Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study
BackgroundThe use of mobile devices to continuously monitor objectively extracted parameters of depressive symptomatology is seen as an important step in the understanding and prevention of upcoming depressive episodes. Speech features such as pitch variability, speech pauses...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2024-01-01
|
| Series: | JMIR Mental Health |
| Online Access: | https://mental.jmir.org/2024/1/e49222 |
| _version_ | 1797352058691518464 |
|---|---|
| author | Lisa-Marie Wadle Ulrich W Ebner-Priemer Jerome C Foo Yoshiharu Yamamoto Fabian Streit Stephanie H Witt Josef Frank Lea Zillich Matthias F Limberger Ayimnisagul Ablimit Tanja Schultz Maria Gilles Marcella Rietschel Lea Sirignano |
| author_facet | Lisa-Marie Wadle Ulrich W Ebner-Priemer Jerome C Foo Yoshiharu Yamamoto Fabian Streit Stephanie H Witt Josef Frank Lea Zillich Matthias F Limberger Ayimnisagul Ablimit Tanja Schultz Maria Gilles Marcella Rietschel Lea Sirignano |
| author_sort | Lisa-Marie Wadle |
| collection | DOAJ |
| description |
BackgroundThe use of mobile devices to continuously monitor objectively extracted parameters of depressive symptomatology is seen as an important step in the understanding and prevention of upcoming depressive episodes. Speech features such as pitch variability, speech pauses, and speech rate are promising indicators, but empirical evidence is limited, given the variability of study designs.
ObjectivePrevious research studies have found different speech patterns when comparing single speech recordings between patients and healthy controls, but only a few studies have used repeated assessments to compare depressive and nondepressive episodes within the same patient. To our knowledge, no study has used a series of measurements within patients with depression (eg, intensive longitudinal data) to model the dynamic ebb and flow of subjectively reported depression and concomitant speech samples. However, such data are indispensable for detecting and ultimately preventing upcoming episodes.
MethodsIn this study, we captured voice samples and momentary affect ratings over the course of 3 weeks in a sample of patients (N=30) with an acute depressive episode receiving stationary care. Patients underwent sleep deprivation therapy, a chronotherapeutic intervention that can rapidly improve depression symptomatology. We hypothesized that within-person variability in depressive and affective momentary states would be reflected in the following 3 speech features: pitch variability, speech pauses, and speech rate. We parametrized them using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) from open-source Speech and Music Interpretation by Large-Space Extraction (openSMILE; audEERING GmbH) and extracted them from a transcript. We analyzed the speech features along with self-reported momentary affect ratings, using multilevel linear regression analysis. We analyzed an average of 32 (SD 19.83) assessments per patient.
ResultsAnalyses revealed that pitch variability, speech pauses, and speech rate were associated with depression severity, positive affect, valence, and energetic arousal; furthermore, speech pauses and speech rate were associated with negative affect, and speech pauses were additionally associated with calmness. Specifically, pitch variability was negatively associated with improved momentary states (ie, lower pitch variability was linked to lower depression severity as well as higher positive affect, valence, and energetic arousal). Speech pauses were negatively associated with improved momentary states, whereas speech rate was positively associated with improved momentary states.
ConclusionsPitch variability, speech pauses, and speech rate are promising features for the development of clinical prediction technologies to improve patient care as well as timely diagnosis and monitoring of treatment response. Our research is a step forward on the path to developing an automated depression monitoring system, facilitating individually tailored treatments and increased patient empowerment. |
| first_indexed | 2024-03-08T13:09:40Z |
| format | Article |
| id | doaj.art-db5e72ef3e5c49369777dd46e56518ad |
| institution | Directory Open Access Journal |
| issn | 2368-7959 |
| language | English |
| last_indexed | 2024-03-08T13:09:40Z |
| publishDate | 2024-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Mental Health |
| spelling | doaj.art-db5e72ef3e5c49369777dd46e56518ad2024-01-18T15:00:34ZengJMIR PublicationsJMIR Mental Health2368-79592024-01-0111e4922210.2196/49222Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot StudyLisa-Marie Wadlehttps://orcid.org/0000-0001-8449-4017Ulrich W Ebner-Priemerhttps://orcid.org/0000-0002-2769-5944Jerome C Foohttps://orcid.org/0000-0003-1067-5725Yoshiharu Yamamotohttps://orcid.org/0000-0002-1132-0355Fabian Streithttps://orcid.org/0000-0003-1080-4339Stephanie H Witthttps://orcid.org/0000-0002-1571-1468Josef Frankhttps://orcid.org/0000-0003-4867-9465Lea Zillichhttps://orcid.org/0000-0001-7457-374XMatthias F Limbergerhttps://orcid.org/0000-0002-6332-397XAyimnisagul Ablimithttps://orcid.org/0009-0008-1883-5336Tanja Schultzhttps://orcid.org/0000-0002-9809-7028Maria Gilleshttps://orcid.org/0000-0002-9604-4459Marcella Rietschelhttps://orcid.org/0000-0002-5236-6149Lea Sirignanohttps://orcid.org/0000-0002-7989-5833 BackgroundThe use of mobile devices to continuously monitor objectively extracted parameters of depressive symptomatology is seen as an important step in the understanding and prevention of upcoming depressive episodes. Speech features such as pitch variability, speech pauses, and speech rate are promising indicators, but empirical evidence is limited, given the variability of study designs. ObjectivePrevious research studies have found different speech patterns when comparing single speech recordings between patients and healthy controls, but only a few studies have used repeated assessments to compare depressive and nondepressive episodes within the same patient. To our knowledge, no study has used a series of measurements within patients with depression (eg, intensive longitudinal data) to model the dynamic ebb and flow of subjectively reported depression and concomitant speech samples. However, such data are indispensable for detecting and ultimately preventing upcoming episodes. MethodsIn this study, we captured voice samples and momentary affect ratings over the course of 3 weeks in a sample of patients (N=30) with an acute depressive episode receiving stationary care. Patients underwent sleep deprivation therapy, a chronotherapeutic intervention that can rapidly improve depression symptomatology. We hypothesized that within-person variability in depressive and affective momentary states would be reflected in the following 3 speech features: pitch variability, speech pauses, and speech rate. We parametrized them using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) from open-source Speech and Music Interpretation by Large-Space Extraction (openSMILE; audEERING GmbH) and extracted them from a transcript. We analyzed the speech features along with self-reported momentary affect ratings, using multilevel linear regression analysis. We analyzed an average of 32 (SD 19.83) assessments per patient. ResultsAnalyses revealed that pitch variability, speech pauses, and speech rate were associated with depression severity, positive affect, valence, and energetic arousal; furthermore, speech pauses and speech rate were associated with negative affect, and speech pauses were additionally associated with calmness. Specifically, pitch variability was negatively associated with improved momentary states (ie, lower pitch variability was linked to lower depression severity as well as higher positive affect, valence, and energetic arousal). Speech pauses were negatively associated with improved momentary states, whereas speech rate was positively associated with improved momentary states. ConclusionsPitch variability, speech pauses, and speech rate are promising features for the development of clinical prediction technologies to improve patient care as well as timely diagnosis and monitoring of treatment response. Our research is a step forward on the path to developing an automated depression monitoring system, facilitating individually tailored treatments and increased patient empowerment.https://mental.jmir.org/2024/1/e49222 |
| spellingShingle | Lisa-Marie Wadle Ulrich W Ebner-Priemer Jerome C Foo Yoshiharu Yamamoto Fabian Streit Stephanie H Witt Josef Frank Lea Zillich Matthias F Limberger Ayimnisagul Ablimit Tanja Schultz Maria Gilles Marcella Rietschel Lea Sirignano Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study JMIR Mental Health |
| title | Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study |
| title_full | Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study |
| title_fullStr | Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study |
| title_full_unstemmed | Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study |
| title_short | Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study |
| title_sort | speech features as predictors of momentary depression severity in patients with depressive disorder undergoing sleep deprivation therapy ambulatory assessment pilot study |
| url | https://mental.jmir.org/2024/1/e49222 |
| work_keys_str_mv | AT lisamariewadle speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT ulrichwebnerpriemer speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT jeromecfoo speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT yoshiharuyamamoto speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT fabianstreit speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT stephaniehwitt speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT joseffrank speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT leazillich speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT matthiasflimberger speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT ayimnisagulablimit speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT tanjaschultz speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT mariagilles speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT marcellarietschel speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy AT leasirignano speechfeaturesaspredictorsofmomentarydepressionseverityinpatientswithdepressivedisorderundergoingsleepdeprivationtherapyambulatoryassessmentpilotstudy |