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

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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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The Internet of Things: A survey
Atzori, L., Iera, A., & Morabito, G. (2010) · Computer Networks · doi:10.1016/j.comnet.2010.05.010 · Cited by 15,613
A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair-Rohani, H., AlMazroa, M. A., Amann, M., Anderson, H. R., Andrews, K. G., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61766-8 · Cited by 11,805
Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010
Murray, C. J. L., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., Ezzati, M., Shibuya, K., Salomon, J. A., Abdalla, S., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61689-4 · Cited by 8,888
Human gut microbes associated with obesity
Ley, R. E., Turnbaugh, P. J., Klein, S., & Gordon, J. I. (2006) · Nature · doi:10.1038/4441022a · Cited by 8,785
The human microbiome project
Turnbaugh, P. J., Ley, R. E., Hamady, M., Fraser-Liggett, C. M., Knight, R., & Gordon, J. I. (2007) · Nature · doi:10.1038/nature06244 · Cited by 6,091
A New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202
On the Self-Regulation of Behavior
Wright, B. R. E., Carver, C. S., & Scheier, M. F. (2000) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2654424 · Cited by 3,327
The Law of Attrition
Eysenbach, G. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e11 · Cited by 2,524
Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA
Bauer, U. E., Briss, P. A., Goodman, R. A., & Bowman, B. A. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60648-6 · Cited by 1,453
Exercise and associated dietary extremes impact on gut microbial diversity
Clarke, S. F., Murphy, E. F., O'Sullivan, O., Lucey, A. J., Humphreys, M., Hogan, A., Hayes, P., O'Reilly, M., Jeffery, I. B., Wood-Martin, R., et al. (2014) · Gut · doi:10.1136/gutjnl-2013-306541 · Cited by 1,439

Show all 72 references

Research works printed but not linked: 3

2014, mHealth App Developer Economics
2013, Mobile Health Market Report 2013-2017
2013, Green paper on Citizen Science for Europe: towards a society of empowered citizens and enhanced research, Socientize Consort, 1

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Self-citation
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Topooco, N. (2018) · doi:10.3384/diss.diva-148016 · Cited by 16
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Self-citation
Retracted
Commentary: Pediatric Digital Health Supported by the National Institutes of Health
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Self-citation
“It depends:” a qualitative study on digital health academic-industry collaboration
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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: Patrick, Kevin
Hekler, Eric B.
Estrin, Deborah
Mohr, David C.
Riper, Heleen
Crane, David
Godino, Job
Riley, William T.
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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