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Kappen M, van der Donckt J, Vanhollebeke G, Allaert J, Degraeve V, Madhu N, Van Hoecke S, Vanderhasselt MA. Acoustic speech features in social comparison: how stress impacts the way you sound. Sci Rep 2022; 12:22022. [PMID: 36539505 PMCID: PMC9767914 DOI: 10.1038/s41598-022-26375-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
The use of speech as a digital biomarker to detect stress levels is increasingly gaining attention. Yet, heterogeneous effects of stress on specific acoustic speech features have been observed, possibly due to previous studies' use of different stress labels/categories and the lack of solid stress induction paradigms or validation of experienced stress. Here, we deployed a controlled, within-subject psychosocial stress induction experiment in which participants received both neutral (control condition) and negative (negative condition) comparative feedback after solving a challenging cognitive task. This study is the first to use a (non-actor) within-participant design that verifies a successful stress induction using both self-report (i.e., decreased reported valence) and physiological measures (i.e., increased heart rate acceleration using event-related cardiac responses during feedback exposure). Analyses of acoustic speech features showed a significant increase in Fundamental Frequency (F0) and Harmonics-to-Noise Ratio (HNR), and a significant decrease in shimmer during the negative feedback condition. Our results using read-out-loud speech comply with earlier research, yet we are the first to validate these results in a well-controlled but ecologically-valid setting to guarantee the generalization of our findings to real-life settings. Further research should aim to replicate these results in a free speech setting to test the robustness of our findings for real-world settings and should include semantics to also take into account what you say and not only how you say it.
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Affiliation(s)
- Mitchel Kappen
- grid.5342.00000 0001 2069 7798Department of Head and Skin, Department of Psychiatry and Medical Psychology, Ghent University, University Hospital Ghent (UZ Ghent), Corneel Heymanslaan 10-13K12, 9000 Ghent, Belgium ,grid.5342.00000 0001 2069 7798Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
| | | | - Gert Vanhollebeke
- grid.5342.00000 0001 2069 7798Department of Head and Skin, Department of Psychiatry and Medical Psychology, Ghent University, University Hospital Ghent (UZ Ghent), Corneel Heymanslaan 10-13K12, 9000 Ghent, Belgium ,grid.5342.00000 0001 2069 7798Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Jens Allaert
- grid.5342.00000 0001 2069 7798Department of Head and Skin, Department of Psychiatry and Medical Psychology, Ghent University, University Hospital Ghent (UZ Ghent), Corneel Heymanslaan 10-13K12, 9000 Ghent, Belgium ,grid.5342.00000 0001 2069 7798Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
| | - Vic Degraeve
- grid.5342.00000 0001 2069 7798IDLab, Ghent University-Imec, Ghent, Belgium
| | - Nilesh Madhu
- grid.5342.00000 0001 2069 7798IDLab, Ghent University-Imec, Ghent, Belgium
| | - Sofie Van Hoecke
- grid.5342.00000 0001 2069 7798IDLab, Ghent University-Imec, Ghent, Belgium
| | - Marie-Anne Vanderhasselt
- grid.5342.00000 0001 2069 7798Department of Head and Skin, Department of Psychiatry and Medical Psychology, Ghent University, University Hospital Ghent (UZ Ghent), Corneel Heymanslaan 10-13K12, 9000 Ghent, Belgium ,grid.5342.00000 0001 2069 7798Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium
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De Brouwer M, Vandenbussche N, Steenwinckel B, Stojchevska M, Van Der Donckt J, Degraeve V, Vaneessen J, De Turck F, Volckaert B, Boon P, Paemeleire K, Van Hoecke S, Ongenae F. mBrain: towards the continuous follow-up and headache classification of primary headache disorder patients. BMC Med Inform Decis Mak 2022; 22:87. [PMID: 35361224 PMCID: PMC8969243 DOI: 10.1186/s12911-022-01813-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 03/09/2022] [Indexed: 12/04/2022] Open
Abstract
Background The diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation. Therefore, the exploratory mBrain study investigates moving to continuous, semi-autonomous and objective follow-up and classification based on both self-reported and objective physiological and contextual data. Methods The data collection set-up of the observational, longitudinal mBrain study involved physiological data from the Empatica E4 wearable, data-driven machine learning (ML) algorithms detecting activity, stress and sleep events from the wearables’ data modalities, and a custom-made application to interact with these events and keep a diary of contextual and headache-specific data. A knowledge-based classification system for individual headache attacks was designed, focusing on migraine, cluster headache (CH) and tension-type headache (TTH) attacks, by using the classification criteria of ICHD-3. To show how headache and physiological data can be linked, a basic knowledge-based system for headache trigger detection is presented. Results In two waves, 14 migraine and 4 CH patients participated (mean duration 22.3 days). 133 headache attacks were registered (98 by migraine, 35 by CH patients). Strictly applying ICHD-3 criteria leads to 8/98 migraine without aura and 0/35 CH classifications. Adapted versions yield 28/98 migraine without aura and 17/35 CH classifications, with 12/18 participants having mostly diagnosis classifications when episodic TTH classifications (57/98 and 32/35) are ignored. Conclusions Strictly applying the ICHD-3 criteria on individual attacks does not yield good classification results. Adapted versions yield better results, with the mostly classified phenotype (migraine without aura vs. CH) matching the diagnosis for 12/18 patients. The absolute number of migraine without aura and CH classifications is, however, rather low. Example cases can be identified where activity and stress events explain patient-reported headache triggers. Continuous improvement of the data collection protocol, ML algorithms, and headache classification criteria (including the investigation of integrating physiological data), will further improve future headache follow-up, classification and trigger detection. Trial registration This trial was retrospectively registered with number NCT04949204 on 24 June 2021 at www.clinicaltrials.gov. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01813-w.
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Affiliation(s)
| | - Nicolas Vandenbussche
- Department of Neurology, Ghent University Hospital, 9000, Ghent, Belgium.,4BRAIN, Institute for Neuroscience, Department of Head and Skin, Ghent University, 9000, Ghent, Belgium
| | | | | | | | - Vic Degraeve
- IDLab, Ghent University - imec, 9052, Ghent, Belgium
| | | | | | | | - Paul Boon
- Department of Neurology, Ghent University Hospital, 9000, Ghent, Belgium.,4BRAIN, Institute for Neuroscience, Department of Head and Skin, Ghent University, 9000, Ghent, Belgium
| | - Koen Paemeleire
- Department of Neurology, Ghent University Hospital, 9000, Ghent, Belgium
| | | | - Femke Ongenae
- IDLab, Ghent University - imec, 9052, Ghent, Belgium
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