Tonn Peter, Degani Yoav, Hershko Shani, Klein Amit, Seule Lea, Schulze Nina
Neuropsychiatric Center of Hamburg-Altona, Hamburg, Germany.
VoiceSense Ltd, Herzelia, Israel.
JMIR Res Protoc. 2020 May 14;9(5):e13852. doi: 10.2196/13852.
The 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.
The 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).
In 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).
The 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.
It 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.
ClinicalTrials.gov NCT03700008; https://clinicaltrials.gov/ct2/show/NCT03700008.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13852.
全球精神障碍的患病率非常高。以指南为导向的患者护理取决于早期诊断以及对其治疗进行定期和有效的评估,以便在患者心理健康恶化时能够迅速干预。为确保有效治疗,医生或治疗师的经验水平在精神疾病的初始诊断和治疗中都很重要。然而,经验丰富的医生和心理治疗师在各地的数量都不足,尤其是在农村地区或欠发达国家。人类的言语可以通过改变其非内容方面(语音旋律、语调、语速等)来揭示说话者的心理状态。无论是在诊所还是日常生活中,通过了解受影响者的正常言语模式,并花费足够的时间倾听患者,这一点都很明显。然而,这些时间和经验往往难以获得,从而错失了获取语言非内容信息的机会。为改善对精神障碍患者的护理,我们开发了一种通过对患者主动言语进行非内容分析来评估其最重要心理参数的概念。将语音分析用于精神健康患者的评估和跟踪,与患者的智能手机配合使用时,就有可能进行远程、自动和持续的评估,这是当前数字和移动健康工具使用日益增加趋势的一部分。
本研究的主要目的是通过将非内容语音参数分析结果与几份心理问卷(症状自评量表90 [SCL - 90]、患者健康问卷[PHQ]和大五人格测试)的结果进行比较,来评估参与者的心理状态测量情况。
在本文中,我们描述了一项病例对照研究(有一个病例组和一个对照组)。参与者将在门诊神经精神治疗中心招募。纳入标准是由专科医生做出的神经或精神诊断、无晚期或危及生命的疾病以及能流利使用德语。排除标准包括精神病、痴呆、神经疾病中的言语或语言障碍、成瘾史、最近或过去12个月内有自杀企图或语言能力不足。测量工具将是VoiceSense数字语音分析工具,它能够分析200个特定的语音参数,并使用心理测量工具和问卷(SCL - 90、PHQ、大五人格测试)对结果进行评估。
截至2019年9月,该研究正在进行,但我们已经招募了254名参与者。在时间点1已经完成了161次测量,共有62名参与者完成了每一项心理和语音分析测量。
言语的语调及调制似乎与内容同样重要,甚至可能更重要,不应被低估。这在对目前所获得的心理结果的解读中尤为明显。因此,应用软件分析工具可以提高评估结果的准确性并改善患者护理。
ClinicalTrials.gov NCT03700008;https://clinicaltrials.gov/ct2/show/NCT03700008。
国际注册报告识别码(IRRID):PRR1 - 10.2196/13852。