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Seven lessons for interdisciplinary research on interactive digital health interventions
Open accessAbstract
Research and development for interactive digital health interventions requires multi-disciplinary expertise in identifying user needs, and developing and evaluating each intervention. Two of the central areas of expertise required are Health (broadly defined) and Human–Computer Interaction. Although these share some research methods and values, they traditionally have deep differences that can catch people unawares, and make interdisciplinary collaborations challenging, resulting in sub-optimal project outcomes. The most widely discussed is the contrast between formative evaluation (emphasised in Human–Computer Interaction) and summative evaluation (emphasised in Health research). However, the differences extend well beyond this, from the nature of accepted evidence to the culture of reporting. In this paper, we present and discuss seven lessons that we have learned about the contrasting cultures, values, assumptions and practices of Health and Human–Computer Interaction. The lessons are structured according to a research lifecycle, from establishing the state of the art for a given digital intervention, moving through the various (iterative) stages of development, evaluation and deployment, through to reporting research results. Although our focus is on enabling people from different disciplinary backgrounds to work together with better mutual understanding, we also highlight ways in which future research in this interdisciplinary space could be better supported.
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“Recent research into digital health intervention development highlights the importance of an iterative, multidisciplinary approach in which the end user is placed at the heart of the system and the technology is grounded in user wants and needs [45-47].”
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“To ensure tools are convenient and useful in the context of day-to-day life, it is necessary to involve end-users across the design and development process (Mohr et al., 2017; Blandford et al., 2018; Borghouts et al., 2021; Poole, 2013).”
From A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype (2023) · cites this paper as Blandford et al., 2018
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Evaluation Methods in Clinical Health Technologies: A Systematic Review
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Metadata
- Authors
- Blandford, A., Gibbs, J., Newhouse, N., Perski, O., Singh, A., & Murray, E.
- Affiliations
- University College London (GB)
- The Royal Free Hospital (GB)
- Corresponding author
- Blandford, Ann
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2018-01
- Volume
- 4
- Pages
- 2055207618770325-2055207618770325
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC6016567
- OpenAlex
- W2800091779
- Cited by
- 209
- References
- 42
- Citations by year
- 2026: 36 · 2025: 30 · 2024: 28 · 2023: 14 · 2022: 24 · 2021: 24 · 2020: 32 · 2019: 17 · 2018: 4
- FWCI
- 27.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Digital Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Engineering and Physical Sciences Research Council (EP/G004560/1)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Interdisciplinary Research and Collaboration
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- interdisciplinary research, human-computer interaction, health research, formative assessment, summative assessment, research culture, research reporting, research lifecycle, user needs, interdisciplinary collaboration
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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.
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