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Past, Present, and Future of eHealth and mHealth Research to Improve Physical Activity and Dietary Behaviors
Open accessAbstract
Because physical inactivity and unhealthy diets are highly prevalent, there is a need for cost-effective interventions that can reach large populations. Electronic health (eHealth) and mobile health (mHealth) solutions have shown promising outcomes and have expanded rapidly in the past decade. The purpose of this report is to provide an overview of the state of the evidence for the use of eHealth and mHealth in improving physical activity and nutrition behaviors in general and special populations. The role of theory in eHealth and mHealth interventions is addressed, as are methodological issues. Key recommendations for future research in the field of eHealth and mHealth are provided.
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“However, recent improvements in digital access in lower income countries and to all sociodemographic groups mean that it is timely and important to consider the extent to which it may be necessary to tailor DBCIs to ensure they are accessible and engaging for people with lower levels of education, literacy or computer literacy.44”
From Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions (2016) · cites this paper as [44]
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“In the past two decades, there has been a large number of published studies using internet and mobile-based behavior interventions to support the effectiveness of using digital technologies to deliver intervention materials to diverse populations [8,14].”
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Self-citation
Metadata
- Authors
- Vandelanotte, C., Müller, A. M., Short, C. E., Hingle, M., Nathan, N., Williams, S. L., Lopez, M. L., Parekh, S., & Maher, C. A.
- Affiliations
- Central Queensland University (AU)
- University of Malaya (MY)
- South Australian Health and Medical Research Institute (AU)
- University of Arizona (US)
- Hunter Medical Research Institute (AU)
- Griffith University (AU)
- University of South Australia (AU)
- Corresponding author
- Vandelanotte, Corneel
- ORCID
- Venue
- Journal of Nutrition Education and Behavior
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 48
- Issue
- 3
- Pages
- 219-228.e1
- ISSN
- 1499-4046 (ISSN-L)
- OpenAlex
- W2294434433
- Cited by
- 396
- References
- 95
- Citations by year
- 2026: 18 · 2025: 30 · 2024: 37 · 2023: 35 · 2022: 59 · 2021: 54 · 2020: 43 · 2019: 43 · 2018: 36 · 2017: 30 · 2016: 11
- FWCI
- 74.51
- Type
- journal article
- Language
- en
- Open access
- green
- papers.ssrn.com
- Locations
- Journal of Nutrition Education and Behavior (publishedVersion)
- PubMed (publishedVersion)
- Acquire (CQUniversity) (submittedVersion)
- National University of Singapore (submittedVersion)
- UniSA Research Outputs Repository (University of South Australia) (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Griffith Research Online (submittedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Funders
- National Heart Foundation of Australia (ID100427)
- University of Malaya/Ministry of Higher Education
- National Health and Medical Research Council Early Career Fellowship
- Postdoctoral Research Fellowship
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Keywords
- e-health, mobile health, physical activity, dietary behavior, nutrition behavior, physical inactivity, unhealthy diet, theory-based intervention, research recommendations, methodological issues
- MeSH terms
- Diet, Health Promotion, Humans, Health Behavior, Exercise, Telemedicine, Behavioral Research
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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 ›
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