Adaptive e-learning to improve dietary behaviour: A systematic review and cost-effectiveness analysis

摘要

Background: UK public health policy strongly advocates dietary change for the improvement of population health and emphasises the importance of individual empowerment to improve health. A new and evolving area in the promotion of dietary behavioural change is 'e-learning', the use of interactive electronic media to facilitate teaching and learning on a range of issues including health. The high level of accessibility, combined with emerging advances in computer processing power, data transmission and data storage, makes interactive e-learning a potentially powerful and cost-effective medium for improving dietary behaviour. Objective: This review aims to assess the effectiveness and cost-effectiveness of adaptive e-learning interventions for dietary behaviour change, and also to explore potential psychological mechanisms of action and components of effective interventions. Data sources: Electronic bibliographic databases (Cumulative Index to Nursing and Allied Health Literature, The Cochrane Library, Dissertation Abstracts, EMBASE, Education Resources Information Center, Global Health, Health Economic Evaluations Database, Health Management Information Consortium, MEDLINE, PsycINFO and Web of Science) were searched for the period January 1990 to November 2009. Reference lists of included studies and previous reviews were also screened; authors were contacted and trial registers were searched. Review methods: Studies were included if they were randomised controlled trials, involving participants aged ≥ 13 years, which evaluated the effectiveness of interactive software programs for improving dietary behaviour. Primary outcomes were measures of dietary behaviours, including estimated intakes or changes in intake of energy, nutrients, dietary fibre, foods or food groups. Secondary outcome measures were clinical outcomes such as anthropometry or blood biochemistry. Psychological mediators of dietary behaviour change were also investigated. Two review authors independently screened results and extracted data from included studies, with any discrepancies settled by a third author. Where studies reported the same outcome, the results were pooled using a random-effects model, with weighted mean differences (WMDs), and 95% confidence intervals (CIs) were calculated. Cost-effectiveness was assessed in two ways: through a systematic literature review and by building a de novo decision model to assess the costeffectiveness of a 'generic' e-learning device compared with dietary advice delivered by a health-care professional. Results: A total of 36,379 titles were initially identified by the electronic searches, of which 43 studies were eligible for inclusion in the review. All e-learning interventions were delivered in high-income countries. The most commonly used behavioural change techniques reported to have been used were goal setting; feedback on performance; information on consequences of behaviour in general; barrier identification/problem solving; prompting self-monitoring of behaviour; and instruction on how to perform the behaviour. There was substantial heterogeneity in the estimates of effect. E-learning interventions were associated with a WMD of +0.24 (95% CI 0.04 to 0.44) servings of fruit and vegetables per day; -0.78 g (95% CI -2.5 g to 0.95 g) total fat consumed per day; -0.24 g (95% CI -1.44 g to 0.96 g) saturated fat intake per day; -1.4% (95% CI -2.5% to -0.3%) of total energy consumed from fat per day; +1.45 g (95% CI -0.02 g to 2.92 g) dietary fibre per day; +4 kcal (95% CI -85 kcal to 93 kcal) daily energy intake; -0.1 kg/m 2 (95% CI -0.7 kg/m 2 to 0.4 kg/m 2) change in body mass index. The base-case results from the E-Learning Economic Evaluation Model suggested that the incremental costeffectiveness ratio was approximately ￡102,112 per quality-adjusted life-year (QALY). Expected value of perfect information (EVPI) analysis showed that although the individuallevel EVPI was arguably negligible, the population-level

机译：背景：英国公共卫生政策大力倡导改变饮食以改善人群健康，并强调赋予个人权力对改善健康的重要性。促进饮食行为改变的一个新的不断发展的领域是“电子学习”，即使用交互式电子媒体促进有关包括健康在内的一系列问题的教与学。高度的可访问性，再加上计算机处理能力，数据传输和数据存储方面的新兴进步，使得交互式电子学习成为改善饮食行为的潜在强大且具有成本效益的媒介。目的：本综述旨在评估适应性电子学习干预措施对饮食行为改变的有效性和成本效益，并探讨潜在的心理作用机制和有效干预措施的组成部分。数据来源：电子书目数据库（护理和相关健康文献的累积索引，Cochrane图书馆，学位论文摘要，EMBASE，教育资源信息中心，全球卫生，卫生经济评估数据库，卫生管理信息联盟，MEDLINE，PsycINFO和Web of Science ）在1990年1月至2009年11月期间进行了搜索。还筛选了纳入研究和以往评论的参考文献列表;联系了作者并搜索了试验登记册。审查方法：如果研究是≥13岁的参与者的随机对照试验，则该研究评估了交互式软件程序改善饮食行为的有效性。主要结果是饮食行为的量度，包括估计的摄入量或能量，营养素，膳食纤维，食物或食物组的摄入量变化。次要指标是临床指标，例如人体测量学或血液生化指标。饮食行为改变的心理中介者也进行了调查。两位评论作者独立筛选结果并从纳入的研究中提取数据，任何差异均由第三位作者解决。如果研究报告的结果相同，则使用随机效应模型汇总结果，并使用加权平均差（WMD）和95％的置信区间（CI）。通过两种方式评估成本效益：通过系统的文献综述以及通过建立从头决策模型来评估“通用”电子学习设备与卫生保健专业人员提供的饮食建议相比的成本效益。结果：通过电子搜索初步鉴定出总共36,379种标题，其中43项研究符合纳入该评价的条件。所有电子学习干预措施都是在高收入国家/地区提供的。据报道已使用的最常用的行为改变技术是目标设定。绩效反馈;有关行为后果的信息;障碍识别/问题解决;促使对行为进行自我监控;以及有关如何执行该行为的说明。在效果评估中存在很大的异质性。电子学习干预与每天提供的水果和蔬菜的WMD为+0.24（95％CI为0.04至0.44）有关;每天消耗的总脂肪为-0.78克（95％CI -2.5到0.95克）;每天-0.24 g（95％CI -1.44 g至0.96 g）饱和脂肪摄入量;每天从脂肪消耗的总能量的-1.4％（95％CI -2.5％至-0.3％）;每天+1.45 g（95％CI -0.02 g至2.92 g）膳食纤维;每日能量摄入量+4 kcal（95％CI -85 kcal至93 kcal）;体重指数变化-0.1 kg / m 2（95％CI -0.7 kg / m 2至0.4 kg / m 2）。电子学习经济评估模型的基本结果表明，每质量调整生命年（QALY）的增量成本效益比约为，102,112。完美信息的期望值（EVPI）分析表明，尽管个人水平EVPI可以忽略不计，但人口水平

Adaptive e-learning to improve dietary behaviour: A systematic review and cost-effectiveness analysis

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