Perceptions of Food Advertising and Association With Consumption of Energy-Dense Nutrient-Poor Foods Among Adolescents in the United States: Results From a National Survey.

The advertising and marketing of energy-dense, nutrient-poor (EDNP) food and drink has been cited as one contributor to unhealthy eating behaviors in adolescents. The present study examines perceptions about and trust in food advertising and their association with consumption of EDNP foods and drinks among adolescents in the United States. Data (n = 1,384) come from the U.S. National Cancer Institute's Family Life, Activity, Sun, Health, and Eating Survey. One way ANOVAs were conducted to assess differences between population subgroups in advertising perceptions. Bivariate and multivariable linear regression models were used to examine the associations between perceptions toward and trust in food advertising and consumption of EDNP foods and drinks, controlling for sociodemographic factors. Results show that there are significant differences between racial/ethnic groups on advertising perceptions (F = 16.32, p = < .0001). As positive perceptions toward food advertising increase among adolescents, there is an associated increase in daily frequency of consumption of EDNP foods and drinks (ß = 0.10, p < .01). Similarly, the more adolescents agreed that they trusted food advertising, the higher the reported daily frequency of EDNP food and drink consumption (ß = 0.08, p = .01). Targeting perceptions about food advertising may be a worthy intervention strategy to reduce the impact of food marketing and the consumption of heavily advertised EDNP foods and drinks among adolescents.

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.

Mining Health App Data to Find More and Less Successful Weight Loss Subgroups.

BACKGROUND: More than half of all smartphone app downloads involve weight, diet, and exercise. If successful, these lifestyle apps may have far-reaching effects for disease prevention and health cost-savings, but few researchers have analyzed data from these apps. OBJECTIVE: The purposes of this study were to analyze data from a commercial health app (Lose It!) in order to identify successful weight loss subgroups via exploratory analyses and to verify the stability of the results. METHODS: Cross-sectional, de-identified data from Lose It! were analyzed. This dataset (n=12,427,196) was randomly split into 24 subsamples, and this study used 3 subsamples (combined n=972,687). Classification and regression tree methods were used to explore groupings of weight loss with one subsample, with descriptive analyses to examine other group characteristics. Data mining validation methods were conducted with 2 additional subsamples. RESULTS: In subsample 1, 14.96% of users lost 5% or more of their starting body weight. Classification and regression tree analysis identified 3 distinct subgroups: "the occasional users" had the lowest proportion (4.87%) of individuals who successfully lost weight; "the basic users" had 37.61% weight loss success; and "the power users" achieved the highest percentage of weight loss success at 72.70%. Behavioral factors delineated the subgroups, though app-related behavioral characteristics further distinguished them. Results were replicated in further analyses with separate subsamples. CONCLUSIONS: This study demonstrates that distinct subgroups can be identified in "messy" commercial app data and the identified subgroups can be replicated in independent samples. Behavioral factors and use of custom app features characterized the subgroups. Targeting and tailoring information to particular subgroups could enhance weight loss success. Future studies should replicate data mining analyses to increase methodology rigor.

Skin Cancer and UV Exposure-Related Behaviors Among Appalachian and Non-Appalachian Adults.

Introduction: Appalachian communities experience elevated rates of cancer incidence and mortality relative to other regions in the U.S. Specifically, melanoma mortality rates are higher in Appalachia compared to the national average, despite comparable incidence rates. Purpose: To examine differences in self-reported history of skin cancer and prevalence of two UV exposure behaviors between Appalachian and non-Appalachian adults in a nationally representative sample. Methods: Data are from four cross-sectional cycles of the Health Information National Trends Survey (2011-2014) (N=14,451). We examined sunscreen use and tanning bed use, and self-reported history of melanoma and non-melanoma skin cancer. Descriptive and weighted multivariable analyses were conducted to examine sunscreen and tanning bed use, controlling for sociodemographic characteristics. Results: Approximately 8% of the study sample resided in Appalachia (n=1,015). Self-reported melanoma (0.6%) and non-melanoma (3.2%) skin cancer histories were low among Appalachians and did not differ statistically from non-Appalachians (p>0.05). Only 21.2% of Appalachians reported using sunscreen often or always when going outside for more than one hour on a warm, sunny day compared to 27.4% of non-Appalachians (pp=0.04), but there were no regional differences in tanning bed use (OR=1.48, p=0.23) when controlling for sociodemographics and general health status. Implications: Appalachians had comparable histories of self-reported melanoma and non-melanoma skin cancer but were less likely to report sunscreen use than non-Appalachians. Enhanced communication efforts to promote sunscreen use and other UV protection behaviors in Appalachia may be valuable.

Beliefs About Behavioral Determinants of Obesity in Appalachia, 2011-2014.

The populations of many Appalachian communities have high rates of unhealthy body weight. This study aimed to identify differences in beliefs about obesity between Appalachians and non-Appalachians. Our analyses explored health beliefs and behaviors among US adults aged ≥18 (n = 14 451) who responded to the Health Information National Trends Survey (2011-2014), of whom 1015 (8%) resided within the 420 counties recognized as Appalachian by the Appalachian Regional Commission. Using weighted regression analyses and controlling for sociodemographic characteristics and general health, we determined that self-reported body mass index was higher among Appalachians than among non-Appalachians ( B = 0.75, P = .03, 95% confidence interval, 0.08-1.47) and that Appalachians were less likely than non-Appalachians to believe that lifestyle factors were related to obesity ( B = -0.37, P = .03, 95% confidence interval, -0.04 to -0.71). Results suggest that Appalachians may regard behavioral prevention differently from non-Appalachians, perhaps with less confidence in the effectiveness of certain behaviors to reduce obesity risk. Future research may determine whether such beliefs could complicate efforts to encourage healthy lifestyles throughout the region.

The science of team science: A review of the empirical evidence and research gaps on collaboration in science.

Collaborations among researchers and across disciplinary, organizational, and cultural boundaries are vital to address increasingly complex challenges and opportunities in science and society. In addition, unprecedented technological advances create new opportunities to capitalize on a broader range of expertise and information in scientific collaborations. Yet rapid increases in the demand for scientific collaborations have outpaced changes in the factors needed to support teams in science, such as institutional structures and policies, scientific culture, and funding opportunities. The Science of Team Science (SciTS) field arose with the goal of empirically addressing questions from funding agencies, administrators, and scientists regarding the value of team science (TS) and strategies for successfully leading, engaging in, facilitating, and supporting science teams. Closely related fields have rich histories studying teams, groups, organizations, and management and have built a body of evidence for effective teaming in contexts such as industry and the military. Yet few studies had focused on science teams. Unique contextual factors within the scientific enterprise create an imperative to study these teams in context, and provide opportunities to advance understanding of other complex forms of collaboration. This review summarizes the empirical findings from the SciTS literature, which center around five key themes: the value of TS, team composition and its influence on TS performance, formation of science teams, team processes central to effective team functioning, and institutional influences on TS. Cross-cutting issues are discussed in the context of new research opportunities to further advance SciTS evidence and better inform policies and practices for effective TS. (PsycINFO Database Record

Characterizing user engagement with health app data: a data mining approach.

The use of mobile health applications (apps) especially in the area of lifestyle behaviors has increased, thus providing unprecedented opportunities to develop health programs that can engage people in real-time and in the real-world. Yet, relatively little is known about which factors relate to the engagement of commercially available apps for health behaviors. This exploratory study examined behavioral engagement with a weight loss app, Lose It! and characterized higher versus lower engaged groups. Cross-sectional, anonymized data from Lose It! were analyzed (n = 12,427,196). This dataset was randomly split into 24 subsamples and three were used for this study (total n = 1,011,008). Classification and regression tree methods were used to identify subgroups of user engagement with one subsample, and descriptive analyses were conducted to examine other group characteristics associated with engagement. Data mining validation methods were conducted with two separate subsamples. On average, users engaged with the app for 29 days. Six unique subgroups were identified, and engagement for each subgroup varied, ranging from 3.5 to 172 days. Highly engaged subgroups were primarily distinguished by the customization of diet and exercise. Those less engaged were distinguished by weigh-ins and the customization of diet. Results were replicated in further analyses. Commercially-developed apps can reach large segments of the population, and data from these apps can provide insights into important app features that may aid in user engagement. Getting users to engage with a mobile health app is critical to the success of apps and interventions that are focused on health behavior change.

Progress on Broadband Access to the Internet and Use of Mobile Devices in the United States.

Healthy People 2020 (HP2020) aims to improve population health outcomes through several objectives, including health communication and health information technology. We used 7 administrations of the Health Information National Trends Survey to examine HP2020 goals toward access to the Internet through broadband and mobile devices (N = 34 080). We conducted descriptive analyses and obtained predicted marginals, also known as model-adjusted risks, to estimate the association between demographic characteristics and use of mobile devices. The HP2020 target (7.7% of the US population) for accessing the Internet through a cellular network was surpassed in 2014 (59.7%), but the HP2020 target (83.2%) for broadband access fell short (63.8%). Sex and age were associated with accessing the Internet through a cellular network throughout the years (Wald F test, P <.05). The increase in the percentage of people accessing the Internet through mobile devices presents an opportunity for technology-based health interventions that should be explored.

Public use of electronic personal health information: Measuring progress of the Healthy People 2020 Objectives.

Use of the internet for seeking and managing health information in the U.S., Europe, and emerging and developing nations is growing. Recent global trends indicate more interactive uses of the internet including online communication with providers. In the U.S., The Healthy People 2020 (HP2020) initiative was created by the Department of Health and Human Services to provide 10-year goals for improving the health of American citizens. Two goals of HP2020 were to increase the proportion of individuals who use the Internet to keep track of their personal health information (PHI) online and to increase the proportion of individuals who use the internet to communicate with their healthcare provider. In the present study, we use data from the seven administrations of the Health Information National Trends Survey (HINTS) to assess progress towards these goals. These data were analyzed using descriptive, bivariate, and logistic regression analytic techniques. Results of this study suggested that the HP2020 target of having 15.7% of individuals manage their PHI online by 2020 has already been exceeded (28.1%); similarly, the goal for proportion of individuals communicating with their provider using the internet (15.0%) was exceeded by 2014 (29.7%). While progress towards these goals was positive in all sociodemographic groups for both goals, differences in the rate of progress were seen by gender, race/ethnicity, income, and education, but not by age group. The rapidly increasing proportion of individuals globally who use the internet to manage their health information provides unique opportunities for patient-centered health information technology interventions.

Advancing Cancer Prevention and Behavior Theory in the Era of Big Data.

The era of "Big Data" presents opportunities to substantively address cancer prevention and control issues by improving health behaviors and refining theoretical models designed to understand and intervene in those behaviors. Yet, the terms "model" and "Big Data" have been used rather loosely, and clarification of these terms is required to advance the science in this area. The objectives of this paper are to discuss conceptual definitions of the terms "model" and "Big Data", as well as examine the promises and challenges of Big Data to advance cancer prevention and control research using behavioral theories. Specific recommendations for harnessing Big Data for cancer prevention and control are offered.

Mobile and Wireless Technologies in Health Behavior and the Potential for Intensively Adaptive Interventions.

Recent advances in mobile and wireless technologies have made real-time assessments of health behaviors and their influences possible with minimal respondent burden. These tech-enabled real-time assessments provide the basis for intensively adaptive interventions (IAIs). Evidence of such studies that adjust interventions based on real-time inputs is beginning to emerge. Although IAIs are promising, the development of intensively adaptive algorithms generate new research questions, and the intensive longitudinal data produced by IAIs require new methodologies and analytic approaches. Research considerations and future directions for IAIs in health behavior research are provided.