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

https://doi.org/10.1016/j.amepre.2016.05.001Get rights and content

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.

Section snippets

The Technologic Infrastructure for Health Is Changing

Digital technologies are increasingly pervasive in all aspects of daily life and the ease with which digital tools are adopted is in part because of the malleability and adaptability of digital technologies. The Internet and Web Architectures that underlie this digital revolution are fundamentally minimalistic and modular and allow for decentralized growth based on minimal commonality. Mobile and wireless technologies, including cellular systems, embedded cameras, smartphones, and the Internet

These Changes Support the Ability to Handle Highly Complex, Multilayered Issues in Health

Concurrent with advances in information technology are three major trends in public health and medicine:

  • 1

    the emergence of chronic diseases as the main causes of poor health, disability, and death;

  • 2

    an increased understanding of the multiple influences on health, including the genome, microbiome, health behaviors, social influences, and the environment; and

  • 3

    collaborative, self, and social health management.

The combination of these poses both an unprecedented challenge to traditional health care and

Health Behavior Research: New Data, Research Designs, and Methods

Technologic advances now illuminate what has been long theorized about behavior, that it is influenced at multiple levels—genetic, biological, social, environmental—and that these influences are reciprocal, dynamic, and temporally based.43, 44 Thus, the complexity of understanding behavior strains current scientific methods and processes—something that is labeled “data poor.” A data-poor science requires researchers first to specify the questions, design the study to answer these questions, and

Conclusions

This is a time of three major trends: increasing capabilities inherent in communication, computing, and data science; unsustainable growth in the complexity and cost of health care; and a movement to a more user-centered and collaborative approach to health promotion and health care. As outlined in this paper, the first and third trends can be leveraged to help address the second if public health is open to incorporating models of research and practice that are already being used in other

Acknowledgments

This 2016 theme section of the American Journal of Preventive Medicine is supported by funding from the NIH Office of Behavioral and Social Sciences Research (OBSSR) to support the dissemination of research on digital health interventions, methods, and implications for preventive medicine.

This paper is one of the outputs of two workshops, one supported by the Medical Research Council (MRC)/National Institute for Health Research (NIHR) Methodology Research Program (PI Susan Michie), the OBSSR

References (78)

  • A. Kleiboer et al.

    A randomized controlled trial on the role of support in Internet-based problem solving therapy for depression and anxiety

    Behav Res Ther

    (2015)
  • T.W. Bickmore et al.

    Automated interventions for multiple health behaviors using conversational agents

    Patient Educ Couns

    (2013)
  • D.C. Mohr et al.

    Continuous evaluation of evolving behavioral intervention technologies

    Am J Prev Med

    (2013)
  • IMS Institute. Patient Adoption of mHealth | Reports....
  • mHealth App Developer Economics

    (2014)
  • PatientView. European Directory of Health Apps 2-12-2013. London;...
  • Mobile Health Market Report 2013-2017

    (2013)
  • E.R. Breton et al.

    Weight loss—there is an app for that! But does it adhere to evidence-informed practices?

    Transl Behav Med

    (2011)
  • L.T. Cowan et al.

    Apps of steel: are exercise apps providing consumers with realistic expectations?: a content analysis of exercise apps for presence of behavior change theory

    Health Educ Behav

    (2013)
  • T. Donker et al.

    Smartphones for smarter delivery of mental health programs: a systematic review

    J Med Internet Res

    (2013)
  • S. Kumar et al.

    Mobile health: revolutionizing healthcare through transdisciplinary research

    Computer (Long Beach Calif)

    (2013)
  • A. Pentland

    Social Physics: How Good Ideas Spread: The Lessons From a New Science

    (2014)
  • D. Estrin

    small data, where n = me

    Commun ACM

    (2014)
  • E.B. Hekler et al.

    Realizing effective behavioral management of health: the metamorphosis of behavioral science methods

    IEEE Pulse

    (2013)
  • Smart toilets and sewer sensors are coming. Wired UK....
  • Green paper on Citizen Science for Europe: towards a society of empowered citizens and enhanced research

    Socientize Consort

    (2013)
  • H.E. Payne et al.

    Behavioral functionality of mobile apps in health interventions: a systematic review of the literature

    JMIR mHealth uHealth

    (2015)
  • G. Eysenbach

    The law of attrition

    J Med Internet Res

    (2005)
  • L. Yardley et al.

    Understanding and promoting engagement with digital behavior change interventions

    Am J Prev Med

    (2016)
  • P. McNamee et al.

    Designing and undertaking a health economics study of digital health interventions

    Am J Prev Med

    (2016)
  • E. Murray et al.

    Evaluating digital health interventions: key questions and approaches

    Am J Prev Med

    (2016)
  • E.B. Hekler et al.

    Developing and refining models and theories suitable for digital health interventions

    Am J Prev Med

    (2016)
  • W.W. Hung et al.

    Recent trends in chronic disease, impairment and disability among older adults in the United States

    BMC Geriatr

    (2011)
  • L.L. Barrett

    Prescription Drug Use Among Midlife and Older Americans

    (2005)
  • Best Care at Lower Cost: The Path to Continuously Learning Health Care in America - Institute of Medicine

    (2012)
  • M.F. Furukawa et al.

    Despite substantial progress in EHR adoption, health information exchange and patient engagement remain low in office settings

    Health Aff (Millwood)

    (2014)
  • A.M. Sisko et al.

    National health expenditure projections, 2013-23: faster growth expected with expanded coverage and improving economy

    Health Aff (Millwood)

    (2014)
  • C.A. Celis-Morales et al.

    Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers

    PLoS One

    (2012)
  • P.J. Turnbaugh et al.

    The human microbiome project

    Nature

    (2007)
  • Cited by (111)

    • Use of smartphone apps in bipolar disorder: An international web-based survey of feature preferences and privacy concerns

      2021, Journal of Affective Disorders
      Citation Excerpt :

      While websites may allow for the delivery of comprehensive information, smartphone-based interventions are portable and, as such, better suited for self-monitoring and delivering real-time support (Stawarz et al., 2019), and therefore necessitates distinct design considerations. Thirdly, the context in which people make decisions regarding the use of mental health apps is rapidly evolving (Patrick et al., 2016). The technological capabilities of smartphones are constantly being added to and refined (for example, passive data monitoring and applications of machine learning), with potential implications for the feature preferences of end-users.

    View all citing articles on Scopus

    This article is part of a theme section titled Digital Health: Leveraging New Technologies to Develop, Deploy, and Evaluate Behavior Change Interventions.

    View full text