Analysis of the health economics portfolio funded by the National Institutes of Health in response to published guidance.

Health services, economics, and outcomes research (referred to as health economics research hereinafter) is one of the interdisciplinary sciences that the National Institutes of Health (NIH) supports in order to pursue its overall mission to improve health. In 2015, NIH guidance was published to clarify the type of health economics research that NIH would continue to fund. This analysis aimed to determine if there were changes in the number of health economics applications received and funded by NIH after the release of the guidance. Health economics applications submitted to NIH both before and after publication of the guidance were identified using a machine learning approach with input from subject matter experts. Application and funding trends were examined by fiscal year, method of application (solicited vs. unsolicited), and activity code. This study found that application and funding rates of health economics research were decreasing prior to guidance. Following publication of this guidance, the application and funding rate of health economics applications increased.

Advancing digital health equity: Directions for behavioral and social science research.

The field of digital health is evolving rapidly and encompasses a wide range of complex and changing technologies used to support individual and population health. The COVID-19 pandemic has augmented digital health expansion and significantly changed how digital health technologies are used. To ensure that these technologies do not create or exacerbate existing health disparities, a multi-pronged and comprehensive research approach is needed. In this commentary, we outline five recommendations for behavioral and social science researchers that are critical to promoting digital health equity. These recommendations include: (i) centering equity in research teams and theoretical approaches, (ii) focusing on issues of digital health literacy and engagement, (iii) using methods that elevate perspectives and needs of underserved populations, (iv) ensuring ethical approaches for collecting and using digital health data, and (v) developing strategies for integrating digital health tools within and across systems and settings. Taken together, these recommendations can help advance the science of digital health equity and justice.

The field of digital health is quickly growing and changing. Digital health technologies have the potential to increase access to health-related information and healthcare and improve wellbeing, but it is important that those technologies don't widen existing health disparities or create new ones. Behavioral and social science researchers have a key role to play in centering equity in their research teams and theoretical approaches, focusing on key barriers to access, uptake, and usage, studying digital health in ways that elevate the voices and needs of historically underserved groups, being thoughtful about how digital health data are collected and used, and making sure that digital health tools are designed to be used in real-world settings.

Social Determinants of Health and Implementation Research: Lessons from the COVID-19 Pandemic.

During the past three decades, the world has experienced many clinical and public health challenges that require implementation of practices and policies informed by an understanding of social determinants of health and health inequities, but perhaps none as global and pervasive as the current COVID-19 pandemic. In the context of this special themed issue on Social Determinants of Health and Implementation Research: Three Decades of Progress and a Need for Convergence, we highlight the application of social determinants of health and implementation research on various aspects of the COVID-19 pandemic.

Publication rates from biomedical and behavioral and social science R01s funded by the National Institutes of Health.

Prior research has shown a serious lack of research transparency resulting from the failure to publish study results in a timely manner. The National Institutes of Health (NIH) has increased its use of publication rate and time to publication as metrics for grant productivity. In this study, we analyze the publications associated with all R01 and U01 grants funded from 2008 through 2014, providing sufficient time for these grants to publish their findings, and identify predictors of time to publication based on a number of variables, including if a grant was coded as a behavioral and social sciences research (BSSR) grant or not. Overall, 2.4% of the 27,016 R01 and U01 grants did not have a publication associated with the grant within 60 months of the project start date, and this rate of zero publications was higher for BSSR grants (4.6%) than for non-BSSR grants (1.9%). Mean time in months to first publication was 15.2 months, longer for BSSR grants (22.4 months) than non-BSSR grants (13.6 months). Survival curves showed a more rapid reduction of risk to publish from non-BSSR vs BSSR grants. Cox regression models showed that human research (vs. animal, neither, or both) and clinical trials research (vs. not) are the strongest predictors of time to publication and failure to publish, but even after accounting for these and other predictors, BSSR grants continued to show longer times to first publication and greater risk of no publications than non-BSSR grants. These findings indicate that even with liberal criteria for publication (any publication associated with a grant), a small percentage of R01 and U01 grantees fail to publish in a timely manner, and that a number of factors, including human research, clinical trial research, child research, not being an early stage investigator, and conducting behavioral and social sciences research increase the risk of time to first publication.

National Institutes of Health social and behavioral research in response to the SARS-CoV2 Pandemic.

The COVID-19 pandemic has been mitigated primarily using social and behavioral intervention strategies, and these strategies have social and economic impacts, as well as potential downstream health impacts that require further study. Digital and community-based interventions are being increasingly relied upon to address these health impacts and bridge the gap in health care access despite insufficient research of these interventions as a replacement for, not an adjunct to, in-person clinical care. As SARS-CoV-2 testing expands, research on encouraging uptake and appropriate interpretation of these test results is needed. All of these issues are disproportionately impacting underserved, vulnerable, and health disparities populations. This commentary describes the various initiatives of the National Institutes of Health to address these social, behavioral, economic, and health disparities impacts of the pandemic, the findings from which can improve our response to the current pandemic and prepare us better for future infectious disease outbreaks.

National Institutes of Health Support of Digital Health Behavior Research.

The National Institutes of Health (NIH) has increasingly supported research in digital health technologies to advance research and deliver behavior change interventions. We highlight some of the research supported by the NIH in eHealth, mHealth, and social media as well as research resources supported by the NIH to accelerate research in this area. We also describe some of the challenges and opportunities in the digital health field and the need to balance the promise of these technologies with rigorous scientific evidence.

Willingness to Exchange Health Information via Mobile Devices: Findings From a Population-Based Survey.

PURPOSE: The rapid proliferation of mobile devices offers unprecedented opportunities for patients and health care professionals to exchange health information electronically, but little is known about patients' willingness to exchange various types of health information using these devices. We examined willingness to exchange different types of health information via mobile devices, and assessed whether sociodemographic characteristics and trust in clinicians were associated with willingness in a nationally representative sample. METHODS: We analyzed data for 3,165 patients captured in the 2013 Health Information National Trends Survey. Multinomial logistic regression analysis was conducted to test differences in willingness. Ordinal logistic regression analysis assessed correlates of willingness to exchange 9 types of information separately. RESULTS: Participants were very willing to exchange appointment reminders (odds ratio [OR] = 6.66; 95% CI, 5.68-7.81), general health tips (OR = 2.03; 95% CI, 1.74-2.38), medication reminders (OR = 2.73; 95% CI, 2.35-3.19), laboratory/test results (OR = 1.76; 95% CI, 1.62-1.92), vital signs (OR = 1.63; 95% CI, 1.48-1.80), lifestyle behaviors (OR = 1.40; 95% CI, 1.24-1.58), and symptoms (OR = 1.62; 95% CI, 1.46-1.79) as compared with diagnostic information. Older adults had lower odds of being more willing to exchange any type of information. Education, income, and trust in health care professional information correlated with willingness to exchange certain types of information. CONCLUSIONS: Respondents were less willing to exchange via mobile devices information that may be considered sensitive or complex. Age, socioeconomic factors, and trust in professional information were associated with willingness to engage in mobile health information exchange. Both information type and demographic group should be considered when developing and tailoring mobile technologies for patient-clinician communication.

Educational attainment and life expectancy: a perspective from the NIH Office of Behavioral and Social Sciences Research.

The NIH Office of Behavioral and Social Sciences Research (OBSSR) furthers the mission of the NIH by stimulating behavioral and social sciences research throughout NIH and integrating these areas of research more fully into the NIH health research enterprise, thereby improving our understanding, treatment, and prevention of disease. OBSSR accomplishes this mission through several strategic priorities: (1) supporting the next generation of basic behavioral and social sciences research, (2) facilitating interdisciplinary research, (3) promoting a multi-level systems perspective of health and behavior, and (4) encouraging a problem-focused perspective on population health.

Program evaluation of remote heart failure monitoring: healthcare utilization analysis in a rural regional medical center.

BACKGROUND: Remote monitoring for heart failure (HF) has had mixed and heterogeneous effects across studies, necessitating further evaluation of remote monitoring systems within specific healthcare systems and their patient populations. "Care Beyond Walls and Wires," a wireless remote monitoring program to facilitate patient and care team co-management of HF patients, served by a rural regional medical center, provided the opportunity to evaluate the effects of this program on healthcare utilization. MATERIALS AND METHODS: Fifty HF patients admitted to Flagstaff Medical Center (Flagstaff, AZ) participated in the project. Many of these patients lived in underserved and rural communities, including Native American reservations. Enrolled patients received mobile, broadband-enabled remote monitoring devices. A matched cohort was identified for comparison. RESULTS: HF patients enrolled in this program showed substantial and statistically significant reductions in healthcare utilization during the 6 months following enrollment, and these reductions were significantly greater compared with those who declined to participate but not when compared with a matched cohort. CONCLUSIONS: The findings from this project indicate that a remote HF monitoring program can be successfully implemented in a rural, underserved area. Reductions in healthcare utilization were observed among program participants, but reductions were also observed among a matched cohort, illustrating the need for rigorous assessment of the effects of HF remote monitoring programs in healthcare systems.

Behavioral research in cancer prevention and control: a look to the future.

Human behavior is central to the etiology and management of cancer outcomes and presents several avenues for targeted and sustained intervention. Psychosocial experiences such as stress and health behaviors including tobacco use, sun exposure, poor diet, and a sedentary lifestyle increase the risk of some cancers yet are often quite resistant to change. Cancer screening and other health services are misunderstood and over-utilized, and vaccination underutilized, in part because of the avalanche of information about cancer prevention. Coordination of cancer care is suboptimal, and only a small fraction of cancer patients enroll in clinical trials essential to the development of new cancer treatments. A growing population of cancer survivors has necessitated a fresh view of cancer as a chronic rather than acute disease. Fortunately, behavioral research can address a wide variety of key processes and outcomes across the cancer control continuum from prevention to end-of-life care. Here we consider effects at the biobehavioral and psychological, social and organizational, and environmental levels. We challenge the research community to address key behavioral targets across all levels of influence, while taking into account the many new methodological tools that can facilitate this important work.

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.

Overview of the Consortium of Hospitals Advancing Research on Tobacco (CHART).

BACKGROUND: The Consortium of Hospitals Advancing Research on Tobacco (CHART) is a network of six projects and a research coordinating unit funded by the National Heart, Lung, and Blood Institute, the National Cancer Institute, the National Institute on Drug Abuse, and the National Institutes of Health (NIH) Office of Behavioral and Social Science Research. The CHART projects will assess the effectiveness and cost-effectiveness of smoking cessation interventions initiated during hospitalization and continued post-discharge. METHODS/DESIGN: Along with a seventh project funded previously under the NIH Challenge grants, the CHART projects will assess smoking cessation strategies delivered to approximately 10,000 hospitalized smokers across a geographically diverse group of nearly 20 private, public, academic, and community hospitals. The CHART research coordinating unit at Kaiser Permanente Center for Health Research provides organizational and data coordination support, facilitating the development of common measures for combining data from multiple CHART projects. DISCUSSION: The targeted enrollment in CHART, if achieved, will represent the largest, most diverse pooled dataset of hospitalized smokers receiving smoking cessation assistance, and is designed to contribute to the dissemination and implementation of smoking cessation interventions provided by hospital systems.

Evaluation of a PDA-based dietary assessment and intervention program: a randomized controlled trial.

OBJECTIVE: To evaluate the capability of DietMatePro, a PDA-based dietary assessment program, to monitor dietary intake and to improve adherence to a dietary regimen. DESIGN: Randomized controlled trial. SUBJECTS: Overweight and obese (Body Mass Index (BMI) 25-40) participants without dietary restrictions. INTERVENTION: Participants (n = 174) were randomized to record usual dietary intake using either DietMatePro or a paper food diary for one week to compare concordance with 24-hr recall. At the week 1 visit, participants were individually counseled to follow the diet recommendations of the Ornish Prevention Diet for three weeks and continue monitoring food intake using the assigned method to estimate adherence to the intervention by monitoring condition. OUTCOME MEASURES: Spearman correlations between week 1 24-hr recall and the assigned recording method were compared to assess validity. Mean pre-post changes in intake measured by 24-hr recall were compared according to monitoring condition to measure adherence to the Ornish diet. RESULTS: Correlations of energy and nutrient values reported on the food label ranged from 0.41 to 0.71 for the DietMatePro condition versus 0.63 to 0.83 for the paper-based diary. Diet adherence was higher among DietMatePro (43%) compared to the paper diary (28%) group (p = 0.039). CONCLUSIONS/APPLICATIONS: DietMatePro does not appear to produce more valid data than paper-based approaches. DietMatePro may improve adherence to dietary regimens compared to paper-based methods.

Enhancing measurement in health outcomes research supported by Agencies within the US Department of Health and Human Services.

Many of the Institutes, Agencies and Centers that make up the US Department of Health and Human Services (DHHS) have recognized the need for better instrumentation in health outcomes research, and provide support, both internally and externally, for research utilizing advances in measurement theory and computer technology (informatics). In this paper, representatives from several DHHS agencies and institutes will discuss their need for better instruments within their discipline and describe current or future initiatives for exploring the benefits of these technologies. Together, the perspectives underscore the importance of developing valid, precise, and efficient measures to capture the full burden of disease and treatment on patients. Initiatives, like the Patient-Reported Outcomes Measurement Information System (PROMIS) to create health-related quality of life item banks, represent a trans-DHHS effort to develop a standard set of measures for informing decision making in clinical research, practice, and health policy.

Accuracy of a PDA-based dietary assessment program.

OBJECTIVE: Study objectives were to assess the accuracy of a food record delivered on a personal digital assistant (PDA) and to examine sources of error from the PDA-based food record. METHODS: Thirty-nine adults recruited with a newspaper advertisement were trained to record food intake using DietMatePro, a dietary assessment program delivered on a PDA. After 3 d of use, subjects returned for a follow-up visit in which a 24-h recall was conducted. Subjects also were timed while recording an observed, weighed lunch. Recalled and actual food intakes were compared with estimates recorded by the subjects when using the PDA. Paired sample t tests and Pearson's correlations assessed means and measurements of association between DietMatePro data compared with the 24-h recall data and observed meal data. Bland-Altman plots were used to assess bias in food recording. Sources of error were quantified by using calories as the unit for comparison. RESULTS: There were no significant differences in daily totals for calories and macronutrients between DietMatePro data and comparison measurements. Pearson's correlations of associations between DietMatePro data and the comparison measurement ranged from 0.505 to 0.797 (P < 0.005, n = 28) for the 24-h recall and from 0.419 to 0.786 (P < 0.005, n = 33) for the observed lunch, depending on the nutrient measured. The largest source of absolute error in caloric estimation was attributable to portion size estimation error (49%). CONCLUSIONS: DietMatePro, a PDA-based dietary assessment program, provides a method of assessing energy and macronutrient intakes comparable to the 24-h recall in samples lacking dietary restrictions.