e.g. mhealth
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Skip search results from other journals and go to results- 15 Journal of Medical Internet Research
- 9 JMIR Formative Research
- 6 JMIR Research Protocols
- 5 JMIR Public Health and Surveillance
- 3 JMIR Cancer
- 3 JMIR Medical Informatics
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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
J Med Internet Res 2025;27:e54524
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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).
JMIR Med Inform 2024;12:e58085
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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].
JMIR Res Protoc 2024;13:e56587
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Despite substantial evidence that Black YMSM-YTW experience sexual racism in online dating, there is no evidence about whether this results in quantifiable differences in online sexual partnering by race and ethnicity.
Research consistently finds high levels of within–race and ethnicity sexual partnering among young MSM [7,13-17]. A recent study suggested that sexual exclusivity among Black sexual minority men may be partially protective against the psychological impacts of racial discrimination [18].
JMIR Public Health Surveill 2024;10:e54215
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By “minoritized,” we refer to systemic oppression across a range of contexts by the majority group (in the United States, by White communities), and by “ethnoracial” we recognize that cultural groups are typically racialized, meaning treated as a race, and thus use a term that combines “ethnicity” and “race” [8]. Ethnoracial disparities are, therefore, inequalities along ethnic and racial group lines.
JMIR Form Res 2024;8:e55759
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We used ANOVA to assess engagement and changes in clinical outcomes by age, race/ethnicity, and gender.
The study was approved by the WCG Institutional Review Board (Wisdo.001.1/26/2023). Since all data were routinely collected during the intervention, this protocol was considered exempt from additional consent. All data were deidentified. Participants received 1 year of free access to the platform but no other compensation.
JMIR Form Res 2024;8:e58263
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By extension, this study evaluated the interaction effect between race and ethnicity of the participants, perceptions of social media health mis- and disinformation, and trust in cancer information. We hypothesized that the association between perceptions of a lot of mis- and disinformation on social media and trust in cancer information sources would vary by race and ethnicity.
JMIR Cancer 2024;10:e54162
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We obtained a sample of adults meant to be representative of the intersection of age, race and ethnicity, and sex-assigned at birth using US census data.
We collected information on age (in years), gender identity (male, female, and nonbinary), yearly income (in US dollars), highest educational attainment, race, and ethnicity. Race and ethnicity were combined and defined as Asian, Hispanic, non-Hispanic Black, non-Hispanic White, or other (eg, multiracial or Middle Eastern).
JMIR Form Res 2024;8:e49780
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The Political Economy of Digital Health Equity: Structural Analysis
However, a growing body of research suggests that digital health can also exacerbate health inequities for those excluded from its benefits for reasons of cost, digital literacy, and structural discrimination based on age, race, ethnicity, or socioeconomic status [7,8].
Often referred to as the “health-related digital divide,” the exacerbation of health inequities by digital health technologies has been widely discussed in health informatics and digital health literature [7,9,10].
J Med Internet Res 2024;26:e46971
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For example, in a study by Mak and colleagues [10], they reviewed 379 National Institute of Mental Health–funded clinical trials for various mental health disorders published between 1995 and 2004 to investigate how many trials reported sex, race, and ethnicity. They found that 91.6% of the National Institute of Mental Health–funded published trials reported sex. However, only 47.8% included race or ethnicity in their demographics, and 25.6% had incomplete race or ethnicity information.
J Med Internet Res 2024;26:e50780
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