Wendy Nilsen

Health behavior models in the age of mobile interventions: are our theories up to the task?

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.

Mobile health technology evaluation: the mHealth evidence workshop.

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, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

Self-report measures of medication adherence behavior: recommendations on optimal use

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 self-report 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.

Advancing the science of mHealth.

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.

Understanding the context of health for persons with multiple chronic conditions: moving from what is the matter to what matters.

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.

Dynamic Models of Behavior for Just-in-Time Adaptive Interventions

Improvements in health behavior theory will be central to creating successful interventions that encourage and support behavior change and maintenance. The authors discuss dynamic, multimethod, conceptually driven, and data-rich approaches for the development of testable computational models of health-related behaviors in real time.

News from the NIH: potential contributions of the behavioral and social sciences to the precision medicine initiative

At this year’s State of the Union address, the President announced a new

Using Contemporary Technologies in the Assessment and Treatment of Serious Mental Illness

215 million Precision Medicine Initiative in the 2016 budget that will pioneer a new model of patient-empowered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients [1, 2]. Concurrently, Directors of the National Institutes of Health and the National Cancer Institute, Drs. Francis Collins and Harold Varmus, respectively, published an article in the New England Journal of Medicine that describes two main components of this initiative, a near-term focus on cancer therapy and a longer-term effort to generate knowledge applicable to a wide range of health and disease [3]. This longer-term initiative seeks to generate a cohort of one million or more Americans to “enable better assessment of disease risk, understanding of disease mechanisms, and the prediction of optimal therapy for many more diseases, with the goal of expanding the benefits of precision medicine into myriad aspects of health and healthcare” [3]. Matching treatment to the unique biological, behavioral, or environmental characteristics of the individual is nothing new. Matching blood type for transfusions has been common practice for nearly a century [4]. The guidelines for the management of cholesterol have been based on individual patient factors for over a decade [5]. Tailored behavioral interventions have been evaluated for over two decades. Although early treatment matching studies were disappointing [6], tailored behavioral interventions, especially computerized tailored interventions, have generally been found more efficacious than untailored interventions [7, 8]. The concept of precision medicine is not new, but recent advances in genome sequencing, cohort study designs, health informatics, and mobile/wireless technologies make now an opportune time for a large precision medicine cohort initiative.

Validating office-based screening for psychosocial strengths and difficulties among youths in foster care.

Technologies that have the potential to improve the quality of mental health services are developing at a phenomenal rate. In the current paper, experts from a wide array of disciplines and settings share their perspectives, experiences, and visions for technology-assisted assessment and treatment of serious mental illness in the present and not-too-distant future. First, we examine how technological advancements may help consumers better understand and utilize complex treatment systems, obtain information on mental health conditions and viable interventions, enhance treatment engagement, learn and maintain effective coping strategies, overcome some of the barriers associated with stigma, reduce treatment costs, and increase treatment access and outcomes. We then discuss the advantages of using ubiquitous mobile devices and mobile health approaches specifically, highlighting their potential in facilitating real-place/real-time individualized care. Finally, we identify some of the unique challenges emerging in the development and testing of technological platforms for services, providing suggestions for areas for future research.

Learning Difficulties Among Children Separated From a Parent

OBJECTIVES: To assess the effectiveness of social-emotional screening in the primary care setting for youths in foster care. METHODS: The setting was a primary care practice for all youth in home-based foster care in 1 county. Subjects were youths, aged 11 to 17 years, and their foster parents; both completed a Strengths and Difficulties Questionnaire at well-child visits. The Strengths and Difficulties Questionnaire is a previously validated 25-item tool that has 5 domains: emotional symptoms; conduct problems; hyperactivity/inattention; peer problems; and prosocial behaviors and an overall total difficulties score. We first compared youth versus parent Strengths and Difficulties Questionnaire scores and then assessed the accuracy of these Strengths and Difficulties Questionnaire scores by comparing them in a subsample of youths (n = 50) with results of home-based structured clinical interviews using the Childrens Interview for Psychiatric Syndromes. RESULTS: Of 138 subjects with both youth and parent reports, 78% had prosocial behaviors (strengths), and 70% had 1 or more social-emotional problems. Parents reported significantly more conduct problems (38% vs 16%; P < .0001) and total difficulties (30% vs 16%; P = .002) than did youth. The Strengths and Difficulties Questionnaire had better agreement with the Childrens Interview for Psychiatric Syndromes (n = 50) for any Strengths and Difficulties Questionnaire–identified problem for combined youth and foster-parent reports (93%), compared with youth report alone (54%) or parent report alone (71%). CONCLUSIONS: Although most youths in foster care have social-emotional problems, most have strengths as well. Youth and foster-parent perspectives on these problems differ. Systematic social-emotional screening in primary care that includes both youth and parent reports can identify youths who may benefit from services.