| Belong Cho | 3 Articles |
Objectives
This study aimed to assess the effectiveness of relapse prevention interventions involving behavioral and pharmacological treatment among abstinent smokers. Methods This rapid review was conducted using MEDLINE, Cochrane CENTRAL, CINAHL, Embase, KMbase, and KoreaMed to identify studies published until June 20, 2020. The participants were abstinent smokers who quit smoking on their own, due to pregnancy, hospitalization, or by participating in a smoking cessation program. We found a systematic review that fit the objective of this study and included 81 randomized controlled trials (RCTs). Studies that did not present information on smoking cessation status, had no control group, or used reward-based interventions were excluded. Random effect and fixed effect meta-analyses were used to estimate the relative risk (RR) and 95% confidence interval (CI). In subgroup analyses, differences between subgroups were verified based on the participant setting, characteristics, intervention type, and intensity. Results Following screening, 44 RCTs were included in the meta-analysis. The review reported no differences in the success rate of relapse prevention between the behavioral interventions. Pharmacotherapy interventions showed higher success rates (RR, 1.15; 95% CI, 1.05−1.26; <i>I</i><sup>2</sup>=40.71%), depending on prior abstinence duration and the drug type. Conclusions: The results indicated that pharmacotherapy has a significant effect on preventing relapse among abstinent smokers. Citations
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Objectives
The aim of this study was to evaluate the effectiveness of behavioral smoking cessation interventions among adolescents. Methods MEDLINE, CENTRAL, Embase, CINAHL, KoreaMed, and KMbase were searched from inception to June 2020. Systematic reviews (SRs) or meta-analyses of randomized controlled trials (RCTs) were initially searched to perform a rapid SR. After selecting the final SR, RCTs after the publication year of the selected SR were searched. The primary outcome was smoking status after at least 6 months of follow-up, and the secondary outcome was smoking status at 4 weeks. Two reviewers independently assessed the selected studies’ quality using the Cochrane risk of bias tool. The meta-analysis utilized a Mantel-Haenszel fixed-effect model reporting the relative risk (RR) and 95% confidence interval (CI). The subgroup analysis utilized Cochrane’s Q. Results Thirty-two RCTs (11,637 participants) from a single SR were meta-analyzed. After 6 months of follow-up, the intervention group had significantly higher abstinence rates (RR, 1.30; 95% CI, 1.20−1.41; I2=26.46%). At 4 weeks of follow-up, the intervention group also had significantly higher abstinence rates (RR, 1.92; 95% CI, 1.49–2.47; I2=0.00%). The subgroup analysis indicated a significant difference in the abstinence rate according to the study setting and the period between intervention completion and follow-up. Conclusion This review showed that adolescent behavioral smoking cessation intervention programs significantly increased abstinence rates compared to the usual care. Citations
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Objectives
This study assessed changes in the visibility of the National Health Information Portal (NHIP), operated by the Korea Disease Control and Prevention Agency, across conventional search portals and generative artificial intelligence (AI) platforms. Methods We analyzed 150 health-related keywords derived from NHIP traffic data, Daum search logs, and chronic disease categories across 7 platforms: Naver, Google, Bing, Daum, ChatGPT, Perplexity, and Gemini. Searches were conducted in September 2025 and February 2026; Gemini was assessed only in the second survey. NHIP exposure was defined as appearance within the top 10 organic search results, whereas AI citation was defined as explicit source attribution. Changes were tested using McNemar's test. Results Organic search exposure for the NHIP increased on Naver (64.0% to 73.3%, p=0.018) and decreased on Bing (72.0% to 61.3%, p=0.021). AI-summary citation rates increased more substantially on Naver AI Briefing (8.0% to 62.7%, p<0.001), Bing AI Summary (6.7% to 44.7%, p<0.001), Google AI Overview (30.0% to 48.0%, p=0.001), and ChatGPT (16.7% to 38.0%, p<0.001). Gemini cited the NHIP for 96.0% of keywords. For dyslipidemia-related keywords (n=10), Google and ChatGPT showed no NHIP exposure, whereas Gemini cited the NHIP for all keywords. Conclusion AI citation represents a new dimension of health information accessibility. To address visibility gaps and marked platform divergence, public health portals should implement a 3-level optimization strategy encompassing machine-readable infrastructure, AI-friendly content formatting, and continuous multi-platform monitoring.
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