Ignite Creation Date:
2024-10-26 @ 3:38 PM
Last Modification Date:
2024-10-26 @ 3:38 PM
Study NCT ID:
NCT06566417
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-07-30
Brief Title:
Social Media as a Risk Tool for HIV Prevention Needs
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Use of Sentiment Analysis and Social Media to Understand HIV Prevention Needs Among Young Women in Kenya
Status:
RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
SMaaRT
Brief Summary:
The impact of effective HIV prevention tools is limited because many people do not know that they are at risk for HIV acquisition despite the availability of various risk assessment scores and criteria This proposal aims to use a novel data science approach to assessing HIV prevention needs among 400 young women in Kisumu Kenya- namely topic modeling and network analysis of text andor social media messages eg WhatsApp Instagram Twitter The study will involve in-depth assessment of relevant ethical and logistical factors to ensure appropriate and optimized use of a sentiment analysis tool for implementation in routine clinical care
Detailed Description:
In the Social Media as a Risk Tool SMaaRT Study the investigators hypothesize that topic modeling of SMSsocial media data combined with network analysis among young women in Kenya will correlate well with existing HIV risk scales and ultimately yield a better understanding of HIV prevention needs The investigators propose the following aims
1 Explore ethical factors that may influence analysis of SMS and social media messages Research assistants will conduct individual qualitative interviews with up to 32 young women 16 who would and 16 who would not provide SMSsocial media data stratified among four clinic sites and one focus group of five Kenyan bioethicists Questions will explore ethical concerns from individual and bystander eg contacts involved in SMSsocial media perspectives and differences in ethical issues by type of social media eg conversations vs posts Follow-up interviews will be conducted with the women who provide SMS andor social media data in Aim 2
2 Conduct topic modeling and network analysis of SMS and social media messages to predict HIV prevention needs among young women in Kenya Working with four clinical sites in Kisumu study staff will ask approximately 400 women ages 18-24 seeking HIV testing PrEP and other health services to download six months of SMSsocial media messages eg WhatsApp Instagram Twitter as a one-time procedure For those providing data research assistants will assess social networks engaged via SMSsocial media eg anonymously labeled as peers sexual partners administer multiple HIV risk assessments eg VOICE Wand risk scores and obtain HIV test results Data analysts will use automated structural topic modelling to determine topics word clusters and assess for association with other risk assessments primary outcome and HIV test results exploratory outcome and will also evaluate the impact of social networks SMSsocial media type data volume and language type on outcomes Data collection and analysis will conform to Aim 1 findings
3 Assess practical factors that may influence use of a sentiment analysis tool in routine care In a needs assessment based on Implementation Mapping research assistants will conduct four focus groups with five staff per clinic and two focus groups with five young women each to explore staffing best suited to implement a sentiment analysis tool and how it could be best integrated into routine care The investigators will also assess available resources to determine optimal efficiency in developing a preliminary implementation strategy
1 Explore ethical factors that may influence analysis of SMS and social media messages Research assistants will conduct individual qualitative interviews with up to 32 young women 16 who would and 16 who would not provide SMSsocial media data stratified among four clinic sites and one focus group of five Kenyan bioethicists Questions will explore ethical concerns from individual and bystander eg contacts involved in SMSsocial media perspectives and differences in ethical issues by type of social media eg conversations vs posts Follow-up interviews will be conducted with the women who provide SMS andor social media data in Aim 2
2 Conduct topic modeling and network analysis of SMS and social media messages to predict HIV prevention needs among young women in Kenya Working with four clinical sites in Kisumu study staff will ask approximately 400 women ages 18-24 seeking HIV testing PrEP and other health services to download six months of SMSsocial media messages eg WhatsApp Instagram Twitter as a one-time procedure For those providing data research assistants will assess social networks engaged via SMSsocial media eg anonymously labeled as peers sexual partners administer multiple HIV risk assessments eg VOICE Wand risk scores and obtain HIV test results Data analysts will use automated structural topic modelling to determine topics word clusters and assess for association with other risk assessments primary outcome and HIV test results exploratory outcome and will also evaluate the impact of social networks SMSsocial media type data volume and language type on outcomes Data collection and analysis will conform to Aim 1 findings
3 Assess practical factors that may influence use of a sentiment analysis tool in routine care In a needs assessment based on Implementation Mapping research assistants will conduct four focus groups with five staff per clinic and two focus groups with five young women each to explore staffing best suited to implement a sentiment analysis tool and how it could be best integrated into routine care The investigators will also assess available resources to determine optimal efficiency in developing a preliminary implementation strategy
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None