Ethical Challenges Using Mobile Technology for HIV ......– Limiting URL placement through...

Ethical Challenges Using Internet and Mobile Technology for HIV

Prevention Research

Brian Mustanski, Ph.D.

www.impactprogram.org

Overview

• My experience • Why use the Internet for HIV and other health

research? • What are the risks of everyday internet use

and how to people approach privacy? • Case studies • Tension of privacy versus data quality


My experience with online sexual health research 1999 LGBT College Student Survey of

how “having sex” is defined Recruited through LGBT University Student

Organizations. Online survey.

2002 Online daily diary study Recruited through free banner ads on high volume gay websites. Online survey and diaries.

2007 Project Q2 Recruited using respondent driven sampling (RDS). Follow up surveys could be done online.

2007 1.0 Recruited when testing HIV negative at clinics in Chicago. Online intervention RCT.

2011 Keep it up! 1.5 Recruited through Chicago community organization. Online intervention service.

2012 Recruited online with Facebook ads. Online sexual health education.

2012 Recruited online through banner ads. Evaluation of rapid at home HIV testing

2012 Recruited when testing HIV negative at clinics in Atlanta, Chicago, and New York. Online

intervention RCT.

2012 Guy to Guy Recruited online through LGBT youth oriented websites. Focus groups and text intervention RCT.

Presenter

Presentation Notes

First online study at IU.


Share your experiences

• With online data collection • How did you go about detecting or preventing

fraudulent participation?


Why use the internet/technology for HIV and other health research

• High rates of use. • Some aspects of data collection are facilitated

– No need for data entry (still an issue offline?) – 24/7 data collection

• Access to geographically diverse populations • Assess to “hard to reach” populations

(sometimes through direct advertising on “mainstream sites” and sometimes through specific websites).


Internet use


Cell phone use


Use of technology for health purposes

• 80% of internet users, or 59% of U.S. adults, look online for health information (Pew, 2012).

• 17% of cell phone owners, or 15% of adults, have used their phone to look up health or medical information. Young people, Latinos, and African Americans are significantly more likely than other groups to have mobile internet access and to conduct health searches on-the-go.


What are the risks of every day internet/technology use?


How do teens think about privacy online?

Presenter

Presentation Notes

Source: The Pew Research Center Internet & American Life Project Teen & Parent surveys. Source: Teen data taken from surveys of teens age 12-17 conducted October-November 2006, September-November 2007, November 2007-February 2008, June-September 2009, April-July 2011, and July-September 2012 (n=802). Adult data taken from surveys of adults ages 18+ conducted August 2006, April-May 2009, August-September 2009, July-August 2011, and November-December 2012 (n=2,261). Methodological information for each survey is available from http://pewrsr.ch/ZLGBUL

Presenter

Presentation Notes

Greater diversification of platforms used, end of MySpace. FB still dominant, though focus group data suggests its not always enjoyed. Source: The Pew Research Center's Internet & American Life Teen-Parent survey, July 26-September 30, 2012. n=802 for teens 12-17 and parents, including oversample of minority families. Interviews were conducted in English and Spanish. The margin of error for teen social media users is +/- 5.1 percentage points. Note: This chart is based on an open-ended question that asks: “On which social networking site or sites do you have a profile or account?” and was asked of anyone who had answered yes to one or both of two previous questions “Do you ever use an online social networking site like MySpace or Facebook?” and “Do you ever use Twitter?” Sites listed for comparison were those that were reported in 2012 by at least one respondent. Asterisks (*) indicate that less than 1% of respondents gave the corresponding answer.

Presenter

Presentation Notes

Important to remember than platforms themselves have shifted and changed over this time MySpace -> Facebook and then iterations on Facebook itself. Source: Pew Internet Parent/Teen Privacy Survey, July 26-September 30, 2012. n=802 teens ages 12-17. Interviews were conducted in English and Spanish and on landline and cell phones. Margin of error for results based on teen social media users is +/- 5.1 percentage points. Comparison data for 2006 comes from the Pew Internet Parents & Teens Survey, October 23-November 19, 2006. n=487 teens with a profile online. Margin of error is +/- 5.2 percentage points.

Presenter

Presentation Notes

Source: Pew Internet Parent/Teen Privacy Survey, July 26-September 30, 2012. n=802 teens ages 12-17. Interviews were conducted in English and Spanish and on landline and cell phones. Margin of error for results based on teen Facebook users is +/- 5.3 percentage points.

Presenter

Presentation Notes

Source: Pew Internet Parent/Teen Privacy Survey, July 26-September 30, 2012. n=802 teens ages 12-17. Interviews were conducted in English and Spanish and on landline and cell phones. Margin of error for results based on teen social media users is +/- 5.1 percentage points. Note: Columns marked with a superscript letter (a) or indicate a statistically significant difference between that row and the row designated by that superscript letter. Statistical significance is determined inside the specific section covering each demographic trait.

Presenter

Presentation Notes

Source: Pew Research Center Internet & American Life Project Teens and Privacy Survey, July 26 – September 30, 2012. n=802 parents of 12-17 year olds, including an oversample of minority families. N=781 for parents of teen internet users. Interviews were conducted in English and Spanish and on landline and cell phones. Margin of error is +/- 4.5 percentage points for the total sample of parents and +/- 4.6 percentage points for parents of teen internet users. Note: A full report that examines parents’ attitudes and actions around their children’s online privacy is available here: http://www.pewinternet.org/Reports/2012/Teens-and-Privacy.aspx

Teens don’t always have a good understanding about how their personal data is used: Middle Schooler: “Anyone who isn’t friends with me cannot see anything about my profile except my name and gender. I don’t believe that [Facebook] would do anything with my info.” High Schooler: “I don’t know if Facebook gives access to others. I hope not.” High School Boy: “I don’t think [Facebook] should give anyone access to profile information.” High School Girl: “It depends on what kind of profile information they’d share. If it was only my age and gender, I wouldn’t mind. If they went into detail and shared personal things, I would mind!” High school boy: “I don’t think it would be fair because it is my information and should not be shared with others, unless I decide to.”

Presenter

Presentation Notes

When asked whether they thought Facebook gives anyone else access to the information they share: Data presented in this slide is from a series of online focus groups in mid-late 2012.


Impactprogram.org: What our analytics show

Repository for IMPACT’s innovative research Sexual health resources for LGBT youth Original videos Weekly blogs for youth and researchers

Presenter

Presentation Notes

(from Website Strategic Plan) Purpose of Website: To establish IMPACT’s visibility and credibility by sharing information about our mission, faculty and staff, research studies, and work or volunteer opportunities. To house IMPACT’s research findings in one online location and disseminate them to the public through a manuscript page and research blog. To serve as an online resource for LGBT youth through a blog, videos, quizzes, and links. To provide a space for us to experiment with technology that will keep us on the cutting edge in our field. Audience Research colleagues; LGBT youth and adults; Parents, teachers, allies; College and graduate students; Potential research participants


• Keywords searched for • How they got to site • What network they are on (e.g. Comcast,

Northwestern, US Department of Justice, etc).


Case Study


Overview of Just/Us Study • Goal to reach African American and Latino youth with HIV

prevention education on Facebook. – These groups are at higher risk compared to white youth and less

likely to be engaged in HIV prevention research.

• Enrolled N = 1,588 in an RCT through community recruitment, newspapers ads, and online network recruitment. Ages 16-25 with waiver of parental permission.

• Intervention involved multiple updates each day about sexual health, plus a facebook page with more information.

• Outcomes include attitudes towards, norms for, and implementation of healthy sexual behavior.


Ethical Issues


Case Study 2: G2G online focus groups


Tension of confidentiality/ease of enrollment versus data quality


Why does data quality matter? • What are the implications of fraud in online research?

– Compromised validity of findings – Waste of resources

• Increasing adoption of web-based data collection in health research

means we need to be aware of these issues or risk general skepticism of this method.

• Important to make sure data collected through this modality are valid

and reliable, and that conclusions are accurate. – Duplication – Falsification – Validity and reliability of responses (not the major focus of this

presentation)


How common are these issues?

• Invalid entries occur commonly in web-based research with MSM. – Konstan and colleagues (2005) found that 11% of

entries in their sample (N=1,150) were duplicate entries from participants.

– Bowen and colleagues (2008) found that

approximately 1/3 of the 1,900 total submissions were multiple entries.


How common are these issues?

• In Bauermeister’s work with young adults (18-24)... – In a sample of single YMSM filling out a survey,

10% were fraudulent/duplicate cases. • iTunes gift card ($15)

– In a sample of emerging adults (N=3,447), 16%

were fraudulent/duplicate cases. • VISA e-card ($20-$75)


Bockting’s NICHD-Funded “All Gender Health” Study

• ~70% fraudulent cases • Prevention used

– Phone number verification

• Detection used – IP address lookup – Mailing address lookup – PayPal name in receipt – Inconsistencies in longitudinal responses


Suggested Best Practices

• To be published in a forthcoming commentary


Preventing falsified data… • Recruitment

– Eligibility screeners that don’t explicitly state eligibility criteria and include sham questions

– Limiting URL placement through robot.txt instructions – Monitoring ULR awareness (google alerts, regular linkto

searches) – Targeting ads to only those who meet some eligibility criteria – Multiple steps in enrollment procedure to allow chance to very

email address and potentially other contact information. Run in waiting period (don’t find out if eligible until after a set time).

– Restriction of one submission per IP address or computer (using cookies)

– Use of CAPTCHAS (Completely Automated Public Turing test to tell Computers and Humans Apart)

– Make payment require provision of identifying information (name, mailing address)


Detecting falsified data… • Automated Procedures

– IP match – Time to completion – Consistency of responses (i.e. age matches DOB) – Track referral URL – Creating statistical algorithms for matches – Must call from phone into system to confirm reported number

• Manual Procedures – Cross-checking entries from similar IPs – Reverse IP lookup (issues with server location;

http://www.networksolutions.com/whois/index.jsp) – Flagging cases – The “you have been flagged” email – Compare information against public sources like facebook

http://www.networksolutions.com/whois/index.jsp


Next generation fraud detection • Algorithms that can predict users demographic

characteristics based on provided information.

• Identity verification systems that seek to establish a mapping from a person’s online identity to their real life identity (e.g., LexisNexis Identity Management). Participants must provide personal data online, to the identity verification firm. The firm then checks public and private databases for a match and prompts the user to answer questions based on the records. An identity "score" is calculated, and the identity of the online user is either given the "verified" status, or not, based on the score. E.g. “What is your mother’s maiden name?”


Points for discussion

• Tension between making enrollment easy to facilitate participation and needing to prevent fraud.

• Use of precious financial and staff resources for fraud detection and prevention.

• IRB consideration.


My experience with online sexual health research 1999 LGBT College Student Survey of

how “having sex” is defined Recruited through LGBT University Student

Organizations. Online survey.

2002 Online daily diary study Recruited through free banner ads on high volume gay websites. Online survey and diaries.

2007 Project Q2 Recruited using respondent driven sampling (RDS). Follow up surveys could be done online.

2007 1.0 Recruited when testing HIV negative at clinics in Chicago. Online intervention RCT.

2011 Keep it up! 1.5 Recruited through Chicago community organization. Online intervention service.

2012 Recruited online with Facebook ads. Online sexual health education.

2012 Recruited online through banner ads. Evaluation of rapid at home HIV testing

2012 Recruited when testing HIV negative at clinics in Atlanta, Chicago, and New York. Online

intervention RCT.

2012 Guy to Guy Recruited online through LGBT youth oriented websites. Focus groups and text intervention RCT.

Presenter

Presentation Notes

First online study at IU.


Thank you

• [email protected] • Impactprogram.org

mailto:[email protected]

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