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Cited by 170Mobile Health and mHealth ApplicationsDigital Mental Health InterventionsInnovative Human-Technology Interaction

The Pace of Technologic Change

Patrick, Kevin, Hekler, Eric B., Estrin, Deborah, Mohr, David C., Riper, Heleen, Crane, David, Godino, Job, Riley, William T.·2016·American Journal of Preventive Medicine
DOI 10.1016/j.amepre.2016.05.001
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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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  • Agile science states that eHealth development should be iterative and flexible, with short sprints to allow for constant changes to the process and products.

    “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]

  • Supervised learning predicts adherence and effects from early use patterns, which enables early intervention for users at risk.

    “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]

  • More advanced and predictive information for pattern recognition within complex data sets can be obtained by applying machine-learning algorithms.

    “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]

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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)
DOI
10.1016/j.amepre.2016.05.001
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
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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