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移动健康干预对改善青少年健康结局的影响:一项荟萃分析。

Mobile Health Interventions for Improving Health Outcomes in Youth: A Meta-analysis.

作者信息

Fedele David A, Cushing Christopher C, Fritz Alyssa, Amaro Christina M, Ortega Adrian

机构信息

Department of Clinical & Health Psychology, University of Florida, Gainesville.

Clinical Child Psychology Program, University of Kansas, Lawrence.

出版信息

JAMA Pediatr. 2017 May 1;171(5):461-469. doi: 10.1001/jamapediatrics.2017.0042.

Abstract

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 Studies were excluded if the article was an unpublished dissertation or thesis, the mean age of participants was older than 18 years, the study did not assess a health behavior and disease outcome, or the article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.

摘要

重要性

移动健康干预在儿科学中越来越受欢迎;然而,这些干预措施在改变健康结果方面的效果尚不清楚。

目的

确定移动健康干预对改善18岁及以下青少年健康结果的有效性。

数据来源

通过PubMed、护理及相关健康文献累积索引、教育资源信息中心和PsycINFO收集截至2016年11月30日发表的研究。对符合研究纳入标准的文章进行了前后文献检索。检索词包括远程医疗、电子健康、移动健康、移动医疗、应用程序和移动应用。

研究选择

搜索结果尽可能限于婴儿、儿童、青少年或青年。如果使用定量方法评估移动干预技术在主要或次要能力方面的应用,以促进或改变18岁及以下青少年的健康行为,则纳入研究。如果文章是未发表的学位论文或毕业论文、参与者的平均年龄超过18岁、研究未评估健康行为和疾病结果,或文章未包含足够的统计数据,则排除该研究。纳入和排除标准由两名独立编码员应用,重叠率为20%。在9773篇独特的文章中,36篇文章(包含37项独特研究,共29822名参与者)符合纳入标准。

数据提取与综合

在9773篇独特的文章中,36篇文章(包含37项独特研究),共29822名参与者符合纳入标准。效应量通过可转换为标准化均值差异的统计检验计算得出。所有汇总效应量和调节变量均使用随机效应模型进行检验。

主要结局和指标

健康行为或疾病控制的变化。

结果

研究共纳入29822名参与者。在报告性别的研究中,女性总数为11226名(53.2%)。在报告年龄的研究中,平均年龄为11.35岁。移动健康干预的随机效应汇总效应量显著(n = 37;Cohen d = 0.22;95% CI,0.14 - 0.29)。随机效应模型表明,向照顾者提供移动健康干预可增强干预效果。干预中涉及照顾者的研究产生的效应量(n = 16;Cohen d = 0.28;95% CI,0.18 - 0.39)大于未纳入照顾者的研究(n = 21;Cohen d = 0.

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