Weighting Nonprobability and Probability Sample Surveys in Describing Cancer Catchment Areas.

BACKGROUND: The Population Health Assessment initiative by NCI sought to enhance cancer centers' capacity to acquire, aggregate, and integrate data from multiple sources, as well as to plan, coordinate, and enhance catchment area analysis activities. METHODS: Key objectives of this initiative are pooling data and comparing local data with national data. A novel aspect of analyzing data from this initiative is the methodology used to weight datasets from sites that collected both probability and nonprobability samples. This article describes the methods developed to weight data, which cancer centers collected with combinations of probability, and nonprobability sampling designs. RESULTS: We compare alternative weighting methods in particular for the hybrid probability and nonprobability sampling designs employed by different cancer centers. We also include comparisons of local center data with national survey data from large probability samples. CONCLUSIONS: This hybrid approach to calculating statistical weights can be implemented both within cancer centers that collect both probability and nonprobability samples with common measures. Aggregation can also apply to cancer centers that share common data elements, and target similar populations, but differ in survey sampling designs. IMPACT: Researchers interested in local versus national comparisons for cancer surveillance and control outcomes should consider various weighting approaches, including hybrid approaches, when analyzing their data.

Developing international open science collaborations: Funder reflections on the Open Science Prize.

The Open Science Prize was established with the following objectives: first, to encourage the crowdsourcing of open data to make breakthroughs that are of biomedical significance; second, to illustrate that funders can indeed work together when scientific interests are aligned; and finally, to encourage international collaboration between investigators with the intent of achieving important innovations that would not be possible otherwise. The process for running the competition and the successes and challenges that arose are presented.

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.

A Feasibility Study on Using an Internet-Panel Survey to Measure Perceptions of E-cigarettes in 3 Metropolitan Areas, 2015.

OBJECTIVES: Internet-panel surveys are emerging as a means to quickly and cost-effectively collect health data, and because of their large memberships, they could be used for community-level surveys. To determine the feasibility of using an internet-panel survey to quickly provide community-level data, we conducted a pilot test of a health survey in 3 US metropolitan areas. METHODS: We conducted internet-panel surveys in Cleveland, Ohio; New York, New York; and Seattle, Washington, in 2015. Slightly more than 500 people responded to the survey in each city. We compared weighted unadjusted prevalence estimates from the internet-panel data with estimates from the 2014 Health Information National Trends Survey (HINTS) for the following question in each survey: "Compared to smoking cigarettes, would you say that electronic cigarettes are…much less harmful, less harmful, just as harmful, more harmful, much more harmful, or I've never heard of electronic cigarettes." We used multivariable logistic regression to compare associations of respondents' demographic and health characteristics with perceived harm from e-cigarettes. RESULTS: The prevalence of the perception that e-cigarettes are less harmful than smoking cigarettes ranged from 35.9% to 39.9% in the internet-panel sites and was 43.0% in HINTS. Most patterns of beliefs and respondent characteristics in the internet-panel data were consistent with patterns in HINTS. We found inconsistent patterns between internet-panel sites and HINTS by race/ethnicity and education. CONCLUSIONS: This feasibility study found that internet-panel surveys could quickly produce community-level data for targeted public health interventions and evaluation, but they may be limited in producing estimates among subgroups.

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.

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.

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.

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.

Consumer Attitudes and Perceptions on mHealth Privacy and Security: Findings From a Mixed-Methods Study.

This study examined consumers' attitudes and perceptions regarding mobile health (mHealth) technology use in health care. Twenty-four focus groups with 256 participants were conducted in 5 geographically diverse locations. Participants were also diverse in age, education, race/ethnicity, gender, and rural versus urban settings. Several key themes emerged from the focus groups. Findings suggest that consumer attitudes regarding mHealth privacy/security are highly contextualized, with concerns depending on the type of information being communicated, where and when the information is being accessed, who is accessing or seeing the information, and for what reasons. Consumers frequently considered the tradeoffs between the privacy/security of using mHealth technologies and the potential benefits. Having control over mHealth privacy/security features and trust in providers were important issues for consumers. Overall, this study found significant diversity in attitudes regarding mHealth privacy/security both within and between traditional demographic groups. Thus, to address consumers' concerns regarding mHealth privacy and security, a one-size-fits-all approach may not be adequate. Health care providers and technology developers should consider tailoring mHealth technology according to how various types of information are communicated in the health care setting, as well as according to the comfort, skills, and concerns individuals may have with mHealth technology.

Self-Reported Versus Accelerometer-Measured Physical Activity and Biomarkers Among NHANES Youth.

BACKGROUND: Discrepancies in self-report and accelerometer-measured moderate-to-vigorous physical activity (MVPA) may influence relationships with obesity-related biomarkers in youth. METHODS: Data came from 2003-2006 National Health and Nutrition Examination Surveys (NHANES) for 2174 youth ages 12 to 19. Biomarkers were: body mass index (BMI, kg/m2), BMI percentile, height and waist circumference (WC, cm), triceps and subscapular skinfolds (mm), systolic & diastolic blood pressure (BP, mmHg), high-density lipoprotein (HDL, mg/dL), total cholesterol (mg/dL), triglycerides (mg/dL), insulin (µU/ml), C-reactive protein (mg/dL), and glycohemoglobin (%). In separate sex-stratified models, each biomarker was regressed on accelerometer variables [mean MVPA (min/day), nonsedentary counts, and MVPA bouts (mean min/day)] and self-reported MVPA. Covariates were age, race/ethnicity, SES, physical limitations, and asthma. RESULTS: In boys, correlations between self-report and accelerometer MVPA were stronger (boys: r = 0.14-0.21; girls: r = 0.07-0.11; P < .010) and there were significant associations with BMI, WC, triceps skinfold, and SBP and accelerometer MVPA (P < .01). In girls, there were no significant associations between biomarkers and any measures of physical activity. CONCLUSIONS: Physical activity measures should be selected based on the outcome of interest and study population; however, associations between PA and these biomarkers appear to be weak regardless of the measure used.

The promise of intensive longitudinal data capture for behavioral health research.

Advances in technology and the associated cultural norms, especially the advent of the smartphone, offer an unprecedented opportunity to collect data on relevant health behaviors and experiences unobtrusively at a greater frequency and in greater volumes than ever before. This special issue will acquaint the readership of Nicotine and Tobacco Research with the potential for intensive longitudinal data and will illustrate some innovative analytic techniques for addressing research questions associated with this type of complex data. This introductory article will provide a brief history of the analytic techniques for intensive longitudinal data and will point to some resources that support and enable the use of these techniques.

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.

Association between self-reported household practices and body mass index of US children and adolescents, 2005.

INTRODUCTION: Parents can set household practices that influence children's behaviors. The objective of this study was to determine whether children (children and adolescents aged 9-18 y) who live in a household that has healthful practices related to behaviors associated with obesity have a lower body mass index (BMI). METHODS: We analyzed data from the 2005 Styles mail panel survey (N = 1,685 parents and children). We used multiple logistic regression to assess associations between 4 household practices and 3 children's behaviors: watching television, participating in vigorous physical activity, and purchasing sodas and snacks at school. RESULTS: Children watched more television if they had a television in their bedrooms, were less active as a family, and had no junk food restrictions at home. Children in less active families participated in about half as much VPA as children in more active families. Children purchased more sodas and snacks at school if they had a television in their bedrooms and their family consumed more meals at fast-food restaurants. Children whose families were less active were more likely to have a self-reported BMI at or above the 85th percentile. In addition, children who watched more television were more likely to have a self-reported BMI at or above the 85th percentile. CONCLUSION: Household practices were associated with children's behaviors and self-reported BMI. A household profile that includes being active as a family may counteract the increase in childhood obesity.

Differences in the intensity and duration of adolescents' sports and exercise across physical and social environments.

We used data from the American Time Use Survey (years 2003-06) to analyze whether the intensity and duration of high school students' (ages 15-18 years) sports and exercise bouts differed across physical and social environments. Boys' sports and exercise bouts were more likely to reach a vigorous intensity when taking place at school and with friends/acquaintances/other people; whereas girls' sports and exercise bouts were more likely to reach a vigorous intensity when outdoors and alone. For boys and girls, bout durations were greater at school and with friends/acquaintances/other people than in other environments. Overall, environmental influences on the intensity but not duration of sports and exercise bouts appear to differ between boys and girls.

The association of state law to physical education time allocation in US public schools.

OBJECTIVES: We examined whether public schools in states with specific and stringent physical education (PE) laws, as assessed by the Physical Education-Related State Policy Classification System (PERSPCS), available on the Classification of Laws Associated with School Students (C.L.A.S.S.) Web site, reported more weekly PE time in the most recent School Health Policies and Programs Survey (SHPPS). METHODS: Schools (n=410) were grouped by their state's PERSPCS time requirement scores (none, nonspecific requirement, or specific requirement). Average weekly school-level PE was calculated using the SHPPS-reported PE minutes. Weighted analyses determined if PE minutes/week differed by PERSPCS group. RESULTS: Schools in states with specific requirement laws averaged over 27 and 60 more PE minutes/week at the elementary and middle school levels, respectively, compared with schools within states with nonspecific laws and over 40 and 60 more PE minutes per week, respectively, compared with elementary and middle schools in states with no laws. High school results were nonsignificant. CONCLUSIONS: Public health guidelines recommend at least 60 minutes of daily physical activity for children, and PE may further this goal. Strong codified law with specific time requirements for PE may be an important tool contributing toward adequate PE time and daily physical activity recommendations.

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.

Are daily fluctuations in perceived environment associated with walking?

The physical environment is thought to influence walking; however, daily variations in perceived environment have received little attention. The current study sought to examine if key within-person factors (i.e., implementation intentions, social support, affect and self-efficacy) would be associated with walking and if perceived access to supportive environments (e.g., access to nice walking paths) and perceived environmental barriers (e.g., bad weather and safety issues) were uniquely associated with walking after controlling for other constructs. Participants (N = 14, 50.0% men, 78.6% White, M age = 59.4 ± 6.4) were in the intervention arm of an 8-week controlled trial promoting walking via personal digital assistants. Participants completed electronic surveys twice a day (total entries = 804) in which they reported brisk walking levels and psychosocial and environmental factors. Multilevel modelling was used to examine within-person variations in constructs as determinants of walking. Results suggested that daily variations in implementation intentions, social support and positive affect were positively associated with walking. Further, perceived access to supportive environments, though not perceived environmental barriers, was positively associated with walking after controlling for other constructs (p < 0.05). Future research should explore intervention components that target context-specific information about perceived access to supportive environments as part of a broader perspective on intervention development.

Racial variation in the cancer caregiving experience: a multisite study of colorectal and lung cancer caregivers.

BACKGROUND: As cancer care shifts from hospital to outpatient settings, the number of cancer caregivers continues to grow. However, little is known about the cancer caregiving experience. This gap in knowledge is especially evident for racially diverse caregivers. OBJECTIVE: This study, part of a multisite study of care recipients with either lung or colorectal cancer and their caregivers, examined the caregiving experiences of African American (AA) and white caregivers. METHODS: Caregivers were identified by cancer patients in the Cancer Care Outcomes Research and Surveillance consortium. Caregivers completed a self-administered, mailed questionnaire that assessed their characteristics and experiences. Analysis of covariance was used to compare racial groups by objective burden and caregiving resources while controlling for covariates. RESULTS: Despite greater preparedness for the caregiving role (P = .006), AA caregivers reported more weekly hours caregiving than whites did (26.5 ± 3.1 vs 18.0 ± 1.7; P = .01). In later phases of caregiving, AAs reported having more social support (P = .02), spending more hours caregiving (31.9 ± 3.5 vs 16.9 ± 1.9; P < .001), and performing more instrumental activities of daily living on behalf of their care recipient (P = .021). CONCLUSION: Racial differences in the caregiving experience exist. IMPLICATIONS FOR PRACTICE: Nurses play a key role in educating cancer patients and their caregivers on how to effectively cope with and manage cancer. Because AA caregivers seem to spend more time in the caregiving role and perform more caregiving tasks, AA caregivers may benefit from interventions tailored to their specific caregiving experience.

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.

Pediatricians' and family physicians' weight-related care of children in the U.S.

BACKGROUND: Few national data exist to assess primary care physicians' (PCPs') clinical practices with regard to childhood obesity. PURPOSE: To survey pediatricians and family practice physicians regarding their assessment, counseling, and management of diet, physical activity, and weight status among pediatric patients in the primary care setting. METHODS: A nationally representative cross-sectional survey of pediatricians and family practice physicians sampled from the American Medical Association (AMA) Masterfile was conducted in 2008 and analyzed in 2010. Outcomes included physicians' self-reported practice behaviors regarding assessments of pediatric patients' weight status, counseling of diet and physical activity, and referrals and follow-ups. RESULTS: Response rate excluding physicians listed as "no-contact" by the AMA was 73.7% among pediatricians and 66.9% among family physicians. Less than 50% of all PCPs assessed BMI percentiles regularly in children. Eighteen percent of all PCPs reported referring children for further evaluation or management. Fifty-eight percent of all PCPs reported never, rarely, or only sometimes tracking patients over time concerning weight or weight-related behaviors. Pediatricians were more likely than family physicians to assess weight status and provide behavioral counseling (p's<0.001). CONCLUSIONS: Active PCP participation in assessing or managing childhood obesity in the primary care setting appears low relative to the frequency of the problem in the U.S. Interventions to reduce the barriers to physician engagement in the assessment and management of healthy lifestyles are needed to prevent and control childhood obesity.