JMIR Publications

ABSTRACT

Background:

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.

Objective:

Previous research studies find 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 (e.g., 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.

Methods:

In the present study, we captured voice samples and momentary affect ratings over the course of three weeks in a sample of 30 patients 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-subject variability in depressive and affective momentary states would be reflected in the following three speech features: pitch variability, speech pauses, and speech rate. We parametrized them using the eGeMAPS parameter set from openSMILE 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 assessments per patient.

Results:

Analyses revealed that pitch variability, speech pauses and speech rate were associated with depression severity, positive affect, valence, and energetic arousal; additionally, 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 (i.e., lower pitch variability linked to lower depression severity, higher positive affect, valence and energetic arousal). Speech pauses were negatively, and speech rate was positively associated with improved momentary states.

Conclusions:

Pitch 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.