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Exploring Different Incentive Structures Among US Adults Who Use e-Cigarettes to Optimize Retention in Longitudinal Web-Based Surveys: Case Study

Exploring Different Incentive Structures Among US Adults Who Use e-Cigarettes to Optimize Retention in Longitudinal Web-Based Surveys: Case Study

In this case study, we examine participant progress through a survey and resulting participant demographics for 2 incentive delivery structures among a sample of US adults who frequently use e-cigarettes to determine the most effective incentive structure for optimizing survey retention while maintaining representativeness.

Elizabeth Crespi, Johanna Heller, Jeffrey J Hardesty, Qinghua Nian, Joshua K Sinamo, Kevin Welding, Ryan David Kennedy, Joanna E Cohen

J Med Internet Res 2023;25:e49354

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A Survey of Patient Demographics in Inflammatory Skin Disease Case Reports

A Survey of Patient Demographics in Inflammatory Skin Disease Case Reports

However, little work has been carried out to assess which diseases and demographics have a greater number of published case reports associated with them. In this paper, we use a novel data set to explore the potential biases in case report publications in inflammatory skin conditions by disease for demographic factors.

Ross O'Hagan, Stella A Caldas, Patrick M Brunner, Benjamin Ungar

JMIR Dermatol 2023;6:e49070

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Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study

Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study

Among those with no formal education, demographics, cognitive tests, and IADL had AUCs of 0.79 (95% CI 0.78-0.79), 0.72 (95% CI 0.72-0.73), and 0.71 (95% CI 0.70-0.71), respectively. When making predictions among those with some education, demographics, cognitive tests, and IADL had average AUCs of 0.73 (95% CI 0.72-0.74), 0.64 (95% CI 0.63-0.66), and 0.64 (95% CI 0.62-0.65), respectively. The existing prediction models recreated in this study all had good predictive ability in the general population.

Collin Sakal, Tingyou Li, Juan Li, Xinyue Li

JMIR Aging 2024;7:e53240

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Dermatologic Data From the Global Burden of Disease Study 2019 and the PatientsLikeMe Online Support Community: Comparative Analysis

Dermatologic Data From the Global Burden of Disease Study 2019 and the PatientsLikeMe Online Support Community: Comparative Analysis

Given PLM’s popularity, this study and our previous work [4] analyze user demographics and illuminate the daily struggles, treatment challenges, and emotional impact of high-burden dermatologic conditions identified by the GBD. Greater understanding could build awareness of patient concerns, identify trends and unmet needs in disease management, and ultimately contribute to improved patient-centered care and outcomes.

Mindy D Szeto, Lina Alhanshali, Chandler W Rundle, Madeline Adelman, Michelle Hook Sobotka, Emily Woolhiser, Jieying Wu, Colby L Presley, Jalal Maghfour, John Meisenheimer, Jaclyn B Anderson, Robert P Dellavalle

JMIR Dermatol 2024;7:e50449

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Racial and Ethnic Differences in COVID-19 Outcomes, Stressors, Fear, and Prevention Behaviors Among US Women: Web-Based Cross-sectional Study

Racial and Ethnic Differences in COVID-19 Outcomes, Stressors, Fear, and Prevention Behaviors Among US Women: Web-Based Cross-sectional Study

Other demographics included age, education, employment status, sexual orientation, relationship status, parenthood, household composition and type of occupants, type of residential community (eg, urban or rural), current living situation, and household income. We modified COVID-19 measures developed by the World Health Organization (WHO) for rapid behavioral studies about COVID-19 [20].

Jamila K Stockman, Brittany A Wood, Katherine M Anderson

J Med Internet Res 2021;23(7):e26296

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Telehealth Uptake Among Hispanic People During COVID-19: Retrospective Observational Study

Telehealth Uptake Among Hispanic People During COVID-19: Retrospective Observational Study

Research suggested that telehealth use differed by demographics such as gender, age, and education [14]. Studies have noted gender differences in telehealth use, as male participants are less likely to use telehealth than female participants [3,9,15,16]. Among racial and ethnic groups, Hispanic patients often opt for in-person rather than telehealth visits [5,10]. Whether a patient has access to and knowledge to use telehealth depends on socioeconomic factors [5].

Di Shang, Cynthia Williams, Hera Culiqi

JMIR Med Inform 2024;12:e57717

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The Impact of UK Medical Students’ Demographics and Socioeconomic Factors on Their Self-Reported Familiarity With the Postgraduate Training Pathways and Application Process: Cross-Sectional Study

The Impact of UK Medical Students’ Demographics and Socioeconomic Factors on Their Self-Reported Familiarity With the Postgraduate Training Pathways and Application Process: Cross-Sectional Study

The primary aim of this study was to investigate any difference between medical students’ demographics and their self-reported familiarity with Post-Foundation Training Pathways (PFTPs) and Post-Foundation Application Process (PFAP). The secondary aim was to investigate the difference between demographics and training pathway choices. This was a cross-sectional study using a web-based questionnaire. Bristol Online Surveys (University of Bristol) was used to collect responses.

Kaveh Davoudi, Tushar Rakhecha, Anna Chiara Corriero, Kar Chang Natalie Ko, Roseanne Ismail, Esther R B King, Linda Hollén

JMIR Med Educ 2023;9:e49013

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