Study Design and Lessons Learned From Using Google Takeout Donations for Suicide Prevention Research: Prospective Cohort Study
7 affiliations
- 1Department of Psychiatry and Behavioral Sciences, School of Medicine, University of Washington, 325 9th Avenue, Box 359911, Seattle, WA, 98104, United States, 1 206-744-1752, 1 206-744-3231
- 2Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- 3Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Boston, MA, United States
- 4School of Medicine, University of Washington, Seattle, WA, United States
- 5University of Michigan, Ann Arbor, MI, United States
- 6VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States
- 7Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, United States
Abstract
Background: Social media and search engine tech companies, such as Google, use the wealth of data at their disposal to create algorithms to predict suicide risk and provide interventions based on company policies and outputs from these algorithms. Neither the details of these algorithms nor their accuracy in risk prediction are shared with the public; thus, research is needed to verify whether algorithms applied to search engine data can accurately predict suicide risk in a platform user. This paper provides new information about the methodological challenges in studying the value of search engine data for suicide prediction.
Objective: This paper describes the study methods and experiences regarding recruiting and retaining participants in a year-long, fully remote prospective cohort study of suicide prediction in people with variable risk of suicide, using Google Search data.
Methods: We aimed to recruit 1000 United States–dwelling and English-speaking adults with current suicidal ideation who fell into 1 of 3 categories: participants with a suicide attempt in the past year, participants with a lifetime suicide attempt but not one in the past year, and participants with no suicide attempt in their lifetime. Recruitment sources included online advertisements hosted on various websites, to differing levels of success. Participants completed online surveys and an interview via Zoom to enroll in the study and then were sent follow-up surveys every 2 weeks for the year following initial enrollment. Recruitment was conducted from March 2022 to January 2024, with follow-up data being collected through February 2025.
Results: Of 15,120 records created in our Research Electronic Data Capture (REDCap) survey system, 485 participants were eligible to participate in the prospective year of the study. Recruitment challenges were mainly due to participant dropout across all enrollment time points, with 63.7% (9636/15,120) of the prescreening sample dropping out or refusing. Other issues included the difficulty of downloading Google Search data (446/3277, 13.6%) and not having enough data to meet the inclusion criteria among those who were able to successfully donate (252/3277, 7.7%). Lastly, the study encountered fraudulent behavior, with 489 of 15,120 records (3.2%) being identified as fraudulent over the course of the study.
Conclusions: Despite past success with remote clinical research methods, we found that recruitment can be significantly hampered by fraudulent participation, despite the use of state-of-the-art methods to mitigate this problem. Further, the ability to acquire useful Google Search data is extremely challenging and necessitates the use of technical assistance and continuous monitoring of Google Takeout data structure to ensure accurate and complete data donations. The solutions we provide for this type of research should be helpful for future endeavors. However, research is still limited by the barriers to obtaining data from technology companies. These limitations have significant implications in detecting the accuracy and utility of suicide prediction from search engine data.
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BibTeX
@article{comtois2026study,
title = {{Study Design and Lessons Learned From Using Google Takeout Donations for Suicide Prevention Research: Prospective Cohort Study}},
author = {Katherine Anne Comtois and Payton Smythe and Amanda Kerbrat and Ethan H Kim and Natalie Crouch and Ava Homiar and Brittany Mosser and Nichole Sams and Michelle L Harvey and Courtney L Bagge and Trevor Cohen and Patricia A Arean},
journal = {Journal of Medical Internet Research},
year = {2026},
month = nov,
doi = {10.2196/83637},
url = {https://doi.org/10.2196/83637},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Study Design and Lessons Learned From Using Google Takeout Donations for Suicide Prevention Research: Prospective Cohort Study
AU - Katherine Anne Comtois
AU - Payton Smythe
AU - Amanda Kerbrat
AU - Ethan H Kim
AU - Natalie Crouch
AU - Ava Homiar
AU - Brittany Mosser
AU - Nichole Sams
AU - Michelle L Harvey
AU - Courtney L Bagge
AU - Trevor Cohen
AU - Patricia A Arean
PY - 2026
DA - 2026/11/03
JO - Journal of Medical Internet Research
DO - 10.2196/83637
UR - https://doi.org/10.2196/83637
AB - Background: Social media and search engine tech companies, such as Google, use the wealth of data at their disposal to create algorithms to predict suicide risk and provide interventions based on company policies and outputs from these algorithms. Neither the details of these algorithms nor their accuracy in risk prediction are shared with the public; thus, research is needed to verify whether algorithms applied to search engine data can accurately predict suicide risk in a platform user. This paper provides new information about the methodological challenges in studying the value of search engine data for suicide prediction. Objective: This paper describes the study methods and experiences regarding recruiting and retaining participants in a year-long, fully remote prospective cohort study of suicide prediction in people with variable risk of suicide, using Google Search data. Methods: We aimed to recruit 1000 United States–dwelling and English-speaking adults with current suicidal ideation who fell into 1 of 3 categories: participants with a suicide attempt in the past year, participants with a lifetime suicide attempt but not one in the past year, and participants with no suicide attempt in their lifetime. Recruitment sources included online advertisements hosted on various websites, to differing levels of success. Participants completed online surveys and an interview via Zoom to enroll in the study and then were sent follow-up surveys every 2 weeks for the year following initial enrollment. Recruitment was conducted from March 2022 to January 2024, with follow-up data being collected through February 2025. Results: Of 15,120 records created in our Research Electronic Data Capture (REDCap) survey system, 485 participants were eligible to participate in the prospective year of the study. Recruitment challenges were mainly due to participant dropout across all enrollment time points, with 63.7% (9636/15,120) of the prescreening sample dropping out or refusing. Other issues included the difficulty of downloading Google Search data (446/3277, 13.6%) and not having enough data to meet the inclusion criteria among those who were able to successfully donate (252/3277, 7.7%). Lastly, the study encountered fraudulent behavior, with 489 of 15,120 records (3.2%) being identified as fraudulent over the course of the study. Conclusions: Despite past success with remote clinical research methods, we found that recruitment can be significantly hampered by fraudulent participation, despite the use of state-of-the-art methods to mitigate this problem. Further, the ability to acquire useful Google Search data is extremely challenging and necessitates the use of technical assistance and continuous monitoring of Google Takeout data structure to ensure accurate and complete data donations. The solutions we provide for this type of research should be helpful for future endeavors. However, research is still limited by the barriers to obtaining data from technology companies. These limitations have significant implications in detecting the accuracy and utility of suicide prediction from search engine data.
SN - 1439-4456
SN - 1438-8871
ER -