Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.

Medication adherence plays an important role in optimizing the outcomes of many treatment and preventive regimens in chronic illness. Self-report is the most common method for assessing adherence behavior in research and clinical care, but there are questions about its validity and precision. The NIH Adherence Network assembled a panel of adherence research experts working across various chronic illnesses to review selfreport medication adherence measures and research on their validity. Self-report medication adherence measures vary substantially in their question phrasing, recall periods, and response items. Self-reports tend to overestimate adherence behavior compared with other assessment methods and generally have high specificity but low sensitivity. Most evidence indicates that self-report adherence measures show moderate correspondence to other adherence measures and can significantly predict clinical outcomes. The quality of self-report adherence measures may be enhanced through efforts to use validated scales, assess the proper construct, improve estimation, facilitate recall, reduce social desirability bias, and employ technologic delivery. Self-report medication adherence measures can provide actionable information despite their limitations. They are preferred when speed, efficiency, and low-cost measures are required, as is often the case in clinical care. KeywordsAdherence, Compliance, Self-management, Medication, Self-report Valid measurement of medication adherence plays a crucial role in healthcare and health research. When a patient is not benefiting from a medication regimen, clinicians need sound adherence information to determine whether the medication is ineffective or not being taken as prescribed. Assessing medication adherence during routine clinical care can further ensure that individuals in need of adherence support interventions receive them, ideally before deleterious outcomes occur. In the context of clinical research, proper interpretation of proof-of-concept trials testing new pharmacologic regimens requires valid adherence data, because any null findings may stem from poor adherence rather than a lack of drug efficacy. Research designed to understand and promote medication adherence also requires precise methods of adherence assessment.Among many approaches to assessing medication adherence, patient self-report measures remain the most common method [1][2][3][4][5][6]. These measures are defined by asking respondents to characterize their medication adherence behavior. Self-report measures of medication adherence range from simple singleitem questions regarding missed doses to complex multi-item assessments that incorporate reasons for nonadherence [7]. The widespread use of self-report adherence measures in clinical care and research reflects their low cost and ease of implementation across a large variety of medication regimens.There are two primary challenges related to selfreport measures of medication adherence. ImplicationsPractice: Routine assessment of medication...

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and, (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, future possibilities and set a grand goal for the emerging field of mHealth research.

PURPOSE An isolated focus on 1 disease at a time is insufficient to generate the scientific evidence needed to improve the health of persons living with more than 1 chronic condition. This article explores how to bring context into research efforts to improve the health of persons living with multiple chronic conditions (MCC).METHODS Forty-five experts, including persons with MCC, family and friend caregivers, researchers, policy makers, funders, and clinicians met to critically consider 4 aspects of incorporating context into research on MCC: key contextual factors, needed research, essential research methods for understanding important contextual factors, and necessary partnerships for catalyzing collaborative action in conducting and applying research.RESULTS Key contextual factors involve complementary perspectives across multiple levels: public policy, community, health care systems, family, and person, as well as the cellular and molecular levels where most research currently is focused. Needed research involves moving from a disease focus toward a person-driven, goal-directed research agenda. Relevant research methods are participatory, flexible, multilevel, quantitative and qualitative, conducive to longitudinal dynamic measurement from diverse data sources, sufficiently detailed to consider what works for whom in which situation, and generative of ongoing communities of learning, living and practice. Important partnerships for collaborative action include cooperation among members of the research enterprise, health care providers, community-based support, persons with MCC and their family and friend caregivers, policy makers, and payers, including government, public health, philanthropic organizations, and the business community.CONCLUSION Consistent attention to contextual factors is needed to enhance health research for persons with MCC. Rigorous, integrated, participatory, multimethod approaches to generate new knowledge and diverse partnerships can be used to increase the relevance of research to make health care more sustainable, safe, equitable and effective, to reduce suffering, and to improve quality of life. INTRODUCTIONM ore than 1 in 4 Americans lives with the burden of more than 1 ongoing health condition, [1][2][3] and the number of persons living with multiple chronic health conditions is growing dramatically. 2,4 Medical costs for persons with chronic illnesses account for 75% of US health care spending, 4 and more than 90% of the Medicare spending on older adults is devoted to persons suffering from multiple chronic conditions (MCC). 5 This heavy expenditure has not yielded the desired increase in quality of life for those affected. 4 A strategic framework of the Department of Health and Human Services (DHHS) 6 and multiple proposals and programs from the private sector highlight the growing concern about persons living with MCC. 261Current health care and research approaches are largely mismatched to the challenge of persons living with MCC. Both health care and research are p...

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Mobile health (mHealth) technologies have the potential to greatly impact health research, health care, and health outcomes, but the exponential growth of the technology has outpaced the science. This article outlines two initiatives designed to enhance the science of mHealth. The mHealth Evidence Workshop used an expert panel to identify optimal methodological approaches for mHealth research. The NIH mHealth Training Institutes address the silos among the many academic and technology areas in mHealth research and is an effort to build the interdisciplinary research capacity of the field. Both address the growing need for high quality mobile health research both in the United States and internationally. mHealth requires a solid, interdisciplinary scientific approach that pairs the rapid change associated with technological progress with a rigorous evaluation approach. The mHealth Evidence Workshop and the NIH mHealth Training Institutes were both designed to address and further develop this scientific approach to mHealth.

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

M utable health-related behaviors and habits are responsible for upwards of 40 percent of preventable deaths. 1 However, it has proven difficult to develop successful scalable interventions to change common unhealthy yet persistent behaviors-such as poor dietary choices, physical inactivity, tobacco use, and unsafe sex. Furthermore, it has been ever harder develop programs that yield behavioral changes that are maintained over time. For example, up to 60 percent of alcohol, tobacco, and drug users relapse after successful quit attempts, and weight regain is so common that maintaining weight loss has been termed the "Achilles' heel" of obesity therapy. 2 These findings suggest that there's definitely room for improvement in our current understanding of what drives human behavior.Improvements in health behavior theory will be central to creating successful interventions that encourage and support behavior change and maintenance. Data from pervasive and mobile technologies used to measure, motivate, and sustain behavior change could play a major role in shaping a new, dynamic health behavior theory. Collaboration between behavioral scientists, computer scientists, and engineers will be needed to move this field forward.Here, we discuss dynamic, multimethod, conceptually driven, and data-rich approaches for the development of testable computational models of health-related behaviors in real time. ThEorIES of human BEhavIorTheories of human behavior have typically provided a needed conceptual framework for examining interrelationships between inputs (determinants) and outputs (behaviors or health outcomes). They help us estimate the relative impact of the various inputs that can guide further intervention and research. Importantly, theory can guide development of interventions by delineating factors to be studied, identifying facilitating situations and relevant processes, guiding timing and sequencing, and indicating possible methods of intervention and evaluation. However, our theories of human behavior might not be "up to the task" 3 of understanding behavior in a digital world. This might be because current theories of human behavior are largely based on data that provides static snapshots of behavior.The development of theories of human behavior has, to date, taken a predominantly intuition-based approach, postulating a set of concepts, definitions, and propositions that are meant to explain behavior by assuming the relationships between variables, usually in terms of linear functions, and then exposing the theory to "piecewise" empirical testing-testing only parts of the theory at one time. This approach was partially an artifact of the nature and temporal density of available data, which was frequently collected only pre-and post-intervention.However, data acquisition is no longer restricted to infrequent questionnaires or clinical tests. Now, rich streams of continuous data are becoming available through existing and emerging technologies, such as wearable and deployable sensors and mobile phones, leaving digi...