Department of Psychological Sciences, University of Missouri - St. Louis, One University Blvd, 325 Stadler Hall, St. Louis, MO 63121, USA.
Department of Psychological Sciences, University of Missouri - St. Louis, One University Blvd, 325 Stadler Hall, St. Louis, MO 63121, USA.
Addict Behav. 2022 Dec;135:107455. doi: 10.1016/j.addbeh.2022.107455. Epub 2022 Aug 1.
Ecological momentary assessment (EMA) is well-suited to measure adolescent substance use. Previous research with adolescents, particularly racially minoritized adolescents, has predominantly provided mobile devices to participants as a strategy to reduce structural barriers to technology access. This report examined feasibility and acceptability of a text-message-delivered EMA protocol to adolescents' personal phones.
Non-Hispanic Black and White adolescents aged 14-18 years with mobile phone access and past-30-day substance use were recruited from community settings. Respondents (n = 36; 55.5 % female; 55.5 % White) completed a 14-day diary assessing substance use.
Respondents completed M = 13.8 (SD = 1.36) diaries for a compliance rate of 93.5 %. Black respondents completed significantly fewer diaries (87.9 %) than White respondents (97.9 %) although compliance rates were high among both groups. Adolescents reported high acceptability of the protocol, with 97.1 % willing to participate again.
Findings suggest text-message-based EMA delivered to personal phones is acceptable and feasible for assessing substance use among adolescents. As the sociodemographic "digital divide" narrows among adolescents, this cost-effective and equitable method becomes more feasible.
生态瞬时评估（EMA）非常适合测量青少年的物质使用情况。先前针对青少年，特别是少数族裔青少年的研究主要向参与者提供移动设备，作为减少技术获取结构性障碍的一种策略。本报告探讨了向青少年个人手机发送短信的 EMA 协议的可行性和可接受性。
从社区环境中招募了具有移动电话访问权限且过去 30 天内有物质使用史的 14-18 岁非西班牙裔黑人和白人青少年。受访者（n=36；55.5%女性；55.5%白人）完成了为期 14 天的日记，评估物质使用情况。
受访者完成了 M=13.8（SD=1.36）份日记，符合率为 93.5%。黑人受访者完成的日记明显少于白人受访者（分别为 87.9%和 97.9%），尽管两组的符合率都很高。青少年对该方案的接受度很高，97.1%的人愿意再次参与。
研究结果表明，通过个人手机发送短信的基于文本的 EMA 用于评估青少年的物质使用情况是可以接受且可行的。随着青少年在社会人口统计学上的“数字鸿沟”缩小，这种具有成本效益和公平性的方法变得更加可行。
