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Designing Digital Mental Health Tools to Support the Needs of Black Adults in the United States: Qualitative Analysis

Designing Digital Mental Health Tools to Support the Needs of Black Adults in the United States: Qualitative Analysis

When given the choice, many Black individuals express a preference for providers who are the same race as them (ie, patient-provider race concordance) [20]; however, underrepresentation of providers of color has made it difficult for most Black adults to achieve such concordance [21,22], which may lead to challenges building rapport with their provider [23] and less satisfaction with treatment [24].

Sarah Alexandra Popowski, Jonah Meyerhoff, Olivia Marin Allen, Theresa Nguyen, Terika McCall, Aderonke Bamgbose Pederson, Madhu Reddy, David Mohr, Rachel Kornfield

JMIR Form Res 2025;9:e73279


Community Comfort With Automatic Sharing of Race, Ethnicity, and Language Data Between Health Care Settings: Cross-Sectional Study

Community Comfort With Automatic Sharing of Race, Ethnicity, and Language Data Between Health Care Settings: Cross-Sectional Study

Patient data collection pertaining to race, ethnicity, and language (REL) was first mandated in the United States as part of the Civil Rights Act of 1964 as a means to document race-based discrimination in health care settings and drive institutional and social change [1]. This groundbreaking legislative change was one of the first steps to improve health care disparities among racially and ethnically minoritized people who face marginalization because of systemic oppression.

Noah Brazer, Baylah Tessier-Sherman, Deron Galusha, Sakinah C Suttiratana, Corrine Liu, Katherine K Kim, Mark E Abraham, Marcella Nunez-Smith, Karen H Wang

Interact J Med Res 2025;14:e67288


Race and Ethnicity in Facebook Images and Text: Thematic Analysis

Race and Ethnicity in Facebook Images and Text: Thematic Analysis

Facebook activity varies by race, with racially minoritized groups practicing more content creation and engagement related to race and ethnicity compared to White users and engaging more in the explicit self-presentation of racial and ethnic identities as salient [2-5].

Shaniece Criss, Sarah M Gonzales, Heran Mane, Katrina Makres, Dalmondeh D Nayreau, Vaishnavi Bharadwaj, Hannah G Kim, Thu T Nguyen

JMIR Form Res 2025;9:e62713


Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health

Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health

Appropriate inclusion of race within AI and ML models can identify differences in the outcomes of people with different backgrounds, creating opportunities for mitigation [2]. Yet, numerous examples exist of inappropriate inclusion of race or proxies of race in health-related models, which can harm large segments of the population [3].

Martin C Were, Ang Li, Bradley A Malin, Zhijun Yin, Joseph R Coco, Benjamin X Collins, Ellen Wright Clayton, Laurie L Novak, Rachele Hendricks-Sturrup, Abiodun O Oluyomi, Shilo Anders, Chao Yan

J Med Internet Res 2025;27:e73996


Differential Analysis of Age, Gender, Race, Sentiment, and Emotion in Substance Use Discourse on Twitter During the COVID-19 Pandemic: A Natural Language Processing Approach

Differential Analysis of Age, Gender, Race, Sentiment, and Emotion in Substance Use Discourse on Twitter During the COVID-19 Pandemic: A Natural Language Processing Approach

Substance use (SU) prevalence varies across demographics such as age, gender, race, and ethnicity. During the COVID-19 pandemic, these differences became more pronounced. The pandemic not only increased global SU, with overdose deaths rising by 29.4% [1], but also exacerbated societal and racial inequalities [2,3] and significantly impacted mental health [4-7].

Julina Maharjan, Ruoming Jin, Jennifer King, Jianfeng Zhu, Deric Kenne

JMIR Infodemiology 2025;5:e67333


Association of Social Media Recruitment and Depression Among Racially and Ethnically Diverse Metabolic and Bariatric Surgery Candidates: Prospective Cohort Study

Association of Social Media Recruitment and Depression Among Racially and Ethnically Diverse Metabolic and Bariatric Surgery Candidates: Prospective Cohort Study

Covariates including age, sex, race and ethnicity, BMI, education level, and perceived financial well-being were obtained through self-report. Age was treated as a continuous variable, representing the participants’ age in years. Sex was a categorical variable, categorized as male, female, or other. Race and ethnicity were also categorical variables, encompassing diverse categories such as NHW, NHB, Hispanic, and Other.

Jackson M Francis, Sitapriya S Neti, Dhatri Polavarapu, Folefac Atem, Luyu Xie, Olivia Kapera, Matthew S Mathew, Elisa Marroquin, Carrie McAdams, Jeffrey Schellinger, Sophia Ngenge, Sachin Kukreja, Benjamin E Schneider, Jaime P Almandoz, Sarah E Messiah

JMIR Form Res 2025;9:e58916


Use of mHealth Technology for Improving Exercise Adherence in Patients With Heart Failure: Systematic Review

Use of mHealth Technology for Improving Exercise Adherence in Patients With Heart Failure: Systematic Review

Specifically, this review was done to (1) describe study characteristics of m Health interventions for exercise adherence in HF including details of sample demographics, sample sizes, exercise program, and theoretical frameworks; (2) summarize types of m Health technology used to improve exercise adherence in patients with HF; (3) highlight how the term “adherence” was defined and how it was measured across m Health studies and adherence achieved; and (4) highlight the effect of age, sex, race, NYHA functional

Pallav Deka, Erin Salahshurian, Teresa Ng, Susan W Buchholz, Leonie Klompstra, Windy Alonso

J Med Internet Res 2025;27:e54524


Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study

Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study

We estimated odds ratios (OR) for diabetes by race or ethnicity and asthma status under 4 EHR-based estimation methods that we describe herein. First, “naïve” models were estimated by fitting a logistic regression model for observed diabetes status (DM*) on the total sample (n=454,612).

Sarah Conderino, Rebecca Anthopolos, Sandra S Albrecht, Shannon M Farley, Jasmin Divers, Andrea R Titus, Lorna E Thorpe

JMIR Med Inform 2024;12:e58085


Implementation and Evaluation of a Home-Based Pre-Exposure Prophylaxis Monitoring Option: Protocol for a Randomized Controlled Trial

Implementation and Evaluation of a Home-Based Pre-Exposure Prophylaxis Monitoring Option: Protocol for a Randomized Controlled Trial

Younger age, Black race, and unstable or lower income are each associated with discontinuations and being lost to follow-up [14]. We have observed similar levels of Pr EP retention in the PHSKC Sexual Health Clinic (SHC), with 40% of Pr EP users discontinuing the intervention at least once within 12 months [15].

Chase Cannon, Katherine Holzhauer, Matthew Golden

JMIR Res Protoc 2024;13:e56587