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Akademický článek

Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach

Autor: Silvan Hornstein, Valerie Forman-Hoffman, Albert Nazander, Kristian Ranta, Kevin Hilbert

Publikováno v: Digital Health, Vol 7 (2021)

Objective Predicting the outcomes of individual participants for treatment interventions appears central to making mental healthcare more tailored and effective. However, little work has been done to investigate the performance of machine learning-ba

Externí odkaz: https://doaj.org/article/a1c87f1e7a2d4da89785a980cb563b2c

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Akademický článek

Significant reduction in depressive symptoms among patients with moderately-severe to severe depressive symptoms after participation in a therapist-supported, evidence-based mobile health program delivered via a smartphone app

Autor: Valerie L. Forman-Hoffman, Benjamin W. Nelson, Kristian Ranta, Albert Nazander, Outi Hilgert, Joao de Quevedo

Publikováno v: Internet Interventions, Vol 25, Iss , Pp 100408- (2021)

Depression is a debilitating disorder associated with poor health outcomes, including increased comorbidity and early mortality. Despite the advent of new digital health interventions, few have been tested among patients with more severe forms of dep

Externí odkaz: https://doaj.org/article/dbc032733a7645a4a1ce263cca386ea9

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Akademický článek

Long-Term Outcomes of a Therapist-Supported, Smartphone-Based Intervention for Elevated Symptoms of Depression and Anxiety: Quasiexperimental, Pre-Postintervention Study

Autor: Economides, Marcos, Ranta, Kristian, Nazander, Albert, Hilgert, Outi, Goldin, Philippe R, Raevuori, Anu, Forman-Hoffman, Valerie

Publikováno v: JMIR mHealth and uHealth, Vol 7, Iss 8, p e14284 (2019)

BackgroundDepression is one of the most common mental health disorders and severely impacts one’s physical, psychological, and social functioning. To address access barriers to care, we developed Ascend—a smartphone-delivered, therapist-supported

Externí odkaz: https://doaj.org/article/5fbfed70d3d94a488793195e5ee8538f

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Feasibility and Efficacy of the Addition of Heart Rate Variability Biofeedback to a Remote Digital Health Intervention for Depression

Autor: Paul M. Lehrer, Valerie L. Forman-Hoffman, Albert Nazander, Kristian Ranta, Richard Gevirtz, Marcos Economides, Inna Khazan, Anu Raevuori, Outi Hilgert

Publikováno v: Applied Psychophysiology and Biofeedback

A rise in the prevalence of depression underscores the need for accessible and effective interventions. The objectives of this study were to determine if the addition of a treatment component showing promise in treating depression, heart rate variabi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1cb099e60f7c82b1a12d089457cdcdb
http://europepmc.org/articles/PMC7250954

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Akademický článek

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Resonance breathing is associated with improvements in depression and anxiety symptoms during heart rate variability biofeedback sessions

Autor: Nazander A, Adam Pettitt, Lehrer P, Ranta K, Benjamin W. Nelson, Hoffman, Gevirtz R

Heart rate variability (HRV) appears to be a transdiagnostic biomarker for health and disease. Although initial studies using HRV biofeedback (HRVB) to regulate HRV as a potential adjunctive treatment to gold-standard interventions seem promising, mo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3569bd7341e747d5747d687892455c51
https://doi.org/10.31234/osf.io/7g5q8

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Predicting Therapy Outcome in a Digital Mental Health Intervention for Depression and Anxiety: A Machine Learning Approach (Preprint)

Autor: Silvan Hornstein, Valerie Forman-Hoffman, Albert Nazander, Kristian Ranta, Kevin Hilbert

BACKGROUND Predicting the outcomes of individual participants for treatment interventions appears central for making mental healthcare more tailored and effective. Machine Learning (ML) has been proven to be able to make such predictions with notable

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7d9816bd21a28fb8301d70a353418cca
https://doi.org/10.2196/preprints.26432

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Significant reduction in depressive symptoms among patients with at least moderately severe depressive symptoms after participation in a therapist-supported, evidence-based program delivered via a smartphone app

Autor: Valerie Forman-Hoffman, Kristian Ranta, Albert Nazander, Outi Hilgert, Joao de Quevedo

Depression is a debilitating disorder associated with many poor health outcomes, including increased comorbidity and early mortality. Despite the advent of new digital health interventions, few have been tested among patients with more severe forms o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8900aeee2fc344e93ccdce7417eaa96e
https://psyarxiv.com/bx79a

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