Health Condition 1: J449- Chronic obstructive pulmonary disease, unspecified Health Condition 2: J452- Mild intermittent asthma
Conditions
Interventions
Control Intervention1: NIL: NIL
Sponsors
Sonde Health
Eligibility
Inclusion criteria
Inclusion criteria: 1. Agreement with the subject consent information presented on the Sonde app. 2. Stated willingness and ability to comply with all study procedures 3. Male or female, aged 18 or above 4. Fluent in any of the designated languages selected for the study 5. Pregnant women are allowed to participate 6. Have a medical diagnosis of asthma or COPD if participating as a patient (asthma and COPD as comorbidities are allowed) 7. No respiratory diagnosis if participating as non-patient volunteer. Non-respiratory diagnoses are allowed unless mentioned in the exclusion criteria
Exclusion criteria
Exclusion criteria: 1. Speech or voice disorder (known diagnosis or clinician judgment) 2. Patient in critical conditions requiring immediate medical attention 3. Chronic respiratory conditions other than asthma or COPD 4. Acute respiratory conditions (upper or lower respiratory tract viral or bacteriological infections) 5. Participation in medication studies or trials 6. Severe psychiatric diagnosis (e.g. schizophrenia, psychotic disorder) 7. Dementia, Alzheimer’s Disease, or similar cognitive impairment diagnosis 8. Movement disorder (e.g. Parkinson’s Disease, Huntington’s Disease)
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| 1.Dataset Compilation: To compile a comprehensive reference dataset of vocal samples, gathered passively from individuals with respiratory conditions as well as healthy volunteers, to serve as a benchmark for analysis.Timepoint: Day zero (baseline) | — |
Secondary
| Measure | Time frame |
|---|---|
| 2. Authentication Algorithm Assessment: To assess the feasibility & accuracy of user authentication algorithms by analyzing voice samples collected passively, ensuring reliable participant identification. 3. Vocal Feature Analysis: To conduct a comparative analysis of vocal & prosodic features between passively collected voice samples & those obtained through cued vocal elicitations, to validate the efficacy of passive collection methods. 4. Respiratory Condition Modeling: To develop robust predictive models that utilize passively collected voice samples to diagnose & monitor respiratory conditions & symptoms. 5. Mental Health Assessment Models: To create predictive models that can evaluate the severity of self-reported mental health symptoms using the vocal features derived from passively collected voice samples. Timepoint: Day zero (baseline) | — |
Countries
India
Contacts
Public ContactDr Sharvari Rangnekar
Questt Clinicals and Ayurceuticals Pvt. Ltd.
Outcome results
None listed