The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Developing and Evaluating Digital Interventions to Promote Behavior Change in Health and Health Care: Recommendations Resulting From an International Workshop
Open accessAbstract
Devices and programs using digital technology to foster or support behavior change (digital interventions) are increasingly ubiquitous, being adopted for use in patient diagnosis and treatment, self-management of chronic diseases, and in primary prevention. They have been heralded as potentially revolutionizing the ways in which individuals can monitor and improve their health behaviors and health care by improving outcomes, reducing costs, and improving the patient experience. However, we are still mainly in the age of promise rather than delivery. Developing and evaluating these digital interventions presents new challenges and new versions of old challenges that require use of improved and perhaps entirely new methods for research and evaluation. This article discusses these challenges and provides recommendations aimed at accelerating the rate of progress in digital behavior intervention research and practice. Areas addressed include intervention development in a rapidly changing technological landscape, promoting user engagement, advancing the underpinning science and theory, evaluating effectiveness and cost-effectiveness, and addressing issues of regulatory, ethical, and information governance. This article is the result of a two-day international workshop on how to create, evaluate, and implement effective digital interventions in relation to health behaviors. It was held in London in September 2015 and was supported by the United Kingdom's Medical Research Council (MRC), the National Institute for Health Research (NIHR), the Methodology Research Programme (PI Susan Michie), and the Robert Wood Johnson Foundation of the United States (PI Kevin Patrick). Important recommendations to manage the rapid pace of change include considering using emerging techniques from data science, machine learning, and Bayesian approaches and learning from other disciplines including computer science and engineering. With regard to assessing and promoting engagement, a key conclusion was that sustained engagement is not always required and that for each intervention it is useful to establish what constitutes "effective engagement," that is, sufficient engagement to achieve the intended outcomes. The potential of digital interventions for testing and advancing theories of behavior change by generating ecologically valid, real-time objective data was recognized. Evaluations should include all phases of the development cycle, designed for generalizability, and consider new experimental designs to make the best use of rich data streams. Future health economics analyses need to recognize and model the complex and potentially far-reaching costs and benefits of digital interventions. In terms of governance, developers of digital behavior interventions should comply with existing regulatory frameworks, but with consideration for emerging standards around information governance, ethics, and interoperability.
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“To date, low uptake and poor engagement are commonly observed with digital interventions, which are often insufficient to sustain behavior change [15,16].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [16]
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In the citing paper
“Within the realm of digital interventions, using suitable strategies, such as user-centered participatory design, can heighten user engagement, thereby rendering intervention measures more appealing and efficacious [26].”
From Assessing the Effectiveness of Digital Health Behavior Strategies on Type 2 Diabetes Management: Systematic Review and Network Meta-Analysis (2025) · cites this paper as [26]
In the citing paper
“Behavioural theory can guide the design of mHealth interventions aimed at changing health behaviours.60”
From mHealth text and voice communication for monitoring people with chronic diseases in low-resource settings: a realist review (2018) · cites this paper as [60]
In the citing paper
“An important reason for the lack of justifications for the intended use of eHealth technologies might be that there is a lack of knowledge regarding the working mechanisms of technology-based applications [8].”
From Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence (2017) · cites this paper as [8]
In the citing paper
“the results of a recent literature review of theoretical perspectives on adherence showed that adherence is a multidimensional concept, influenced by a range of technological, environmental, and individual factors altogether that cannot be evaluated by technology usage alone [8,84].”
From Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence (2017) · cites this paper as [8]
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Cites
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Metadata
- Authors
- Michie, S., Yardley, L., West, R., Patrick, K., & Greaves, F.
- Affiliations
- University College London (GB)
- University of Oxford (GB)
- University of California San Diego (US)
- Public Health England (GB)
- Corresponding author
- Michie, Susan
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2017-06-29
- Volume
- 19
- Issue
- 6
- Pages
- e232
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5509948
- OpenAlex
- W2724146920
- Cited by
- 865
- References
- 32
- Citations by year
- 2026: 75 · 2025: 94 · 2024: 98 · 2023: 94 · 2022: 122 · 2021: 120 · 2020: 94 · 2019: 98 · 2018: 59 · 2017: 11
- FWCI
- 81.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Explore Bristol Research (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- Robert Wood Johnson Foundation
- Cancer Research UK (22962)
- National Institute for Health and Care Research
- Medical Research Council (MR/K023195/1)
- Economic and Social Research Council (ES/F029624/1)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Keywords
- digital interventions, behavior change, health behavior, health care, intervention development, user engagement, cost-effectiveness, health economics, regulatory framework, information governance, ethics, interoperability, machine learning, data science, Bayesian approach, chronic disease self-management, primary prevention
- MeSH terms
- Delivery of Health Care, Humans, Health Behavior, Telemedicine, Internet
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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