CoChat
Sign Up
Cited by 109Nutrition, Health and Food BehaviorBehavioral Health and InterventionsMobile Health and mHealth Applications

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

Open access
Harris, J, Felix, L, Miners, A, Murray, E, Michie, S, Ferguson, E, Free, C, Lock, K +2 more·2011·Health Technology Assessment
DOI 10.3310/hta15370PMC PMC4781244
Open this paper in CoChat — free accountOpen PDF

Abstract

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 cost-effectiveness 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/m2 (95% CI -0.7 kg/m2 to 0.4 kg/m2) change in body mass index. The base-case results from the E-Learning Economic Evaluation Model suggested that the incremental cost-effectiveness ratio was approximately £102,112 per quality-adjusted life-year (QALY). Expected value of perfect information (EVPI) analysis showed that although the individual-level EVPI was arguably negligible, the population-level value was between £37M and £170M at a willingness to pay of £20,000-30,000 per additional QALY. LIMITATIONS: The limitations of this review include potential reporting bias, incomplete retrieval of completed research studies and data extraction errors. CONCLUSION: The current clinical and economic evidence base suggests that e-learning devices designed to promote dietary behaviour change will not produce clinically significant changes in dietary behaviour and are at least as expensive as other individual behaviour change interventions. FUTURE WORK RECOMMENDATIONS: Despite the relatively high EVPI results from the cost-effectiveness modelling, further clinical trials of individual e-learning interventions should not be undertaken until theoretically informed work that addresses the question of which characteristics of the target population, target behaviour, content and delivery of the intervention are likely to lead to positive results, is completed. FUNDING: The National Institute for H

Integrity signals

Registry

Crossref · 10.3310/hta15370

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Digital health interventions can promote healthy behaviours such as smoking cessation, healthy eating, physical activity, safer sex, or reduced alcohol consumption.

    “Digital health interventions (DHI) can be used to promote healthy behaviours (e.g. smoking cessation, 1, 2 healthy eating, 3 physical activity, 4 safer sex 5 or alcohol consumption 6)”

    From Evaluating Digital Health Interventions (2016) · cites this paper as [2]

  • Technology-based interventions are becoming increasingly popular for delivering health-related behavioral changes.

    “Today, technology-based interventions are becoming increasingly popular for delivering health-related behavioral changes [5–7].”

    From Engaging women with an embodied conversational agent to deliver mindfulness and lifestyle recommendations: A feasibility randomized control trial (2017) · cites this paper as [7]

  • Changing dietary behaviors appears to be more challenging, with a recent systematic review for the UK Health Technology Agency finding little or no impact of adaptive e-learning technologies across a wide range of dietary behaviors and no impact on weight loss or body mass index.

    “Changing dietary behaviors appears to be more challenging, with a recent systematic review for the UK Health Technology Agency finding little or no impact of adaptive e-learning technologies across a wide range of dietary behaviors and no impact on weight loss or body mass index [39].”

    From Web-Based Interventions for Behavior Change and Self-Management: Potential, Pitfalls, and Progress (2012) · cites this paper as [39]

  • Among target behaviors, weight loss seems particularly difficult to achieve with Web-based interventions.

    “Among target behaviors, weight loss seems particularly difficult to achieve with Web-based interventions [39].”

    From Web-Based Interventions for Behavior Change and Self-Management: Potential, Pitfalls, and Progress (2012) · cites this paper as [39]

Cites

Still being checked.

Cited by

Sorted by citations

Related

Authors
Harris, J., Felix, L., Miners, A., Murray, E., Michie, S., Ferguson, E., Free, C., Lock, K., Landon, J., & Edwards, P.
Affiliations
  • London School of Hygiene & Tropical Medicine (GB)
  • University College London (GB)
  • UK Health Forum (GB)
Corresponding author
Edwards, P
ORCID
Venue
Health Technology Assessment
Publisher
National Institute for Health and Care Research
Published
2011-10
Volume
15
Issue
37
Pages
1-160
ISSN
  • 1366-5278 (ISSN-L)
  • 2046-4924
DOI
10.3310/hta15370
PMC
PMC4781244
OpenAlex
W2116226630
Cited by
109
References
0
Citations by year
2026: 1 · 2025: 7 · 2023: 7 · 2022: 5 · 2021: 14 · 2020: 8 · 2019: 10 · 2018: 7 · 2017: 9 · 2016: 13 · 2015: 6 · 2014: 10 · 2013: 10 · 2012: 2
FWCI
0.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Health Technology Assessment (publishedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • IFPRI E-brary (International Food Policy Research Institute) (submittedVersion)
  • CGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • International Food Policy Research Institute (International Food Policy Research Institute) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
Health Technology Assessment programme (08/57/02)
Topics
  • Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
  • Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
adaptive e-learning, dietary behavior change, cost-effectiveness, interactive software, behaviour change techniques, goal setting, performance feedback, self-monitoring, psychological mediators, dietary recommendations, health promotion, energy intake, nutrient intake, dietary fiber, anthropometry, serum biochemistry, decision models, self-empowerment, computer-based intervention
MeSH terms
Computer-Assisted Instruction, Cost-Benefit Analysis, Diet, Feeding Behavior, Female, Humans, Male, Middle Aged, Education, Distance, Internet, Risk Reduction Behavior

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review