Factors associated with patterns of mobile technology use among persons who inject drugs.
Factors associated with patterns of mobile technology use among persons who inject drugs.
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DOI：
10.1080/08897077.2016.1176980
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发表时间：
2016-10
期刊：
影响因子：
3.5
通讯作者：
Garfein RS
中科院分区：
文献类型：
--
作者：
Collins KM;Armenta RF;Cuevas-Mota J;Liu L;Strathdee SA;Garfein RS
需要新的和创新的方法来提供干预措施，以进一步减少危险行为，提高注射毒品者的整体健康水平。移动的健康（mHealth）干预措施有可能达到PWID;然而，对这一人群中的移动的技术使用（MTU）知之甚少。在这项研究中，作者确定了MTU的模式，并确定了PWID队列中与MTU相关的因素。通过一项纵向队列研究收集数据，研究加州圣地亚哥PWID人群的药物使用、危险行为和健康状况。使用潜在类别分析（LCA）来定义MTU的模式（即，进行语音呼叫、文本消息收发和移动的因特网接入）。然后使用多项逻辑回归来确定与移动的技术使用类别相关的人口统计学特征、风险行为和健康指标。在LCA中，4类解决方案最适合数据。1类定义为低MTU（22%，n = 100）; 2类定义为使用移动终端访问互联网但不使用语音或文本消息的PWID（20%，n = 95）; 3类定义为主要使用语音、文本和连接互联网（17%，n = 91）; 4类定义为高MTU（41%，n = 175）。与低MTU相比，高MTU类别成员更有可能更年轻、具有更高的社会经济地位、贩卖毒品并每天注射甲基苯丙胺。圣地亚哥的大多数PWID使用移动的技术进行语音、文本和/或互联网访问，这表明在这一人群中快速采用移动健康干预措施是可能的。然而，在残疾人中实施移动保健干预措施时，需要考虑老年人和/或无家可归者对移动的技术的拥有率和使用率较低的问题。
New and innovative methods of delivering interventions are needed to further reduce risky behaviors and increase overall health among persons who inject drugs (PWID). Mobile health (mHealth) interventions have potential for reaching PWID; however, little is known about mobile technology use (MTU) in this population. In this study, the authors identify patterns of MTU and identified factors associated with MTU among a cohort of PWID. Data were collected through a longitudinal cohort study examining drug use, risk behaviors, and health status among PWID in San Diego, California. Latent class analysis (LCA) was used to define patterns of MTU (i.e., making voice calls, text messaging, and mobile Internet access). Multinomial logistic regression was then used to identify demographic characteristics, risk behaviors, and health indicators associated with mobile technology use class. In LCA, a 4-class solution fit the data best. Class 1 was defined by low MTU (22%, n = 100); class 2, by PWID who accessed the Internet using a mobile device but did not use voice or text messaging (20%, n = 95); class 3, by primarily voice, text, and connected Internet use (17%, n = 91); and class 4, by high MTU (41%, n = 175). Compared with low MTU, high MTU class members were more likely to be younger, have higher socioeconomic status, sell drugs, and inject methamphetamine daily. The majority of PWID in San Diego use mobile technology for voice, text, and/or Internet access, indicating that rapid uptake of mHealth interventions may be possible in this population. However, low ownership and use of mobile technology among older and/or homeless individuals will need to be considered when implementing mHealth interventions among PWID.