e.g. mhealth
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Skip search results from other journals and go to results- 2 JMIR Formative Research
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Unhealthy alcohol use (UAU) costs the United States nearly US $250 billion per year [1], and alcohol-related deaths among those aged 16 years and older recently increased by 25% [2]. Emergency departments (EDs), where alcohol-related visits are rising [3-5], are sometimes the only health care touchpoint for patients with UAU, making it a promising point of intervention [6,7]. SMS text messages are one of the most ubiquitous and salient modes of mobile health interventions.
JMIR Form Res 2025;9:e65187
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One randomized trial examining the impact of influencer marketing—the promotion of products by popular web-based celebrities—showed that influencer marketing of unhealthy foods on social media significantly increased children’s consumption of unhealthy snacks [40,41].
Exposure to unhealthy food advertisements that feature celebrity influencers is concerning as it can capture adolescents’ attention through its appealing features.
J Med Internet Res 2025;27:e53188
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Television advertising of unhealthy foods is a big driver of children’s exposure to unhealthy food marketing [10-12]. Without question, children who are highly exposed to powerful marketing of HFSS foods are vulnerable to negative food behaviors that are conducive to overweight and obesity development [13-15]. Increased exposure of children to the marketing of unhealthy foods increases purchase requests [13,14] and develops tastes, preferences, and habits [16-19] for these foods.
JMIR Pediatr Parent 2024;7:e63410
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There were significant differences in the distribution of all covariates between “less unhealthy” and “more unhealthy” groups. Nearly half (1010/2080, 48.6%) of the participants in the “more unhealthy” group were single, 60.7% (1263/2080) of them were male, 67.3% (1400/2080) of them had at least a university education, and 71.9% (1495/2080) of “more unhealthy” participants were dependent workers.
JMIR Public Health Surveill 2024;10:e50189
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The first layer acted as a screening mechanism, directing unhealthy CXR images to the second layer for further classification into COVID-19 and non-COVID-19 images. This model has been thoroughly compared with other preprocessing techniques and methods to assess its effectiveness.
The first layer of the MLHC-COVID-19 uses the highest performance model between DTs, SVMs, and neural networks (NNs) to differentiate between healthy and unhealthy CXR images.
JMIR Form Res 2023;7:e42324
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