对血液酒精浓度相关技术的使用和年轻人饮酒结果的探索性分析。

IF 3 Q2 SUBSTANCE ABUSE
Alcohol (Hanover, York County, Pa.) Pub Date : 2024-11-01 Epub Date: 2024-09-30 DOI:10.1111/acer.15455
Sayre E Wilson, Hannah A Lavoie, Benjamin L Berey, Tessa Frohe, Bonnie H P Rowland, Liana S E Hone, Robert F Leeman
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引用次数: 0

摘要

背景:使用移动医疗(mHealth)技术可以减少饮酒及相关不良后果;然而,如果没有研究人员的提示或按使用付费,人们对其功效知之甚少。这项探索性分析评估了移动医疗技术使用频率与酒精使用结果之间的关系:酗酒的年轻成年人(N = 97,Mage = 23,51%为男性,64%为非西班牙裔白人,Mdrinks/week = 21)可选择在饮酒时使用三种移动医疗技术(呼气式酒精检测仪/应用程序、血液酒精含量估算器应用程序、通过短信计算饮酒量),为期两周。通过双变量相关性和多元回归评估了酒精相关结果与在研究日和饮酒日使用任何、多种和特定移动医疗技术之间的关系:参与者在大约 68% 的饮酒日(33% 的现场日)使用了一种或多种移动医疗技术,其中 34% 的饮酒日使用了多种技术。双变量相关性显示,使用任何移动保健技术的研究日百分比越高,每周平均饮酒量就越高。然而,使用任何移动保健技术的饮酒日百分比越高,每周平均饮酒量、大量和高强度饮酒日百分比以及负面影响就越低。酒精变量与使用提供个性化反馈的移动医疗技术之间存在几种明显的反相关关系。多元回归分析(在性别和基线酒精变量不变的情况下)表明,使用任何移动医疗技术的饮酒天数百分比越高,每周平均饮酒量和大量饮酒天数百分比就越低:结论:在没有研究小组直接提示或每次使用奖励的情况下,使用移动医疗技术来控制饮酒与总体饮酒量和大量饮酒的减少有关。这表明在现实世界中使用移动医疗应用程序来帮助即时饮酒是有潜力的。将饮酒期间使用移动医疗技术正常化有助于遏制年轻成年人群中有害饮酒的公共卫生危机。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploratory analysis of blood alcohol concentration-related technology use and drinking outcomes among young adults.

Background: Mobile health (mHealth) technology use may reduce alcohol use and related negative consequences; however, little is known about its efficacy without prompting from researchers or pay-per-use. This exploratory analysis assessed relationships between mHealth technology use frequency and alcohol-use outcomes.

Methods: Young adults who drink heavily (N = 97, Mage = 23, 51% male, 64% non-Hispanic White, Mdrinks/week = 21) had the option to use three mHealth technologies (breathalyzer device/app, blood alcohol content estimator app, drink counting via text message) while drinking for 2 weeks. Relationships between alcohol-related outcomes and any, multiple, and specific mHealth technology use across study days and drinking days were evaluated via bivariate correlations and multiple regressions.

Results: Participants used one or more mHealth technologies on approximately 68% of drinking days (33% of field days), with multiple technologies used on 34% of drinking days. Bivariate correlations revealed that a higher percentage of study days with any mHealth technology use was related to higher mean weekly drinks. However, a higher percentage of drinking days with any mHealth technology use was related to lower mean weekly drinks, percent of heavy and high-intensity drinking days, and negative consequences. There were several significant, inverse correlations between alcohol variables and using the mHealth technologies that provided personalized feedback. Multiple regression analyses (holding sex and baseline alcohol variables constant) indicated that a higher percentage of drinking days with any mHealth technology use was related to lower mean weekly drinks and lower percentage of heavy drinking days.

Conclusions: Using mHealth technologies to moderate drinking without direct prompting from the research team or per-use incentives was related to less overall alcohol use and heavy drinking. This indicates potential real-world engagement with mHealth apps to assist with in-the-moment drinking. Normalizing mHealth technology use during drinking could help curb the public health crisis around harmful alcohol use in young adult populations.

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