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Am J Prev Med. 2016 Nov;51(5):816-824. doi: 10.1016/j.amepre.2016.05.001.

The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research.

Author information

1
Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, California. Electronic address: kpatrick@ucsd.edu.
2
School of Nutrition and Health Promotion, Arizona State University, Phoenix, Arizona.
3
Cornell NYC Tech, Department of Computer Science, Cornell University, New York, New York.
4
Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
5
Faculty of Behavioural and Movement Sciences, Section of Clinical Psychology, VU University Amsterdam, Amsterdam, the Netherlands.
6
Centre for Behaviour Change, University College London, London, United Kingdom.
7
Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, California.
8
NIH, Bethesda, Maryland.

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.

PMID:
27745681
DOI:
10.1016/j.amepre.2016.05.001
[Indexed for MEDLINE]

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