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. Author manuscript; available in PMC: 2015 Mar 1.
Published in final edited form as: Am J Prev Med. 2014 Mar;46(3):303–311. doi: 10.1016/j.amepre.2013.10.004

Behavioral Research in Cancer Prevention and Control

A Look to the Future

William M P Klein 1, Michele Bloch 1, Bradford W Hesse 1, Paige G McDonald 1, Linda Nebeling 1, Mary E O’Connell 1, William T Riley 1, Stephen H Taplin 1, Gina Tesauro 1
PMCID: PMC3921666  NIHMSID: NIHMS538734  PMID: 24512871

Abstract

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 controbiol 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.

Introduction

Although rates of some cancers have decreased during the past 40 years, others have grown.1, 2 Cancer remains a leading cause of mortality, with an estimated U.S. economic impact of $263.8 billion in 2010.3 It is estimated that in 2013, about 580,350 Americans will die of cancer, accounting for approximately 25% of all deaths.4

The more general health landscape has also evolved dramatically in that time. In 2010, Congress passed the U.S. Patient Protection and Affordable Care Act, which spotlights prevention and patient-reported outcomes and established the Patient-Centered Outcomes Research Institute (PCORI).5 The 2009 Family Smoking Prevention and Tobacco Control Act provides the U.S. Food and Drug Administration (FDA) with broad authority to regulate the manufacturing, marketing, and distribution of tobacco products. The U.S. Preventive Services Task Force has revised guidelines for mammography, cervical, lung, and prostate screening. The guidelines call for more shared decisions between physicians and patients given the importance of weighing costs and benefits of different screening modalities.6, 7 Social media and crowd-sourcing platforms have altered the decision-making process and patient-provider dynamic8, 9; and the platform for health messages has evolved from print media to websites and mobile applications. Individuals can now obtain access to their genetic profiles (outside of the healthcare system), access personal health records, and share concerns and symptoms with thousands of online acquaintances.

At least three IOM reports10-12 recognize the need to embrace multi-disciplinary, multilevel, and multiple-method approaches to quality health care and communication directly applicable to cancer control research. National Cancer Institute Director Dr. Harold Varmus observed in a 2006 Science editorial that

concerted national efforts to ensure the vitality of all of the components of modern oncology—academic research, industrial development, and the delivery of new methods throughout the healthcare arena—are essential to an optimistic view of the prospects for transforming an understanding of oncogenic mechanisms into therapeutic benefits for our entire society.13

Given the centrality of human behavior to the etiology and management of cancer outcomes, Varmus’ sentiments easily extend to behavioral mechanisms and many benefits beyond the therapeutic. In the end, people smoke, arrive at diagnoses, communicate risk, use information technology, use (and overuse) screening tests, and implement policies. A cancer diagnosis changes family dynamics, consequential decision-making, and other psychosocial processes.

Hiatt and Rimer’s14 seminal essay on the importance of population science research in cancer prevention and control makes clear that behavior is both a cause and consequence of cancer.15 Most Healthy People 202016 goals center on improvements in health-related behaviors. Any successful cancer prevention and control strategy hinges on the effective application of what is known about the basics of human behavior. In this paper, we consider the promise of research on behavioral processes that cause, prevent, detect, or ameliorate the effects of cancer—processes that include not only tangible behaviors such as tobacco use but also a range of behavioral processes including stress responses, social interaction, and group dynamics. The focus is particularly on topics with the most potential impact (in terms of reduced morbidity, mortality, and healthcare costs), in lieu of focusing on potentially interesting topics with little downstream effect. Instead of critiquing previous work and untenable or unfeasible research paths, the emphasis is on new scientific opportunities for behavioral researchers.

Key Behavioral Targets

Tobacco use remains the nation’s leading cause of preventable death, responsible for approximately one third of all cancer deaths in the U.S. Cigarette smoking causes the vast majority of lung cancers, and is causally linked to other cancers including cancers of the larynx, oral cavity and pharynx, esophagus, pancreas, bladder and kidney, cervix, and stomach. Despite decades of progress, about one in five U.S. adults were current cigarette smokers in 2011.17 These overall prevalence figures mask large disparities in use based on educational attainment, race/ethnicity, geographic region, and other variables.18 In addition, increasingly available alternative tobacco products such as hookahs and e-cigarettes will present a new set of challenges.

Rising obesity rates continue to capture the attention and concern of researchers, policymakers, health providers, and the media. Recent evidence suggests that 35.7% of the U.S. adult population is obese and an additional 33.3% are overweight.19 Fewer than 10% of U.S. adults engage in recommended physical activity.20 Many parts of the country are considered “food deserts,” given lack of access to nutritional foods.21 Left unchecked, the epidemic of obesity will exact considerable costs on the healthcare system. Obesity has already been linked with numerous diseases including cancers of the breast, colon, and bladder.22

Other behaviors that increase cancer risk include human papillomavirus (HPV) exposure,23 exposure to the sun,24 and failure to ameliorate environmental exposures (e.g., radon in the home).25 Moreover, increasing evidence suggests that interactions among health behaviors and other psychosocial experiences (such as stress, chronic depression, and lack of social support) may be related to cancer progression.26, 27 Health behaviors themselves are intimately linked together by psychological processes such as executive control and impulsivity and by social processes such as peer relationships and socioeconomic status. It is important to understand how health behaviors interrelate, and how to address new health behaviors such as those that emerge from new consumer products.

Once a patient enters the healthcare system additional challenges ensue. Growing demands on physicians and the healthcare system highlight the importance of understanding how to maximize coordination in healthcare teams, reduce medical errors, maintain patient engagement and satisfaction, and keep costs manageable.28 Patients are increasingly engaged in decisions about their care.29,30 Many decisions are shared among family members or between patients and physicians, necessitating a better understanding of how these decisions are made and whether they lead to positive outcomes. These include HPV vaccination in adolescents, choice of cancer screening tests, and watchful waiting for slow-growing cancers. Clinical trial enrollment, an important shared decision, is abysmally low (estimates range from 3% to 5% of eligible patients),31 slowing development of new therapeutic targets and undermining generalizability of clinical trial findings. Finally, as cancer treatments improve and the population of cancer survivors grows, the cognitive and emotional needs of survivors must be met as they re-enter the work force, re-define relationships, cope with other diseases, and make reproductive and other life decisions.

Elucidating the Levels of Influence

Behavioral research can address these and other challenges but must do so from the perspective of multiple levels of influence. Here we consider biobehavioral and psychological, social and organizational, and environmental influences in particular.

Biobehavioral and Psychological Influences

Depression, psychological distress, social isolation, stressful life events, and trauma have all been associated with higher risk of cancer progression and mortality (although less so with cancer initiation).26, 32-34 Although causal evidence is lacking that would indicate that stress reduction helps to prevent and/or slow the progression of cancer, dampening the stress pathways (e.g., with beta-blockers) does appear to be associated with lower recurrence, progression and mortality of some cancers.35, 36 When assessing the influence on cancer, new behavioral methodologies and paradigms are needed to capture the highly dynamic contributions of psychosocial experiences that vary over time, within individuals and between populations.37 Calls from the research and clinical community have risen following the influx of genome-wide association studies for objective quantitative assessments of environmental exposures, including stress, lifestyle factors and behaviors.37 Combining rich contextual information from the exposome (the comprehensive characterization of an individual’s exogenous and endogenous exposure history from conception onwards)38 with the genome promises to expand our ability to understand the relative contributions of psychosocial experiences and corresponding biological signatures.

Behavioral research can also help in understanding and improving cancer decision-making. People—including physicians—rely on decisional heuristics or “rules of thumb” when making judgments and decisions.39 Used improperly, these heuristics can produce unfortunate consequences such as overestimation of risk (e.g., believing that one’s breast cancer risk is higher than it is) and misuse of base rate information (e.g., ignoring cancer prevalence when interpreting a screening test result).40 Behavioral researchers understand the factors that influence intertemporal choices (e.g., preferring a smaller short-term gain to a larger long-term gain), and the way in which people cope with uncertainty and ambiguity in predicting future outcomes.41 This work might provide clues about how to communicate risk in difficult decision contexts, and how to help individuals prioritize health behaviors and navigate new health information.

Basic cognitive processes such as memory, language, and attention help individuals deliver and receive health messages about prevention, side effects, clinical trials, and more. Key attentional processes (e.g., cognitive load, figure/ground contrast, stimuli saturation) influence how people detect stimuli, with implications for clinical practice such as radiography. Language and comprehension are related to how patients make sense of health warnings, consent forms, physician communications, and personal health records. A nuanced understanding of taste, smell, hearing, touch, and vision – and how combinations of these senses interact – can elucidate health behaviors linked to cancer (e.g., smoking, consumption of bitter greens, preferences for fats) and documented side effects of chemotherapy.42 Despite these possibilities, research connecting basic knowledge of perceptual, affective, and cognitive processes with health behavior and other cancer targets is surprisingly limited.

Health decisions are also influenced by motives such as self-enhancement, social comparison, predictability/control, favorable self-presentation, effective resource management, preparedness for bad news, goal attainment, and existential meaning.43 These motivational processes easily influence the more basic attentional processes above. For example, the desire to counter potentially threatening information can direct attention toward or away from aspects of that information.44 Fortunately, intervention efforts that protect the self from threat can reduce defensive responding to threatening health information.45 More research is needed to help us understand how people balance multiple motives and under what circumstances such intervention efforts work (and why).

Many targets such as obesity are the product of repeated and consistent action such as over-eating and sedentary behavior, highlighting the need to understand not only how and when people initiate behavior but also how such behavior is maintained over time. A burgeoning literature on self-regulation46 helps elucidate how people form and carry out short- and long-term goals, and how access to resources (cognitive, relational, emotional and otherwise) facilitates achievement of those goals. This research can be leveraged to address maintenance concerns, such as medical adherence. A timely example where such processes are important is HPV vaccination (for prevention of cervical cancer), which requires more than one dose over multiple visits to be maximally effective.

Social and Organizational Influences

Social-ecologic models provide a useful framework for public health interventions that focus on health behavior change within the population. These models acknowledge individual influences on behavior and mechanisms of behavior change, but also include socio-cultural and environmental influences.47 Communities (worksites, churches, schools, community centers, military, and healthcare centers) have been highlighted as key settings for many health behavior interventions. More research is needed to identify mediating variables at the social and organizational level that are most clearly related to the health outcomes and behaviors of concern.48-50

The starting point for most health care is a social interaction between patient and provider, which can be influenced by a number of factors including nonverbal signals and establishment of trust.51 Implicit prejudice can emerge in healthcare interactions with downstream effects on satisfaction, adherence, and health outcomes52—thereby fueling health disparities—but little is known about how to prevent these effects. Research on relationship processes demonstrates that social support and intimate relationships buffer the effects of stress on health53 and cancer survivors depend greatly on, and benefit from, social support.54, 55 Less is known about how relationships influence everyday behaviors (e.g., how parenting influences adolescent diet decisions). Many health decisions are made by relationship dyads (e.g., patient-physician, parent-child, and spouse-spouse)56 and yet the preponderance of research on medical decision-making takes an individual approach. Patient-provider interactions, which at one time were focused on one-way media, have become increasingly interactive, participative, and personalized with distinct implications for the design of health campaigns.57

Health decisions are also made in the organizational contexts where individuals seek care. Targeted research on medical team functioning can help reduce medical errors and associated costs and improve coordination of care.58, 59 The current evidence does not yet provide an overarching definition for a well-functioning team; nor is there an evidence base of how to provide incentives for teamwork and what relationship there is between teamwork and cancer care outcomes. 60, 61 For example, within healthcare organizations, research suggests that healthcare workers do a better job when they have clear duties, acknowledged roles, and relational integration.60

Environmental Influences

These personal and social factors influence health outcomes within the larger (and richer) context of the information, policy, and product/physical environment.

Information environment

The disruptive influence62 of a wholly new information environment must be marshaled to extend the reach, efficiency, and effectiveness of cancer control interventions.63, 64 For example, patients often bring web-based information to clinic visits and providers may not be prepared to integrate this information into their care.65 Cancer myths can spread virally through social networking sites, making it difficult for consumers to disentangle fact from fiction, or objective evidence from profit-motivated hyperbole.66 At the same time, content analyses of reputable information sites hosted by government or academic sponsors reveal that the language in these sites is not accessible to all audiences, exposing disparities in health literacy.67, 68 Continued work is needed to explore ways of supporting better processes of care by re-engineering communication channels and patient workflows within and outside medical environments.69-73

Policy environment

Laws, regulations, and political and institutional entities have profound effects on individual and organizational behaviors.74 For example, in the 1990s, growing health concerns about trans-fatty acids led to policy changes in product labeling and increased consumer demand for reduced trans fats.50 In an effort to address childhood obesity, policies have come under increasing scrutiny to address unhealthy food options and the amount of physical activity in schools. As policies have changed to reduce unhealthy snack options and sugar-sweetened beverage sales in schools and modify menu labeling to include caloric information, the consumption of healthier alternatives has increased.74-79 Yet we are only at the beginning of understanding what types of policies are most effective and why.

Tobacco taxes, smoke-free air, and other policies have long been a powerful force in tobacco control.80, 81 The importance of tobacco control policy research has accelerated with the enactment of the 2009 Tobacco Act. The Act authorizes FDA to require disclosure of tobacco product ingredients; create standards for tobacco products; restrict tobacco sales, distribution, and marketing; and require stronger health warnings on packaging and in advertisements.82 Within the framework of the Act, the National Institutes of Health has formed an interagency partnership with FDA to foster tobacco control regulatory research.83 This research will help FDA understand tobacco and its ingredients and constituents, tobacco addiction, tobacco marketing and labeling, and adolescent tobacco use.

Physical environment

Complementing the communication and policy environment is the larger environment within which most health behaviors take place. These include products that influence health behavior (e.g., novel tobacco products and new foods) and the extent to which the physical environment helps or hinders adaptive behavior. The design of tobacco products is intended to facilitate addiction and long-term use. The wide variety of tobacco products including cigarettes, cigars, pipes, water pipe tobacco, and smokeless tobacco is intended to appeal to diverse consumers. Today, noncombustible tobacco products are often marketed with messages that encourage their use in indoor environments where smoking is prohibited. Dual use of smoked and smokeless products is already substantial,84 and is likely to grow as consumers face more choices to facilitate and sustain addiction.

With respect to obesity, the design of neighborhoods will influence supermarket accessibility to purchase healthier foods or access to safe playgrounds for physical activity.85 Environmental changes that improve access, combined with affordable pricing, can reduce the economic disparities of access to healthier foods and have a positive impact on health.86 We need to better understand the relationship between the local food environment and health, and identify potential points for intervention.85 In general, there is increased need for research on the macro-environment, including media; technology; food production, distribution, and marketing; urban development and transportation systems; and health systems. Without further research on these outside influences, interventions targeting the individual, home, or local neighborhood will continue to demonstrate minimal to modest effects in producing long-term, sustainable public health impact.87 In addition, further efforts should be made to strengthen our understanding of key environmental changes that may address the needs of hard-to-reach population groups (e.g., those with lower educational attainment, lower incomes and language barriers).75,88,89

Methodologic Innovation

The impact of behavioral research at all of these levels—biobehavioral and psychological; social and organizational; and environmental—requires a keen understanding of methodologic barriers as well as effective use of new methodologic approaches. As illustrated in examples throughout this paper, cancer risk behavior data are increasingly multi-disciplinary and multilevel; as well as dense and longitudinal in nature, which require new analytic research approaches.90, 91

Several of the behavioral constructs discussed relevant to cancer control research are difficult to observe (e.g., self-efficacy, relational dynamics), necessitating precise, efficient, and flexible self-report measures. Newly developed methods need to be up to the task. As an example, Item Response Theory (IRT) introduces greater flexibility of administration given the use of computer-adaptive testing (CAT).92 A benefit of IRT is that existing instruments measuring the same construct can be co-calibrated with these banks. The result is that any existing instrument can be expressed on the same scale or metric.93

It is also becoming increasingly easier to combine measures from more than one study conducted in disparate populations using techniques such as integrative data analysis. Successful data harmonization requires both consensus measure development and co-calibrated measures. It requires research infrastructure, such as the NCI Grid-Enabled Measures portal (GEM),94 to support sharing of measures and their attributes.

Despite attempts to harmonize health behavior theories or their component constructs, the field has more theories and constructs than ever before (many with limited empirical support).95, 96 Consequently, the field remains unable to answer essential questions, such as which theories better predict or explain certain behaviors, or what amount of variance is accounted for by specific theoretical constructs.

New measurement and technologic advances may provide better data and improved precision to answer these questions. Experience sampling, or Ecological Momentary Assessment (EMA), and sensor technologies allow us to capture behaviors and their putative effects in real-time and in the proper context. This provides intensive longitudinal data to explain and predict behaviors that may be associated with cancer risk. Using random or event-based prompts, researchers gather participant experiences (such as smoking or eating behavior) throughout the day.97

With smartphones, researchers can also obtain location and movement, and audio and video capture.98 Sensor technologies build on smartphone features by providing automated direct observation. Accelerometer sensors can identify general categories of movements (e.g., sitting, running, walking) and specific targeted behaviors. Body sensor networks provide real-time physiological monitoring capabilities.99 Additional sensors provide a window into relevant community, environmental, and other multilevel constructs.100 The datasets that result from these tools require newer analytic approaches such as hierarchical linear models,101 latent curve models,102 and time-varying effect models103 to capture the variability and richness of the data provided. These data also provide the opportunity to use system dynamic modeling, both at the macro104 and micro105 level, to model the nonlinear interrelationships of variables over time. New technologies will transform not only measurement and theory testing, but also intervention design. Although technology-delivered interventions may result in less human interaction, they are infinitely scalable with minimal additional cost, and fully maintain treatment fidelity. The wealth of data obtainable from these technologies allows interventions to be adaptable and responsive to behavioral context and prior intervention responses. The advent of mobile, wireless, and near continuous data streams will allow adaptation to interventions not just initially, but throughout the intervention.106, 107 These advances will increasingly require collaboration among the more traditional social and behavioral science fields with the fields of engineering, computer science, biostatistics, research methodology, mathematical modeling, and biological sciences.

At the same time, use of multilevel designs necessitates a nuanced understanding of how interventions interact (and possibly undermine each other), how their impact may vary over time; and the extent to which interventions are effectively implemented.108 For example, the traditional epidemiologic model of agent, host, vector and environment is useful for understanding the diverse influences on tobacco use at the individual and population level. As described by Giovino, the agent (diverse tobacco products and secondhand smoke), host (individual user), and vector (tobacco manufacturers), interact in an environment that includes familial, social, cultural, historical, legal, regulatory, economic, media, and other influences.109 The integration of these influences is central to the Cancer Intervention and Surveillance Modeling Network (CISNET), a consortium of NCI-sponsored investigators that uses statistical modeling to improve understanding of cancer control interventions in prevention, screening, and treatment and their effects on population trends in incidence and mortality. Such integrated paradigms are necessary to best understand what combination of approaches will be most effective.

Conclusion

Behavioral research has made many important contributions to reduce the cancer burden.15 In the last 20 years, we have learned how to frame health messages,110 use new measurement techniques to capture in vivo experiences related to cancer risk factors110, involve communities in health interventions, and much more. Evidence-based approaches that have been shown to alter behavior in some domains can inform other research efforts. For example, increasing taxes on sugar sweetened beverages will likely decrease consumption of these products, just as increasing tobacco taxes on tobacco products has been shown to decrease tobacco use.80

Nevertheless, there are several problems that require sustained attention, all of which involve or are influenced by human behavior. The current paper, rather than reviewing past work, identifies several opportunities for new work from disparate disciplines. Research on biobehavioral and psychological influences; social and organizational influences; and environmental influences is necessary to address the behaviors and behavioral processes that increase cancer risk. Behavioral research in decision-making could improve attempts to decrease tobacco use, sun exposure, virus exposure, and controllable environmental exposures; increase physical activity and healthy diets; increase appropriate use of screening and treatment adherence; inform attempts to improve clinical trial enrollment; identify the circumstances under which shared decision making is most beneficial; reduce medical errors; and improve the performance of medical teams. It can yield a better understanding of how health behaviors are associated with psychosocial experiences; and how to design effective and scalable interventions in both public health and in the healthcare setting. Thoughtful research at all levels of influence by researchers who understand mechanisms underlying human performance will be paramount to addressing key outcomes across the cancer control continuum.

Behavioral scientists must become comfortable with new platforms, new data sources, new methodologies, and new conceptual and analytic approaches. In addition, a multi-disciplinary team approach might be more cost effective and may lead to more interventions capable of dissemination. At the same time, behavioral research will only make inroads in addressing key proximal cancer targets by resisting the temptation to focus exclusively on individual psychological processes, given that those processes emerge in a dynamic social, physical, and informational environment. To the extent that behavioral researchers take on these challenges, they will help in meaningful ways to address concerns raised in multiple IOM reports10-12 about the quality of cancer care, and will advance medicine toward the ultimate goal of decreased cancer morbidity and mortality.

Acknowledgments

Other than the lead author, order of authorship is alphabetical. We thank our colleagues in the Behavioral Research Program at the National Cancer Institute for contributions to many discussions underlying the themes of this paper. The views and opinions expressed in this article are those of the authors and do not necessarily represent the views of the National Institutes of Health or any other governmental agency.

Footnotes

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No financial disclosures were reported by the authors of this paper.

References

  • 1.Kohler BA, Ward E, McCarthy BJ, et al. Annual report to the nation on the status of cancer, 1975-2007, featuring tumors of the brain and other nervous system. J Natl Cancer Inst. 2011;103(9):714–36. doi: 10.1093/jnci/djr077. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Edwards BK, Ward E, Kohler BA, et al. Annual report to the nation on the status of cancer, 1975-2006, featuring colorectal cancer trends and impact of interventions (risk factors, screening, and treatment) to reduce future rates. Cancer. 2010;116(3):544–73. doi: 10.1002/cncr.24760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.American Cancer Society . Cancer Facts & Figures, 2011. American Cancer Society; Atlanta GA: 2011. [Google Scholar]
  • 4.American Cancer Society . Cancer Facts & Figures, 2013. American Cancer Society; Atlanta GA: 2013. [Google Scholar]
  • 5.Selby JV, Beal AC, Frank L. The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda. JAMA. 2012;307(15):1583–4. doi: 10.1001/jama.2012.500. [DOI] [PubMed] [Google Scholar]
  • 6.Screening for prostate cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2012;157(2):120–34. doi: 10.7326/0003-4819-157-2-201207170-00459. [DOI] [PubMed] [Google Scholar]
  • 7.Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2009;151(10):716–26. W-236. doi: 10.7326/0003-4819-151-10-200911170-00008. [DOI] [PubMed] [Google Scholar]
  • 8.Hesse BW, O’Connell M, Augustson EM, Chou WY, Shaikh AR, Finney Rutten LJ. Realizing the promise of web 2.0: engaging community intelligence. J Health Commun. 2011;16(Suppl 1):10–31. doi: 10.1080/10810730.2011.589882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Chou WY, Hunt YM, Beckjord EB, Moser RP, Hesse BW. Social media use in the United States: implications for health communication. J Med Internet Res. 2009;11(4):e48. doi: 10.2196/jmir.1249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Institute of Medicine (IOM) Health IT and Patient Safety: Building Safer Systems for Better Care. The National Academies Press; Washington DC: 2012. Report No.: 0309221129. [PubMed] [Google Scholar]
  • 11.Institute of Medicine (IOM) Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis. National Academy Press; Washington DC: 2013. Report No.: 978-0-309-29309-9. [PubMed] [Google Scholar]
  • 12.Institute of Medicine (IOM) Committee on Quality of Health Care in America . Crossing the quality chasm: a new health system for the 21st century. National Academy Press; Washington DC: 2001. Report No.: 0309072808. [Google Scholar]
  • 13.Varmus H. The new era in cancer research. Science. 2006;312(5777):1162–5. doi: 10.1126/science.1126758. [DOI] [PubMed] [Google Scholar]
  • 14.Hiatt RA, Rimer BK. A new strategy for cancer control research. Cancer Epidemiol Biomarkers Prev. 1999;8(11):957–64. [PubMed] [Google Scholar]
  • 15.Miller SM, Bowen DJ, Croyle RT, Rowland JH, editors. Handbook of cancer control and behavioral science: a resource for researchers, practitioners, and policy makers. American Psychological Association; Washington DC: 2009. [Google Scholar]
  • 16.Koh HK. A 2020 vision for healthy people. N Engl J Med. 2010;362(18):1653–6. doi: 10.1056/NEJMp1001601. [DOI] [PubMed] [Google Scholar]
  • 17.Centers for Disease Control and Prevention (CDC) Current cigarette smoking among adults—United States, 2011. MMWR Morb Mortal Wkly Rep. 2012;61(44):889–94. [PubMed] [Google Scholar]
  • 18.Jha P, Peto R, Zatonski W, Boreham J, Jarvis MJ, Lopez AD. Social inequalities in male mortality, and in male mortality from smoking: indirect estimation from national death rates in England and Wales, Poland, and North America. Lancet. 2006;368(9533):367–70. doi: 10.1016/S0140-6736(06)68975-7. [DOI] [PubMed] [Google Scholar]
  • 19.Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity in the United States, 2009-2010. NCHS Data Brief. 2012;2012(82):1–8. [PubMed] [Google Scholar]
  • 20.Tucker JM, Welk GJ, Beyler NK. Physical activity in U.S.: adult’s compliance with the Physical Activity Guidelines for Americans. Am J Prev Med. 2011;40(4):454–61. doi: 10.1016/j.amepre.2010.12.016. [DOI] [PubMed] [Google Scholar]
  • 21.Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the United States: A review of food deserts literature. Health Place. 2010;16(5):876–84. doi: 10.1016/j.healthplace.2010.04.013. [DOI] [PubMed] [Google Scholar]
  • 22.Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer- and service-specific estimates. Health Aff (Millwood) 2009;28(5):w822–31. doi: 10.1377/hlthaff.28.5.w822. [DOI] [PubMed] [Google Scholar]
  • 23.Jemal A, Simard EP, Dorell C, et al. Annual Report to the Nation on the Status of Cancer, 1975-2009, featuring the burden and trends in human papillomavirus(HPV)-associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst. 2013;105(3):175–201. doi: 10.1093/jnci/djs491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Centers for Disease Control and Prevention (CDC) Sunburn and sun protective behaviors among adults aged 18-29 years--United States, 2000-2010. MMWR Morb Mortal Wkly Rep. 2012;61(18):317–22. [PubMed] [Google Scholar]
  • 25.Neri A, Stewart SL, Angell W. Radon control activities for lung cancer prevention in national comprehensive cancer control program plans, 2005-2011. Prev Chronic Dis. 2013;10(8):E132. doi: 10.5888/pcd10.120337. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Antoni MH, Lutgendorf SK, Cole SW, et al. The influence of bio-behavioural factors on tumour biology: pathways and mechanisms. Nat Rev Cancer. 2006;6(3):240–8. doi: 10.1038/nrc1820. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Armaiz-Pena GN, Cole SW, Lutgendorf SK, Sood AK. Neuroendocrine influences on cancer progression. Brain Behav Immun. 2013;30(Suppl(30)):S19–S25. doi: 10.1016/j.bbi.2012.06.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Taplin SH, Rodgers AB. Toward improving the quality of cancer care: addressing the interfaces of primary and oncology-related subspecialty care. J Natl Cancer Inst Monogr. 2010;2010(40):3–10. doi: 10.1093/jncimonographs/lgq006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Institute of Medicine (IOM) Patients Charting the Course: Citizen Engagement in the Learning Health System—Workshop Summary. National Academies Press; Washington D.C.: 2011. [PubMed] [Google Scholar]
  • 30.Hesse BW, Moser RP, Rutten LJ. Surveys of physicians and electronic health information. N Engl J Med. 2010;362(9):859–60. doi: 10.1056/NEJMc0909595. [DOI] [PubMed] [Google Scholar]
  • 31.Lara PN, Jr, Higdon R, Lim N, et al. Prospective evaluation of cancer clinical trial accrual patterns: identifying potential barriers to enrollment. J Clin Oncol. 2001;19(6):1728–33. doi: 10.1200/JCO.2001.19.6.1728. [DOI] [PubMed] [Google Scholar]
  • 32.Chida Y, Hamer M, Wardle J, Steptoe A. Do stress-related psychosocial factors contribute to cancer incidence and survival? Nat Clin Pract Oncol. 2008;5(8):466–75. doi: 10.1038/ncponc1134. [DOI] [PubMed] [Google Scholar]
  • 33.Costanzo ES, Sood AK, Lutgendorf SK. Biobehavioral influences on cancer progression. Immunol Allergy Clin North Am. 2011;31(1):109–32. doi: 10.1016/j.iac.2010.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Lutgendorf SK, Sood AK. Biobehavioral factors and cancer progression: physiological pathways and mechanisms. Psychosom Med. 2011;73(9):724–30. doi: 10.1097/PSY.0b013e318235be76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Fitzgerald PJ. Beta blockers, norepinephrine, and cancer: an epidemiological viewpoint. Clin Epidemiol. 2012;4:151–6. doi: 10.2147/CLEP.S33695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Wang HM, Liao ZX, Komaki R, et al. Improved survival outcomes with the incidental use of beta-blockers among patients with non-small-cell lung cancer treated with definitive radiation therapy. Ann Oncol. 2013;24(5):1312–9. doi: 10.1093/annonc/mds616. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Rappaport SM, Smith MT. Epidemiology. Environment and disease risks. Science. 2010;330(6003):460–1. doi: 10.1126/science.1192603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Brunekreef B. Exposure science, the exposome, and public health. Environ Mol Mutagen. 2013;54(7):596–8. doi: 10.1002/em.21767. [DOI] [PubMed] [Google Scholar]
  • 39.Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. Science. 1974;185(4157):1124–31. doi: 10.1126/science.185.4157.1124. [DOI] [PubMed] [Google Scholar]
  • 40.Klein WM, Stefanek ME. Cancer risk elicitation and communication: lessons from the psychology of risk perception. CA Cancer J Clin. 2007;57(3):147–67. doi: 10.3322/canjclin.57.3.147. [DOI] [PubMed] [Google Scholar]
  • 41.Han PK, Klein WM, Arora NK. Varieties of Uncertainty in Health Care: A Conceptual Taxonomy. Med Decis Making. 2011 doi: 10.1177/0272989X11393976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Boltong A, Keast R. The influence of chemotherapy on taste perception and food hedonics: a systematic review. Cancer Treat Rev. 2012;38(2):152–63. doi: 10.1016/j.ctrv.2011.04.008. [DOI] [PubMed] [Google Scholar]
  • 43.Klein WMP, Cerully JL. Health-related risk perception and decision-making: Lessons from the study of motives in social psychology. Soc Personal Psychol Compass. 2007;1(1):334–58. [Google Scholar]
  • 44.McQueen A, Vernon SW, Swank PR. Construct definition and scale development for defensive information processing: an application to colorectal cancer screening. Health Psychol. 2013;32(2):190–202. doi: 10.1037/a0027311. [DOI] [PubMed] [Google Scholar]
  • 45.Harris PR, Epton T. The Impact of Self-Affirmation on Health-Related Cognition and Health Behaviour: Issues and Prospects. Soc Personal Psychol Compass. 2010;4(7):439–54. [Google Scholar]
  • 46.Mann T, Sherman D, Updegraff J. Dispositional motivations and message framing: a test of the congruency hypothesis in college students. Health Psychol. 2004;23(3):330–4. doi: 10.1037/0278-6133.23.3.330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Frieden TR. A framework for public health action: the health impact pyramid. Am J Public Health. 2009;100(4):590–5. doi: 10.2105/AJPH.2009.185652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Baranowski T, Cerin E, Baranowski J. Steps in the design, development and formative evaluation of obesity prevention-related behavior change trials. Int J Behav Nutr Phys Act. 2009;6(6):6. doi: 10.1186/1479-5868-6-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Clauser SB, Taplin SH, Foster MK, Fagan P, Kaluzny AD. Multilevel intervention research: lessons learned and pathways forward. J Natl Cancer Inst Monogr. 2012;2012(44):127–33. doi: 10.1093/jncimonographs/lgs019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Freudenberg N, Galea S. The impact of corporate practices on health: implications for health policy. J Public Health Policy. 2008;29(1):86–104. doi: 10.1057/palgrave.jphp.3200158. discussion 105. [DOI] [PubMed] [Google Scholar]
  • 51.Lepper HS, Martin LR, DiMatteo MR. A model of nonverbal exchange in physician-patient expectations for patient involvement. J Nonverbal Behav. 1995;19(4):207–22. [Google Scholar]
  • 52.Shavers VL, Klein WM, Fagan P. Research on race/ethnicity and health care discrimination: where we are and where we need to go. Am J Public Health. 2012;102(5):930–2. doi: 10.2105/AJPH.2012.300708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2):310–57. [PubMed] [Google Scholar]
  • 54.Kroenke CH, Kubzansky LD, Schernhammer ES, Holmes MD, Kawachi I. Social networks, social support, and survival after breast cancer diagnosis. J Clin Oncol. 2006;24(7):1105–11. doi: 10.1200/JCO.2005.04.2846. [DOI] [PubMed] [Google Scholar]
  • 55.Lutgendorf SK, De Geest K, Bender D, et al. Social influences on clinical outcomes of patients with ovarian cancer. J Clin Oncol. 2012;30(23):2885–90. doi: 10.1200/JCO.2011.39.4411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Lewis MA, Butterfield RM. Social Control in Marital Relationships: Effect of One’s Partner on Health Behavior. J Appl Soc Psychol. 2007;37(2):298–319. [Google Scholar]
  • 57.Bernhardt JM, Mays D, Kreuter MW. Dissemination 2.0: closing the gap between knowledge and practice with new media and marketing. J Health Commun. 2011;16(Suppl 1):32–44. doi: 10.1080/10810730.2011.593608. [DOI] [PubMed] [Google Scholar]
  • 58.Manser T. Teamwork and patient safety in dynamic domains of healthcare: a review of the literature. Acta Anaesthesiol Scand. 2009;53(2):143–51. doi: 10.1111/j.1399-6576.2008.01717.x. [DOI] [PubMed] [Google Scholar]
  • 59.Leonard MW, Frankel AS, Knight AP. What Facilitates or Hinders Team Effectiveness in Organizations? In: Salas E, Frush K, editors. Improving Patient Safety Through Teamwork and Team Training. Oxford University Press; New York: 2013. [Google Scholar]
  • 60.Lemieux-Charles L, McGuire WL. What do we know about health care team effectiveness? A review of the literature. Med Care Res Rev. 2006;63(3):263–300. doi: 10.1177/1077558706287003. [DOI] [PubMed] [Google Scholar]
  • 61.Bosch M, Faber MJ, Cruijsberg J, et al. Review article: Effectiveness of patient care teams and the role of clinical expertise and coordination: a literature review. Med Care Res Rev. 2009;66(6 Suppl):5S–35S. doi: 10.1177/1077558709343295. [DOI] [PubMed] [Google Scholar]
  • 62.Topol EJ. The creative destruction of medicine: how the digital revolution will create better health care. Basic Books; New York: 2012. [Google Scholar]
  • 63.Hesse BW, Johnson LE, Davis KL. Extending the reach, effectiveness, and efficiency of communication: Evidence from the centers of excellence in cancer communication research. Patient Educ Couns. 2010;81S1:S1–S5. doi: 10.1016/j.pec.2010.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Viswanath K. Science and society: the communications revolution and cancer control. Nat Rev Cancer. 2005;5(10):828–35. doi: 10.1038/nrc1718. [DOI] [PubMed] [Google Scholar]
  • 65.Murray E, Lo B, Pollack L, et al. The impact of health information on the Internet on health care and the physician-patient relationship: national U.S. survey among 1.050 U.S. physicians. J Med Internet Res. 2003;5(3):e17. doi: 10.2196/jmir.5.3.e17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Science Panel on Interactive Communication and Health . Wired for Health and Well-Being: The Emergence of Interactive Health Communication. Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services; Washington DC: 1999. [Google Scholar]
  • 67.Viswanath K, Breen N, Meissner H, et al. Cancer knowledge and disparities in the information age. J Health Commun. 2006;11(Suppl 1):1–17. doi: 10.1080/10810730600637426. [DOI] [PubMed] [Google Scholar]
  • 68.Berland GK, Elliott MN, Morales LS, et al. Health information on the Internet: accessibility, quality, and readability in English and Spanish. Jama. 2001;285(20):2612–21. doi: 10.1001/jama.285.20.2612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Kim E, Han JY, Moon TJ, et al. The process and effect of supportive message expression and reception in online breast cancer support groups. Psychooncology. 2011 doi: 10.1002/pon.1942. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Han JY, Wise M, Kim E, et al. Factors Associated with Use of Interactive Cancer Communication System: An Application of the Comprehensive Model of Information Seeking. J Comput Mediat Commun. 2010;15(3):367–88. doi: 10.1111/j.1083-6101.2010.01508.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.DuBenske LL, Gustafson DH, Shaw BR, Cleary JF. Web-based cancer communication and decision making systems: connecting patients, caregivers, and clinicians for improved health outcomes. Med Decis Making. 2010;30(6):732–44. doi: 10.1177/0272989X10386382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Blumenthal D, Glaser JP. Information technology comes to medicine. N Engl J Med. 2007;356(24):2527–34. doi: 10.1056/NEJMhpr066212. [DOI] [PubMed] [Google Scholar]
  • 73.Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood) 2011;30(3):464–71. doi: 10.1377/hlthaff.2011.0178. [DOI] [PubMed] [Google Scholar]
  • 74.Larson NI, Story MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the U.S. Am J Prev Med. 2009;36(1):74–81. doi: 10.1016/j.amepre.2008.09.025. [DOI] [PubMed] [Google Scholar]
  • 75.Mello MM, Pomeranz J, Moran P. The interplay of public health law and industry self-regulation: the case of sugar-sweetened beverage sales in schools. Am J Public Health. 2008;98(4):595–604. doi: 10.2105/AJPH.2006.107680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Wang YC, Coxson P, Shen YM, Goldman L, Bibbins-Domingo K. A penny-per-ounce tax on sugar-sweetened beverages would cut health and cost burdens of diabetes. Health Aff (Millwood) 2012;31(1):199–207. doi: 10.1377/hlthaff.2011.0410. [DOI] [PubMed] [Google Scholar]
  • 77.Giesen JC, Payne CR, Havermans RC, Jansen A. Exploring how calorie information and taxes on high-calorie foods influence lunch decisions. Am J Clin Nutr. 2011;93(4):689–94. doi: 10.3945/ajcn.110.008193. [DOI] [PubMed] [Google Scholar]
  • 78.Taber DR, Chriqui JF, Perna FM, Powell LM, Slater SJ, Chaloupka FJ. Association between state physical education (PE) requirements and PE participation, physical activity, and body mass index change. Prev Med. 2013;7435(13):00310–1. doi: 10.1016/j.ypmed.2013.08.018. [DOI] [PubMed] [Google Scholar]
  • 79.Masse LC, Perna F, Agurs-Collins T, Chriqui JF. Change in school nutrition-related laws from 2003 to 2008: evidence from the school nutrition-environment state policy classification system. Am J Public Health. 2013;103(9):1597–603. doi: 10.2105/AJPH.2012.300896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Chaloupka FJ, Yurekli A, Fong GT. Tobacco taxes as a tobacco control strategy. Tob Control. 2012;21(2):172–80. doi: 10.1136/tobaccocontrol-2011-050417. [DOI] [PubMed] [Google Scholar]
  • 81.Hyland A, Barnoya J, Corral JE. Smoke-free air policies: past, present and future. Tob Control. 2012;21(2):154–61. doi: 10.1136/tobaccocontrol-2011-050389. [DOI] [PubMed] [Google Scholar]
  • 82.Overview of the Center for Tobacco Products: Consumer Fact Sheet. U.S. Food and Drug Administration; 2013. www.fda.gov/TobaccoProducts/NewsEvents/ucm265521.htm. [Google Scholar]
  • 83.Tobacco Regulatory Science Program (TRSP) National Institutes of Health Office of Disease Prevention. 2013 prevention.nih.gov/tobacco/
  • 84.Tomar SL, Alpert HR, Connolly GN. Patterns of dual use of cigarettes and smokeless tobacco among U.S. males: findings from national surveys. Tob Control. 2010;19(2):104–9. doi: 10.1136/tc.2009.031070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Kelly B, Flood VM, Yeatman H. Measuring local food environments: an overview of available methods and measures. Health Place. 2011;17(6):1284–93. doi: 10.1016/j.healthplace.2011.08.014. [DOI] [PubMed] [Google Scholar]
  • 86.Drewnowski A, Aggarwal A, Hurvitz PM, Monsivais P, Moudon AV. Obesity and supermarket access: proximity or price? Am J Public Health. 2012;102(8):e74–80. doi: 10.2105/AJPH.2012.300660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Powell LM, Szczypka G, Chaloupka FJ. Trends in exposure to television food advertisements among children and adolescents in the United States. Arch Pediatr Adolesc Med. 2010;164(9):794–802. doi: 10.1001/archpediatrics.2010.139. [DOI] [PubMed] [Google Scholar]
  • 88.Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med. 1999;29(6 Pt 1):563–70. doi: 10.1006/pmed.1999.0585. [DOI] [PubMed] [Google Scholar]
  • 89.Lakerveld J, Verstrate L, Bot SD, et al. Environmental interventions in low-SES neighbourhoods to promote healthy behaviour: enhancing and impeding factors. Eur J Public Health. 2013:20. doi: 10.1093/eurpub/ckt070. [DOI] [PubMed] [Google Scholar]
  • 90.Shiyko MP, Li Y, Rindskopf D. Poisson Growth Mixture Modeling of Intensive Longitudinal Data: An Application to Smoking Cessation Behavior. Struct Equ Modeling. 2012;19(1):65–85. doi: 10.1080/10705511.2012.634722. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Warne RT, Li Y, McKyer EL, Condie R, Diep CS, Murano PS. Managing clustered data using hierarchical linear modeling. J Nutr Educ Behav. 2012;44(3):271–7. doi: 10.1016/j.jneb.2011.06.013. [DOI] [PubMed] [Google Scholar]
  • 92.Thomas ML. The value of item response theory in clinical assessment: a review. Assessment. 2011;18(3):291–307. doi: 10.1177/1073191110374797. [DOI] [PubMed] [Google Scholar]
  • 93.Riley WT, Pilkonis P, Cella D. Application of the National Institutes of Health Patient-reported Outcome Measurement Information System (PROMIS) to mental health research. J Ment Health Policy Econ. 2011;14(4):201–8. [PMC free article] [PubMed] [Google Scholar]
  • 94.Moser RP, Hesse BW, Shaikh AR, et al. Grid-enabled measures: using Science 2.0 to standardize measures and share data. Am J Prev Med. 2011;40(5 Suppl 2):S134–S143. doi: 10.1016/j.amepre.2011.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Weinstein ND. Testing four competing theories of health-protective behavior. Health Psychol. 1993;12(4):324–33. doi: 10.1037//0278-6133.12.4.324. [DOI] [PubMed] [Google Scholar]
  • 96.Noar SM, Zimmerman RS. Health Behavior Theory and cumulative knowledge regarding health behaviors: are we moving in the right direction? Health Educ Res. 2005;20(3):275–90. doi: 10.1093/her/cyg113. [DOI] [PubMed] [Google Scholar]
  • 97.Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1–32. doi: 10.1146/annurev.clinpsy.3.022806.091415. [DOI] [PubMed] [Google Scholar]
  • 98.Dobkin BH, Dorsch A. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair. 2011;25(9):788–98. doi: 10.1177/1545968311425908. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Hao Y, Foster R. Wireless body sensor networks for health-monitoring applications. Physiol Meas. 2008;29(11):R27–56. doi: 10.1088/0967-3334/29/11/R01. [DOI] [PubMed] [Google Scholar]
  • 100.Goodwin MS, Velicer WF, Intille SS. Telemetric monitoring in the behavior sciences. Behav Res Methods. 2008;40(1):328–41. doi: 10.3758/brm.40.1.328. [DOI] [PubMed] [Google Scholar]
  • 101.Hedeker D, Gibbons RD. Longitudinal Data Analysis. Wiley; New York: 2006. [Google Scholar]
  • 102.Bollen KA, Curran PJ. Latent Curve Models: A Structural Equation Perspective. Wiley; New York: 2006. [Google Scholar]
  • 103.Tan X, Shiyko MP, Li R, Li Y, Dierker L. A time-varying effect model for intensive longitudinal data. Psychol Methods. 2012;17(1):61–77. doi: 10.1037/a0025814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.El-Sayed AM, Scarborough P, Seemann L, Galea S. Social network analysis and agent-based modeling in social epidemiology. Epidemiol Perspect Innov. 2012;9(1):1. doi: 10.1186/1742-5573-9-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Schubert C, Geser W, Noisternig B, et al. Stress system dynamics during "life as it is lived": an integrative single-case study on a healthy woman. PLoS One. 2012;7(3):e29415. doi: 10.1371/journal.pone.0029415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106.Patrick K, Intille SS, Zabinski MF. An ecological framework for cancer communication: implications for research. J Med Internet Res. 2005;7(3):e23. doi: 10.2196/jmir.7.3.e23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. 2011;1(1):53–71. doi: 10.1007/s13142-011-0021-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Taplin SH, Anhang Price R, Edwards HM, et al. Introduction: Understanding and influencing multilevel factors across the cancer care continuum. J Natl Cancer Inst Monogr. 2012;2012(44):2–10. doi: 10.1093/jncimonographs/lgs008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Giovino GA. Epidemiology of tobacco use in the United States. Oncogene. 2002;21(48):7326–40. doi: 10.1038/sj.onc.1205808. [DOI] [PubMed] [Google Scholar]
  • 110.Salovey P, Williams-Piehota P, Mowad L, Moret ME, Edlund D, Andersen J. Bridging the digital divide by increasing computer and cancer literacy: community technology centers for head-start parents and families. J Health Commun. 2009;14(3):228–45. doi: 10.1080/10810730902805804. [DOI] [PubMed] [Google Scholar]

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