William T. Riley

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.

Item Banks for Measuring Emotional Distress From the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, Anxiety, and Anger

The authors report on the development and calibration of item banks for depression, anxiety, and anger as part of the Patient-Reported Outcomes Measurement Information System (PROMIS®). Comprehensive literature searches yielded an initial bank of 1,404 items from 305 instruments. After qualitative item analysis (including focus groups and cognitive interviewing), 168 items (56 for each construct) were written in a first person, past tense format with a 7-day time frame and five response options reflecting frequency. The calibration sample included nearly 15,000 respondents. Final banks of 28, 29, and 29 items were calibrated for depression, anxiety, and anger, respectively, using item response theory. Test information curves showed that the PROMIS item banks provided more information than conventional measures in a range of severity from approximately −1 to +3 standard deviations (with higher scores indicating greater distress). Short forms consisting of seven to eight items provided information comparable to legacy measures containing more items.

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.

Obesity Among Those with Mental Disorders: A National Institute of Mental Health Meeting Report

The National Institute of Mental Health convened a meeting in October 2005 to review the literature on obesity, nutrition, and physical activity among those with mental disorders. The findings of this meeting and subsequent update of the literature review are summarized here. Levels of obesity are higher in those with schizophrenia and depression, as is mortality from obesity-related conditions such as coronary heart disease. Medication side effects, particularly the metabolic side effects of antipsychotic medications, contribute to the high levels of obesity in those with schizophrenia, but increased obesity and visceral adiposity have been found in some but not all samples of drug-naïve patients as well. Many of the weight-management strategies used in the general population may be applicable to those with mental disorders, but little is known about the effects of these strategies on this patient population or how these strategies may need to be adapted for the unique needs of those with mental disorders. The minimal research on weight-management programs for those with mental disorders indicates that meaningful changes in dietary intake and physical activity are possible. Physical activity is an important component of any weight-management program, particularly for those with depression, for which a substantial body of research indicates both mental and physical health benefits. Obesity among those with mental disorders has not received adequate research attention, and empirically-based interventions to address the increasing prevalence of obesity and risk of cardiovascular and metabolic diseases in this population are lacking.

Representativeness of the Patient-Reported Outcomes Measurement Information System Internet panel

OBJECTIVES To evaluate the Patient-Reported Outcomes Measurement Information System (PROMIS), which collected data from an Internet polling panel, and to compare PROMIS with national norms. STUDY DESIGN AND SETTING We compared demographics and self-rated health of the PROMIS general Internet sample (N=11,796) and one of its subsamples (n=2,196) selected to approximate the joint distribution of demographics from the 2000 U.S. Census, with three national surveys and U.S. Census data. The comparisons were conducted using equivalence testing with weights created for PROMIS by raking. RESULTS The weighted PROMIS population and subsample had similar demographics compared with the 2000 U.S. Census, except that the subsample had a higher percentage of people with higher education than high school. Equivalence testing shows similarity between PROMIS general population and national norms with regard to body mass index, EQ-5D health index (EuroQol group defined descriptive system of health-related quality of life states consisting of five dimensions including mobility, self-care, usual activities, pain/discomfort, anxiety/depression), and self-rating of general health. CONCLUSION Self-rated health of the PROMIS general population is similar to that of existing samples from the general U.S. population. The weighted PROMIS general population is more comparable to national norms than the unweighted population with regard to subject characteristics. The findings suggest that the representativeness of the Internet data is comparable to those from probability-based general population samples.

Relative to the general US population, chronic diseases are associated with poorer health-related quality of life as measured by the Patient-Reported Outcomes Measurement Information System (PROMIS)

OBJECTIVES The Patient-Reported Outcomes Measurement Information System (PROMIS) allows assessment of the impact of chronic conditions on health-related quality of life (HRQL) across diseases. We report on the HRQL impact of individual and comorbid conditions as well as conditions that are described as limiting activity. STUDY DESIGN AND SETTING Data were collected through online and clinic recruitment as part of the PROMIS item calibration sample (n=21,133). Participants reported the presence or absence of 24 chronic health conditions and whether their activity was limited by each condition. RESULTS Across health status domains, the presence of a chronic condition was associated with poorer scores than those without a diagnosis, particularly for those individuals who reported that their condition was disabling. The magnitude of detriment in HRQL was more pronounced for individuals with two or more chronic conditions and could not be explained by sociodemographic factors. Patterns of HRQL deficits varied across disease and comorbidity status. CONCLUSION The impact of chronic conditions, particularly when experienced with comorbid disease, is associated with detriments in HRQL. The negative impact on HRQL varies across symptoms and functional areas within a given condition.

Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention A Scientific Statement From the American Heart Association

Although mortality for cardiovascular disease (CVD) has declined for several decades, heart disease and stroke continue to be the leading causes of death, disability, and high healthcare costs. Unhealthy behaviors related to CVD risk (eg, smoking, sedentary lifestyle, and unhealthful eating habits) remain highly prevalent. The high rates of overweight, obesity, and type 2 diabetes mellitus (T2DM); the persistent presence of uncontrolled hypertension; lipid levels not at target; and the ≈18% of adults who continue to smoke cigarettes pose formidable challenges for achieving improved cardiovascular health.1,2 It is apparent that the performance of healthful behaviors related to the management of CVD risk factors has become an increasingly important facet of the prevention and management of CVD.3 In 2010, the American Heart Association (AHA) made a transformative shift in its strategic plan and added the concept of cardiovascular health.2 To operationalize this concept, the AHA targeted 4 health behaviors in the 2020 Strategic Impact Goals: reduction in smoking and weight, healthful eating, and promotion of regular physical activity. Three health indicators also were included: glucose, blood pressure (BP), and cholesterol. On the basis of the AHA Life’s Simple 7 metrics for improved cardiovascular health, 30% have not reached the target levels for lipids or BP. National Health and Nutrition Examination Survey (NHANES) data revealed that people who met ≥6 of the cardiovascular health metrics had a significantly better risk profile (hazard ratio for all-cause mortality, 0.49) compared with individuals who had achieved only 1 metric or none.2 The studies reviewed in this statement targeted these behaviors (ie, smoking, physical activity, healthful eating, and maintaining a healthful weight) and cardiovascular health indicators (ie, blood …

Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise

Our current health research enterprise is painstakingly slow and cumbersome, and its results seldom translate into practice. The slow pace of health research contributes to findings that are less relevant and potentially even obsolete. To produce more rapid, responsive, and relevant research, we propose approaches that increase relevance via greater stakeholder involvement, speed research via innovative designs, streamline review processes, and create and/or better leverage research infrastructure. Broad stakeholder input integrated throughout the research process can both increase relevance and facilitate study procedures. More flexible and rapid research designs should be considered before defaulting to the traditional two-arm randomized controlled trial (RCT), but even traditional RCTs can be designed for more rapid findings. Review processes for grant applications, IRB protocols, and manuscript submissions can be better streamlined to minimize delays. Research infrastructures such as rapid learning systems and other health information technologies can be leveraged to rapidly evaluate new and existing treatments, and alleviate the extensive recruitment delays common in traditional research. These and other approaches are feasible but require a culture shift among the research community to value not only methodological rigor, but also the pace and relevance of research.

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.

The development of a clinical outcomes survey research application: Assessment CenterSM

IntroductionThe National Institutes of Health sponsored Patient-Reported Outcome Measurement Information System (PROMIS) aimed to create item banks and computerized adaptive tests (CATs) across multiple domains for individuals with a range of chronic diseases.PurposeWeb-based software was created to enable a researcher to create study-specific Websites that could administer PROMIS CATs and other instruments to research participants or clinical samples. This paper outlines the process used to develop a user-friendly, free, Web-based resource (Assessment CenterSM) for storage, retrieval, organization, sharing, and administration of patient-reported outcomes (PRO) instruments.MethodsJoint Application Design (JAD) sessions were conducted with representatives from numerous institutions in order to supply a general wish list of features. Use Cases were then written to ensure that end user expectations matched programmer specifications. Program development included daily programmer “scrum” sessions, weekly Usability Acceptability Testing (UAT) and continuous Quality Assurance (QA) activities pre- and post-release.ResultsAssessment Center includes features that promote instrument development including item histories, data management, and storage of statistical analysis results.ConclusionsThis case study of software development highlights the collection and incorporation of user input throughout the development process. Potential future applications of Assessment Center in clinical research are discussed.