TY - JOUR AU - Parker, N. Jayelin AU - Rager, L. Theresa AU - Burns, Jade AU - Mmeje, Okeoma PY - 2024/12/9 TI - Data Verification and Respondent Validity for a Web-Based Sexual Health Survey: Tutorial JO - JMIR Form Res SP - e56788 VL - 8 KW - sexually transmitted infections KW - adolescent and young adults KW - sexual health KW - recruitment KW - survey design KW - social media KW - data verification KW - web-based surveys KW - data integrity KW - social media advertisements KW - online advertisements KW - STI KW - STD KW - sexual health survey KW - sexually transmitted disease N2 - Background: As technology continues to shape the landscape of health research, the utilization of web-based surveys for collecting sexual health information among adolescents and young adults has become increasingly prevalent. However, this shift toward digital platforms brings forth a new set of challenges, particularly the infiltration of automated bots that can compromise data integrity and the reliability of survey results. Objective: We aimed to outline the data verification process used in our study design, which employed survey programming and data cleaning protocols. Methods: A 26-item survey was developed and programmed with several data integrity functions, including reCAPTCHA scores, RelevantID fraud and duplicate scores, verification of IP addresses, and honeypot questions. Participants aged 15?24 years were recruited via social media advertisements over 7 weeks and received a US $15 incentive after survey completion. Data verification occurred through a 2-part cleaning process, which removed responses that were incomplete, flagged as spam by Qualtrics, or from duplicate IP addresses, or those that did not meet the inclusion criteria. Final comparisons of reported age with date of birth and reported state with state inclusion criteria were performed. Participants who completed the study survey were linked to a second survey to receive their incentive. Responses without first and last names and full addresses were removed, as were those with duplicate IP addresses or the exact same longitude and latitude coordinates. Finally, IP addresses used to complete both surveys were compared, and consistent responses were eligible for an incentive. Results: Over 7 weeks, online advertisements for a web-based survey reached 1.4 million social media users. Of the 20,585 survey responses received, 4589 (22.3%) were verified. Incentives were sent to 462 participants; of these, 14 responses were duplicates and 3 contained discrepancies, resulting in a final sample of 445 responses. Conclusions: Confidential web-based surveys are an appealing method for reaching populations?particularly adolescents and young adults, who may be reluctant to disclose sensitive information to family, friends, or clinical providers. Web-based surveys are a useful tool for researchers targeting hard-to-reach populations due to the difficulty in obtaining a representative sample. However, researchers face the ongoing threat of bots and fraudulent participants in a technology-driven world, necessitating the adoption of evolving bot detection software and tailored protocols for data collection in unique contexts. UR - https://formative.jmir.org/2024/1/e56788 UR - http://dx.doi.org/10.2196/56788 ID - info:doi/10.2196/56788 ER - TY - JOUR AU - Golden, Ashleigh AU - Aboujaoude, Elias PY - 2024/10/18 TI - Describing the Framework for AI Tool Assessment in Mental Health and Applying It to a Generative AI Obsessive-Compulsive Disorder Platform: Tutorial JO - JMIR Form Res SP - e62963 VL - 8 KW - artificial intelligence KW - ChatGPT KW - generative artificial intelligence KW - generative AI KW - large language model KW - chatbots KW - machine learning KW - digital health KW - telemedicine KW - psychotherapy KW - obsessive-compulsive disorder UR - https://formative.jmir.org/2024/1/e62963 UR - http://dx.doi.org/10.2196/62963 UR - http://www.ncbi.nlm.nih.gov/pubmed/39423001 ID - info:doi/10.2196/62963 ER - TY - JOUR AU - Pagoto, Sherry AU - Lueders, Natalie AU - Palmer, Lindsay AU - Idiong, Christie AU - Bannor, Richard AU - Xu, Ran AU - Ingels, Spencer PY - 2024/9/4 TI - Best Practices for Designing and Testing Behavioral and Health Communication Interventions for Delivery in Private Facebook Groups: Tutorial JO - JMIR Form Res SP - e58627 VL - 8 KW - social media KW - Facebook KW - behavioral intervention KW - health communication KW - Facebook groups UR - https://formative.jmir.org/2024/1/e58627 UR - http://dx.doi.org/10.2196/58627 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58627 ER - TY - JOUR AU - Pretorius, Kelly PY - 2024/7/9 TI - A Simple and Systematic Approach to Qualitative Data Extraction From Social Media for Novice Health Care Researchers: Tutorial JO - JMIR Form Res SP - e54407 VL - 8 KW - social media analysis KW - data extraction KW - health care research KW - extraction tutorial KW - Facebook extraction KW - Facebook analysis KW - safe sleep KW - sudden unexpected infant death KW - social media KW - analysis KW - systematic approach KW - qualitative data KW - Facebook KW - health-related KW - maternal perspective KW - maternal perspectives KW - sudden infant death syndrome KW - mother KW - mothers KW - women KW - United States KW - SIDS KW - SUID KW - post KW - posts UR - https://formative.jmir.org/2024/1/e54407 UR - http://dx.doi.org/10.2196/54407 UR - http://www.ncbi.nlm.nih.gov/pubmed/38980712 ID - info:doi/10.2196/54407 ER - TY - JOUR AU - Bandiera, Carole AU - Pasquier, Jérôme AU - Locatelli, Isabella AU - Schneider, P. Marie PY - 2024/5/22 TI - Using a Semiautomated Procedure (CleanADHdata.R Script) to Clean Electronic Adherence Monitoring Data: Tutorial JO - JMIR Form Res SP - e51013 VL - 8 KW - medication adherence KW - digital technology KW - digital pharmacy KW - electronic adherence monitoring KW - data management KW - data cleaning KW - research methodology KW - algorithms KW - R KW - semiautomated KW - code KW - coding KW - computer science KW - computer programming KW - medications KW - computer script N2 - Background: Patient adherence to medications can be assessed using interactive digital health technologies such as electronic monitors (EMs). Changes in treatment regimens and deviations from EM use over time must be characterized to establish the actual level of medication adherence. Objective: We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users. Methods: In addition to raw EM data, we collected adherence start and stop monitoring dates and identified the prescribed regimens, the expected number of EM openings per day based on the prescribed regimen, EM use deviations, and patients? demographic data. The script formats the data longitudinally and calculates each day?s medication implementation. Results: We provided a simulated data set for 10 patients, for which 15 EMs were used over a median period of 187 (IQR 135-342) days. The median patient implementation before and after EM raw data cleaning was 83.3% (IQR 71.5%-93.9%) and 97.3% (IQR 95.8%-97.6%), respectively (?+14%). This difference is substantial enough to consider EM data cleaning to be capable of avoiding data misinterpretation and providing a cleaned data set for the adherence analysis in terms of implementation and persistence. Conclusions: The CleanADHdata.R script is a semiautomated procedure that increases standardization and reproducibility. This script has broader applicability within the realm of digital health, as it can be used to clean adherence data collected with diverse digital technologies. UR - https://formative.jmir.org/2024/1/e51013 UR - http://dx.doi.org/10.2196/51013 UR - http://www.ncbi.nlm.nih.gov/pubmed/38776539 ID - info:doi/10.2196/51013 ER - TY - JOUR AU - Draucker, Claire AU - Carrión, Andrés AU - Ott, A. Mary AU - Knopf, Amelia PY - 2023/12/13 TI - Assessing Facilitator Fidelity to Principles of Public Deliberation: Tutorial JO - JMIR Form Res SP - e51202 VL - 7 KW - public deliberation KW - deliberative democracy KW - bioethics KW - engagement KW - theory KW - process KW - ethical conflict KW - ethical KW - ethics KW - coding KW - evaluation KW - tutorial KW - biomedical KW - HIV KW - HIV prevention KW - HIV research UR - https://formative.jmir.org/2023/1/e51202 UR - http://dx.doi.org/10.2196/51202 UR - http://www.ncbi.nlm.nih.gov/pubmed/38090788 ID - info:doi/10.2196/51202 ER - TY - JOUR AU - Sudre, Gustavo AU - Bagi?, I. Anto AU - Becker, T. James AU - Ford, P. John PY - 2023/4/27 TI - An Emerging Screening Method for Interrogating Human Brain Function: Tutorial JO - JMIR Form Res SP - e37269 VL - 7 KW - screening KW - brain function KW - cognition KW - magnetoencephalography KW - MEG KW - neuroimaging KW - tutorial KW - tool KW - cognitive test KW - signal KW - cognitive function UR - https://formative.jmir.org/2023/1/e37269 UR - http://dx.doi.org/10.2196/37269 UR - http://www.ncbi.nlm.nih.gov/pubmed/37103988 ID - info:doi/10.2196/37269 ER -