Publications
Christine Vinci, Aaron Haslam, Cho Y Lam, Santosh Kumar and David W Wetter.
The use of ambulatory assessment in smoking cessation.. Addictive behaviors, 2018. BibTeX@article{Vinci2018, author = "Vinci, Christine and Haslam, Aaron and Lam, Cho Y. and Kumar, Santosh and Wetter, David W.", title = "The use of ambulatory assessment in smoking cessation.", journal = "Addictive behaviors", year = 2018, abstract = "Ambulatory assessment of smoking behavior has greatly advanced our knowledge of the smoking cessation process. The current article first provides a brief overview of ecological momentary assessment for smoking cessation and highlights some of the primary advantages and scientific advancements made from this data collection method. Next, a discussion of how certain data collection tools (i.e., smoking topography and carbon monoxide detection) that have been traditionally used in lab-based settings are now being used to collect data in the real world. The second half of the paper focuses on the use of wearable wireless sensors to collect data during the smoking cessation process. Details regarding how these sensor-based technologies work, their application to newer tobacco products, and their potential to be used as intervention tools are discussed. Specific focus is placed on the opportunity to utilize novel intervention approaches, such as Just-In-Time Adaptive Interventions, to intervene upon smoking behavior. Finally, a discussion of some of the current challenges and limitations related to using sensor-based tools for smoking cessation are presented, along with suggestions for future research in this area." }James M Rehg, Susan A Murphy and Santosh Kumar (eds.).
Detecting Eating and Smoking Behaviors Using Smartwatches. pages 175–201, Springer International Publishing, 2017. URL, DOI BibTeX@inbook{Parate2017, pages = "175--201", title = "Detecting Eating and Smoking Behaviors Using Smartwatches", publisher = "Springer International Publishing", year = 2017, author = "Parate, Abhinav and Ganesan, Deepak", editor = "Rehg, James M. and Murphy, Susan A. and Kumar, Santosh", abstract = "Inertial sensors embedded in commercial smartwatches and fitness bands are among the most informative and valuable on-body sensors for monitoring human behavior. This is because humans perform a variety of daily activities that impacts their health, and many of these activities involve using hands and have some characteristic hand gesture associated with it. For example, activities like eating food or smoking a cigarette require the direct use of hands and have a set of distinct hand gesture characteristics. However, recognizing these behaviors is a challenging task because the hand gestures associated with these activities occur only sporadically over the course of a day, and need to be separated from a large number of irrelevant hand gestures. In this chapter, we will look at approaches designed to detect behaviors involving sporadic hand gestures. These approaches involve two main stages: (1) spotting the relevant hand gestures in a continuous stream of sensor data, and (2) recognizing the high-level activity from the sequence of recognized hand gestures. We will describe and discuss the various categories of approaches used for each of these two stages, and conclude with a discussion about open questions that remain to be addressed.", booktitle = "Mobile Health: Sensors, Analytic Methods, and Applications", doi = "10.1007/978-3-319-51394-2_10", url = "https://doi.org/10.1007/978-3-319-51394-2_10" }Lucy Yardley, Bonnie J Spring, Heleen Riper, Leanne Morrisonand David G H Crane, Kristina Curtis, Gina C Merchant, Felix Naughton and Ann Blandford.
Understanding and promoting effective engagement with digital behavior change interventions. American Journal of Preventive Medicine 51(5):833–842, 2016. URL, DOI BibTeX@article{yardley2016understanding, author = "Lucy Yardley and Bonnie J Spring and Heleen Riper and Leanne G Morrisonand David H Crane and Kristina Curtis and Gina C Merchant and Felix Naughton and Ann Blandford", title = "Understanding and promoting effective engagement with digital behavior change interventions", journal = "American Journal of Preventive Medicine", year = 2016, volume = 51, number = 5, pages = "833--842", abstract = "This paper is one in a series developed through a process of expert consensus to provide an overview of questions of current importance in research into engagement with digital behavior change interventions, identifying guidance based on research to date and priority topics for future research. The first part of this paper critically reflects on current approaches to conceptualizing and measuring engagement. Next, issues relevant to promoting effective engagement are discussed, including how best to tailor to individual needs and combine digital and human support. A key conclusion with regard to conceptualizing engagement is that it is important to understand the relationship between engagement with the digital intervention and the desired behavior change. This paper argues that it may be more valuable to establish and promote “effective engagement,” rather than simply more engagement, with “effective engagement” defined empirically as sufficient engagement with the intervention to achieve intended outcomes. Appraisal of the value and limitations of methods of assessing different aspects of engagement highlights the need to identify valid and efficient combinations of measures to develop and test multidimensional models of engagement. The final section of the paper reflects on how interventions can be designed to fit the user and their specific needs and context. Despite many unresolved questions posed by novel and rapidly changing technologies, there is widespread consensus that successful intervention design demands a user-centered and iterative approach to development, using mixed methods and in-depth qualitative research to progressively refine the intervention to meet user requirements.", doi = "10.1016/j.amepre.2016.06.015", publisher = "Elsevier", url = "https://md2k.org/images/papers/jitai/AmJPrevMed_Spring2016.pdf" }Angela Pfammatter, Bonnie Spring, Nalini Saligram, Raj Dav'e, Arun Gowda, Linelle Blais, Monika Arora, Harish Ranjani, Om Ganda, Donald Hedeker and others.
mHealth Intervention to Improve Diabetes Risk Behaviors in India: A Prospective, Parallel Group Cohort Study. Journal of Medical Internet Research 18(8):e207, 2016. URL, DOI BibTeX@article{pfammatter2016mhealth, author = "Angela Pfammatter and Bonnie Spring and Nalini Saligram and Raj Dav'e and Arun Gowda and Linelle Blais and Monika Arora and Harish Ranjani and Om Ganda and Donald Hedeker and others", title = "mHealth Intervention to Improve Diabetes Risk Behaviors in India: A Prospective, Parallel Group Cohort Study", journal = "Journal of Medical Internet Research", year = 2016, volume = 18, number = 8, pages = "e207", abstract = "BACKGROUND: In low/middle income countries like India, diabetes is prevalent and health care access limited. Most adults have a mobile phone, creating potential for mHealth interventions to improve public health. To examine the feasibility and initial evidence of effectiveness of mDiabetes, a text messaging program to improve diabetes risk behaviors, a global nonprofit organization (Arogya World) implemented mDiabetes among one million Indian adults. OBJECTIVE: A prospective, parallel cohort design was applied to examine whether mDiabetes improved fruit, vegetable, and fat intakes and exercise. METHODS: Intervention participants were randomly selected from the one million Nokia subscribers who elected to opt in to mDiabetes. Control group participants were randomly selected from non-Nokia mobile phone subscribers. mDiabetes participants received 56 text messages in their choice of 12 languages over 6 months; control participants received no contact. Messages were designed to motivate improvement in diabetes risk behaviors and increase awareness about the causes and complications of diabetes. Participant health behaviors (exercise and fruit, vegetable, and fat intake) were assessed between 2012 and 2013 via telephone surveys by blinded assessors at baseline and 6 months later. Data were cleaned and analyzed in 2014 and 2015. RESULTS: 982 participants in the intervention group and 943 in the control group consented to take the phone survey at baselne. At the end of the 6-month period, 611 (62.22%) in the intervention and 632 (67.02%) in the control group completed the follow-up telephone survey. Participants receiving texts demonstrated greater improvement in a health behavior composite score over 6 months, compared with those who received no messages F(1, 1238) = 30.181, P<.001, 95% CI, 0.251-0.531. Fewer intervention participants demonstrated health behavior decline compared with controls. Improved fruit, vegetable, and fat consumption (P<.01) but not exercise were observed in those receiving messages, as compared with controls. CONCLUSIONS: A text messaging intervention was feasible and showed initial evidence of effectiveness in improving diabetes-related health behaviors, demonstrating the potential to facilitate population-level behavior change in a low/middle income country. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ACTRN): 12615000423516; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367946&isReview=true (Archived by WebCite at http://www.webcitation.org/6j5ptaJgF).", doi = "10.2196/jmir.5712", publisher = "JMIR Publications Inc., Toronto, Canada", url = "http://www.jmir.org/2016/8/e207/" }Kevin Patrick, Eric B Hekler, Deborah Estrin, David C Mohr, Heleen Riper, David Crane, Job Godino and William T Riley.
The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research. American Journal of Preventive Medicine 51(5):816 - 824, 2016. URL, DOI BibTeX@article{Patrick2016816, author = "Kevin Patrick and Eric B. Hekler and Deborah Estrin and David C. Mohr and Heleen Riper and David Crane and Job Godino and William T. Riley", title = "The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research", journal = "American Journal of Preventive Medicine", year = 2016, volume = 51, number = 5, pages = "816 - 824", abstract = {This paper addresses the rapid pace of change in the technologies that support digital interventions; the complexity of the health problems they aim to address; and the adaptation of scientific methods to accommodate the volume, velocity, and variety of data and interventions possible from these technologies. Information, communication, and computing technologies are now part of every societal domain and support essentially every facet of human activity. Ubiquitous computing, a vision articulated fewer than 30 years ago, has now arrived. Simultaneously, there is a global crisis in health through the combination of lifestyle and age-related chronic disease and multiple comorbidities. Computationally intensive health behavior interventions may be one of the most powerful methods to reduce the consequences of this crisis, but new methods are needed for health research and practice, and evidence is needed to support their widespread use. The challenges are many, including a reluctance to abandon timeworn theories and models of health behavior—and health interventions more broadly—that emerged in an era of self-reported data; medical models of prevention, diagnosis, and treatment; and scientific methods grounded in sparse and expensive data. There are also many challenges inherent in demonstrating that newer approaches are, indeed, effective. Potential solutions may be found in leveraging methods of research that have been shown to be successful in other domains, particularly engineering. A more "agile science" may be needed that streamlines the methods through which elements of health interventions are shown to work or not, and to more rapidly deploy and iteratively improve those that do. There is much to do to advance the issues discussed in this paper, and the papers in this theme issue. It remains an open question whether interventions based in these new models and methods are, in fact, equally if not more efficacious as what is available currently. Economic analyses of these new approaches are needed because assumptions of net worth compared to other approaches are just that, assumptions. Human-centered design research is needed to ensure that users ultimately benefit. Finally, a translational research agenda will be needed, as the status quo will likely be resistant to change.}, doi = "10.1016/j.amepre.2016.05.001", url = "https://md2k.org/images/papers/jitai/AmJPrevMed_Patrick2016.pdf" }Inbal Nahum-Shani, Shawna N Smith, Bonnie J Spring, Linda M Collins, Katie Witkiewitz, Ambuj Tewari and Susan A Murphy.
Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine, pages 1–17, 2016. URL, DOI BibTeX@article{Nahum-Shani2016, author = "Inbal Nahum-Shani and Shawna N. Smith and Bonnie J. Spring and Linda M. Collins and Katie Witkiewitz and Ambuj Tewari and Susan A. Murphy", title = "Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support", journal = "Annals of Behavioral Medicine", year = 2016, pages = "1--17", abstract = "Background. The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual’s state can change rapidly, unexpectedly, and in his/her natural environment. Purpose. Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods. Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusion. As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implica-tions of providing timely and ecologically sound support for intervention adherence and retention.", doi = "10.1007/s12160-016-9830-8", url = "https://md2k.org/images/papers/jitai/BehavMed_Nahum-Shani16.pdf" }Korkut Bekiroglu, Constantino Lagoa, Susan A Murphy and Stephanie T Lanza.
Control Engineering Methods for the Design of Robust Behavioral Treatments. IEEE Transactions on Control Systems Technology PP(99):1-12, 2016. URL, DOI BibTeX@article{7501575, author = "Korkut Bekiroglu and Constantino Lagoa and Susan A. Murphy and Stephanie T. Lanza", title = "Control Engineering Methods for the Design of Robust Behavioral Treatments", journal = "IEEE Transactions on Control Systems Technology", year = 2016, volume = "PP", number = 99, pages = "1-12", abstract = "In this paper, a robust control approach is used to address the problem of adaptive behavioral treatment design. Human behavior (e.g., smoking and exercise) and reactions to treatment are complex and depend on many unmeasurable external stimuli, some of which are unknown. Thus, it is crucial to model human behavior over many subject responses. We propose a simple (low order) uncertain affine model subject to uncertainties whose response covers the most probable behavioral responses. The proposed model contains two different types of uncertainties: uncertainty of the dynamics and external perturbations that patients face in their daily life. Once the uncertain model is defined, we demonstrate how least absolute shrinkage and selection operator (lasso) can be used as an identification tool. The lasso algorithm provides a way to directly estimate a model subject to sparse perturbations. With this estimated model, a robust control algorithm is developed, where one relies on the special structure of the uncertainty to develop efficient optimization algorithms. This paper concludes by using the proposed algorithm in a numerical experiment that simulates treatment for the urge to smoke.", doi = "10.1109/TCST.2016.2580661", url = "https://md2k.org/images/papers/jitai/ieee_murphy2016.pdf" }C Yang, J P Maher and D E Conroy.
Implementation of Behavior Change Techniques in Mobile Applications for Physical Activity.. American Journal of Preventive Medicine, 2015. URL, DOI BibTeX@article{Yang2015a, author = "C. Yang and J.P. Maher and D.E. Conroy", title = "Implementation of Behavior Change Techniques in Mobile Applications for Physical Activity.", journal = "American Journal of Preventive Medicine", year = 2015, abstract = "Mobile applications (apps) for physical activity are popular and hold promise for promoting behavior change and reducing non-communicable disease risk. App marketing materials describe a limited number of behavior change techniques (BCTs), but apps may include unmarketed BCTs, which are important as well.To characterize the extent to which BCTs have been implemented in apps from a systematic user inspection of apps.Top-ranked physical activity apps (N=100) were identified in November 2013 and analyzed in 2014. BCTs were coded using a contemporary taxonomy following a user inspection of apps.Users identified an average of 6.6 BCTs per app and most BCTs in the taxonomy were not represented in any apps. The most common BCTs involved providing social support, information about others' approval, instructions on how to perform a behavior, demonstrations of the behavior, and feedback on the behavior. A latent class analysis of BCT configurations revealed that apps focused on providing support and feedback as well as support and education.Contemporary physical activity apps have implemented a limited number of BCTs and have favored BCTs with a modest evidence base over others with more established evidence of efficacy (e.g., social media integration for providing social support versus active self-monitoring by users). Social support is a ubiquitous feature of contemporary physical activity apps and differences between apps lie primarily in whether the limited BCTs provide education or feedback about physical activity.", doi = "10.1016/j.amepre.2014.10.010", school = "Department of Preventive Medicine, Northwestern University, Chicago, Illinois. Electronic address: conroy@northwestern.edu.", url = "https://md2k.org/images/papers/jitai/amjprevmed_conroy.pdf" }Bonnie Spring, Abby C King, Sherry L Pagoto, Linda Van Horn and Jeffery D Fisher.
Fostering multiple healthy lifestyle behaviors for primary prevention of cancer. American Psychologist 70(2):75-90, 2015. URL, DOI BibTeX@article{Spring2015, author = "Bonnie Spring and Abby C. King and Sherry L. Pagoto and Linda Van Horn and Jeffery D. Fisher", title = "Fostering multiple healthy lifestyle behaviors for primary prevention of cancer", journal = "American Psychologist", year = 2015, volume = 70, number = 2, pages = "75-90", abstract = "The odds of developing cancer are increased by specific lifestyle behaviors (tobacco use, excess energy and alcohol intakes, low fruit and vegetable intake, physical inactivity, risky sexual behaviors, and inadequate sun protection) that are established risk factors for developing cancer. These behaviors are largely absent in childhood, emerge and tend to cluster over the life span, and show an increased prevalence among those disadvantaged by low education, low income, or minority status. Even though these risk behaviors are modifiable, few are diminishing in the population over time. We review the prevalence and population distribution of these behaviors and apply an ecological model to describe effective or promising healthy lifestyle interventions targeted to the individual, the sociocultural context, or environmental and policy influences. We suggest that implementing multiple health behavior change interventions across these levels could substantially reduce the prevalence of cancer and the burden it places on the public and the health care system. We note important still-unresolved questions about which behaviors can be intervened upon simultaneously in order to maximize positive behavioral synergies, minimize negative ones, and effectively engage underserved populations. We conclude that interprofessional collaboration is needed to appropriately determine and convey the value of primary prevention of cancer and other chronic diseases. (PsycINFO Database Record (c) 2015 APA, all rights reserved)", doi = "10.1037/a0038806", editor = ". , http://dx.doi.org/10.1037/a0038806", url = "https://md2k.org/images/papers/jitai/nihms729492_spring.pdf" }Donna Spruijt-Metz, Eric Hekler, Niilo Saranummi, Stephen Intille, Ilkka Korhonen, Wendy Nilsen, Daniel E Rivera, Bonnie Spring, Susan Michie, David A Asch, Alberto Sanna, Vicente Traver Salcedo, Rita Kukakfa and Misha Pavel.
Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Translational Behavioral Medicine 5(3):335-346, 2015. URL, DOI BibTeX@article{raey, author = "Donna Spruijt-Metz and Eric Hekler and Niilo Saranummi and Stephen Intille and Ilkka Korhonen and Wendy Nilsen and Daniel E. Rivera and Bonnie Spring and Susan Michie and David A. Asch and Alberto Sanna and Vicente Traver Salcedo and Rita Kukakfa and Misha Pavel", title = "Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research", journal = "Translational Behavioral Medicine", year = 2015, volume = 5, number = 3, pages = "335-346", abstract = "Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static “snapshots” of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing “gold standard” measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a “knowledge commons,” which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.", doi = "10.1007/s13142-015-0324-1", publisher = "Springer US", url = "https://md2k.org/images/papers/jitai/TBM_Spring.pdf" }M Rabbi, A Pfammatter, M Zhang, B Spring and T Choudhury.
Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults. Journal of Medical Internet Research 3(2):e42, 2015. URL, DOI BibTeX@article{Rabbi2015, author = "M Rabbi and A Pfammatter and M Zhang and B Spring and T Choudhury", title = "Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults", journal = "Journal of Medical Internet Research", year = 2015, volume = 3, number = 2, pages = "e42", abstract = "Background: A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users’ behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement. Objective: MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user’s environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions. Methods: MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior’s personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions. Results: In qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior’s personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P<.001). Conclusions: MyBehavior is a simple-to-use mobile phone app with preliminary evidence of efficacy. To the best of our knowledge, MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information (ie, manual food logging and automatic tracking of activity). Lessons learned about the difficulty of manual logging and usability concerns, as well as future directions, are discussed.", doi = "10.2196/mhealth.4160", url = "../images/papers/jitai/jmir_spring.pdf" }C A Pellegrini, J Steglitz, W Johnston, J Warnick, T Adams, H G McFadden, J Siddique, D Hedeker and B Spring.
Design and protocol of a randomized multiple behavior change trial: Make Better Choices 2 (MBC2).. Contemporary Clinical Trials 41C:85–92, 2015. URL BibTeX@article{Pellegrini2015, author = "C.A. Pellegrini and J. Steglitz and W. Johnston and J. Warnick and T. Adams and H.G. McFadden and J. Siddique and D. Hedeker and B. Spring", title = "Design and protocol of a randomized multiple behavior change trial: Make Better Choices 2 (MBC2).", journal = "Contemporary Clinical Trials", year = 2015, volume = "41C", pages = "85--92", abstract = "Suboptimal diet and inactive lifestyle are among the most prevalent preventable causes of premature death. Interventions that target multiple behaviors are potentially efficient; however the optimal way to initiate and maintain multiple health behavior changes is unknown.The Make Better Choices 2 (MBC2) trial aims to examine whether sustained healthful diet and activity change are best achieved by targeting diet and activity behaviors simultaneously or sequentially. Study design approximately 250 inactive adults with poor quality diet will be randomized to 3 conditions examining the best way to prescribe healthy diet and activity change. The 3 intervention conditions prescribe: 1) an increase in fruit and vegetable consumption (F/V+), decrease in sedentary leisure screen time (Sed-), and increase in physical activity (PA+) simultaneously (Simultaneous); 2) F/V+ and Sed- first, and then sequentially add PA+ (Sequential); or 3) Stress Management Control that addresses stress, relaxation, and sleep. All participants will receive a smartphone application to self-monitor behaviors and regular coaching calls to help facilitate behavior change during the 9month intervention. Healthy lifestyle change in fruit/vegetable and saturated fat intakes, sedentary leisure screen time, and physical activity will be assessed at 3, 6, and 9months.MBC2 is a randomized m-Health intervention examining methods to maximize initiation and maintenance of multiple healthful behavior changes. Results from this trial will provide insight about an optimal technology supported approach to promote improvement in diet and physical activity.", school = "Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680N. Lake Shore Drive, Suite 1400, Chicago, IL 60611, United States.", url = "http://dx.doi.org/10.1016/j.cct.2015.01.009" }I Nahum-Shani, S N Smith, A Tewari, K Witkiewitz, L M Collins, B Spring and S A Murphy.
Just-in-time adaptive interventions (JITAIs): An organizing framework for ongoing health behavior support. Number 126, 2015. URL BibTeX@techreport{nahumshani2014b, author = "I. Nahum-Shani and S.N. Smith and A. Tewari and K. Witkiewitz and L.M. Collins and B. Spring and S.A. Murphy", title = "Just-in-time adaptive interventions (JITAIs): An organizing framework for ongoing health behavior support", year = 2015, number = 126, abstract = "An emerging mobile phone intervention design, called the just-in-time adaptive intervention (JITAI), holds enormous potential for adapting mobile phone-delivered interventions to the dynamics of an individuals emotional, social, physical and contextual state, so as to prevent negative health outcomes and promote the adoption and maintenance of healthy behaviors. A JITAI is an intervention design aiming to address the dynamically changing needs of individuals via the provision of the type/amount of support needed, at the right time, and only when needed. Despite the increasing use and the practical and conceptual appeal of JITAIs, a comprehensive organizing scientific framework to guide the construction of efficacious evidence-based JITAIs has yet to be provided. To bridge this gap, the current manuscript provides an organizing scientific framework to guide the construction of JITAIs. The key components of a JITAI are described and illustrated using examples of JITAIs from various domains of psychological and health behavior research. Design principles are discussed, as well as practical and theoretical challenges that require consideration in the process of constructing efficacious JITAIs.", journal = "Technical Report No. 14-126 The Methodology Center, Penn State", school = "The Methodology Center, Pennsylvania State University", url = "http://methodology.psu.edu/media/techreports/14-126.pdf", volume = 14 }Y Cui, J D Robinson, J M Engelmann, C Y Lam, J A Minnix, M Karam-Hage, D W Wetter, J A Dani, T R Kosten and P M Cinciripini.
Reinforcement Sensitivity Underlying Treatment-Seeking Smokers' Affect, Smoking Reinforcement Motives, and Affective Responses. Psychology of Addictive Behaviors, 2015. URL BibTeX@article{Cui2015, author = "Y. Cui and J.D. Robinson and J.M. Engelmann and C.Y. Lam and J.A. Minnix and M. Karam-Hage and D.W. Wetter and J.A. Dani and T.R. Kosten and P.M. Cinciripini", title = "Reinforcement Sensitivity Underlying Treatment-Seeking Smokers' Affect, Smoking Reinforcement Motives, and Affective Responses", journal = "Psychology of Addictive Behaviors", year = 2015, abstract = "Nicotine dependence has been suggested to be related to reinforcement sensitivity, which encompasses behavioral predispositions either to avoid aversive (behavioral inhibition) or to approach appetitive (behavioral activation) stimuli. Reinforcement sensitivity may shape motives for nicotine use and offer potential targets for personalized smoking cessation therapy. However, little is known regarding how reinforcement sensitivity is related to motivational processes implicated in the maintenance of smoking. Additionally, women and men differ in reinforcement sensitivity, and such difference may cause distinct relationships between reinforcement sensitivity and motivational processes for female and male smokers. In this study, the authors characterized reinforcement sensitivity in relation to affect, smoking-related reinforcement motives, and affective responses, using self-report and psychophysiological measures, in over 200 smokers before treating them. The Behavioral Inhibition/Activation Scales (BIS/BAS; Carver & White, 1994) was used to measure reinforcement sensitivity. In female and male smokers, BIS was similarly associated with negative affect and negative reinforcement of smoking. However, positive affect was positively associated with BAS Drive scores in male smokers, and this association was reversed in female smokers. BIS was positively associated with corrugator electromyographic reactivity toward negative stimuli and left frontal electroencephalogram alpha asymmetry. Female and male smokers showed similar relationships for these physiological measures. These findings suggest that reinforcement sensitivity underpins important motivational processes (e.g., affect), and gender is a moderating factor for these relationships. Future personalized smoking intervention, particularly among more dependent treatment-seeking smokers, may experiment to target individual differences in reinforcement sensitivity. (PsycINFO Database Record (c) 2015 APA, all rights reserved).", url = "http://dx.doi.org/10.1037/adb0000050" }S Kumar, M al'Absi, J G Beck, E Ertin and M Scott.
Behavioral Monitoring and Assessment via Mobile Sensing Technologies. In L Marsch, S Lord and J Dallery (eds.). Leveraging Technology to Transform Behavioral Healthcare. Oxford Press, 2014, pages 27-39. BibTeX@incollection{UMemphisCS2014-503, author = "S. Kumar and M. al'Absi and J.G. Beck and E. Ertin and M. Scott", title = "Behavioral Monitoring and Assessment via Mobile Sensing Technologies", booktitle = "Leveraging Technology to Transform Behavioral Healthcare", publisher = "Oxford Press", year = 2014, editor = "L. Marsch and S. Lord and J. Dallery", pages = "27-39", abstract = "Mobile sensing technologies can now be used to obtain continuous measures of health, behavior, and the environment in naturalistic settings. These technologies have the potential to revolutionize healthcare by introducing novel delivery channels for behavioral assessment and intervention. This chapter reviews recent advancements in converting wearable physiological sensor data into measures of behaviors—using the examples of stress, conversation, and smoking. The chapter describes the authors work with a suite of sensors and mobile software to illustrate the process of obtaining measures of behaviors from mobile sensor data. The chapter concludes with a discussion of research challenges that must be addressed to scale these technologies for broad use." }D Swendeman, W S Comulada, N Ramanathan, M Lazar and D Estrin.
Reliability and Validity of Daily Self-Monitoring by Smartphone Application for Health-Related Quality-of-Life, Antiretroviral Adherence, Substance Use, and Sexual Behaviors Among People Living with HIV. AIDS and Behavior 19(2):330-340, 2014. URL BibTeX@article{Swendeman2015, author = "D. Swendeman and W.S. Comulada and N. Ramanathan and M. Lazar and D. Estrin", title = "Reliability and Validity of Daily Self-Monitoring by Smartphone Application for Health-Related Quality-of-Life, Antiretroviral Adherence, Substance Use, and Sexual Behaviors Among People Living with HIV", journal = "AIDS and Behavior", year = 2014, volume = 19, number = 2, pages = "330-340", abstract = "This paper examines inter-method reliability and validity of daily self-reports by smartphone application compared to 14-day recall web-surveys repeated over 6 weeks with people living with HIV (PLH). A participatory sensing framework guided participant-centered design prioritizing external validity of methods for potential applications in both research and self-management interventions. Inter-method reliability correlations were consistent with prior research for physical and mental health quality-of-life (r = 0.260.61), antiretroviral adherence (r = 0.700.73), and substance use (r = 0.650.92) but not for detailed sexual encounter surveys (r = 0.150.61). Concordant and discordant pairwise comparisons show potential trends in reporting biases, for example, lower recall reports of unprotected sex or alcohol use, and rounding up errors for frequent events. Event-based reporting likely compensated for modest response rates to daily time-based prompts, particularly for sexual and drug use behaviors that may not occur daily. Recommendations are discussed for future continuous assessment designs and", publisher = "Springer US", url = "http://dx.doi.org/10.1007/s10461-014-0923-8" }D Swendeman, N Ramanathan, W S Comulada, M J Rotheram-Borus and D Estrin.
Efficacy Of Daily Self-Monitoring Of Health Behaviors And Quality Of Life By Mobile Phone: Mixed-Methods Results From Two Studies With Diverse Populations. In Annals of Behavioral Medicine. Volume 47, 2014, pages S263–S263. BibTeX@incollection{Swendeman2014, author = "D. Swendeman and N. Ramanathan and W.S. Comulada and M.J. Rotheram-Borus and D. Estrin", title = "Efficacy Of Daily Self-Monitoring Of Health Behaviors And Quality Of Life By Mobile Phone: Mixed-Methods Results From Two Studies With Diverse Populations", booktitle = "Annals of Behavioral Medicine", year = 2014, volume = 47, pages = "S263--S263" }A L Rebar, J P Maher, S E Doerksen, S Elavsky and D E Conroy.
Intention-behavior gap is wider for walking and moderate physical activity than for vigorous physical activity in university students.. Journal of Science and Medicine in Sport, 2014. URL BibTeX@article{Rebar2014a, author = "A.L. Rebar and J.P. Maher and S.E. Doerksen and S. Elavsky and D.E. Conroy", title = "Intention-behavior gap is wider for walking and moderate physical activity than for vigorous physical activity in university students.", journal = "Journal of Science and Medicine in Sport", year = 2014, abstract = "The theory of planned behavior proposes that physical activity is the result of intentions; however little is known about whether the relation between intentions and behavior differs between vigorous, moderate physical activity, and walking. For university students, vigorous physical activity is oftentimes enacted as a goal-directed behavior; whereas walking is oftentimes a means to achieving a goal other than physical activity (e.g., transportation).The study was a one-week prospective study.Undergraduate students (N=164) reported intentions for walking, moderate physical activity, and vigorous physical activity and self-reported these behaviors one week later.Hierarchical linear modeling revealed that intentions were more strongly related to vigorous physical activity than to moderate physical activity or walking.Intention-enhancing interventions may effectively promote vigorous physical activity, but other motivational processes may be more appropriate to target in interventions of walking and moderate physical activity.", school = "Center for Behavior and Health - Institute for Public Health and Medicine, Northwestern University, USA.", url = "http://dx.doi.org/10.1016/j.jsams.2014.11.392" }M Carbunar, B Ballesteros, J Burri, D H Chau and D Rahman.
Turning the Tide: Curbing Deceptive Yelp Behaviors. Prodeedings of SIAM International Conference on Data Mining (SDM), 2014. URL BibTeX@article{Rahman2014b, author = "M. Carbunar and B. Ballesteros and J. Burri and D.H. Chau and D. Rahman", title = "Turning the Tide: Curbing Deceptive Yelp Behaviors", journal = "Prodeedings of SIAM International Conference on Data Mining (SDM)", year = 2014, abstract = "The popularity and influence of reviews, make sites like Yelp ideal targets for malicious behaviors. We present Marco, a novel system that exploits the unique combination of social, spatial and temporal signals gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Marco increases the cost and complexity of attacks, by imposing a tradeoff on fraudsters, between their ability to impact venue ratings and their ability to remain undetected. We contribute a new dataset to the community, which consists of both ground truth and gold standard data. We show that Marco significantly outperforms state-of-the-art approaches, by achieving 94% accuracy in classifying reviews as fraudulent or genuine, and 95.8% accuracy in classifying venues as deceptive or legitimate. Marco successfully flagged 244 deceptive venues from our large dataset with 7,435 venues, 270,121 reviews and 195,417 users. Among the San Francisco car repair and moving companies that we analyzed, almost 10% exhibit fraudulent behaviors.", url = "http://users.cis.fiu.edu/ carbunar/deceptive.pdf" }L Mauriello, K Evers, W Nilsen and K Patrick.
The Use Of mHealth With Special Populations: Theory, Research And Practice. In Annals of Behavioral Medicine. Volume 47, 2014, pages S149–S149. BibTeX@incollection{Mauriello2014, author = "L. Mauriello and K. Evers and W. Nilsen and K. Patrick", title = "The Use Of mHealth With Special Populations: Theory, Research And Practice", booktitle = "Annals of Behavioral Medicine", year = 2014, volume = 47, pages = "S149--S149" }J P Maher, S E Doerksen, S Elavsky and D E Conroy.
Daily satisfaction with life is regulated by both physical activity and sedentary behavior.. Journal of Sport & Exercise Psychology 36(2):166–178, 2014. URL BibTeX@article{Maher2014, author = "J.P. Maher and S.E. Doerksen and S. Elavsky and D.E. Conroy", title = "Daily satisfaction with life is regulated by both physical activity and sedentary behavior.", journal = "Journal of Sport \& Exercise Psychology", year = 2014, volume = 36, number = 2, pages = "166--178", abstract = "Recent research revealed that on days when college students engage in more physical activity than is typical for them, they also experience greater satisfaction with life (SWL). That work relied on self-reported physical activity and did not differentiate between low levels of physical activity and sedentary behavior. This study was designed to (1) determine if the association between self-reported physical activity and SWL would exist when physical activity was monitored objectively and (2) examine the between- and within-person associations among physical activity, sedentary behavior, and SWL. During a 14-day ecological momentary assessment study, college students (N = 128) wore an accelerometer to objectively measure physical activity and sedentary behavior, and they self-reported their physical activity, sedentary behavior, and SWL at the end of each day. Physical activity and sedentary behavior had additive, within-person associations with SWL across self-reported and objective-measures of behavior. Strategies to promote daily well-being should encourage college students to incorporate greater amounts of physical activity as well as limit their sedentary behavior.", url = "http://www.ncbi.nlm.nih.gov/pubmed/24686953" }J Kang, C C Ciecierski, E L Malin, A J Carroll, M Gidea, L L Craft, B Spring and B Hitsman.
A latent class analysis of cancer risk behaviors among U.S. college students. Elsevier Preventive Medicine 64:121-126, 2014. URL BibTeX@article{Kang2014a, author = "J. Kang and C.C. Ciecierski and E.L. Malin and A.J. Carroll and M. Gidea and L.L. Craft and B. Spring and B. Hitsman", title = "A latent class analysis of cancer risk behaviors among U.S. college students", journal = "Elsevier Preventive Medicine", year = 2014, volume = 64, pages = "121-126", abstract = "Objective The purpose of this study is to understand how cancer risk behaviors cluster in U.S. college students and vary by race and ethnicity. Methods Using the fall 2010 wave of the National College Health Assessment (NCHA), we conducted a latent class analysis (LCA) to evaluate the clustering of cancer risk behaviors/conditions: tobacco use, physical inactivity, unhealthy diet, alcohol binge drinking, and overweight/obesity. The identified clusters were then examined separately by students' self-reported race and ethnicity. Results Among 30,093 college students surveyed, results show a high prevalence of unhealthy diet as defined by insufficient fruit and vegetable intake (> 95%) and physical inactivity (> 60%). The LCA identified behavioral clustering for the entire sample and distinct clustering among Black and American Indian students. Conclusions Cancer risk behaviors/conditions appear to cluster among college students differentially by race. Understanding how risk behaviors cluster in young adults can lend insight to racial disparities in cancer through adulthood. Health behavior interventions focused on modifying multiple risk behaviors and tailored to students' racial group could potentially have a much larger effect on cancer prevention than those targeting any single behavior.", url = "http://www.sciencedirect.com/science/article/pii/S0091743514001170" }K Heron, S Smyth, D E Conroy, C Sciamanna and L Rovniak.
The Development And Evaluation Of A Mobile Real-Time Physical Activity Intervention. In Annals of Behavioral Medicine. Volume 47, Springer, 233 Spring St., New York, NY 10013 USA, 2014, pages S53–S53. BibTeX@incollection{Heron2014, author = "K. Heron and S. Smyth and D.E. Conroy and C. Sciamanna and L. Rovniak", title = "The Development And Evaluation Of A Mobile Real-Time Physical Activity Intervention", booktitle = "Annals of Behavioral Medicine", publisher = "Springer, 233 Spring St., New York, NY 10013 USA", year = 2014, volume = 47, pages = "S53--S53" }P T Fitzsimmons, J P Maher, S E Doerksen, S Elavsky, A L Rebar and D E Conroy.
A daily process analysis of physical activity, sedentary behavior, and perceived cognitive abilities. Psychology of Sport and Exercise 15(5):498–504, 2014. URL BibTeX@article{Fitzsimmons2014, author = "P.T. Fitzsimmons and J.P. Maher and S.E. Doerksen and S. Elavsky and A.L. Rebar and D.E. Conroy", title = "A daily process analysis of physical activity, sedentary behavior, and perceived cognitive abilities", journal = "Psychology of Sport and Exercise", year = 2014, volume = 15, number = 5, pages = "498--504", abstract = "Objectives This study evaluated the role of both physical activity and sedentary behavior in daily perceptions of cognitive abilities and whether these relations exist within-person, between-person, or both. Design Non-experimental, intensive longitudinal research using ecological momentary assessments. Method College students wore accelerometers and provided end-of-day reports on physical activity, sedentary behavior, and perceived cognitive abilities for 14 days. Results Across self-reports and objective measures of behavior, daily deviations in physical activity were positively associated with perceived cognitive abilities. Daily deviations in self-reported, but not objectively-assessed, sedentary behavior also were negatively associated with perceived cognitive abilities. Contrary to previous research, overall levels of physical activity and sedentary behaviors were not associated with perceived cognitive abilities. Conclusions These findings indicate that physical activity has a within- rather than between-person association with perceived cognitive abilities although between-person associations effects may require longer monitoring periods to manifest. Further research is needed to establish the direction of causality and resolve whether the nature (rather than quantity) of sedentary activities influences cognition.", publisher = "Elsevier", url = "http://www.sciencedirect.com/science/article/pii/S1469029214000557" }J Dallery, W T Riley and I Nahum-Shani.
Research designs to develop and evaluate technology-based health behavior interventions. In L Marsch, S Lord and J Dallery (eds.). Behavioral Healthcare and Technology. Oxford University Press, 2014, page 168. BibTeX@incollection{Dallery2014, author = "J. Dallery and W.T. Riley and I. Nahum-Shani", title = "Research designs to develop and evaluate technology-based health behavior interventions", booktitle = "Behavioral Healthcare and Technology", publisher = "Oxford University Press", year = 2014, editor = "L. Marsch and S. Lord and J. Dallery", pages = 168, journal = "Behavioral Health Care and Technology: Using Science-Based Innovations to Transform Practice" }D E Conroy, C Yang and J P Maher.
Behavior Change Techniques in Top-Ranked Mobile Apps for Physical Activity. American journal of preventive medicine 46(6):649–652, 2014. URL BibTeX@article{Conroy2014, author = "D.E. Conroy and C. Yang and J.P. Maher", title = "Behavior Change Techniques in Top-Ranked Mobile Apps for Physical Activity", journal = "American journal of preventive medicine", year = 2014, volume = 46, number = 6, pages = "649--652", abstract = "Background: Mobile applications (apps) have potential for helping people increase their physical activity, but little is known about the behavior change techniques marketed in these apps. Purpose: The aim of this study was to characterize the behavior change techniques represented in online descriptions of top-ranked apps for physical activity. Methods: Top-ranked apps (n¼167) were identified on August 28, 2013, and coded using the Coventry, Aberdeen and London–Revised (CALO-RE) taxonomy of behavior change techniques during the following month. Analyses were conducted during 2013. Results: Most descriptions of apps incorporated fewer than four behavior change techniques. The most common techniques involved providing instruction on how to perform exercises, modeling how to perform exercises, providing feedback on performance, goal-setting for physical activity, and planning social support/change. A latent class analysis revealed the existence of two types of apps, educational and motivational, based on their configurations of behavior change techniques. Conclusions: Behavior change techniques are not widely marketed in contemporary physical activity apps. Based on the available descriptions and functions of the observed techniques in contemporary health behavior theories, people may need multiple apps to initiate and maintain behavior change. This audit provides a starting point for scientists, developers, clinicians, and consumers to evaluate and enhance apps in this market.", publisher = "Elsevier", url = "http://www.ajpmonline.org/pb/assets/raw/Health%20Advance/journals/amepre/AMEPRE_4004_Embargo.pdf" }L M Collins, I Nahum-Shani and D Almirall.
Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART). Clinical Trials 11(4):426-434, 2014. URL BibTeX@article{Collins2014, author = "L.M. Collins and I. Nahum-Shani and D. Almirall", title = "Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART)", journal = "Clinical Trials", year = 2014, volume = 11, number = 4, pages = "426-434", abstract = "A behavioral intervention is a program aimed at modifying behavior for the purpose of treating or preventing disease, promoting health, and/or enhancing well-being. Many behavioral interventions are dynamic treatment regimens, that is, sequential, individualized multicomponent interventions in which the intensity and/or type of treatment is varied in response to the needs and progress of the individual participant. The multiphase optimization strategy (MOST) is a comprehensive framework for development, optimization, and evaluation of behavioral interventions, including dynamic treatment regimens. The objective of optimization is to make dynamic treatment regimens more effective, efficient, scalable, and sustainable. An important tool for optimization of dynamic treatment regimens is the sequential, multiple assignment, randomized trial (SMART). The purpose of this article is to discuss how to develop optimized dynamic treatment regimens within the MOST framework. Methods and results The article discusses the preparation, optimization, and evaluation phases of MOST. It is shown how MOST can be used to develop a dynamic treatment regimen to meet a prespecified optimization criterion. The SMART is an efficient experimental design for gathering the information needed to optimize a dynamic treatment regimen within MOST. One signature feature of the SMART is that randomization takes place at more than one point in time. Conclusion MOST and SMART can be used to develop optimized dynamic treatment regimens that will have a greater public health impact.", publisher = "SAGE Publications", url = "http://ctj.sagepub.com/content/11/4/426.long" }D Almirall, I Nahum-Shani, N E Sherwood and S A Murphy.
Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research. Translational Behavioral Medicine 4(3):260–274, 2014. URL BibTeX@article{almirall2014introduction, author = "D. Almirall and I. Nahum-Shani and N.E. Sherwood and S.A. Murphy", title = "Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research", journal = "Translational Behavioral Medicine", year = 2014, volume = 4, number = 3, pages = "260--274", abstract = "The management of many health disorders often entails a sequential, individualized approach whereby treatment is adapted and readapted over time in response to the specific needs and evolving status of the individual. Adaptive interventions provide one way to operationalize the strategies (e.g., continue, augment, switch, step-down) leading to individualized sequences of treatment. Often, a wide variety of critical questions must be answered when developing a high-quality adaptive intervention. Yet, there is often insufficient empirical evidence or theoretical basis to address these questions. The Sequential Multiple Assignment Randomized Trial (SMART)—a type of research design—was developed explicitly for the purpose of building optimal adaptive interventions by providing answers to such questions. Despite increasing popularity, SMARTs remain relatively new to intervention scientists. This manuscript provides an introduction to adaptive interventions and SMARTs. We discuss SMART design considerations, including common primary and secondary aims. For illustration, we discuss the development of an adaptive intervention for optimizing weight loss among adult individuals who are overweight.", publisher = "Springer", url = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4167891/pdf/13142_2014_Article_265.pdf" }P S Advani, LR. Reitzel, N T Nguyen, F D Fisher, E J Savoy, A G Cuevas, D W Wetter and L H McNeill.
Financial strain and cancer risk behaviors among African Americans.. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2014. URL BibTeX@article{advani2014, author = "P.S. Advani and LR. Reitzel and N.T. Nguyen and F.D. Fisher and E.J. Savoy and A.G. Cuevas and D.W. Wetter and L.H. McNeill", title = "Financial strain and cancer risk behaviors among African Americans.", journal = "Cancer epidemiology, biomarkers \& prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology", year = 2014, abstract = "BACKGROUND: African Americans suffer disproportionately from the adverse consequences of behavioral risk factors for cancer relative to other ethnic groups. Recent studies have assessed how financial strain might uniquely contribute to engagement in modifiable behavioral risk factors for cancer, but not among African Americans. The current study examined associations between financial strain and modifiable cancer risk factors (smoking, at-risk alcohol use, overweight/obesity, insufficient physical activity, inadequate fruit and vegetable intake, and multiple risk factors) among 1,278 African American adults (age, 46.5 ± 12.6 years; 77% female) and explored potential mediators (stress and depressive symptoms) of those associations. METHODS: Logistic regression models were used to examine associations between financial strain and cancer risk factors. Analyses were adjusted for age, sex, partner status, income, educational level, and employment status. Analyses involving overweight/obesity status additionally controlled for fruit and vegetable intake and physical activity. Nonparametric bootstrapping procedures were used to assess mediation. RESULTS: Greater financial strain was associated with greater odds of insufficient physical activity (P < 0.003) and smoking (P = 0.005) and was positively associated with the total number of cancer risk factors (P < 0.0001). There was a significant indirect effect of both stress and depressive symptoms on the relations of financial strain with physical inactivity and multiple risk factors, respectively. CONCLUSIONS: Future interventions aimed at reducing cancer disparities should focus on African Americans experiencing higher financial strain while addressing their stress and depressive symptoms. IMPACT: Longitudinal studies are needed to assess the temporal and causal relations between financial strain and modifiable behavioral cancer risk factors among African Americans", url = "http://www.ncbi.nlm.nih.gov/pubmed/24740200" }J M Rehg, A Rozga, G D Abowd and M S Goodwin.
Behavioral Imaging and Autism. Pervasive Computing, IEEE 13(2):84-87, 2014. URL BibTeX@article{6818509, author = "J.M. Rehg and A. Rozga and G.D. Abowd and M.S. Goodwin", title = "Behavioral Imaging and Autism", journal = "Pervasive Computing, IEEE", year = 2014, volume = 13, number = 2, pages = "84-87", abstract = "Behavioral imaging encompasses the use of computational sensing and modeling techniques to measure and analyze human behavior. This article discusses a research program focused on the study of dyadic social interactions between children and their caregivers and peers. The study has resulted in a dataset containing semi-structured play interactions between children and adults. Behavioral imaging could broadly affect the quality of care for individuals with a developmental or behavioral disorder.", url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6818509" }