sing Voice Samples to develop computer algorithms that will help to predict psychological distress and postpartum depressio
Not Applicable
- Conditions
- Health Condition 1: F39- Unspecified mood [affective] disorder
- Registration Number
- CTRI/2023/08/056105
- Lead Sponsor
- ICMR
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- ot Yet Recruiting
- Sex
- Not specified
- Target Recruitment
- 0
Inclusion Criteria
College students & Post-partum mothers who fulfill the following criteria:
1.Age 18 â?? 45 years
2.Gender as stated
3.Able to read, speak and comprehend English and/or Kannada
Exclusion Criteria
1.Visual or hearing impairment
2.Voice-related disorders, neurological impairment or extrapyramidal symptoms - e.g. vocal tremors
3.Respiratory ailments such as chronic obstructive pulmonary disease (COPD).
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method 1. To generate high-quality, clinically labelled voice datasets using state-of-art technologies & algorithms <br/ ><br>2. To develop machine-learning (ML) algorithms based on voice features for detection of psychological distress among college students and post-partum depression. <br/ ><br>Timepoint: There will be a single time of point assessment at the 24 months when the model will be generated
- Secondary Outcome Measures
Name Time Method a) To evaluate the usefulness of voice based ML algorithms for categorising the severity of psychological distress & post-partum depression. <br/ ><br>The sampling design is convenience sampling. It is estimated that voice data of 400 subjects each have to collected. <br/ ><br>Timepoint: 2 years