A machine learning-based ecological momentary intervention for mental health promotion in youth: a micro-randomized trial

Wykaz prac cytowanych wewnątrz artykułu (relacja cites → Publication), pobrany ze zweryfikowanych metadanych PubMed / Crossref:

1. Kieling C, Buchweitz C, Caye A, Silvani J, Ameis SH, Brunoni AR, et al. Worldwide prevalence and disability from mental disorders across childhood and adolescence: evidence from the global burden of disease study. JAMA Psychiatry. 2024;81:347–56.
   pubmed_reference_list
2. Saunders DC, Knapp FM, Veenstra-VanderWeele J. Age-not just a number in youth mental health. JAMA Psychiatry. 2024;81:327–8.
   pubmed_reference_list
3. Singh B, Ahmed M, Staiano AE, Gough C, Petersen J, Vandelanotte C, et al. A systematic umbrella review and meta-meta-analysis of eHealth and mHealth interventions for improving lifestyle behaviours. NPJ Digit Med. 2024;7:179.
   pubmed_reference_list
4. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2018;52:446–62.
   pubmed_reference_list
5. Balaskas A, Schueller SM, Cox AL, Doherty G. Ecological momentary interventions for mental health: A scoping review. PLoS ONE. 2021;16:e0248152.
   pubmed_reference_list
6. Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol. 2010;15:1–39.
   pubmed_reference_list
7. Schulte-Strathaus JCC, Rauschenberg C, Baumeister H, Reininghaus U. Ecological momentary interventions in public mental health provision. In: Montag C, Baumeister H, editors. Digital Phenotyping and Mobile Sensing. Studies in Neuroscience, Psychology and Behavioral Economics. Cham: Springer International Publishing; 2023. p. 427-39.
   pubmed_reference_list
8. Myin-Germeys I, Kasanova Z, Vaessen T, Vachon H, Kirtley O, Viechtbauer W, et al. Experience sampling methodology in mental health research: new insights and technical developments. World Psychiatry. 2018;17:123–32.
   pubmed_reference_list
9. Reininghaus U, Daemen M, Postma MR, Schick A, Hoes-van der Meulen I, Volbragt N, et al. Transdiagnostic ecological momentary intervention for improving self-esteem in youth exposed to childhood adversity: the SELFIE randomized clinical trial. JAMA Psychiatry. 2024;81:227–39.
   pubmed_reference_list
10. Reininghaus U, Paetzold I, Rauschenberg C, Hirjak D, Banaschewski T, Meyer-Lindenberg A, et al. Effects of a novel, transdiagnostic ecological momentary intervention for prevention, and early intervention of severe mental disorder in youth (EMIcompass): findings from an exploratory randomized controlled trial. Schizophr Bull. 2023;49:592–604.
    pubmed_reference_list
11. Versluis A, Verkuil B, Spinhoven P, van der Ploeg MM, Brosschot JF. Changing mental health and positive psychological well-being using ecological momentary interventions: a systematic review and meta-analysis. J Med Internet Res. 2016;18:e152.
    pubmed_reference_list
12. Loo Gee B, Griffiths KM, Gulliver A. Effectiveness of mobile technologies delivering ecological momentary interventions for stress and anxiety: a systematic review. J Am Med Inform Assoc. 2016;23:221–9.
    pubmed_reference_list
13. Koppe G, Guloksuz S, Reininghaus U, Durstewitz D. Recurrent neural networks in mobile sampling and intervention. Schizophr Bull. 2019;45:272–6.
    pubmed_reference_list
14. Fechtelpeter J, Rauschenberg C, Jalalabadi H, Boecking B, van Amelsvoort T, Reininghaus U, et al. A control theoretic approach to evaluate and inform ecological momentary interventions. Int J Methods Psychiatr Res. 2024;33:e70001.
    pubmed_reference_list
15. Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, et al. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry. 2021;20:154–70.
    pubmed_reference_list
16. Choo TH, Wall M, Brodsky BS, Herzog S, Mann JJ, Stanley B, et al. Temporal prediction of suicidal ideation in an ecological momentary assessment study with recurrent neural networks. J Affect Disord. 2024;360:268–75.
    pubmed_reference_list
17. Jacobson NC, Bhattacharya S. Digital biomarkers of anxiety disorder symptom changes: personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behav Res Ther. 2022;149:104013.
    pubmed_reference_list
18. Perski O, Kale D, Leppin C, Okpako T, Simons D, Goldstein SP, et al. Supervised machine learning to predict smoking lapses from ecological momentary assessments and sensor data: implications for just-in-time adaptive intervention development. PLOS Digit Health. 2024;3:e0000594.
    pubmed_reference_list
19. Rabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T. Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults. JMIR Mhealth Uhealth. 2015;3:e42.
    pubmed_reference_list
20. Walters ST, Businelle MS, Suchting R, Li X, Hebert ET, Mun EY. Using machine learning to identify predictors of imminent drinking and create tailored messages for at-risk drinkers experiencing homelessness. J Subst Abuse Treat. 2021;127:108417.
    pubmed_reference_list
21. Vandelanotte C, Trost S, Hodgetts D, Imam T, Rashid M, To QG, et al. Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions. J Biomed Inform. 2023;144:104435.
    pubmed_reference_list
22. Rauschenberg C, Schick A, Hirjak D, Seidler A, Paetzold I, Apfelbacher C, et al. Evidence synthesis of digital interventions to mitigate the negative impact of the COVID-19 pandemic on public mental health: rapid meta-review. J Med Internet Res. 2021;23:e23365.
    pubmed_reference_list
23. Gotzl C, Hiller S, Rauschenberg C, Schick A, Fechtelpeter J, Fischer Abaigar U, et al. Artificial intelligence-informed mobile mental health apps for young people: a mixed-methods approach on users’ and stakeholders’ perspectives. Child Adolesc Psychiatry Ment Health. 2022;16:86.
    pubmed_reference_list
24. Rauschenberg C, Schick A, Goetzl C, Roehr S, Riedel-Heller SG, Koppe G, et al. Social isolation, mental health, and use of digital interventions in youth during the COVID-19 pandemic: A nationally representative survey. Eur Psychiatry. 2021;64:e20.
    pubmed_reference_list
25. Krumm S, Reininghaus U, Durstewitz D, Koppe G, Fechtelpeter J, Götzl C, et al. Gemeinsam gestalten?! wie junge menschen, praxis und wissenschaft bei der entwicklung einer smartphone-app zusammenfinden. unsere jugend. 2023;75:77–91.
    pubmed_reference_list
26. Hiller S, Gotzl C, Rauschenberg C, Fechtelpeter J, Koppe G, Wierzba E, et al. Health-promoting effects and everyday experiences with a mental health app using ecological momentary assessments and AI-based ecological momentary interventions among young people: qualitative interview and focus group study. JMIR Mhealth Uhealth. 2025;13:e65106.
    pubmed_reference_list
27. Fechtelpeter J, Rauschenberg C, Goetzl C, Hiller S, Wierzba E, Emonds N, et al. Computational network models for forecasting and control of mental health trajectories in digital applications. NPJ Digit Med. 2025;9:70.
    pubmed_reference_list
28. Wierzba E, Schick A, Rauschenberg C, Fechtelpeter J, Hiller S, Götzl C, et al. Promoting psychological resilience and well-being in youth with a smartphone-based ecological momentary mhealth intervention: secondary analysis of a microrandomized trial. J Med Internet Res. 2026;28:e85552.
    pubmed_reference_list
29. Fechtelpeter J, Rauschenberg C, Götzl C, Hiller S, Wierzba E, Krumm S, et al. KI-basierte Personalisierung digitaler Mikrointerventionen. Die Psychotherapie. 2026.
    pubmed_reference_list
30. Bidargaddi N, Schrader G, Klasnja P, Licinio J, Murphy S. Designing m-Health interventions for precision mental health support. Transl Psychiatry. 2020;10:222.
    pubmed_reference_list
31. Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, et al. Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychol. 2015;34S:1220–8.
    pubmed_reference_list
32. Hess F, Monfared Z, Brenner M, Durstewitz D. Generalized teacher forcing for learning chaotic dynamics. Proceedings of the 40th International Conference on Machine Learning; Honolulu, Hawaii, USA: JMLR.org; 2023. p. Article 529.
    pubmed_reference_list
33. Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019;24:1583–98.
    pubmed_reference_list
34. Schmidt D, Koppe G, Monfared Z, Beutelspacher M, Durstewitz D. Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies. In International Conference on Learning Representations. 2019.
    pubmed_reference_list
35. Brenner M, Hess F, Mikhaeil JM, Bereska LF, Monfared Z, Kuo P-C, et al. editors. Tractable dendritic RNNs for reconstructing nonlinear dynamical systems. In Proc 39th International Conference on Machine Learning; 2022: Pmlr.
    pubmed_reference_list
36. Durstewitz D, Koppe G, Thurm MI. Reconstructing computational system dynamics from neural data with recurrent neural networks. Nat Rev Neurosci. 2023;24:693–710.
    pubmed_reference_list
37. Reininghaus U, Gayer-Anderson C, Valmaggia L, Kempton MJ, Calem M, Onyejiaka A, et al. Psychological processes underlying the association between childhood trauma and psychosis in daily life: an experience sampling study. Psychol Med. 2016;46:2799–813.
    pubmed_reference_list
38. Terhorst Y, Philippi P, Sander LB, Schultchen D, Paganini S, Bardus M, et al. Validation of the mobile application rating scale (MARS). PLoS ONE. 2020;15:e0241480.
    pubmed_reference_list
39. Zhou L, Bao J, Setiawan IMA, Saptono A, Parmanto B. The mHealth app usability questionnaire (MAUQ): development and validation study. JMIR Mhealth Uhealth. 2019;7:e11500.
    pubmed_reference_list
40. Allan S, Ward T, Eisner E, Bell IH, Cella M, Chaudhry IB, et al. Adverse events reporting in digital interventions evaluations for psychosis: a systematic literature search and individual level content analysis of adverse event reports. Schizophr Bull. 2024;50:1436–55.
    pubmed_reference_list
41. Slade T, Grove R, Burgess P. Kessler psychological distress scale: normative data from the 2007 Australian national survey of mental health and wellbeing. Aust N Z J Psychiatry. 2011;45:308–16.
    pubmed_reference_list
42. Hasani J. The psychometric properties of the cognitive emotion regulation questionnaire (CERQ). J Clin Psychol. 2010;2:73–84.
    pubmed_reference_list
43. Connor KM, Davidson JR. Development of a new resilience scale: the connor-davidson resilience scale (CD-RISC). Depress Anxiety. 2003;18:76–82.
    pubmed_reference_list
44. Qian TX, S.; Cheng, Z.; Boruvka A. Primary and secondary analyses for micro-randomized trials. 1.0.2 ed. Comprehensive R Archive Network (CRAN)2023
    pubmed_reference_list
45. Liao P, Klasnja P, Tewari A, Murphy SA. Sample size calculations for micro-randomized trials in mHealth. Stat Med. 2016;35:1944–71.
    pubmed_reference_list
46. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013;4:863.
    pubmed_reference_list
47. Reininghaus U, Reinhold AS, Priebe S, Rauschenberg C, Fleck L, Schick A, et al. Toward equitable interventions in public mental health: a review. JAMA Psychiatry. 2024;81:1270–5.
    pubmed_reference_list
48. Rauschenberg C, Boecking B, Paetzold I, Schruers K, Schick A, van Amelsvoort T, et al. A Compassion-Focused Ecological Momentary Intervention for Enhancing Resilience in Help-Seeking Youth: Uncontrolled Pilot Study. JMIR Ment Health. 2021;8:e25650.
    pubmed_reference_list
49. De la Barrera U, Arrigoni F, Monserrat C, Montoya-Castilla I, Gil-Gomez JA. Using ecological momentary assessment and machine learning techniques to predict depressive symptoms in emerging adults. Psychiatry Res. 2024;332:115710.
    pubmed_reference_list
50. Borsboom D. A network theory of mental disorders. World Psychiatry. 2017;16:5–13.
    pubmed_reference_list
51. Bringmann LF, Albers C, Bockting C, Borsboom D, Ceulemans E, Cramer A, et al. Psychopathological networks: Theory, methods and practice. Behav Res Ther. 2022;149:104011.
    pubmed_reference_list
52. Hofmann SG, Curtiss J, McNally RJ. A complex network perspective on clinical science. Perspect Psychol Sci. 2016;11:597–605.
    pubmed_reference_list
53. Dao KP, De Cocker K, Tong HL, Kocaballi AB, Chow C, Laranjo L. Smartphone-delivered ecological momentary interventions based on ecological momentary assessments to promote health behaviors: systematic review and adapted checklist for reporting ecological momentary assessment and intervention studies. JMIR Mhealth Uhealth. 2021;9:e22890.
    pubmed_reference_list
54. Gugel J, Rauschenberg C, Paetzold Jaehme I, Schick A, Wierzba E, et al. Proximal Effects of a Transdiagnostic, Ecological Momentary Intervention for Enhancing Resilience in Help-Seeking Young People (EMIcompass): Findings From a Secondary Analysis of an Exploratory Randomized Controlled Trial. J Med Internet Res. 2026;28:e77150.
    pubmed_reference_list

Dokładne dopasowania identyfikatorów

Nie znaleziono dopasowań w tej grupie.

Kandydaci do ręcznej kontroli

Nie znaleziono dopasowań w tej grupie.

Konflikty

Nie znaleziono dopasowań w tej grupie.

Podobieństwo nie scala rekordów i nie potwierdza tożsamości publikacji.

Zakres: jawne relacje claims[].source_ids[] powiązane z publikacją.