Are health behavior change interventions that use online social networks effective? A systematic review.

Are health behavior change interventions that use online social networks effective? A systematic review.
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DOI:
10.2196/jmir.2952
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发表时间:
2014-02-14
影响因子:
7.4
通讯作者:
Vandelanotte C
Vandelanotte C
中科院分区:
医学2区
文献类型:
--
作者:
Maher CA;Lewis LK;Ferrar K;Marshall S;De Bourdeaudhuij I;Vandelanotte C

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Web2.0技术和在线社交网络的巨大增长为健康行为改变运动的交付提供了巨大的潜力。然而,目前还不清楚如何最好地利用在线社交网络来实现健康行为的改变。这项研究的目的是系统地审查关于在线社交网络健康行为干预有效性的当前证据水平。从2000年至今,使用综合检索策略检索了8个数据库(SCOPUS、CINAHL、Medline、ProQuest、EMBASE、emocINFO、Cochrane、Web of Science and Communication&MASTIC Complete)。研究资格标准基于PICOS格式,其中“人口”包括儿童或成人人口,包括健康和疾病人口;“干预”包括针对关键的可改变的健康行为(烟酒消费、饮食摄取、体力活动和久坐行为)的行为改变干预措施,全部或部分利用在线社交网络提供;“对照”是对照组或在研究后设计的情况下在受试者范围内;“结果”包括健康行为变化和密切相关的变量(如健康行为变化的理论化中介,如自我效能);“研究设计”包括在完整的同行评议资料中报告的实验研究。总结干预效果报告,并尽可能计算效果大小(Cohen‘s d和95%可信区间)。对干预措施的自然流失率(完成研究的人的百分比)、参与度(实际使用量)和保真度(实际使用量/预期使用量)进行了仔细审查。在去除重复项后,从数据库搜索中确定了总共2040项研究,其中10项符合纳入标准。这些研究共涉及113,988名参与者(从n=10到n=107,907)。干预措施包括商业性在线健康社交网站(n=2)、研究健康社交网站(n=3),以及部分通过先前存在的流行在线社交网站(Facebook n=4和Twitter n=1)提供的多成分干预。在纳入的10项研究中,有9项报告在健康行为改变的某些方面或与行为改变相关的结果方面有显着改善。行为改变的效应大小从−0.0 5(95%可信区间0.45~0.35)到0.84(95%可信区间0.49~1.19),但总体来说幅度较小,在统计学上无显著意义。参与者的流失率在0-84%之间。参与度和保真度相对较低,大多数研究达到5%-15%的保真度(有一个例外,达到105%的保真度)。到目前为止,有非常有限的证据表明,结合在线社交网络的干预措施可能是有效的;然而,这一领域的研究还处于初级阶段。需要进一步的研究来确定如何最大限度地保持和参与,行为改变是否可以长期持续,并确定如何利用在线社交网络实现大规模传播。对未来的研究提出了具体的建议。
The dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change. The intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions. Eight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where “population” included child or adult populations, including healthy and disease populations; “intervention” involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; “comparator” was either a control group or within subject in the case of pre-post study designs; “outcomes” included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg, self-efficacy); and “study design” included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen’s d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized. A total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online health social network websites (n=2), research health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine of the 10 included studies reported significant improvements in some aspect of health behavior change or outcomes related to behavior change. Effect sizes for behavior change ranged widely from −0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement and fidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity). To date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether behavior change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided.
DOI: 10.1186/1471-2458-10-538
发表时间: 2010-09-08
期刊: BMC public health
影响因子: 4.5
作者:
Jepson RG;Harris FM;Platt S;Tannahill C
通讯作者: Tannahill C
DOI: 10.1186/2046-4053-4-1
发表时间: 2015-01-01
期刊: Systematic reviews
影响因子: 3.7
作者:
Moher D;Shamseer L;Clarke M;Ghersi D;Liberati A;Petticrew M;Shekelle P;Stewart LA;PRISMA-P Group
通讯作者: PRISMA-P Group
DOI: 10.1016/s0140-6736(12)61766-8
发表时间: 2012-12-15
期刊: LANCET
影响因子: 168.9
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DOI: 10.1186/1479-5868-9-52
发表时间: 2012-04-30
期刊: The international journal of behavioral nutrition and physical activity
影响因子: --
作者:
Davies CA;Spence JC;Vandelanotte C;Caperchione CM;Mummery WK
通讯作者: Mummery WK
DOI: 10.1111/j.1083-6101.2007.00367.x
发表时间: 2007-07-01
影响因子: 7.2
作者:
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