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ArticleJMIR research protocols2020

Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study.

Peter Tonn, Yoav Degani, Shani Hershko, Amit Klein, Lea Seule, Nina Schulze

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03700008 (Development of a Digital Content-free Speech Analysis for the Measurement of Mental Health and Follow-up of Mental Disorders), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03700008 nacompletednot on this map

Development of a Digital Content-free Speech Analysis for the Measurement of Mental Health and Follow-up of Mental Disorders

TypeinterventionalSponsorNeuropsychiatrischen Zentrums Hamburg-AltonaRan2018 to 2020Enrolled210ConditionsMental Disorder, Conditions Influencing Health StatusArmsSpeech analysis tool, Conventional psychological measurements
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Peter TonnNeuropsychiatric Center of Hamburg-Altona, Hamburg, Germany.ORCID https://orcid.org/0000-0002-7460-1189
Yoav DeganiVoiceSense Ltd, Herzelia, Israel.ORCID https://orcid.org/0000-0002-7716-4260
Shani HershkoVoiceSense Ltd, Herzelia, Israel.ORCID https://orcid.org/0000-0003-4467-2223
Amit KleinVoiceSense Ltd, Herzelia, Israel.ORCID https://orcid.org/0000-0001-7506-9262
Lea SeuleNeuropsychiatric Center of Hamburg-Altona, Hamburg, Germany.ORCID https://orcid.org/0000-0002-0948-174X
Nina SchulzeNeuropsychiatric Center of Hamburg-Altona, Hamburg, Germany.ORCID https://orcid.org/0000-0002-3614-4562
Asklepios Klinik Altona · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of mental disorders worldwide is very high. The guideline-oriented care of patients depends on early diagnosis and regular and valid evaluation of their treatment to be able to quickly intervene should the patient's mental health deteriorate. To ensure effective treatment, the level of experience of the physician or therapist is of importance, both in the initial diagnosis and in the treatment of mental illnesses. Nevertheless, experienced physicians and psychotherapists are not available in enough numbers everywhere, especially in rural areas or in less developed countries. Human speech can reveal a speaker's mental state by altering its noncontent aspects (speech melody, intonations, speech rate, etc). This is noticeable in both the clinic and everyday life by having prior knowledge of the normal speech patterns of the affected person, and with enough time spent listening to the patient. However, this time and experience are often unavailable, leaving unused opportunities to capture linguistic, noncontent information. To improve the care of patients with mental disorders, we have developed a concept for assessing their most important mental parameters through a noncontent analysis of their active speech. Using speech analysis for the assessment and tracking of mental health patients opens up the possibility of remote, automatic, and ongoing evaluation when used with patients' smartphones, as part of the current trends toward the increasing use of digital and mobile health tools.

objectiveThe primary objective of this study is to evaluate measurements of participants' mental state by comparing the analysis of noncontent speech parameters to the results of several psychological questionnaires (Symptom Checklist-90 [SCL-90], the Patient Health Questionnaire [PHQ], and the Big 5 Test).

methodsIn this paper, we described a case-controlled study (with a case group and one control group). The participants will be recruited in an outpatient neuropsychiatric treatment center. Inclusion criteria are a neurological or psychiatric diagnosis made by a specialist, no terminal or life-threatening illnesses, and fluent use of the German language. Exclusion criteria include psychosis, dementia, speech or language disorders in neurological diseases, addiction history, a suicide attempt recently or in the last 12 months, or insufficient language skills. The measuring instrument will be the VoiceSense digital voice analysis tool, which enables the analysis of 200 specific speech parameters, and the assessment of findings using psychometric instruments and questionnaires (SCL-90, PHQ, Big 5 Test).

resultsThe study is ongoing as of September 2019, but we have enrolled 254 participants. There have been 161 measurements completed at timepoint 1, and a total of 62 participants have completed every psychological and speech analysis measurement.

conclusionsIt appears that the tone and modulation of speech are as important, if not more so, than the content, and should not be underestimated. This is particularly evident in the interpretation of the psychological findings thus far acquired. Therefore, the application of a software analysis tool could increase the accuracy of finding assessments and improve patient care.

trial registrationClinicalTrials.gov NCT03700008; https://clinicaltrials.gov/ct2/show/NCT03700008. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13852.

Indexed as

content-free speech analysisdepressive disordermobile health appvoice detection

Identifiers

PMID32406862
PMCPMC7256747
OpenAlexW2969438137

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.