Min Jin Zhang, Tzu Tsun Luk, Sai Yin Ho, Man Ping Wang, Tai Hing Lam, Yee Tak Derek Cheung
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Over 14 consecutive days, each participant completed 5 fixed-interval, signal-contingent EMAs daily via a smartphone app. Each EMA asked about the number and types of alcohol marketing exposures, the amount and types of alcohol used, and whether any alcohol was purchased, all within the past 3 hours. We used 2-part models, including multilevel logistic regressions and multilevel gamma regressions, to examine if the number of alcohol marketing exposure was associated with subsequent alcohol use and alcohol purchase.</p><p><strong>Results: </strong>A total of 49 students participated, with 33% (16/49) being male. The mean age was 22.6 (SD 2.6) years. They completed 2360 EMAs (completion rate: 2360/3430, 68.8%). Participants reported exposure to alcohol marketing in 5.9% (140/2360), alcohol use in 6.1% (145/2360), and alcohol purchase in 2.4% (56/2360) of all the EMAs. At the between-person level, exposure to more alcohol marketing predicted a higher likelihood of alcohol use (adjusted odd ratio [AOR]=3.51, 95% CI 1.29-9.54) and a higher likelihood of alcohol purchase (AOR=4.59, 95% CI 1.46-14.49) the following day. Exposure to more alcohol marketing did not increase the amount of alcohol use or frequency of alcohol purchases the following day in participants who used or purchased alcohol. At the within-person level, exposure to more alcohol marketing was not associated with a higher likelihood of alcohol use, amount of alcohol use, higher likelihood of alcohol purchase, or frequency of alcohol purchases the following day (all P<sub>s</sub>>.05). Each additional exposure to alcohol marketing within 1 week predicted an increase of 0.85 alcoholic drinks consumed in the following week (adjusted B=0.85, 95% CI 0.09-1.61). On days of reporting alcohol use, the 3 measures for alcohol marketing receptivity were not associated with more alcohol use or purchase (all P<sub>s</sub>>.05).</p><p><strong>Conclusions: </strong>By using EMA, we provided the first evidence for the effect of alcohol marketing exposure on initiating alcohol use and purchase in current-drinking university students. 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引用次数: 0
摘要
背景:酒精营销接触、酒精使用和购买之间的关系已被广泛研究。然而,利用移动健康工具在真实世界环境中检验因果关系的前瞻性研究却很有限:我们使用生态瞬间评估(EMA)来研究酒精营销暴露对大学生中任何酒精使用、酒精使用量、任何酒精购买和酒精购买频率的人内和人际影响:从 2020 年 1 月到 6 月,我们通过 EMA 对报告当前饮酒的香港大学生进行了一项前瞻性队列研究。在连续 14 天内,每位受试者每天通过智能手机应用程序完成 5 次固定间隔、信号相关的 EMA。每次 EMA 都会询问过去 3 小时内接触酒精营销的次数和类型、使用酒精的数量和类型以及是否购买了酒精。我们使用了两部分模型,包括多层次逻辑回归和多层次伽马回归,来研究酒精营销接触的次数是否与随后的饮酒和购酒有关:共有 49 名学生参与调查,其中男生占 33%(16/49)。平均年龄为 22.6 (SD 2.6)岁。他们完成了 2360 次 EMA(完成率:2360/3430,68.8%)。在所有 EMA 中,5.9%(140/2360)的参与者报告接触过酒精营销,6.1%(145/2360)的参与者报告使用过酒精,2.4%(56/2360)的参与者报告购买过酒精。在人与人之间的水平上,接触更多的酒精营销可预测次日饮酒的可能性更高(调整奇数比 [AOR]=3.51,95% CI 1.29-9.54),购买酒精的可能性更高(AOR=4.59,95% CI 1.46-14.49)。在使用或购买酒精的参与者中,接触更多的酒精营销并不会增加次日的酒精使用量或酒精购买频率。在个人层面上,接触更多的酒精营销与次日更高的饮酒可能性、饮酒量、更高的购酒可能性或购酒频率无关(Ps均>.05)。在一周内每多接触一次酒类营销,就预示着在接下来的一周内饮酒量会增加 0.85(调整后的 B=0.85,95% CI 0.09-1.61)。在报告饮酒的日子里,酒精营销接受度的 3 个测量值与更多的饮酒或购酒无关(Ps 均>.05):通过使用 EMA,我们首次证明了酒精营销暴露对当前饮酒大学生开始使用和购买酒精的影响。我们的研究结果提供了酒精营销对减少年轻人使用和购买酒精的调节作用。
Ecological Momentary Assessment of Alcohol Marketing Exposure, Alcohol Use, and Purchases Among University Students: Prospective Cohort Study.
Background: The relationships between alcohol marketing exposure, alcohol use, and purchase have been widely studied. However, prospective studies examining the causal relationships in real-world settings using mobile health tools are limited.
Objective: We used ecological momentary assessment (EMA) to examine both the within-person- and between-person-level effects of alcohol marketing exposure on any alcohol use, amount of alcohol use, any alcohol purchase, and frequency of alcohol purchase among university students.
Methods: From January to June 2020, we conducted a prospective cohort study via EMA among university students in Hong Kong who reported current drinking. Over 14 consecutive days, each participant completed 5 fixed-interval, signal-contingent EMAs daily via a smartphone app. Each EMA asked about the number and types of alcohol marketing exposures, the amount and types of alcohol used, and whether any alcohol was purchased, all within the past 3 hours. We used 2-part models, including multilevel logistic regressions and multilevel gamma regressions, to examine if the number of alcohol marketing exposure was associated with subsequent alcohol use and alcohol purchase.
Results: A total of 49 students participated, with 33% (16/49) being male. The mean age was 22.6 (SD 2.6) years. They completed 2360 EMAs (completion rate: 2360/3430, 68.8%). Participants reported exposure to alcohol marketing in 5.9% (140/2360), alcohol use in 6.1% (145/2360), and alcohol purchase in 2.4% (56/2360) of all the EMAs. At the between-person level, exposure to more alcohol marketing predicted a higher likelihood of alcohol use (adjusted odd ratio [AOR]=3.51, 95% CI 1.29-9.54) and a higher likelihood of alcohol purchase (AOR=4.59, 95% CI 1.46-14.49) the following day. Exposure to more alcohol marketing did not increase the amount of alcohol use or frequency of alcohol purchases the following day in participants who used or purchased alcohol. At the within-person level, exposure to more alcohol marketing was not associated with a higher likelihood of alcohol use, amount of alcohol use, higher likelihood of alcohol purchase, or frequency of alcohol purchases the following day (all Ps>.05). Each additional exposure to alcohol marketing within 1 week predicted an increase of 0.85 alcoholic drinks consumed in the following week (adjusted B=0.85, 95% CI 0.09-1.61). On days of reporting alcohol use, the 3 measures for alcohol marketing receptivity were not associated with more alcohol use or purchase (all Ps>.05).
Conclusions: By using EMA, we provided the first evidence for the effect of alcohol marketing exposure on initiating alcohol use and purchase in current-drinking university students. Our findings provide evidence of the regulation of alcohol marketing for the reduction of alcohol use and purchase among young adults.
期刊介绍:
JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636.
The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.
JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.