The DOI matches its Crossref record; 69 of the 72 references it cites were found in a registry or an index.
The Pace of Technologic Change
Abstract
This paper addresses the rapid pace of change in the technologies that support digital interventions; the complexity of the health problems they aim to address; and the adaptation of scientific methods to accommodate the volume, velocity, and variety of data and interventions possible from these technologies. Information, communication, and computing technologies are now part of every societal domain and support essentially every facet of human activity. Ubiquitous computing, a vision articulated fewer than 30 years ago, has now arrived. Simultaneously, there is a global crisis in health through the combination of lifestyle and age-related chronic disease and multiple comorbidities. Computationally intensive health behavior interventions may be one of the most powerful methods to reduce the consequences of this crisis, but new methods are needed for health research and practice, and evidence is needed to support their widespread use. The challenges are many, including a reluctance to abandon timeworn theories and models of health behavior-and health interventions more broadly-that emerged in an era of self-reported data; medical models of prevention, diagnosis, and treatment; and scientific methods grounded in sparse and expensive data. There are also many challenges inherent in demonstrating that newer approaches are, indeed, effective. Potential solutions may be found in leveraging methods of research that have been shown to be successful in other domains, particularly engineering. A more "agile science" may be needed that streamlines the methods through which elements of health interventions are shown to work or not, and to more rapidly deploy and iteratively improve those that do. There is much to do to advance the issues discussed in this paper, and the papers in this theme issue. It remains an open question whether interventions based in these new models and methods are, in fact, equally if not more efficacious as what is available currently. Economic analyses of these new approaches are needed because assumptions of net worth compared to other approaches are just that, assumptions. Human-centered design research is needed to ensure that users ultimately benefit. Finally, a translational research agenda will be needed, as the status quo will likely be resistant to change.
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In the citing paper
“This is in line with agile science, which states that eHealth development should be iterative and flexible, with short sprints to allow for constant changes to the process and products [14,183].”
From Methods for Human-Centered eHealth Development: Narrative Scoping Review (2022) · cites this paper as [183]
In the citing paper
“Supervised learning predicts adherence and effects from early use patterns, which enables early intervention for users at risk [31,33,34].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [31]
In the citing paper
“More advanced and predictive information for pattern recognition within complex data sets can be obtained by applying machine-learning algorithms [31].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [31]
Citations
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Cited by
Sorted by citations
Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions
Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., Merchant, G. C., Naughton, F., & Blandford, A. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.015 · Cited by 1,118
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“Thus, the methods by which we deploy, evaluate, and improve these DBCIs are moving from relatively data-poor, infrequent, and typically post-hoc assessments to methods that incorporate continuous measurement of the intervention effects in real time [3].”
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Self-citationAttitudes towards digital treatment for depression: A European stakeholder survey
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Self-citation
Metadata
- Authors
- Patrick, K., Hekler, E. B., Estrin, D., Mohr, D. C., Riper, H., Crane, D., Godino, J., & Riley, W. T.
- Affiliations
- University of California San Diego (US)
- Arizona State University (US)
- Cornell University (US)
- Northwestern University (US)
- Vrije Universiteit Amsterdam (NL)
- University College London (GB)
- National Institutes of Health (US)
- Corresponding author
- Patrick, Kevin
- ORCID
- Venue
- American Journal of Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 51
- Issue
- 5
- Pages
- 816-824
- ISSN
- 0749-3797 (ISSN-L)
- OpenAlex
- W2530300749
- Cited by
- 169
- References
- 72
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 14 · 2023: 13 · 2022: 17 · 2021: 22 · 2020: 19 · 2019: 23 · 2018: 27 · 2017: 19 · 2016: 3
- FWCI
- 44.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- VU Research Portal (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- digital health interventions, health behavior change, ubiquitous computing, chronic disease, comorbidities, evidence-based assessment, human-centered design, translational research, economic evaluation, health behavior theory
- MeSH terms
- Health Promotion, Humans, Research Design, Telecommunications, Health Behavior
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