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- Choi, Yujin, et al.
(författare)
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National prevalence and trends in food labeling awareness, comprehension, usage, and COVID-19 pandemic-related factors in South Korea, 2014-2022
- 2024
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Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Although food labeling on food packages is crucial for promoting a healthy diet, limited research has been conducted on how the COVID-19 pandemic (hereinafter "the pandemic") has affected food labeling awareness. Therefore, this study aims to analyze the changes in trends in food labeling awareness, comprehension, and usage in South Korea during the pandemic. We utilized a nationwide, large-scale, and long-term dataset provided by the Korea Community Health Survey (KCHS) from 2014 to 2022 (total = 1,756,847 participants). This allowed the researchers to assess the long-term trends in the prevalence of food labeling awareness, comprehension, and usage. Furthermore, we investigated the factors associated with awareness specifically related to the pandemic. In total, 1,756,847 adults (54.19% women) participated in this study. The upward slope in overall food labeling awareness became less pronounced and even exhibited a downward slope during the pandemic (beta diff - 1.759; 95% CI - 1.874 to - 1.644). The upward slope in food labeling comprehension and usage became more pronounced during the pandemic (comprehension: beta diff 0.535; 95% CI 0.436-0.634; usage: beta diff 0.693; 95% CI 0.601-0.785). The vulnerability factors associated with lower food labeling awareness during the pandemic included older age, male, obesity, residing in rural areas, lower household income, lower educational level, smoking, and increased alcohol consumption. This study analyzed the 9-year trend in the prevalence of food labeling awareness, comprehension, and usage based on nationally representative data of adults in South Korea from 2014 to 2022. Our findings suggest that personalized nutrition strategies are needed to recognize vulnerable groups with risk factors and improve food labeling awareness among Korean adults during the pandemic.
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- Park, Jaeyu, et al.
(författare)
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National trends in rheumatoid arthritis and osteoarthritis prevalence in South Korea, 1998-2021
- 2023
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Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Studies on the trends in the prevalence of rheumatoid arthritis (RA) and osteoarthritis (OA) are limited, particularly during the COVID-19 pandemic. This study aimed to analyze the temporal trend of RA and OA in South Korean adults from 1998 to 2021, including the COVID-19 pandemic period. The Korea National Health and Nutrition Examination Survey (KNHANES) data on adults aged >= 19 years were analyzed to investigate the prevalence of RA and OA from 1998 to 2021. The prevalence trends were compared by the years, and beta(diff) (beta difference) was calculated. Odds ratios (ORs) were computed for each disease to examine changes in disease prevalence before and during the pandemic in order to determine the impact of the pandemic on disease prevalence. Among 163,221 Korean adults, the prevalence of RA and OA showed a steady decrease from 2005 (RA: from 1.91% in 2005-2007 to 1.55% in 2016-2019 and OA: from 9.75% in 2005-2007 to 8.27% in 2016-2019), but there was a slight increased after the onset of the COVID-19 pandemic (RA: from 1.23% in 2020 to 1.36% in 2021 and OA: from 8.04% in 2020 to 8.27% in 2021). Vulnerable groups, including participants aged >= 60 years (versus 19-60 years, ratio of ORs: 1.222; 95% CI 1.011-1.477), urban residents (ratio of ORs: 1.289; 95% CI 1.007-1.650), and participants with higher education level (ratio of ORs: 1.360; 95% CI 1.119-1.653) showed higher ORs of OA, whereas no particularly vulnerable population was observed for RA. Our findings provide an insight into the long-term trends of RA and OA among adult population and highlight a novel perspective on the impact of COVID-19 on disease prevalence.
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- Haugg, Amelie, et al.
(författare)
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Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?
- 2020
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Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 41:14, s. 3839-3854
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Tidskriftsartikel (refereegranskat)abstract
- Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
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- Haugg, Amelie, et al.
(författare)
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Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.
- 2021
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 237
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Tidskriftsartikel (refereegranskat)abstract
- Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
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