Exploring Factors Associated With Mobile Phone Behaviors and Attitudes Toward Technology Among Adults With Alcohol Use Disorder and Implications for mHealth Interventions: Exploratory Study.

Exploring Factors Associated With Mobile Phone Behaviors and Attitudes Toward Technology Among Adults With Alcohol Use Disorder and Implications for mHealth Interventions: Exploratory Study.
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DOI:
10.2196/32768
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发表时间:
2022-08-15
影响因子:
2.2
通讯作者:
--
中科院分区:
其他
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酒精使用障碍(AUD)与严重的慢性疾病和过早死亡有关。扩大有效循证治疗的覆盖范围或获得途径以帮助AUD患者是一项公共卫生目标。移动的电话或智能手机技术有可能增加临床和行为干预(移动的健康干预)的传播,从而增加AUD患者清醒的开始和维持。关于这一群体如何使用他们的移动的手机以及他们对技术的态度的研究可能对参与者参与这些干预措施有意义。这项探索性研究考察了人口统计学特征(种族、性别、年龄、婚姻状况和收入)、物质使用特征(酒精和大麻使用频率)和临床变量(焦虑和抑郁症状)与移动的手机使用行为和对技术态度指标之间的潜在关系。71名患有AUD的成年人样本(平均年龄42.9岁,标准差10.9岁)参与了酒精部分住院计划,完成了媒体技术使用和态度评估的4个分量表:智能手机使用测量各种移动的手机行为和活动,积极态度和消极态度测量对技术的态度,技术焦虑/依赖测量评估当个体与他们的手机分离时的焦虑水平和对该设备的依赖。参与者还提供了人口统计学信息,并完成了流行病学研究抑郁量表(CES-D)和广泛性焦虑症(GAD-7)量表。最后,参与者使用药物使用频率量表报告了他们过去3个月的饮酒频率。人口统计学因素的结果显示,年龄、智能手机使用(P= 0.003; ηp2=0.14)和积极态度(P= 0.01; ηp2=0.07)具有显著的主效应。婚姻状况(P= 0.03; ηp2=0.13)和收入(P= 0.03; ηp2=0.14)仅与技术焦虑和依赖子量表相关。此外,发现酒精使用和技术焦虑/依赖子量表(P= 0.06; R2=0.02)的显着趋势。最后,CES-D评分(P= 0.03; R2=0.08)和GAD症状(P= 0.004; R2=0.13)仅是技术焦虑/依赖子量表的显著预测因子。研究结果表明,AUD患者在人口统计学、物质使用和临床指标方面的移动的电话使用模式和对技术的态度存在差异。这些结果可能有助于为该人群未来的移动健康干预措施的发展提供信息。
Alcohol use disorder (AUD) is associated with severe chronic medical conditions and premature mortality. Expanding the reach or access to effective evidence-based treatments to help persons with AUD is a public health objective. Mobile phone or smartphone technology has the potential to increase the dissemination of clinical and behavioral interventions (mobile health interventions) that increase the initiation and maintenance of sobriety among individuals with AUD. Studies about how this group uses their mobile phone and their attitudes toward technology may have meaningful implications for participant engagement with these interventions. This exploratory study examined the potential relationships among demographic characteristics (race, gender, age, marital status, and income), substance use characteristics (frequency of alcohol and cannabis use), and clinical variables (anxiety and depression symptoms) with indicators of mobile phone use behaviors and attitudes toward technology. A sample of 71 adults with AUD (mean age 42.9, SD 10.9 years) engaged in an alcohol partial hospitalization program completed 4 subscales from the Media Technology Usage and Attitudes assessment: Smartphone Usage measures various mobile phone behaviors and activities, Positive Attitudes and Negative Attitudes measure attitudes toward technology, and the Technological Anxiety/Dependence measure assesses level of anxiety when individuals are separated from their phone and dependence on this device. Participants also provided demographic information and completed the Epidemiologic Studies Depression Scale (CES-D) and the Generalized Anxiety Disorder (GAD-7) scale. Lastly, participants reported their frequency of alcohol use over the past 3 months using the Drug Use Frequency Scale. Results for the demographic factors showed a significant main effect for age, Smartphone Usage (P=.003; ηp2=0.14), and Positive Attitudes (P=.01; ηp2=0.07). Marital status (P=.03; ηp2=0.13) and income (P=.03; ηp2=0.14) were associated only with the Technological Anxiety and Dependence subscale. Moreover, a significant trend was found for alcohol use and the Technological Anxiety/Dependence subscale (P=.06; R2=0.02). Lastly, CES-D scores (P=.03; R2=0.08) and GAD symptoms (P=.004; R2=0.13) were significant predictors only of the Technological Anxiety/Dependence subscale. Findings indicate differences in mobile phone use patterns and attitudes toward technology across demographic, substance use, and clinical measures among patients with AUD. These results may help inform the development of future mHealth interventions among this population.
DOI: 10.1016/b978-0-444-62619-6.00012-4
发表时间: 2014-01-01
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