Clinical trial NCT05783401: Digital Voice Analysis as a Measure of Frailty and Distress
Official title: Digital Voice Analysis as a Measure of Frailty and Distress. A Feasibility Study (DIVAN)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Frailty
Distress, Emotional
Keywords: Frailty monitoring
Distress monitoring
Digital voice analysis (DVA)
Machine learning
Distress Thermometer
G8 geriatric screening tool
Lead sponsor: University Hospital, Basel, Switzerland
Sponsor class: OTHER
First posted: 2023-03-24
Enrollment: 100
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patient recruitment will be performed at the different participating clinical sites by the investigator

Brief 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Change of mean fundamental frequency extracted from the patient's audio data
timeFrame: during a 16-week period for each patient
measure: Change of first few formants (F1, F2)
timeFrame: during a 16-week period for each patient
measure: Change of jitter (variation in F0 from cycle to cycle)
timeFrame: during a 16-week period for each patient
measure: Change of shimmer (variation in peak-to-peak amplitude)
timeFrame: during a 16-week period for each patient
measure: Change of skewness
timeFrame: during a 16-week period for each patient
measure: Change of kurtosis
timeFrame: during a 16-week period for each patient
measure: Change of voice strength (volume) of the vowel
timeFrame: during a 16-week period for each patient
measure: Change of duration of length of the answer
timeFrame: during a 16-week period for each patient
measure: Change of verbal fluency
timeFrame: during a 16-week period for each patient
measure: Change of word duration of individual words
timeFrame: during a 16-week period for each patient
measure: Change of duration of the breaks between the words
timeFrame: during a 16-week period for each patient
Secondary outcomes:
Other outcomes:
Locations:
city: Basel
country: Switzerland
facility: Palliativzentrum Hildegard, Basel
city: Liestal
country: Switzerland
facility: Kantonsspital Baselland, Klinik für Onkologie, Hämatologie und Immuntherapie
Study officials:
affiliation: Kantonsspital Baselland, Klinik für Onkologie, Hämatologie und Immuntherapie
name: Marcus Vetter, PD Dr. med.
role: PRINCIPAL_INVESTIGATOR
References: