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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05783401
Other study ID # 2022-01461; th23Vetter
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date November 19, 2022
Est. completion date November 2024

Study information

Verified date April 2024
Source University Hospital, Basel, Switzerland
Contact Marcus Vetter, PD Dr. med.
Phone +41 61 925 2525
Email marcus.vetter@ksbl.ch
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

This study evaluates if it is possible to identify quantitative parameters from audio signals to describe the changes in patient's state in relation to frailty and distress.


Description:

Frailty is a common clinical syndrome especially in older adults that carries an increased risk for poor health outcomes including falls, incident disability, hospitalization, and mortality. The early detection of frailty is of importance in many patient populations to predict treatment outcomes, identify patient needs and coordinate efficient and meaningful care. An electronic assessment of the degree of distress in patients, who are unable to report, would be important to be able to routinely and objectively identify suffering in these patients. Digital voice analysis (DVA) gathers speech samples from individuals via different kinds of recording devices (smartphone, tablet, etc.) and examines a large variety of specific acoustic parameters such as for example frequency and voice quality features. This study is to analyse the potential to evaluate distress and frailty through digital voice analysis. On the contrary to the existing studies, it is intended to record audio and clinical evaluation data from the same subject multiple times during several weeks to be able to analyse temporal changes. This will allow to not only perform inter-subject but as well intra-subject comparisons of changes in audio features with changes of the patient's wellbeing over time. To make the patient speak as freely and relaxed as possible, the patient will describe different images. Different features will be extracted from the audios and potential candidates for a larger patient study will be identified, if data quantity permits using machine learning algorithms. Therefore this study evaluates if it is feasible to gather digital voice samples for voice analyses from cancer patients alongside conventional assessments for frailty (G8 questionnaire and distress (Distress Thermometer) to conduct first, preliminary analyses for identification of potential correlates between voice features and frailty or distress and between changes over time.


Recruitment information / eligibility

Status Recruiting
Enrollment 100
Est. completion date November 2024
Est. primary completion date November 2024
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - Active cancer or haemato-oncological malignancy - Adults (= 18 years) - Ability to understand, speak and read German language fluently - Ability to provide written consent - Sufficient or corrected vision to see the images - Sufficient auditory comprehension for participation in the study based on the therapist's clinical opinion - Ability to concentrate for 20-30 minutes based on the investigator's clinical opinion - Signed informed consent to the study Exclusion Criteria: - Aphonia, dysphonia or other obvious voice alterations of patient's voice - Life-expectancy shorter = 14 days as judged by a physician or nurse via "surprise question" - Breathlessness whilst speaking - Cognitive impairment as judged by physician or Mini-Cog in the G8 screening tool - Severe physical, emotional or existential suffering because of which the enrollment and participation in the study would result in patient burden, as judged by the treating physicians and their multiprofessional team members

Study Design


Related Conditions & MeSH terms


Intervention

Other:
Data acquisition: Speech acquisition
Speech test with synchronized audio and video recording. The speaking exercises and the audio and video recording will be done using a tablet computer and an application developed in-house. Freely available images showing different scenes will be integrated and displayed on the tablet to be described by the patient. The goal is to have at least two and maximum four of them described with several sentences by each patient per session. Parameters will be extracted from the patient's audio data to estimate the changes in distress and frailty.
Diagnostic Test:
Data acquisition: G8 Screening tool
The G8 screening tool consists of seven items dealing with food intake, weight loss, mobility, neuropsychological problem, body mass index, prescription drug, and self-perception of health, from the Mini-Nutritional Assessment (MNA) questionnaire and was developed specifically for elderly cancer patients. The total G-8 score lies between 0 and 17. A higher score indicates a better health status.
Data acquisition: Distress Thermometer
The distress thermometer (DT) is a measure of psychological distress in cancer patients. The instrument is a self-reported tool using a 0-to-10 rating scale.

Locations

Country Name City State
Switzerland Palliativzentrum Hildegard, Basel Basel
Switzerland Kantonsspital Baselland, Klinik für Onkologie, Hämatologie und Immuntherapie Liestal

Sponsors (1)

Lead Sponsor Collaborator
University Hospital, Basel, Switzerland

Country where clinical trial is conducted

Switzerland, 

Outcome

Type Measure Description Time frame Safety issue
Primary Change of mean fundamental frequency extracted from the patient's audio data Change of mean fundamental frequency extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of first few formants (F1, F2) Change of first few formants (F1, F2) extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of jitter (variation in F0 from cycle to cycle) Change of jitter (variation in F0 from cycle to cycle) extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of shimmer (variation in peak-to-peak amplitude) Change of shimmer (variation in peak-to-peak amplitude) extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of skewness Change of skewness extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of kurtosis Change of kurtosis extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of voice strength (volume) of the vowel Change of voice strength (volume) of the vowel extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of duration of length of the answer Change of duration of length of the answer extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of verbal fluency Change of verbal fluency extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of word duration of individual words Change of word duration of individual words extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
Primary Change of duration of the breaks between the words Change of duration of the breaks between the words extracted from the patient's audio data to estimate the changes in distress and frailty. during a 16-week period for each patient
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