Published on in Vol 20, No 10 (2018): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10754, first published .
Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial

Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial

Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial

Journals

  1. Olsen M, Stechuchak K, Hung A, Oddone E, Damschroder L, Edelman D, Maciejewski M. A data-driven examination of which patients follow trial protocol. Contemporary Clinical Trials Communications 2020;19:100631 View
  2. Oehler C, Görges F, Rogalla M, Rummel-Kluge C, Hegerl U. Efficacy of a Guided Web-Based Self-Management Intervention for Depression or Dysthymia: Randomized Controlled Trial With a 12-Month Follow-Up Using an Active Control Condition. Journal of Medical Internet Research 2020;22(7):e15361 View
  3. Mukhiya S, Wake J, Inal Y, Lamo Y. Adaptive Systems for Internet-Delivered Psychological Treatments. IEEE Access 2020;8:112220 View
  4. Fürer L, Schenk N, Roth V, Steppan M, Schmeck K, Zimmermann R. Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research. Frontiers in Psychology 2020;11 View
  5. Robila M, Robila S. Applications of Artificial Intelligence Methodologies to Behavioral and Social Sciences. Journal of Child and Family Studies 2020;29(10):2954 View
  6. Schover L, Strollo S, Stein K, Fallon E, Smith T. Effectiveness trial of an online self-help intervention for sexual problems after cancer. Journal of Sex & Marital Therapy 2020;46(6):576 View
  7. Bischoff T, Hynes K, Tambling R, Kingzette A. Marriage and Family Therapists’ Reporting of Telehealth Use on Practice Websites during COVID-19: A Linguistic Analysis. The American Journal of Family Therapy 2022;50(2):159 View
  8. Mukhiya S, Wake J, Inal Y, Pun K, Lamo Y. Adaptive Elements in Internet-Delivered Psychological Treatment Systems: Systematic Review. Journal of Medical Internet Research 2020;22(11):e21066 View
  9. Bendig E, Bauereiß N, Buntrock C, Habibović M, Ebert D, Baumeister H. Lessons learned from an attempted randomized-controlled feasibility trial on “WIDeCAD” - An internet-based depression treatment for people living with coronary artery disease (CAD). Internet Interventions 2021;24:100375 View
  10. Humphries S, Wallert J, Norlund F, Wallin E, Burell G, von Essen L, Held C, Olsson E. Internet-Based Cognitive Behavioral Therapy for Patients Reporting Symptoms of Anxiety and Depression After Myocardial Infarction: U-CARE Heart Randomized Controlled Trial Twelve-Month Follow-up. Journal of Medical Internet Research 2021;23(5):e25465 View
  11. Chekroud A, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021;20(2):154 View
  12. Zhang Q, Hu Q, Li Y, Sun Y, He J, Qiu M, Zhang J, Liang Y, Han Y. Efficacy of CPET Combined with Systematic Education of Cardiac Rehabilitation After PCI: A Real-World Evaluation in ACS Patients. Advances in Therapy 2021;38(9):4836 View
  13. Yao L, Wang Z, Gu H, Zhao X, Chen Y, Liu L. Prediction of Chinese clients’ satisfaction with psychotherapy by machine learning. Frontiers in Psychiatry 2023;14 View
  14. Graf R, Zeldovich M, Friedrich S. Comparing linear discriminant analysis and supervised learning algorithms for binary classification—A method comparison study. Biometrical Journal 2024;66(1) View
  15. Pizga A, Karatzanos E, Tsikrika S, Gioni V, Vasileiadis I, Nanas S, Kordoutis P. Psychosocial Interventions to Enhance Treatment Adherence to Lifestyle Changes in Cardiovascular Disease: A Review of the Literature 2011-2021. European Journal of Environment and Public Health 2022;6(1):em0102 View
  16. Lüdtke T, Rüegg N, Moritz S, Berger T, Westermann S. Insight and the number of completed modules predict a reduction of positive symptoms in an Internet-based intervention for people with psychosis. Psychiatry Research 2021;306:114223 View
  17. Linardon J, Fuller‐Tyszkiewicz M, Shatte A, Greenwood C. An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms. International Journal of Eating Disorders 2022;55(6):845 View
  18. Lee K, Ham B. Machine Learning on Early Diagnosis of Depression. Psychiatry Investigation 2022;19(8):597 View
  19. Linnet J, Hertz S, Jensen E, Runge E, Tarp K, Holmberg T, Mathiasen K, Lichtenstein M. Days between sessions predict attrition in text-based internet intervention of Binge Eating Disorder. Internet Interventions 2023;31:100607 View
  20. Fife D, D’Onofrio J. Common, uncommon, and novel applications of random forest in psychological research. Behavior Research Methods 2022;55(5):2447 View
  21. Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Mental Health 2023;10:e42045 View
  22. Wallert J, Boberg J, Kaldo V, Mataix-Cols D, Flygare O, Crowley J, Halvorsen M, Ben Abdesslem F, Boman M, Andersson E, Hentati Isacsson N, Ivanova E, Rück C. Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data. Translational Psychiatry 2022;12(1) View
  23. Linnet J, Jensen E, Runge E, Hansen M, Hertz S, Mathiasen K, Lichtenstein M. Text based internet intervention of Binge Eating Disorder (BED): Words per message is associated with treatment adherence. Internet Interventions 2022;28:100538 View
  24. Torres R, Zurita C, Mellado D, Nicolis O, Saavedra C, Tuesta M, Salinas M, Bertini A, Pedemonte O, Querales M, Salas R. Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning. Diagnostics 2023;13(3):508 View
  25. Dai R, Kannampallil T, Zhang J, Lv N, Ma J, Lu C. Multi-Task Learning for Randomized Controlled Trials. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1 View
  26. Adhikary D, Barman S, Ranjan R. Internet-Based Cognitive Behavioural Therapy for Individuals With Depression and Chronic Health Conditions: A Systematic Review. Cureus 2023 View
  27. Flygare O, Boberg J, Rück C, Hofmann R, Leosdottir M, Mataix-Cols D, de la Cruz L, Richman P, Wallert J. Association of anxiety or depression with risk of recurrent cardiovascular events and death after myocardial infarction: A nationwide registry study. International Journal of Cardiology 2023;381:120 View
  28. Boberg J, Kaldo V, Mataix-Cols D, Crowley J, Roelstraete B, Halvorsen M, Forsell E, Isacsson N, Sullivan P, Svanborg C, Andersson E, Lindefors N, Kravchenko O, Mattheisen M, Danielsdottir H, Ivanova E, Boman M, Fernández de la Cruz L, Wallert J, Rück C. Swedish multimodal cohort of patients with anxiety or depression treated with internet-delivered psychotherapy (MULTI-PSYCH). BMJ Open 2023;13(10):e069427 View
  29. Andersson P, Coyne J. Caution Warranted Regarding the Efficacy of iCBT in Patients With Symptomatic Paroxysmal Atrial Fibrillation. Journal of the American College of Cardiology 2023;82(19):e181 View
  30. Humphries S, Mars K, Hofmann R, Held C, Olsson E, Banach M. Randomized evaluation of routine beta-blocker therapy after myocardial infarction quality of life (RQoL): design and rationale of a multicentre, prospective, randomized, open, blinded endpoint study. European Heart Journal Open 2023;3(3) View
  31. Côté-Allard U, Pham M, Schultz A, Nordgreen T, Torresen J. Adherence Forecasting for Guided Internet-Delivered Cognitive Behavioral Therapy: A Minimally Data-Sensitive Approach. IEEE Journal of Biomedical and Health Informatics 2023;27(6):2771 View
  32. Ekpezu A, Wiafe I, Oinas-Kukkonen H. Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review. JMIR AI 2023;2:e46779 View
  33. Arntz A, Weber F, Handgraaf M, Lällä K, Korniloff K, Murtonen K, Chichaeva J, Kidritsch A, Heller M, Sakellari E, Athanasopoulou C, Lagiou A, Tzonichaki I, Salinas-Bueno I, Martínez-Bueso P, Velasco-Roldán O, Schulz R, Grüneberg C. Technologies in Home-Based Digital Rehabilitation: Scoping Review. JMIR Rehabilitation and Assistive Technologies 2023;10:e43615 View
  34. Demiray O, Gunes E, Kulak E, Dogan E, Karaketir S, Cifcili S, Akman M, Sakarya S. Classification of patients with chronic disease by activation level using machine learning methods. Health Care Management Science 2023;26(4):626 View
  35. Lyamina N, Golubev M, Zaitsev V. Internet technologies in the psychological rehabilitation of patients with cardiovascular diseases: literature review. CardioSomatics 2023;14(3):197 View
  36. Heyat M, Akhtar F, Munir F, Sultana A, Muaad A, Gul I, Sawan M, Asghar W, Iqbal S, Baig A, de la Torre Díez I, Wu K. Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function. Complex & Intelligent Systems 2024 View

Books/Policy Documents

  1. Safinianaini N, Boström H, Kaldo V. Artificial Intelligence in Medicine. View
  2. Harrer M, Terhorst Y, Baumeister H, Ebert D. Digitale Gesundheitsinterventionen. View