The biological processes, health behaviors, and environmental and ecological factors that influence health can be optimally understood through transdisciplinary science that uses research methods capable of spanning these domains. Ideally, these methods should provide valid information about individuals, families, social networks and entire populations and be capable of gathering information over time. If intervention on health states is desired, data collected could be included in a feedback loop to influence elements of the intervention and yield new information from which to act. The recent proliferation of wireless and mobile technologies provides the opportunity to connect information in the real-world via wearable sensors and, when coupled with fixed sensors embedded in the environment, to produce continuous streams of data on an individual's biology, psychology (attitudes, cognitions and emotions), behavior, and daily environment. These data have the potential to yield new insights into the factors that lead to disease. They also have the potential to be analyzed and used in “real time” to prompt changes in behaviors or environmental exposures that can reduce health risks or optimize health outcomes. This new area of mobile health—now often called simply “mHealth”—has the potential to be a transformative force. mHealth has the potential to change when, where, and how healthcare is provided; to ensure that important social, behavioral, and environmental data are used to understand the determinants of health; and to improve health outcomes.
Mobile phones, particularly smartphones (i.e., sophisticated internet-accessible cellular phones) and other mobile computing devices, are becoming increasingly ubiquitous, which enhances the potential to assess and improve health. There are over 285 million wireless subscribers in the United States alone (CTIA) and an estimated 67.6% of adults worldwide own cell phones (International Communications Union, 2010). Additionally, approximately 75% of U.S. high school students own a phone (Pew Internet, 2010). In contrast to the Internet digital divide that limited for years, if not decades, the reach of computerized health behavior interventions for lower socioeconomic groups, mobile phone use has been rapidly and widely adopted among virtually all demographic groups. In fact, mobile phone usage appears greater among those populations most in need of these interventions (Pew Internet, 2010). According to the Securities and Exchange Commission (SEC, 2010), 96% of the United States is covered by at least one mobile network. In rural areas, this coverage declines to 82%, but, it increases to 99% in urban areas. Mobile penetration in developing countries, where wireless technologies have leapfrogged the wired computer infrastructure, have produced considerable excitement in the global health community with the prospects of reaching and following individuals who were previously unreachable (Kaplan, 2006; Kossaraju, Barrigan, Poropatich & Casscells, 2010).
Given the high penetration and level of computing capacity available in even basic cell phones, it is possible that these technologies can make a significant difference to public health and health care delivery. Through mHealth technologies, researchers have the ability to capture multiple sources of health data, such as in-depth information about the environment for genome-wide association studies (GWAS); detailed information about subjects' physical activity, location and travel areas; physiological responses (e.g., through small sensors attached to the body and connected wirelessly to the telecommunications network); and activities (e.g., through text messaging surveys) over extended periods of time (http://www.gei.nih.gov/). Further, the accessibility and data availability of mHealth methodologies could be utilized to change public health and health care on a large scale, for example, by employing mobile tools to decrease the number of people who develop diabetes, prevent falls at home, and help people who need medication to take them as scheduled.
Consumer demand for health "apps" and sensors has far outpaced the science needed to understand their benefits, risks and impact—positive, neutral or negative—on health outcomes. Moreover, in the small amount of mHealth research conducted to date, issues of privacy, confidentiality, regulatory control, human subjects‘ protection, and logistics (e.g., interoperability among carriers) have been known to hamper researchers' efforts. While addressing these challenges, researchers will need to develop and assess the full spectrum of mobile health technologies, as they create safe, scalable and effective programs.
mhealth at NIH
NIH has history of funding investigators in developing and using mobile technologies to improve health. Recent example include the Exposure Biology portion of the NIH Genes, Environment and Health Initiative (see http://www.gei.nih.gov/exposurebiology/index.asp). The Exposure Biology Program focuses on the development of innovative technologies to measure environmental exposures, diet, physical activity, psychosocial stress, and addictive substances that contribute to the development of disease. The program supports: development of environmental sensors for measurement of chemicals, dietary intake, physical activity, and psychosocial stressors and addictive substances; development of "fingerprints" (markers) of biological response that are indicative of activation of common pathogenic mechanisms such as oxidative stress, epigenetic modifications, and DNA damage; integration of biological responses with the development of biosensors; and application of these biomarkers to genome-wide association (GWA) studies of gene-environment interaction.
NIH also recently sponsored the NIH-Wide Geospatial Infrastructure Workshop. This workshop, co-sponsored by NIH's NCI and NIDA with AAG (Association of American Geographers) took place on February 22-23, 2011 in Rockville, Maryland. The goal of the workshop was to assess and document the demand for a common geospatial infrastructure across the NIH community. Speakers and presenters included NIH leaders, NIH medical research, and the GIScience communities (see the agenda). The workshop participants agreed that developing NIH-wide GIS infrastructure throughout NIH for medical research is needed. The immediate steps following the workshop will include: dissemination of the forthcoming Workshop Report to the NIH leadership and research communities; assisting NLM in adding spatial MeSH terms and journals that feature health related geospatial research; inventory geospatial activities across NIH; development of NIH training track for geospatial education. For more information contact : Zaria Tatalovich, NCI – email@example.com; Bethany Deeds, NIDA – firstname.lastname@example.org; Doug Richardson, AAG – email@example.com
The mHealth Training Institutes provides investigators with a thorough introduction to selected mHealth methodologies that may be used to study behavioral and social dimensions of public health. Participants in these week-long Institutes work with expert mentors to creating their own inter-disciplinary mobile health projects. For further information on the Institute visit the mHealth Training Institute home page
mHealth Training Listserv
mHealth-Training@list.nih.gov: The mHealth Training listserv is a communication tool for keeping members apprised of relevant news and events; it is not a discussion board. The messages disseminated on this list are focused on those related to mHealth. For example, the listserv is used to announce lectures of interest, training opportunities, and funding announcements. Join the electronic mailing list (LISTSERV) for forthcoming announcements by — Sending an e-mail message to firstname.lastname@example.org from the mailing address at which you want to receive announcements.
The body of the message should read SUBscribe mHealth-Training [your full name].
- The message is case sensitive; so capitalize as indicated!
- Don't include the brackets.
- The Subject line should be blank
For example, for Robin Smith to subscribe, the message would read
- SUBscribe mHealth-Training Robin Smith.
You will receive a confirmation of your subscription along with instructions on using the listserv.