Mobile Health Interventions for Improving Health Outcomes in Youth A Meta-analysis
Mobile Health Interventions for Improving Health Outcomes in Youth A Meta-analysis
复制标题
DOI：
10.1001/jamapediatrics.2017.0042
复制
发表时间：
2017-05-01
期刊：
影响因子：
26.1
通讯作者：
Ortega, Adrian
中科院分区：
文献类型：
--
作者：
Fedele, David A.;Cushing, Christopher C.;Ortega, Adrian
重要性 移动健康干预措施在儿科中越来越受欢迎；然而，尚不清楚这些干预措施在改变健康结果方面的效果如何。 目的 确定移动健康干预措施对改善 18 岁或以下青少年健康结果的有效性。 数据来源 截至 2016 年 11 月 30 日发表的研究通过 PubMed、护理和联合健康文献累积索引、教育资源信息中心和 PsychINFO 收集。对符合研究纳入标准的文章进行了向后和向前的文献检索。搜索词包括远程医疗、电子医疗、移动医疗、移动医疗、应用程序和移动应用程序。 研究选择 搜索结果尽可能限于婴儿、儿童、青少年或年轻人。如果使用定量方法来评估移动干预技术在促进或改变 18 岁或以下青少年健康行为的主要或次要能力中的应用，则纳入研究。如果该文章是未发表的论文或论文、参与者的平均年龄超过 18 岁、该研究未评估健康行为和疾病结果，或者该文章未包含足够的统计数据，则该研究将被排除。纳入和排除标准由 2 名独立编码员应用，有 20% 的重叠。在 9773 篇独特文章中，36 篇文章（包含 37 项独特研究，总共 29 822 名参与者）符合纳入标准。 数据提取和综合 在 9773 篇独特文章中，36 篇文章（包含 37 项独特研究），总共 29 822 名参与者符合纳入标准。效应大小是根据统计测试计算得出的，可以转换为标准化平均差。所有聚合效应大小和调节变量均使用随机效应模型进行测试。主要成果和措施 健康行为或疾病控制的变化。结果 研究共有 29 822 名参与者。在报告性别的研究中，女性总数为 11 226 人（53.2%）。报告年龄的平均年龄为 11.35 岁。移动健康干预措施的随机效应总效应大小显着（n = 37；Cohen d = 0.22；95% CI，0.14-0.29）。随机效应模型表明，向护理人员提供移动健康干预增加了干预效果的强度。让护理人员参与干预的研究产生的效应大小（n = 16；Cohen d = 0.28；95% CI，0.18-0.39）大于不包括护理人员的研究（n = 21；Cohen d = 0.13；95% CI，0.02-0.25）。其他编码变量并没有调节研究效果的大小。结论和相关性移动健康干预措施似乎是一种可行的青少年健康行为改变干预方式。鉴于移动电话的普及，移动健康干预措施为改善公众健康带来了希望。
IMPORTANCE Mobile health interventions are increasingly popular in pediatrics; however, it is unclear how effective these interventions are in changing health outcomes.OBJECTIVE To determine the effectiveness of mobile health interventions for improving health outcomes in youth 18 years or younger.DATA SOURCES Studies published through November 30, 2016, were collected through PubMed, Cumulative Index to Nursing and Allied Health Literature, Educational Resources Information Center, and PsychINFO. Backward and forward literature searches were conducted on articles meeting study inclusion criteria. Search terms included telemedicine, eHealth, mobile health, mHealth, app, and mobile application.STUDY SELECTION Search results were limited to infants, children, adolescents, or young adults when possible. Studies were included if quantitative methods were used to evaluate an application of mobile intervention technology in a primary or secondary capacity to promote or modify health behavior in youth 18 years or younger. Studies were excluded if the article was an unpublished dissertation or thesis, the mean age of participants was older than 18 years, the study did not assess a health behavior and disease outcome, or the article did not include sufficient statistics. Inclusion and exclusion criteria were applied by 2 independent coders with 20% overlap. Of 9773 unique articles, 36 articles (containing 37 unique studies with a total of 29 822 participants) met the inclusion criteria.DATA EXTRACTION AND SYNTHESIS Of 9773 unique articles, 36 articles (containing 37 unique studies) with a total of 29 822 participants met the inclusion criteria. Effect sizes were calculated from statistical tests that could be converted to standardized mean differences. All aggregate effect sizes and moderator variables were tested using random-effects models. MAIN OUTCOMES AND MEASURES Change in health behavior or disease control. RESULTS A total of 29 822 participants were included in the studies. In studies that reported sex, the total number of females was 11 226 (53.2%). Of those reporting age, the average was 11.35 years. The random effects aggregate effect size of mobile health interventions was significant (n = 37; Cohen d = 0.22; 95% CI, 0.14-0.29). The random effects model indicated that providing mobile health intervention to a caregiver increased the strength of the intervention effect. Studies that involved caregivers in the intervention produced effect sizes (n = 16; Cohen d = 0.28; 95% CI, 0.18-0.39) larger than those that did not include caregivers (n = 21; Cohen d = 0.13; 95% CI, 0.02-0.25). Other coded variables did not moderate study effect size.CONCLUSIONS AND RELEVANCE Mobile health interventions appear to be a viable health behavior change intervention modality for youth. Given the ubiquity of mobile phones, mobile health interventions offer promise in improving public health.