128208 20241125101200.0 doi 10.1016/j.jad.2023.08.097 sideral 135446 ART-2023-135446 eng Cummins, Nicholas Multilingual markers of depression in remotely collected speech samples: A preliminary analysis 2023 Access copy available to the general public Unrestricted Background: Speech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data. Methods: We collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18 months. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features. Results: Increased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses. Limitations: Our findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features. Conclusions: Participants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD. info:eu-repo/grantAgreement/EC/H2020/115902/EU/Remote Assessment of Disease and Relapse in Central Nervous System Disorders/RADAR-CNS This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 115902-RADAR-CNS info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 4.9 2023 CLINICAL NEUROLOGY 35 / 280 = 0.125 2023 Q1 T1 PSYCHIATRY 38 / 279 = 0.136 2023 Q1 T1 PSYCHIATRY 38 / 279 = 0.136 2023 Q1 T1 2.082 2023 Psychiatry and Mental Health 2023 Q1 Clinical Psychology 2023 Q1 10.9 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Dineley, Judith Conde, Pauline Matcham, Faith Siddi, Sara Lamers, Femke Carr, Ewan Lavelle, Grace Leightley, Daniel White, Katie M. Oetzmann, Carolin Campbell, Edward L. Simblett, Sara Bruce, Stuart Haro, Josep Maria Penninx, Brenda W.J.H. Ranjan, Yatharth Rashid, Zulqarnain Stewart, Callum Folarin, Amos A. Bailón, Raquel Universidad de Zaragoza (orcid)0000-0003-1272-0550 Schuller, Björn W. Wykes, Til Vairavan, Srinivasan Dobson, Richard J.B. Narayan, Vaibhav A. Hotopf, Matthew 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 341 (2023), 128-136 J. affect. disord. Journal of Affective Disorders 0165-0327 1735313 http://zaguan.unizar.es/record/128208/files/texto_completo.pdf Versión publicada 2227457 http://zaguan.unizar.es/record/128208/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:128208 articulos driver 2024-11-22-12:11:25 ARTICLE