0033291711 000900.
Cuijpers P, Kleiboer A, Karyotaki E, Riper H. Internet and mobile interventions for depression: opportunities and challenges: Cuijpers et al. Depress Anxiety. 2017;34(7):596–602. https://doi.org/10.1002/da.22641.
Newman MG, Szkodny LE, Llera SJ, Przeworski A. A review of technologyassisted self-help and minimal contact therapies for anxiety and depression: Is human contact necessary for therapeutic efficacy? Clin Psychol Rev. 2011; 31(1):89–103. https://doi.org/10.1016/j.cpr.2010.09.008.
Simmonds-Buckley M, Bennion MR, Kellett S, Millings A, Hardy GE, Moore RK. Acceptability and effectiveness of NHS-recommended e-therapies for depression, anxiety, and stress: meta-analysis. J Med Internet Res. 2020; 22(10):e17049. https://doi.org/10.2196/17049
Duffy D, Enrique A, Connell S, Connolly C, Richards D. Internet-delivered cognitive behavior therapy as a prequel to face-to-face therapy for depression and anxiety: a naturalistic observation. Front Psychiatry. 2020;10: 902. https://doi.org/10.3389/fpsyt.2019.00902.
Enrique A, Palacios JE, Ryan H, Richards D. Exploring the relationship between usage and outcomes of an internet-based intervention for individuals with depressive symptoms: secondary analysis of data from a randomized controlled trial. J Med Internet Res. 2019;21(8):e12775. https:// doi.org/10.2196/12775.
Salamanca-Sanabria A, Richards D, Timulak L. Adapting an internet-delivered intervention for depression for a Colombian college student population: an illustration of an integrative empirical approach. Internet Interv. 2019;15:76– 86. https://doi.org/10.1016/j.invent.2018.11.005.
Bo Borghouts J, Eikey E, Mark G, De Leon C, Schueller SM, Schneider M, et al. Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review. J Med Internet Res. 2021;23(3): e24387. https://doi.org/10.2196/24387.
Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. https://doi.org/10.1 046/j.1525-1497.2001.016009606.x.
Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med. 2006;166(10): 1092–7. https://doi.org/10.1001/archinte.166.10.1092.
Kroenke K, Wu J, Yu Z, Bair MJ, Kean J, Stump T, et al. Patient health questionnaire anxiety and depression scale: Initial validation in three clinical trials. Psychosom Med. 2016;78(6):716–27. https://doi.org/10.1097/psy. 0000000000000322.
Plummer F, Manea L, Trepel D, McMillan D. Screening for anxiety disorders with the GAD-7 and GAD-2: a systematic review and diagnostic metaanalysis. Gen Hosp Psychiatry. 2016;39:24–31. https://doi.org/10.1016/j. genhosppsych.2015.11.005.
McMillan D, Gilbody S, Richards D. Defining successful treatment outcome in depression using the PHQ-9: A comparison of methods. J Affect Disord. 2010;127(1–3):122–9. https://doi.org/10.1016/j.jad.2010.04.030.
Zimmerman M, Walsh E, Friedman M, Boerescu DA, Attiullah N. Identifying remission from depression on 3 self-report scales. J Clin Psychiatry. 2017; 78(02):177–83. https://doi.org/10.4088/JCP.16m10641
Sheehan DV, Mancini M, Wang J, Berggren L, Cao H, Dueñas HJ, et al. Assessment of functional outcomes by Sheehan Disability Scale in patients with major depressive disorder treated with duloxetine versus selective serotonin reuptake inhibitors. Hum Psychopharmacol. 2016;31(1):53–63. https://doi.org/10.1002/hup.2500.
Kessler RC, Calabrese JR, Farley PA, Gruber MJ, Jewell MA, Katon W, et al. Composite International Diagnostic Interview screening scales for DSM-IV anxiety and mood disorders. Psychol Med. 2013;43(8):1625–37. https://doi. org/10.1017/s0033291712002334.
Kessler RC, Santiago PN, Colpe LJ, Dempsey CL, First MB, Heeringa SG, et al. Clinical reappraisal of the composite international diagnostic interview screening scales (CIDI-SC) in the army study to assess risk and resilience in servicemembers (army STARRS): Clinical reappraisal of the CIDI-SC in army STARRS. Int J Methods Psychiatr Res. 2013;22(4):303–21. https://doi.org/10.1 002/mpr.1398.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 (5th ed.). Arlington: American Psychiatric Association; 2013.
Spitzer RL. Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. JAMA. 1999;282(18):1737–44. https://doi.org/10.1 001/jama.282.18.1737.
Zuromski KL, Ustun B, Hwang I, Keane TM, Marx BP, Stein MB, et al. Developing an optimal short-form of the PTSD Checklist for DSM-5 (PCL-5). Depress Anxiety. 2019;36(9):790–800. https://doi.org/10.1002/da.22942.
Morin CM, Belleville G, Bélanger L, Ivers H. The insomnia severity index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34(5):601–8. https://doi.org/10.1093/sleep/34.5.601.
Posner K, Brown GK, Stanley B, Brent DA, Yershova KV, Oquendo MA, et al. The Columbia–suicide severity rating scale: Initial validity and internal consistency findings from three multisite studies with adolescents and adults. Am J Psychiatry. 2011;168(12):1266–77. https://doi.org/10.1176/appi.a jp.2011.10111704.
Nock MK, Holmberg EB, Photos VI, Michel BD. Self-Injurious Thoughts and Behaviors Interview: development, reliability, and validity in an adolescent sample. Psychol Assess. 2007;19(3):309–17. https://doi.org/10.1037/1040-3 590.19.3.309.
Luedtke A, Sadikova E, Kessler RC. Sample size requirements for multivariate models to predict between-patient differences in best treatments of major depressive disorder. Clin Psychol Sci. 2019;7(3):445–61. https://doi.org/10.11 77/2167702618815466.
Kessler RC, van Loo HM, Wardenaar KJ, Bossarte RM, Brenner LA, Ebert DD, et al. Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiol Psychiatr Sci. 2017;26(1):22–36. https://doi.org/10.1017/S2045796016000020.
Driessen E, Hollon SD. Cognitive behavioral therapy for mood disorders: efficacy, moderators and mediators. Psychiatr Clin North Am. 2010;33(3): 537–55. https://doi.org/10.1016/j.psc.2010.04.005.
Schneider RL, Arch JJ, Wolitzky-Taylor KB. The state of personalized treatment for anxiety disorders: a systematic review of treatment moderators. Clin Psychol Rev. 2015;38:39–54. https://doi.org/10.1016/j.cpr.2 015.02.004.
Jakubovski E, Bloch MH. Anxiety disorder-specific predictors of treatment outcome in the Coordinated Anxiety Learning and Management (CALM) Trial. Psychiatr Q. 2016;87(3):445–64. https://doi.org/10.1007/s11126-015-93 99-6.
Webb CA, Rosso IM, Rauch SL. Internet-based cognitive-behavioral therapy for depression: current progress and future directions. Harv Rev Psychiatry. 2017;25(3):114–22. https://doi.org/10.1097/HRP.0000000000000139.
Lowe B, Spitzer RL, Grafe K, Kroenke K, Quenter A, Zipfel S, et al. Comparative validity of three screening questionnaires for DSM-IV depressive disorders and physicians’ diagnoses. J Affect Disord. 2004;78(2): 131–40. https://doi.org/10.1016/s0165-0327(02)00237-9.
Scott KM, Pd J, Stein DJ, Kessler RC, editors. Mental disorders around the world: facts and figures from the WHO World Mental Health Surveys. New York: Cambridge University Press; 2018. https://doi.org/10.1017/97813163361 68.
Kessler RC, Akiskal HS, Angst J, Guyer M, Hirschfeld RMA, Merikangas KR, et al. Validity of the assessment of bipolar spectrum disorders in the WHO CIDI 3.0. J Affect Disord. 2006;96(3):259–69. https://doi.org/10.1016/j.jad.2006. 08.018.
Kessler RC, Adler LA, Gruber MJ, Sarawate CA, Spencer T, Van Brunt DL. Validity of the World Health Organization Adult ADHD Self-Report Scale (ASRS) Screener in a representative sample of health plan members. Int J Methods Psychiatr Res. 2007;16(2):52–65. https://doi.org/10.1002/mpr.208
Kessler RC, Ustün TB. The World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). Int J Methods Psychiatr Res. 2004;13(2):93–121. doi: 0.1002/mpr.168
Norman CD, Skinner HA. EHEALS: the eHealth literacy scale. J Med Internet Res. 2006;8(4):e27. https://doi.org/10.2196/jmir.8.4.e27
Kessler RC, Hamilton L, Mickelson KD, Walters EE, Zhao S. Age and depression in the MIDUS survey. In: Brim OG, Ryff CD, Kessler RC, editors. How healthy are we? A national study of well-being at midlife. Chicago: University of Chicago Press; 2003. p. 227–51.
Campbell-Sills L,Kessler RC, UrsanoRJ, Sun X, TaylorCT, Heeringa SG, et al. Predictive validity and correlatesofself-assessed resilience among U.S. Army soldiers. DepressAnxiety. 2018;35(2):122–31.https://doi.org/10.1002/da.22694.
Campbell-Sills L, Stein MB. Psychometric analysis and refinement of the Connor-Davidson Resilience Scale (CD-RISC): validation of a 10-item measure of resilience. J Trauma Stress. 2007;20(6):1019–28. https://doi.org/1 0.1002/jts.20271.
Kelly PJ, Kyngdon F, Ingram I, Deane FP, Baker AL, Osborne BA. The Client Satisfaction Questionnaire-8: psychometric properties in a cross-sectional survey of people attending residential substance abuse treatment: Client satisfaction in residential treatment. Drug Alcohol Rev. 2018;37(1):79–86. https://doi.org/10.1111/dar.12522.
Gupta S. Intention-to-treat concept: a review. Perspect Clin Res. 2011;2(3): 109–12. https://doi.org/10.4103/2229-3485.83221.
Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–55. https://doi. org/10.1080/01621459.1996.10476902.
Wooldridge JM. Econometric analysis of cross section and panel data. Cambridge: MIT Press; 2002.
Clarke PS, Windmeijer F. Instrumental variable estimators for binary outcomes. J Am Stat Assoc. 2012;107(500):1638–52. https://doi.org/10.1080/ 01621459.2012.734171.
Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference: Instrumental variable methods for causal inference. Stat Med. 2014;33(13):2297–340. https://doi.org/10.1002/sim.6128
Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. J Am Stat Assoc. 1994;89(427): 846–66. https://doi.org/10.1080/01621459.1994.10476818.
Schnitzer ME, Lok JJ, Gruber S. Variable selection for confounder control, flexible modeling and Collaborative Targeted minimum loss-based estimation in causal inference. Int J Biostat. 2016;12(1):97–115. https://doi. org/10.1515/ijb-2015-0017.
Robins JM, Rotnitzky A. Recovery of information and adjustment for dependent censoring using surrogate markers. In: Jewell NP, Dietz K, Farewell VT, editors. AIDS Epidemiology: Methodological Issues. Boston: Birkhäuser Boston; 1992. p. 297–331. https://doi.org/10.1007/978-1-4757-122 9-2_14.
Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55. https:// doi.org/10.2307/2335942.
Faraway JJ. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. 2nd ed. Boca Raton, FL: CRC Press; 2016. https://doi.org/10.1201/9781315382722.
Bunouf P, Molenberghs G. Implementation of pattern-mixture models in randomized clinical trials. Pharm Stat. 2016;15(6):494–506. https://doi.org/1 0.1002/pst.1780.
Stroup WW. Generalized linear mixed models: modern concepts, methods and applications. Boca Raton: CRC Press; 2013.
Daniels MJ, Jackson D, Feng W, White IR. Pattern mixture models for the analysis of repeated attempt designs. Biometrics. 2015;71(4):1160–7. https:// doi.org/10.1111/biom.12353.
Kessler RC, Bossarte RM, Luedtke A, Zaslavsky AM, Zubizarreta JR. Machine learning methods for developing precision treatment rules with observational data. Behav Res Ther. 2019;120:103412. https://doi.org/10.101 6/j.brat.2019.103412.
VanderWeele TJ, Luedtke AR, van der Laan MJ, Kessler RC. Selecting optimal subgroups for treatment using many covariates. Epidemiology. 2019;30(3): 334–41. https://doi.org/10.1097/ede.0000000000000991.
Kessler RC, Bernecker SL, Bossarte RM, Luedtke AR, McCarthy JF, Nock MK, et al. The role of big data analytics in predicting suicide. In: Passos IC, Mwangi B, Kapczinski F, editors. Personalized Psychiatry: Big Data Analytics in Mental Health. Cham, Switzerland: Springer Nature Switzerland; 2019. p. 77–98. https://doi.org/10.1007/978-3-030-03553-2_5.
Kessler RC, Bossarte RM, Luedtke A, Zaslavsky AM, Zubizarreta JR. Suicide prediction models: a critical review of recent research with recommendations for the way forward. Mol Psychiatry. 2020;25(1):168–79. https://doi.org/10.1038/s41380-019-0531-0.
van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol. 2007;6(25). https://doi.org/10.2202/1544-6115.1309.
DeRubeis RJ, Cohen ZD, Forand NR, Fournier JC, Gelfand LA, Lorenzo-Luaces L. The personalized advantage index: translating research on prediction into individualized treatment recommendations. A demonstration. PLoS One. 2014;9(1):e83875. https://doi.org/10.1371/journal.pone.0083875.
Luedtke AR, van der LaanMJ. Super-learning ofanoptimal dynamictreatment rule. Int J Biostat.2016;12(1):305–32.https://doi.org/10.1515/ijb-2015-0052.
Luedtke A, Chambaz A. Faster rates for policy learning. 2017. Retrieved from: https://arxiv.org/abs/1704.06431.
Van Der Laan MJ, Rubin D. Targeted maximum likelihood learning. Int J Biostat. 2006;2(1): doi: 10.2202/1557-4679.1043.
Boutron I, Altman DG, Moher D, Schulz KF, Ravaud P, for the CONSORT NPT Group. CONSORT statement for randomized trials of nonpharmacologic treatments: A 2017 update and a CONSORT extension for nonpharmacologic trial abstracts. Ann Intern Med. 2017;167(1):40. https:// doi.org/10.7326/M17-0046.
NIH National Institute of Mental Health. Policy Governing Independent Safety Monitors and Independent Data and safety monitoring boards. 2015. https://www.nimh.nih.gov/funding/clinical-research/policy-governingindependent-safety-monitors-and-independent-data-and-safety-monitoringboards.shtml. Accessed 16 Sept 2019.
Kazdin AE. Technology-based interventions and reducing the burdens of mental illness: perspectives and comments onthe specialseries. Cogn Behav Pract. 2015;22(3):359e66–366. https://doi.org/10.1016/j.cbpra.2015.04.004.
Gulliver A, Calear AL, Sunderland M, Kay-Lambkin F, Farrer LM, Batterham PJ. Predictors of acceptability and engagement in a self-guided online program for depression and anxiety. Internet Interv. 2021;100400:100400. https://doi. org/10.1016/j.invent.2021.100400.
Simon N,McGillivray L, Roberts NP, BarawiK,Lewis CE, BissonJI.Acceptability ofinternet-basedcognitive behaviouraltherapy(i-CBT) for post-traumatic stressdisorder (PTSD): a systematic review.Eur J Psychotraumatol. 2019;10(1): 1646092.https://doi.org/10.1080/20008198.2019.1646092.
dc.rights.accessrights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
rights_invalid_str_mv http://purl.org/coar/access_right/c_abf2
dc.format.extent.none.fl_str_mv 1-19
dc.coverage.temporal.none.fl_str_mv 23
dc.publisher.none.fl_str_mv Peter Jüni
Universidad Cooperativa de Colombia
dc.publisher.program.none.fl_str_mv Psicología
dc.publisher.place.none.fl_str_mv Medellín
publisher.none.fl_str_mv Peter Jüni
Universidad Cooperativa de Colombia
institution Universidad Cooperativa de Colombia
bitstream.url.fl_str_mv https://repository.ucc.edu.co/bitstreams/5a5e0206-999f-4b67-b9dc-80881aa67e15/download
bitstream.checksum.fl_str_mv 3bce4f7ab09dfc588f126e1e36e98a45
bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Cooperativa de Colombia
repository.mail.fl_str_mv bdigital@metabiblioteca.com
_version_ 1811565378189393920
spelling UCCBenjet, CorinaCarrasco , NayibKessler, RonaldAlan , KazdinPim , CuijpersYesica , AlborCarlos , Contreras-IbáñezMa Socorro , Durán GonzálezSarah , GildeaNoé , GonzálezJosé Benjamín , Guerrero LópezAlex , LuedtkeMaria Elena, Medina-Mora5Jorge , PalaciosDerek , RichardsAlicia , Salamanca-SanabriaNancy , Sampson232023-10-07T19:22:31Z2023-10-07T19:22:31Z2022-06-021745-6215https://doi.org/10.1186/s13063-022-06255-3https://hdl.handle.net/20.500.12494/52855Benjet, C., et. al. (2022). Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study. Trials, 23 (450), 2-19.Antecedentes: el trastorno depresivo mayor (TDM) y el trastorno de ansiedad generalizada (TAG) son muy prevalentes entre los estudiantes universitarios y predicen un deterioro del rendimiento universitario y del funcionamiento posterior de la vida. Sin embargo, la mayoría de los estudiantes no reciben tratamiento, especialmente en los países de ingresos medianos bajos (PIBM). Nuestro objetivo es evaluar los efectos de ampliar el tratamiento utilizando terapia cognitivo-conductual transdiagnóstico (iCBT) escalable y económica a través de Internet entre estudiantes universitarios con síntomas de TDM y/o TAG en dos países de ingresos bajos y medios de América Latina (Colombia y México) e investigar la viabilidad. de crear una regla de tratamiento de precisión (PTR) para predecir para quién iCBT es más efectiva. Métodos: Primero llevaremos a cabo un ensayo clínico pragmático aleatorio en múltiples sitios (N = 1500) de estudiantes que buscan tratamiento en clínicas de salud mental para estudiantes en universidades participantes o respondiendo a un correo electrónico ofreciendo servicios. Los estudiantes en listas de espera para servicios clínicos serán asignados al azar a iCBT no guiada (33%), iCBT guiada (33%) y tratamiento habitual (TAU) (33%). iCBT se proporcionará inmediatamente, mientras que TAU se proporcionará siempre que haya una cita clínica disponible. Los efectos agregados a corto plazo se evaluarán a los 90 días y los efectos a más largo plazo a los 12 meses después de la aleatorización. Usaremos el aprendizaje automático conjunto para predecir la heterogeneidad de los efectos del tratamiento de iCBT guiada versus no guiada versus TAU y desarrollaremos una regla de tratamiento de precisión (PTR) para optimizar el resultado individual del estudiante. Luego realizaremos un segundo y un tercer ensayo con muestras separadas (n = 500 por brazo), pero con una asignación desigual entre dos brazos: el 25 % se asignará al tratamiento que se determine que produzca resultados óptimos según el PTR desarrollado en el primer ensayo. (PTR para resultados óptimos a corto plazo para el ensayo 2 y resultados a 12 meses para el ensayo 3), mientras que al 75 % restante se le asignará la misma asignación en los tres brazos de tratamiento. Discusión: Al recopilar características de referencia integrales para evaluar la heterogeneidad de los efectos del tratamiento, proporcionaremos información valiosa e innovadora para optimizar los efectos del tratamiento y guiar la planificación universitaria del tratamiento de salud mental. Un esfuerzo de este tipo podría tener enormes implicaciones para la salud pública de la región al aumentar el alcance del tratamiento, disminuir las necesidades insatisfechas y los tiempos de espera en las clínicas, y servir como modelo de planificación e implementación de intervenciones basadas en evidencia.Background: Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent among university students and predict impaired college performance and later life role functioning. Yet most students do not receive treatment, especially in low-middle-income countries (LMICs). We aim to evaluate the effects of expanding treatment using scalable and inexpensive Internet-delivered transdiagnostic cognitive behavioral therapy (iCBT) among college students with symptoms of MDD and/or GAD in two LMICs in Latin America (Colombia and Mexico) and to investigate the feasibility of creating a precision treatment rule (PTR) to predict for whom iCBT is most effective. Methods: We will first carry out a multi-site randomized pragmatic clinical trial (N = 1500) of students seeking treatment at student mental health clinics in participating universities or responding to an email offering services. Students on wait lists for clinic services will be randomized to unguided iCBT (33%), guided iCBT (33%), and treatment as usual (TAU) (33%). iCBT will be provided immediately whereas TAU will be whenever a clinic appointment is available. Short-term aggregate effects will be assessed at 90days and longer-term effects 12months after randomization. We will use ensemble machine learning to predict heterogeneity of treatment effects of unguided versus guided iCBT versus TAU and develop a precision treatment rule (PTR) to optimize individual student outcome. We will then conduct a second and third trial with separate samples (n = 500 per arm), but with unequal allocation across two arms: 25% will be assigned to the treatment determined to yield optimal outcomes based on the PTR developed in the first trial (PTR for optimal short-term outcomes for Trial 2 and 12-month outcomes for Trial 3), whereas the remaining 75% will be assigned with equal allocation across all three treatment arms. Discussion: By collecting comprehensive baseline characteristics to evaluate heterogeneity of treatment effects, we will provide valuable and innovative information to optimize treatment effects and guide university mental health treatment planning. Such an effort could have enormous public-health implications for the region by increasing the reach of treatment, decreasing unmet need and clinic wait times, and serving as a model of evidence-based intervention planning and implementation.https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000342033https://orcid.org/0000-0002-1613-9790https://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000006790nayib.carrasco@ucc.edu.cohttps://scholar.google.com/citations?user=0Jjq1EgAAAAJ&hl=es1-19Peter JüniUniversidad Cooperativa de ColombiaPsicologíaMedellínhttps://pubmed.ncbi.nlm.nih.gov/35658942/TRIALSAuerbach RP, Alonso J, Axinn WG, Cuijpers P, Ebert DD, Green JG, et al. Mental disorders among college students in the World Health Organization World Mental Health Surveys. Psychol Med. 2016;46(14):2955–70. https://doi. org/10.1017/S0033291716001665.Cuijpers P, Auerbach RP, Benjet C, Bruffaerts R, Ebert D, Karyotaki E, et al. Introduction to the special issue: The WHO World Mental Health International College Student (WMH-ICS) initiative. Int J Methods Psychiatr Res. 2019;28(2):e1762. https://doi.org/10.1002/mpr.1762.Auerbach RP, Mortier P, Bruffaerts R, Alonso J, Benjet C, Cuijpers P, et al. Mental disorder comorbidity and suicidal thoughts and behaviors in the World Health Organization World Mental Health Surveys International College Student initiative. Int J Methods Psychiatr Res. 2019;28(2):e1752. https://doi.org/10.1002/mpr.1752.Alonso J, Vilagut G, Mortier P, Auerbach RP, Bruffaerts R, Cuijpers P, et al. The role impairment associated with mental disorder risk profiles in the WHO World Mental Health International College Student Initiative. Int J Methods Psychiatr Res. 2019;28(2):e1750. https://doi.org/10.1002/mpr.1750.Bruffaerts R, Mortier P, Auerbach RP, Alonso J, Hermosillo De la Torre AE, Cuijpers P, et al. Lifetime and 12-month treatment for mental disorders and suicidal thoughts and behaviors among first year college students. Int J Methods Psychiatr Res. 2019;28(2):e1764. https://doi.org/10.1002/mpr.1764.Alonso J, Liu Z, Evans-Lacko S, Sadikova E, Sampson N, Chatterji S, et al. Treatment gap for anxiety disorders is global: Results of the World Mental Health Surveys in 21 countries. Depress Anxiety. 2018;35(3):195–208. https:// doi.org/10.1002/da.22711.Degenhardt L, Glantz M, Evans-Lacko S, Sadikova E, Sampson N, Thornicroft G, et al. Estimating treatment coverage for people with substance use disorders: an analysis of data from the World Mental Health Surveys. World Psychiatry. 2017;16(3):299–307. https://doi.org/10.1002/wps.20457.ThornicroftG,ChatterjiS,Evans-LackoS,GruberM,SampsonN,Aguilar-GaxiolaS, etal.Undertreatmentofpeoplewithmajor depressivedisorderin21countries.BrJ Psychiatry.2017;210(2):119–24. https://doi.org/10.1192/bjp.bp.116.188078.Evans-Lacko S, Thornicroft G. Viewpoint: WHO World Mental Health Surveys International College Student initiative: Implementation issues in low- and middle-income countries. Int J Methods Psychiatr Res. 2019;28(2):e17566. https://doi.org/10.1002/mpr.1756.Mullan F, Frehywot S, Omaswa F, Buch E, Chen C, Greysen SR, et al. Medical schools in sub-Saharan Africa. Lancet. 2011;377(9771):1113–21. https://doi. org/10.1016/s0140-6736(10)61961-7.Schendel R, McCowan T. Expanding higher education systems in low- and middle-income countries: the challenges of equity and quality. High Educ. 2016;72(4):407–11. https://doi.org/10.1007/s10734-016-0028-6.ShamsuddinK,FadzilF,IsmailWSW,ShahSA,OmarK,MuhammadNA,etal. Correlatesofdepression,anxietyandstressamongMalaysianuniversitystudents. AsianJPsychiatr.2013;6(4):318–23. https://doi.org/10.1016/j.ajp.2013.01.014.Ibrahim AK, Kelly SJ, Adams CE, Glazebrook C. A systematic review of studies of depression prevalence in university students. J Psychiatr Res. 2013;47(3):391–400. https://doi.org/10.1016/j.jpsychires.2012.11.015.Brosnan C, Southgate E, Outram S, Lempp H, Wright S, Saxby T, et al. Experiences of medical students who are first in family to attend university. Med Educ. 2016;50(8):842–51. https://doi.org/10.1111/medu.12995.Southgate E, Brosnan C, Lempp H, Kelly B, Wright S, Outram S, et al. Travels in extreme social mobility: how first-in-family students find their way into and through medical education. Crit Stud Educ. 2017;58(2):242–60. https:// doi.org/10.1080/17508487.2016.1263223.Stebleton MJ, Soria KM, Huesman RL Jr. First-generation students’ sense of belonging, mental health, and use of counseling services at public research universities. J Coll Couns. 2014;17(1):6–20. https://doi.org/10.1002/j.2161-1 882.2014.00044.x.Covarrubias R, Romero A, Trivelli M. Family achievement guilt and mental well-being of college students. J Child Fam Stud. 2015;24(7):2031–7. https:// doi.org/10.1007/s10826-014-0003-8.Hakim JG, Chidzonga MM, Borok MZ, Nathoo KJ, Matenga J, Havranek E, et al. Medical education partnership initiative (MEPI) in Zimbabwe: outcomes and challenges. Glob Health Sci Pract. 2018;6(1):82–92. https://doi. org/10.9745/ghsp-d-17-00052.Palacios JE, Richards D, Palmer R, Coudray C, Hofmann SG, Palmieri PA, et al. Supported internet-delivered cognitive behavioral therapy programs for depression, anxiety, and stress in university students: Open, non-randomised trial of acceptability, effectiveness, and satisfaction. JMIR Ment Health. 2018; 5(4):e11467. https://doi.org/10.2196/11467.ArjadiR,NautaMH,ChowdharyN,BocktingCLH.Asystematicreviewofonline interventionsformentalhealthinlowandmiddleincomecountries:aneglected field.GlobMentHealth.2015;2:e12. https://doi.org/10.1017/gmh.2015.10.Fu Z, Burger H, Arjadi R, Bockting CL. Effectiveness of digital psychological interventions for mental health problems in low-income and middleincome countries: a systematic review and meta-analysis. Lancet Psychiatry. 2020;7(10):851–64. https://doi.org/10.1016/S2215-0366(20)30256-X.Jiménez-Molina Á, Franco P, Martínez V, Martínez P, Rojas G, Araya R. Internet-based interventions for the prevention and treatment of mental disorders in Latin America: a scoping review. Front Psychiatry. 2019;10:664. https://doi.org/10.3389/fpsyt.2019.00664.Harrer M, Adam SH, Baumeister H, Cuijpers P, Karyotaki E, Auerbach RP, et al. Internet interventions for mental health in university students: a systematic review and meta-analysis. Int J Methods Psychiatr Res. 2019;28(2): e1759. https://doi.org/10.1002/mpr.1759.Harrer M, Adam SH, Fleischmann RJ, Baumeister H, Auerbach R, Bruffaerts R, et al. Effectiveness of an internet- and app-based intervention for college students with elevated stress: randomized controlled trial. J Med Internet Res. 2018;20(4):e136. https://doi.org/10.2196/jmir.9293.Salamanca-Sanabria A, Richards D, Timulak L, Connell S, Mojica-Perilla M, Parra-Villa Y, et al. A culturally adapted cognitive behavioral internetdelivered intervention for depressive symptoms: randomized controlled trial. JMIR Ment Health. 2020;6(12):1–20. https://doi.org/10.2196/13392.Norton PJ, Roberge P. Transdiagnostic therapy. Psychiatr Clin North Am. 2017;40(4):675–87. https://doi.org/10.1016/j.psc.2017.08.003.Richards D, Enrique A, Eilert N, Franklin M, Palacios J, Duffy D, et al. A pragmatic randomized waitlist-controlled effectiveness and costeffectiveness trial of digital interventions for depression and anxiety. Digital Med. 2020;3:85. https://doi.org/10.1038/s41746-020-0293-8.NIH National Institute of Mental Health. Strategic Objective 3. 2008. https:// www.nimh.nih.gov/about/strategic-planning-reports/strategic-objective-3. shtml. Accessed 16 Sept. 2019.Maj M, Stein DJ, Parker G, Zimmerman M, Fava GA, De Hert M, et al. The clinical characterization of the adult patient with depression aimed at personalization of management. World Psychiatry. 2020;19(3):269–93. https://doi.org/10.1002/wps.20771.Pescosolido BA. Stigma as a mental health policy controversy: positions, options, and strategies for change. In: Goldman H, Frank R, Morrissey J, editors. The Palgrave Handbook of American Mental Health Policy. Cham: Palgrave Macmillan; 2020. p. 543–72. https://doi.org/10.1007/978-3-030-11 908-9_19.Luedtke AR, van der Laan MJ. Evaluating the impact of treating the optimal subgroup. Stat Methods Med Res. 2017;26(4):1630–40. https://doi.org/10.11 77/0962280217708664.Kessler RC. The potential of predictive analytics to provide clinical decision support in depression treatment planning. Curr Opin Psychiatry. 2018;31(1): 32–9. https://doi.org/10.1097/yco.0000000000000377.Cohen ZD, DeRubeis RJ. Treatment selection in depression. Annu Rev Clin Psychol. 2018;14(1):209–36. https://doi.org/10.1146/annurev-clinpsy-050817084746.Karyotaki E,Efthimiou O,Miguel C, Bermpohl FMG,Furukawa TA, Cuijpers P, et al. Internet-basedcognitivebehavioral therapy for depression:a systematic review and individual patient data network meta-analysis.JAMAPsychiatry. 2021;78(4):361–71.https://doi.org/10.1001/jamapsychiatry.2020.4364.Huguet A, Rao S, McGrath PJ, Wozney L, Wheaton M, Conrod J, et al. A systematic review of cognitive behavioral therapy and behavioral activation apps for depression. PLoS One. 2016;11(5):e0154248. https://doi.org/10.1371/ journal.pone.0154248.Coull G, Morris PG. The clinical effectiveness of CBT-based guided self-help interventions for anxiety and depressive disorders: a systematic review. Psychol Med. 2011;41(11):2239–52. https://doi.org/10.1017/s0033291711 000900.Cuijpers P, Kleiboer A, Karyotaki E, Riper H. Internet and mobile interventions for depression: opportunities and challenges: Cuijpers et al. Depress Anxiety. 2017;34(7):596–602. https://doi.org/10.1002/da.22641.Newman MG, Szkodny LE, Llera SJ, Przeworski A. A review of technologyassisted self-help and minimal contact therapies for anxiety and depression: Is human contact necessary for therapeutic efficacy? Clin Psychol Rev. 2011; 31(1):89–103. https://doi.org/10.1016/j.cpr.2010.09.008.Simmonds-Buckley M, Bennion MR, Kellett S, Millings A, Hardy GE, Moore RK. Acceptability and effectiveness of NHS-recommended e-therapies for depression, anxiety, and stress: meta-analysis. J Med Internet Res. 2020; 22(10):e17049. https://doi.org/10.2196/17049Duffy D, Enrique A, Connell S, Connolly C, Richards D. Internet-delivered cognitive behavior therapy as a prequel to face-to-face therapy for depression and anxiety: a naturalistic observation. Front Psychiatry. 2020;10: 902. https://doi.org/10.3389/fpsyt.2019.00902.Enrique A, Palacios JE, Ryan H, Richards D. Exploring the relationship between usage and outcomes of an internet-based intervention for individuals with depressive symptoms: secondary analysis of data from a randomized controlled trial. J Med Internet Res. 2019;21(8):e12775. https:// doi.org/10.2196/12775.Salamanca-Sanabria A, Richards D, Timulak L. Adapting an internet-delivered intervention for depression for a Colombian college student population: an illustration of an integrative empirical approach. Internet Interv. 2019;15:76– 86. https://doi.org/10.1016/j.invent.2018.11.005.Bo Borghouts J, Eikey E, Mark G, De Leon C, Schueller SM, Schneider M, et al. Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review. J Med Internet Res. 2021;23(3): e24387. https://doi.org/10.2196/24387.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. https://doi.org/10.1 046/j.1525-1497.2001.016009606.x.Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med. 2006;166(10): 1092–7. https://doi.org/10.1001/archinte.166.10.1092.Kroenke K, Wu J, Yu Z, Bair MJ, Kean J, Stump T, et al. Patient health questionnaire anxiety and depression scale: Initial validation in three clinical trials. Psychosom Med. 2016;78(6):716–27. https://doi.org/10.1097/psy. 0000000000000322.Plummer F, Manea L, Trepel D, McMillan D. Screening for anxiety disorders with the GAD-7 and GAD-2: a systematic review and diagnostic metaanalysis. Gen Hosp Psychiatry. 2016;39:24–31. https://doi.org/10.1016/j. genhosppsych.2015.11.005.McMillan D, Gilbody S, Richards D. Defining successful treatment outcome in depression using the PHQ-9: A comparison of methods. J Affect Disord. 2010;127(1–3):122–9. https://doi.org/10.1016/j.jad.2010.04.030.Zimmerman M, Walsh E, Friedman M, Boerescu DA, Attiullah N. Identifying remission from depression on 3 self-report scales. J Clin Psychiatry. 2017; 78(02):177–83. https://doi.org/10.4088/JCP.16m10641Sheehan DV, Mancini M, Wang J, Berggren L, Cao H, Dueñas HJ, et al. Assessment of functional outcomes by Sheehan Disability Scale in patients with major depressive disorder treated with duloxetine versus selective serotonin reuptake inhibitors. Hum Psychopharmacol. 2016;31(1):53–63. https://doi.org/10.1002/hup.2500.Kessler RC, Calabrese JR, Farley PA, Gruber MJ, Jewell MA, Katon W, et al. Composite International Diagnostic Interview screening scales for DSM-IV anxiety and mood disorders. Psychol Med. 2013;43(8):1625–37. https://doi. org/10.1017/s0033291712002334.Kessler RC, Santiago PN, Colpe LJ, Dempsey CL, First MB, Heeringa SG, et al. Clinical reappraisal of the composite international diagnostic interview screening scales (CIDI-SC) in the army study to assess risk and resilience in servicemembers (army STARRS): Clinical reappraisal of the CIDI-SC in army STARRS. Int J Methods Psychiatr Res. 2013;22(4):303–21. https://doi.org/10.1 002/mpr.1398.American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 (5th ed.). Arlington: American Psychiatric Association; 2013.Spitzer RL. Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. JAMA. 1999;282(18):1737–44. https://doi.org/10.1 001/jama.282.18.1737.Zuromski KL, Ustun B, Hwang I, Keane TM, Marx BP, Stein MB, et al. Developing an optimal short-form of the PTSD Checklist for DSM-5 (PCL-5). Depress Anxiety. 2019;36(9):790–800. https://doi.org/10.1002/da.22942.Morin CM, Belleville G, Bélanger L, Ivers H. The insomnia severity index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34(5):601–8. https://doi.org/10.1093/sleep/34.5.601.Posner K, Brown GK, Stanley B, Brent DA, Yershova KV, Oquendo MA, et al. The Columbia–suicide severity rating scale: Initial validity and internal consistency findings from three multisite studies with adolescents and adults. Am J Psychiatry. 2011;168(12):1266–77. https://doi.org/10.1176/appi.a jp.2011.10111704.Nock MK, Holmberg EB, Photos VI, Michel BD. Self-Injurious Thoughts and Behaviors Interview: development, reliability, and validity in an adolescent sample. Psychol Assess. 2007;19(3):309–17. https://doi.org/10.1037/1040-3 590.19.3.309.Luedtke A, Sadikova E, Kessler RC. Sample size requirements for multivariate models to predict between-patient differences in best treatments of major depressive disorder. Clin Psychol Sci. 2019;7(3):445–61. https://doi.org/10.11 77/2167702618815466.Kessler RC, van Loo HM, Wardenaar KJ, Bossarte RM, Brenner LA, Ebert DD, et al. Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiol Psychiatr Sci. 2017;26(1):22–36. https://doi.org/10.1017/S2045796016000020.Driessen E, Hollon SD. Cognitive behavioral therapy for mood disorders: efficacy, moderators and mediators. Psychiatr Clin North Am. 2010;33(3): 537–55. https://doi.org/10.1016/j.psc.2010.04.005.Schneider RL, Arch JJ, Wolitzky-Taylor KB. The state of personalized treatment for anxiety disorders: a systematic review of treatment moderators. Clin Psychol Rev. 2015;38:39–54. https://doi.org/10.1016/j.cpr.2 015.02.004.Jakubovski E, Bloch MH. Anxiety disorder-specific predictors of treatment outcome in the Coordinated Anxiety Learning and Management (CALM) Trial. Psychiatr Q. 2016;87(3):445–64. https://doi.org/10.1007/s11126-015-93 99-6.Webb CA, Rosso IM, Rauch SL. Internet-based cognitive-behavioral therapy for depression: current progress and future directions. Harv Rev Psychiatry. 2017;25(3):114–22. https://doi.org/10.1097/HRP.0000000000000139.Lowe B, Spitzer RL, Grafe K, Kroenke K, Quenter A, Zipfel S, et al. Comparative validity of three screening questionnaires for DSM-IV depressive disorders and physicians’ diagnoses. J Affect Disord. 2004;78(2): 131–40. https://doi.org/10.1016/s0165-0327(02)00237-9.Scott KM, Pd J, Stein DJ, Kessler RC, editors. Mental disorders around the world: facts and figures from the WHO World Mental Health Surveys. New York: Cambridge University Press; 2018. https://doi.org/10.1017/97813163361 68.Kessler RC, Akiskal HS, Angst J, Guyer M, Hirschfeld RMA, Merikangas KR, et al. Validity of the assessment of bipolar spectrum disorders in the WHO CIDI 3.0. J Affect Disord. 2006;96(3):259–69. https://doi.org/10.1016/j.jad.2006. 08.018.Kessler RC, Adler LA, Gruber MJ, Sarawate CA, Spencer T, Van Brunt DL. Validity of the World Health Organization Adult ADHD Self-Report Scale (ASRS) Screener in a representative sample of health plan members. Int J Methods Psychiatr Res. 2007;16(2):52–65. https://doi.org/10.1002/mpr.208Kessler RC, Ustün TB. The World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). Int J Methods Psychiatr Res. 2004;13(2):93–121. doi: 0.1002/mpr.168Norman CD, Skinner HA. EHEALS: the eHealth literacy scale. J Med Internet Res. 2006;8(4):e27. https://doi.org/10.2196/jmir.8.4.e27Kessler RC, Hamilton L, Mickelson KD, Walters EE, Zhao S. Age and depression in the MIDUS survey. In: Brim OG, Ryff CD, Kessler RC, editors. How healthy are we? A national study of well-being at midlife. Chicago: University of Chicago Press; 2003. p. 227–51.Campbell-Sills L,Kessler RC, UrsanoRJ, Sun X, TaylorCT, Heeringa SG, et al. Predictive validity and correlatesofself-assessed resilience among U.S. Army soldiers. DepressAnxiety. 2018;35(2):122–31.https://doi.org/10.1002/da.22694.Campbell-Sills L, Stein MB. Psychometric analysis and refinement of the Connor-Davidson Resilience Scale (CD-RISC): validation of a 10-item measure of resilience. J Trauma Stress. 2007;20(6):1019–28. https://doi.org/1 0.1002/jts.20271.Kelly PJ, Kyngdon F, Ingram I, Deane FP, Baker AL, Osborne BA. The Client Satisfaction Questionnaire-8: psychometric properties in a cross-sectional survey of people attending residential substance abuse treatment: Client satisfaction in residential treatment. Drug Alcohol Rev. 2018;37(1):79–86. https://doi.org/10.1111/dar.12522.Gupta S. Intention-to-treat concept: a review. Perspect Clin Res. 2011;2(3): 109–12. https://doi.org/10.4103/2229-3485.83221.Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–55. https://doi. org/10.1080/01621459.1996.10476902.Wooldridge JM. Econometric analysis of cross section and panel data. Cambridge: MIT Press; 2002.Clarke PS, Windmeijer F. Instrumental variable estimators for binary outcomes. J Am Stat Assoc. 2012;107(500):1638–52. https://doi.org/10.1080/ 01621459.2012.734171.Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference: Instrumental variable methods for causal inference. Stat Med. 2014;33(13):2297–340. https://doi.org/10.1002/sim.6128Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. J Am Stat Assoc. 1994;89(427): 846–66. https://doi.org/10.1080/01621459.1994.10476818.Schnitzer ME, Lok JJ, Gruber S. Variable selection for confounder control, flexible modeling and Collaborative Targeted minimum loss-based estimation in causal inference. Int J Biostat. 2016;12(1):97–115. https://doi. org/10.1515/ijb-2015-0017.Robins JM, Rotnitzky A. Recovery of information and adjustment for dependent censoring using surrogate markers. In: Jewell NP, Dietz K, Farewell VT, editors. AIDS Epidemiology: Methodological Issues. Boston: Birkhäuser Boston; 1992. p. 297–331. https://doi.org/10.1007/978-1-4757-122 9-2_14.Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55. https:// doi.org/10.2307/2335942.Faraway JJ. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. 2nd ed. Boca Raton, FL: CRC Press; 2016. https://doi.org/10.1201/9781315382722.Bunouf P, Molenberghs G. Implementation of pattern-mixture models in randomized clinical trials. Pharm Stat. 2016;15(6):494–506. https://doi.org/1 0.1002/pst.1780.Stroup WW. Generalized linear mixed models: modern concepts, methods and applications. Boca Raton: CRC Press; 2013.Daniels MJ, Jackson D, Feng W, White IR. Pattern mixture models for the analysis of repeated attempt designs. Biometrics. 2015;71(4):1160–7. https:// doi.org/10.1111/biom.12353.Kessler RC, Bossarte RM, Luedtke A, Zaslavsky AM, Zubizarreta JR. Machine learning methods for developing precision treatment rules with observational data. Behav Res Ther. 2019;120:103412. https://doi.org/10.101 6/j.brat.2019.103412.VanderWeele TJ, Luedtke AR, van der Laan MJ, Kessler RC. Selecting optimal subgroups for treatment using many covariates. Epidemiology. 2019;30(3): 334–41. https://doi.org/10.1097/ede.0000000000000991.Kessler RC, Bernecker SL, Bossarte RM, Luedtke AR, McCarthy JF, Nock MK, et al. The role of big data analytics in predicting suicide. In: Passos IC, Mwangi B, Kapczinski F, editors. Personalized Psychiatry: Big Data Analytics in Mental Health. Cham, Switzerland: Springer Nature Switzerland; 2019. p. 77–98. https://doi.org/10.1007/978-3-030-03553-2_5.Kessler RC, Bossarte RM, Luedtke A, Zaslavsky AM, Zubizarreta JR. Suicide prediction models: a critical review of recent research with recommendations for the way forward. Mol Psychiatry. 2020;25(1):168–79. https://doi.org/10.1038/s41380-019-0531-0.van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol. 2007;6(25). https://doi.org/10.2202/1544-6115.1309.DeRubeis RJ, Cohen ZD, Forand NR, Fournier JC, Gelfand LA, Lorenzo-Luaces L. The personalized advantage index: translating research on prediction into individualized treatment recommendations. A demonstration. PLoS One. 2014;9(1):e83875. https://doi.org/10.1371/journal.pone.0083875.Luedtke AR, van der LaanMJ. Super-learning ofanoptimal dynamictreatment rule. Int J Biostat.2016;12(1):305–32.https://doi.org/10.1515/ijb-2015-0052.Luedtke A, Chambaz A. Faster rates for policy learning. 2017. Retrieved from: https://arxiv.org/abs/1704.06431.Van Der Laan MJ, Rubin D. Targeted maximum likelihood learning. Int J Biostat. 2006;2(1): doi: 10.2202/1557-4679.1043.Boutron I, Altman DG, Moher D, Schulz KF, Ravaud P, for the CONSORT NPT Group. CONSORT statement for randomized trials of nonpharmacologic treatments: A 2017 update and a CONSORT extension for nonpharmacologic trial abstracts. Ann Intern Med. 2017;167(1):40. https:// doi.org/10.7326/M17-0046.NIH National Institute of Mental Health. Policy Governing Independent Safety Monitors and Independent Data and safety monitoring boards. 2015. https://www.nimh.nih.gov/funding/clinical-research/policy-governingindependent-safety-monitors-and-independent-data-and-safety-monitoringboards.shtml. Accessed 16 Sept 2019.Kazdin AE. Technology-based interventions and reducing the burdens of mental illness: perspectives and comments onthe specialseries. Cogn Behav Pract. 2015;22(3):359e66–366. https://doi.org/10.1016/j.cbpra.2015.04.004.Gulliver A, Calear AL, Sunderland M, Kay-Lambkin F, Farrer LM, Batterham PJ. Predictors of acceptability and engagement in a self-guided online program for depression and anxiety. Internet Interv. 2021;100400:100400. https://doi. org/10.1016/j.invent.2021.100400.Simon N,McGillivray L, Roberts NP, BarawiK,Lewis CE, BissonJI.Acceptability ofinternet-basedcognitive behaviouraltherapy(i-CBT) for post-traumatic stressdisorder (PTSD): a systematic review.Eur J Psychotraumatol. 2019;10(1): 1646092.https://doi.org/10.1080/20008198.2019.1646092.DepresiónAnsiedadTerapia Cognitivo ConductualEstudiantes UniversitariosDepressionAnxietyCognitive Behavioral TherapyUniversity StudentsStudy protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien studyArtículos Científicoshttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-84334https://repository.ucc.edu.co/bitstreams/5a5e0206-999f-4b67-b9dc-80881aa67e15/download3bce4f7ab09dfc588f126e1e36e98a45MD5220.500.12494/52855oai:repository.ucc.edu.co:20.500.12494/528552024-08-10 22:49:49.825metadata.onlyhttps://repository.ucc.edu.coRepositorio Institucional Universidad Cooperativa de Colombiabdigital@metabiblioteca.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