Interactive media for diabetes self-management: issues in maximizing public health impact
Abstract
Background . Diabetes self-management presents a series of challenging tasks, and primary care, where the majority of cases of adult diabetes are treated, is hard-pressed to address these issues given competing demands. This article discusses how interactive media (IM) can be used to support diabetes self-management. Methods . Following a brief review of the literature, the 5 As framework for enhancing the effectiveness of health behavior counseling and the RE-AIM model for estimating and enhancing public health impact are used to frame discussion of the strengths and limitations of IM for diabetes shared decision making and self-management support. Results . Data and lessons learned from a series of randomized trials of IM for diabetes self-management education are summarized around 2 key issues. The first is enhancing patient engagement in decision making and includes enhancing user experience and engagement, improving quality of care, and promoting collaborative action planning and follow-up. The second is getting such resources into place and sustaining them in real-world primary care settings and involves enhancing participation at patient, clinician, and health care system levels and enhancing the generalizability of results. Conclusions . Key opportunities for IM to support diabetes self-management include assessment of information for shared decision making, assistance with problem-solving self-management challenges, and provision of follow-up support. A key current challenge is the linkage of IM supports to the rest of the patient’s care, and collection of cost-effectiveness data is a key need for future research.
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Interventions to improve health literacy have included using simple language, presenting information in audio-visual formats, tailoring content to individual needs, and other forms of interactivity.
“Interventions to improve health literacy have included using simple language, presenting information in audio-visual formats, tailoring content to individual needs, and other forms of interactivity.[45][46][47]”
From Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions (2016) · cites this paper as [47]
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- Authors
- Glasgow, R. E.
- Affiliations
- Kaiser Permanente (US)
- Corresponding author
- Glasgow, Russell E.
- ORCID
- Glasgow, Russell E.
- Venue
- Medical Decision Making
- Publisher
- SAGE Publications
- Published
- 2010-11
- Volume
- 30
- Issue
- 6
- Pages
- 745-758
- ISSN
- 0272-989X (ISSN-L)
- 1552-681X
- OpenAlex
- W2010009875
- Cited by
- 38
- References
- 50
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 5 · 2017: 5 · 2016: 2 · 2015: 4 · 2014: 1 · 2013: 4 · 2012: 5
- FWCI
- 0.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Decision Making (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK035524, R01 DK-35524)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Keywords
- diabetes self-management, interactive media, shared decision-making, self-management support, primary care, RE-AIM framework, patient engagement, diabetes self-management education, follow-up support, cost-effectiveness, generalizability
- MeSH terms
- Cooperative Behavior, Decision Making, Decision Support Techniques, Diabetes Mellitus, Health Education, Humans, Patient Education as Topic, Peer Group, Public Health, Self Care, Health Behavior, Patient-Centered Care
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 ›
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