@Article{info:doi/10.2196/56788,
author="Parker, N. Jayelin
and Rager, L. Theresa
and Burns, Jade
and Mmeje, Okeoma",
title="Data Verification and Respondent Validity for a Web-Based Sexual Health Survey: Tutorial",
journal="JMIR Form Res",
year="2024",
month="Dec",
day="9",
volume="8",
pages="e56788",
keywords="sexually transmitted infections",
keywords="adolescent and young adults",
keywords="sexual health",
keywords="recruitment",
keywords="survey design",
keywords="social media",
keywords="data verification",
keywords="web-based surveys",
keywords="data integrity",
keywords="social media advertisements",
keywords="online advertisements",
keywords="STI",
keywords="STD",
keywords="sexual health survey",
keywords="sexually transmitted disease",
abstract="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. ",
doi="10.2196/56788",
url="https://formative.jmir.org/2024/1/e56788"
}


@Article{info:doi/10.2196/62963,
author="Golden, Ashleigh
and Aboujaoude, Elias",
title="Describing the Framework for AI Tool Assessment in Mental Health and Applying It to a Generative AI Obsessive-Compulsive Disorder Platform: Tutorial",
journal="JMIR Form Res",
year="2024",
month="Oct",
day="18",
volume="8",
pages="e62963",
keywords="artificial intelligence",
keywords="ChatGPT",
keywords="generative artificial intelligence",
keywords="generative AI",
keywords="large language model",
keywords="chatbots",
keywords="machine learning",
keywords="digital health",
keywords="telemedicine",
keywords="psychotherapy",
keywords="obsessive-compulsive disorder",
doi="10.2196/62963",
url="https://formative.jmir.org/2024/1/e62963",
url="http://www.ncbi.nlm.nih.gov/pubmed/39423001"
}


@Article{info:doi/10.2196/58627,
author="Pagoto, Sherry
and Lueders, Natalie
and Palmer, Lindsay
and Idiong, Christie
and Bannor, Richard
and Xu, Ran
and Ingels, Spencer",
title="Best Practices for Designing and Testing Behavioral and Health Communication Interventions for Delivery in Private Facebook Groups: Tutorial",
journal="JMIR Form Res",
year="2024",
month="Sep",
day="4",
volume="8",
pages="e58627",
keywords="social media",
keywords="Facebook",
keywords="behavioral intervention",
keywords="health communication",
keywords="Facebook groups",
doi="10.2196/58627",
url="https://formative.jmir.org/2024/1/e58627"
}


@Article{info:doi/10.2196/54407,
author="Pretorius, Kelly",
title="A Simple and Systematic Approach to Qualitative Data Extraction From Social Media for Novice Health Care Researchers: Tutorial",
journal="JMIR Form Res",
year="2024",
month="Jul",
day="9",
volume="8",
pages="e54407",
keywords="social media analysis",
keywords="data extraction",
keywords="health care research",
keywords="extraction tutorial",
keywords="Facebook extraction",
keywords="Facebook analysis",
keywords="safe sleep",
keywords="sudden unexpected infant death",
keywords="social media",
keywords="analysis",
keywords="systematic approach",
keywords="qualitative data",
keywords="Facebook",
keywords="health-related",
keywords="maternal perspective",
keywords="maternal perspectives",
keywords="sudden infant death syndrome",
keywords="mother",
keywords="mothers",
keywords="women",
keywords="United States",
keywords="SIDS",
keywords="SUID",
keywords="post",
keywords="posts",
doi="10.2196/54407",
url="https://formative.jmir.org/2024/1/e54407",
url="http://www.ncbi.nlm.nih.gov/pubmed/38980712"
}


@Article{info:doi/10.2196/51013,
author="Bandiera, Carole
and Pasquier, J{\'e}r{\^o}me
and Locatelli, Isabella
and Schneider, P. Marie",
title="Using a Semiautomated Procedure (CleanADHdata.R Script) to Clean Electronic Adherence Monitoring Data: Tutorial",
journal="JMIR Form Res",
year="2024",
month="May",
day="22",
volume="8",
pages="e51013",
keywords="medication adherence",
keywords="digital technology",
keywords="digital pharmacy",
keywords="electronic adherence monitoring",
keywords="data management",
keywords="data cleaning",
keywords="research methodology",
keywords="algorithms",
keywords="R",
keywords="semiautomated",
keywords="code",
keywords="coding",
keywords="computer science",
keywords="computer programming",
keywords="medications",
keywords="computer script",
abstract="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 ($\Delta$+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. ",
doi="10.2196/51013",
url="https://formative.jmir.org/2024/1/e51013",
url="http://www.ncbi.nlm.nih.gov/pubmed/38776539"
}


@Article{info:doi/10.2196/51202,
author="Draucker, Claire
and Carri{\'o}n, Andr{\'e}s
and Ott, A. Mary
and Knopf, Amelia",
title="Assessing Facilitator Fidelity to Principles of Public Deliberation: Tutorial",
journal="JMIR Form Res",
year="2023",
month="Dec",
day="13",
volume="7",
pages="e51202",
keywords="public deliberation",
keywords="deliberative democracy",
keywords="bioethics",
keywords="engagement",
keywords="theory",
keywords="process",
keywords="ethical conflict",
keywords="ethical",
keywords="ethics",
keywords="coding",
keywords="evaluation",
keywords="tutorial",
keywords="biomedical",
keywords="HIV",
keywords="HIV prevention",
keywords="HIV research",
doi="10.2196/51202",
url="https://formative.jmir.org/2023/1/e51202",
url="http://www.ncbi.nlm.nih.gov/pubmed/38090788"
}


@Article{info:doi/10.2196/37269,
author="Sudre, Gustavo
and Bagi{\'c}, I. Anto
and Becker, T. James
and Ford, P. John",
title="An Emerging Screening Method for Interrogating Human Brain Function: Tutorial",
journal="JMIR Form Res",
year="2023",
month="Apr",
day="27",
volume="7",
pages="e37269",
keywords="screening",
keywords="brain function",
keywords="cognition",
keywords="magnetoencephalography",
keywords="MEG",
keywords="neuroimaging",
keywords="tutorial",
keywords="tool",
keywords="cognitive test",
keywords="signal",
keywords="cognitive function",
doi="10.2196/37269",
url="https://formative.jmir.org/2023/1/e37269",
url="http://www.ncbi.nlm.nih.gov/pubmed/37103988"
}