Suppr超能文献

数字行为改变干预措施以增加成年人蔬菜摄入量的系统评价。

Digital behaviour change interventions to increase vegetable intake in adults: a systematic review.

机构信息

Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne Burwood Campus, 221 Burwood Highway, VIC, 3125, Melbourne, Australia.

Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, 3220, Australia.

出版信息

Int J Behav Nutr Phys Act. 2023 Mar 27;20(1):36. doi: 10.1186/s12966-023-01439-9.

Abstract

BACKGROUND

Digital interventions may help address low vegetable intake in adults, however there is limited understanding of the features that make them effective. We systematically reviewed digital interventions to increase vegetable intake to 1) describe the effectiveness of the interventions; 2) examine links between effectiveness and use of co-design, personalisation, behavioural theories, and/or a policy framework; and 3) identify other features that contribute to effectiveness.

METHODS

A systematic search strategy was used to identify eligible studies from MEDLINE, Embase, PsycINFO, Scopus, CINAHL, Cochrane Library, INFORMIT, IEEE Xplore and Clinical Trial Registries, published between January 2000 and August 2022. Digital interventions to increase vegetable intake were included, with effective interventions identified based on statistically significant improvement in vegetable intake. To identify policy-action gaps, studies were mapped across the three domains of the NOURISHING framework (i.e., behaviour change communication, food environment, and food system). Risk of bias was assessed using Cochrane tools for randomized, cluster randomized and non-randomized trials.

RESULTS

Of the 1,347 records identified, 30 studies were included. Risk of bias was high or serious in most studies (n = 25/30; 83%). Approximately one quarter of the included interventions (n = 8) were effective at improving vegetable intake. While the features of effective and ineffective interventions were similar, embedding of behaviour change theories (89% vs 61%) and inclusion of stakeholders in the design of the intervention (50% vs 38%) were more common among effective interventions. Only one (ineffective) intervention used true co-design. Although fewer effective interventions included personalisation (67% vs 81%), the degree of personalisation varied considerably between studies. All interventions mapped across the NOURISHING framework behaviour change communication domain, with one ineffective intervention also mapping across the food environment domain.

CONCLUSION

Few digital interventions identified in this review were effective for increasing vegetable intake. Embedding behaviour change theories and involving stakeholders in intervention design may increase the likelihood of success. The under-utilisation of comprehensive co-design methods presents an opportunity to ensure that personalisation approaches better meet the needs of target populations. Moreover, future digital interventions should address both behaviour change and food environment influences on vegetable intake.

摘要

背景

数字干预措施可能有助于解决成年人蔬菜摄入量低的问题,但对于使其有效的特征,我们的了解有限。我们系统地回顾了旨在增加蔬菜摄入量的数字干预措施,以 1)描述干预措施的有效性;2)检查有效性与共同设计、个性化、行为理论和/或政策框架的使用之间的联系;3)确定有助于有效性的其他特征。

方法

使用系统搜索策略从 MEDLINE、Embase、PsycINFO、Scopus、CINAHL、Cochrane 图书馆、INFORMIT、IEEE Xplore 和临床试验登记处中确定了符合条件的研究,这些研究的发表时间为 2000 年 1 月至 2022 年 8 月。纳入了旨在增加蔬菜摄入量的数字干预措施,基于蔬菜摄入量的统计学显著改善来确定有效干预措施。为了确定政策行动差距,研究按照 NOURISHING 框架的三个领域(即行为改变沟通、食品环境和食品系统)进行了映射。使用 Cochrane 工具评估随机、整群随机和非随机试验的偏倚风险。

结果

在确定的 1347 条记录中,有 30 项研究被纳入。大多数研究(n=25/30;83%)的偏倚风险较高或严重。大约四分之一的干预措施(n=8)在改善蔬菜摄入量方面有效。虽然有效和无效干预措施的特征相似,但行为改变理论的嵌入(89%比 61%)和利益相关者参与干预设计(50%比 38%)在有效干预措施中更为常见。只有一项(无效)干预措施真正采用了共同设计。虽然个性化干预措施较少(67%比 81%),但研究之间的个性化程度差异很大。所有干预措施都映射到了 NOURISHING 框架的行为改变沟通领域,一项无效干预措施也映射到了食品环境领域。

结论

本综述中确定的数字干预措施中,很少有能有效增加蔬菜摄入量。嵌入行为改变理论并让利益相关者参与干预设计可能会增加成功的可能性。综合共同设计方法的利用不足为确保个性化方法更好地满足目标人群的需求提供了机会。此外,未来的数字干预措施应同时解决行为改变和食品环境对蔬菜摄入量的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/966e/10045422/fbe3b9069411/12966_2023_1439_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验