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BACKGROUND: Obesity is a substantial public health concern; however, gold-standard behavioral treatments for obesity are costly and burdensome. Existing adaptations to the efficacious Diabetes Prevention Program (DPP) demonstrate mixed results. Our prior research applying the Multiphase Optimization Strategy (MOST) to DPP identifies a more parsimonious, less costly intervention (EVO) resulting in significant weight loss. OBJECTIVE: The aim of the remotely conducted EVO trial is to test the non-inferiority of EVO against DPP. We will conduct economic evaluations alongside the trial to estimate delivery and patient costs, cost-effectiveness, and lifetime healthcare costs of EVO as compared to DPP. Exploratory analyses will examine maintenance, moderators, and mediators of the treatment effect. STUDY DESIGN: The EVO trial will recruit nationally to randomize 524 participants with obesity. Participants will receive either EVO or DPP over a 6 month period. EVO participants will be provided online lessons, a smartphone application to self-monitor diet, physical activity, and weight, and attend 12 brief calls with a Health Promotionist. DPP participants will receive the first 6 months of the Center for Disease Control’s T2D materials and attend 16 one-hour video call sessions with staff certified in DPP delivery. Weight will be measured at baseline, 3-, 6-, and 12-months. Itemized delivery cost will be collected. Staff and participants will also provide information to estimate costs for intervention-related activities. SIGNIFICANCE: The EVO trial could establish evidence supporting dissemination of a scalable, cost-effective behavioral treatment with potential to shift clinical practice guidelines, inform policy, and reduce the prevalence of obesity.

To improve understanding of how interventions work or why they do not work, there is need for methods of testing hypotheses about the causal mechanisms underlying the individual and combined effects of the components that make up interventions. Factorial mediation analysis, i.e., mediation analysis applied to data from a factorial optimization trial, enables testing such hypotheses. In this commentary, we demonstrate how factorial mediation analysis can contribute detailed information about an intervention’s causal mechanisms. We briefly review the multiphase optimization strategy (MOST) and the factorial experiment. We use an empirical example from a 25 factorial optimization trial to demonstrate how factorial mediation analysis opens possibilities for better understanding the individual and combined effects of intervention components. Factorial mediation analysis has important potential to advance theory about interventions and to inform intervention improvements.

Background/Objective: Growing recognition that collaboration among scientists from diverse disciplines fosters the emergence of solutions to complex scientific problems has spurred initiatives to train researchers to collaborate in interdisciplinary teams. Evaluations of collaboration patterns in these initiatives have tended to be cross-sectional, rather than clarifying temporal changes in collaborative dynamics. Mobile health (mHealth), the science of using mobile, wireless devices to improve health outcomes, is a field whose advancement needs interdisciplinary collaboration. The NIH-supported annual mHealth Training Institute (mHTI) was developed to meet that need and provides a unique testbed. Methods: In this study, we applied a longitudinal social network analysis technique to evaluate how well the program fostered communication among the disciplinarily diverse scholars participating in the 2017−2019 mHTIs. By applying separable temporal exponential random graph models, we investigated the formation and persistence of project-based and fun conversations during the mHTIs. Results: We found that conversations between scholars of different disciplines were just as likely as conversations within disciplines to form or persist in the 2018 and 2019 mHTI, suggesting that the mHTI achieved its goal of fostering interdisciplinary conversations and could be a model for other team science initiatives; this finding is also true for scholars from different career stages. The presence of team and gender homophily effects in certain years suggested that scholars tended to communicate within the same team or gender. Conclusion: Our results demonstrate the usefulness of longitudinal network models in evaluating team science initiatives while clarifying the processes driving interdisciplinary communications during the mHTIs.

Personalized weight management strategies are gaining interest. However, knowledge is limited regarding eating habits and association with energy intake, and current technologies limit assessment in free-living situations. We assessed associations between eating behavior and time of day with energy intake using a wearable camera under free-living conditions and explored if obesity modifies the associations. Sixteen participants (50% with obesity) recorded free-living eating behaviors using a wearable fish-eye camera for 14 days. Videos were viewed by trained annotators who confirmed number of bites, eating speed, and time of day for each eating episode. Energy intake was determined by a trained dietitian performing 24-h diet recalls. Greater number of bites, reduced eating speed, and increased BMI significantly predicted higher energy intake among all participants (P < 0.05, each). There were no significant interactions between obesity and number of bites, eating speed, or time of day (p > 0.05). Greater number of bites and reduced eating speed were significantly associated with higher energy intake in participants without obesity. Results show that under free-living conditions, more bites and slower eating speed predicted higher energy intake when examining consumption of foods with beverages. Obesity did not modify these associations. Findings highlight how eating behaviors can impact energy balance and can inform weight management interventions using wearable technology.

BACKGROUND: Stepped care is a rational resource allocation approach to reduce population obesity. Evidence is lacking to guide decisions on use of low cost treatment components such as mobile health (mHealth) tools without compromising weight loss of those needing more expensive traditional treatment components (e.g., coaching, meal replacement). A sequential multiple assignment randomization trial (SMART) will be conducted to inform the development of an empirically based stepped care intervention that incorporates mHealth and traditional treatment components. OBJECTIVE: The primary aim tests the non-inferiority of app alone, compared to app plus coaching, as first line obesity treatment, measured by weight change from baseline to 6 months. Secondary aims are to identify the best tactic to address early treatment non-response and the optimal treatment sequence for resource efficient weight loss. STUDY DESIGN: Four hundred participants, 18–60 years old with Body Mass Index between 27–45 kg/m(2) will be randomized to receive a weight loss smartphone app (APP) or the app plus weekly coaching (APP + C) for a 12 week period. Those achieving less than 0.5 lb weight loss on average per week, assessed by wireless scale at 2, 4, and 8 weeks, will be classified as non-responders and re-randomized once to step-up modestly (adding another mHealth component) or vigorously (adding mHealth and traditional treatment components) for the remaining treatment period. Weight will be assessed in person at baseline, 3, 6, and 12 months. SIGNIFICANCE: Results will inform construction of an obesity treatment algorithm that balances weight loss outcomes with resource consumption.

College is a critical developmental time when many emerging adults engage in unhealthy behaviors (i.e., lack of exercise, poor diet, smoking) and consequently experience an increased risk for a decline in cardiovascular health. Understanding the beliefs and opinions of the target population is important to develop effective health promotion interventions. The goal of this study was to understand opinions regarding health and health-related mobile technology of college students at an academically elite Midwestern university in order to inform a mobile health promotion intervention following the integrated behavioral model framework. Eighteen college students between the ages of 18 and 22 participated in one of four focus groups, where they discussed perceptions of health behaviors, technology use, and their college environment. Data were analyzed using inductive thematic analysis as well as consensus and conformity analysis. Students reported prioritizing academic success over health and believed in a cultural norm within the university that unhealthy behavioral practices lead to increased academic success. Other identified barriers to achieving good health were (a) low self-efficacy for engaging in healthy behaviors when presented with conflicting academic opportunities and (b) low estimation of the importance of engaging in health behaviors. Regarding mobile health applications (apps), students reported preferring apps that were visually attractive, personalized to each user, and that did not involve competing against other users. These results have implications for the development of mobile health promotion interventions for college students, as they highlight facilitators and barriers to health behavior change in an academically elite student body.

Background Compared with national averages, rural Appalachians experience extremely elevated rates of premature morbidity and mortality. New opportunities, including approaches incorporating personal technology, may help improve lifestyles and overcome health inequities. Objective This study aims to gather perspectives on whether a healthy lifestyle intervention, specifically an app originally designed for urban users, may be feasible and acceptable to rural residents. In addition to a smartphone app, this program—Make Better Choices 2—consists of personalized health coaching, accelerometer use, and financial incentives. Methods We convened 4 focus groups and 16 key informant interviews with diverse community stakeholders to assess perspectives on this novel, evidence-based diet and physical activity intervention. Participants were shown a slide presentation and asked open-ended follow-up questions. The focus group and key informant interview sessions were audiotaped, transcribed, and subjected to thematic analysis. Results We identified 3 main themes regarding Appalachian residents’ perspectives on this mobile health (mHealth) intervention: personal technology is feasible and desirable; challenges persist in implementing mHealth lifestyle interventions in Appalachian communities; and successful mHealth interventions should include personal connections, local coaches, and educational opportunities. Although viewed as feasible and acceptable overall, lack of healthy lifestyle awareness, habitual behavior, and financial constraints may challenge the success of mHealth lifestyle interventions in Appalachia. Finally, participants described several minor elements that require modification, including expanding the upper age inclusion, providing extra coaching on technology use, emphasizing personal and supportive connections, employing local coaches, and ensuring adequate educational content for the program. Conclusions Blending new technologies, health coaching, and other features is not only acceptable but may be essential to reach vulnerable rural residents.

Background Mobile messaging is often used in behavioral weight loss interventions, yet little is known as to the extent to which they contribute to weight loss when part of a multicomponent treatment package. The multiphase optimization strategy (MOST) is a framework that researchers can use to systematically investigate interventions that achieve desirable outcomes given specified constraints. Objective This study describes the use of MOST to develop a messaging intervention as a component to test as part of a weight loss treatment package in a subsequent optimization trial. Methods On the basis of our conceptual model, a text message intervention was created to support self-regulation of weight-related behaviors. We tested the messages in the ENLIGHTEN feasibility pilot study. Adults with overweight and obesity were recruited to participate in an 8-week weight loss program. Participants received a commercially available self-monitoring smartphone app, coaching calls, and text messages. The number and frequency of text messages sent were determined by individual preferences, and weight was assessed at 8 weeks. Results Participants (n=9) in the feasibility pilot study lost 3.2% of their initial body weight over the 8-week intervention and preferred to receive 1.8 texts per day for 4.3 days per week. Researcher burden in manually sending messages was high, and the cost of receiving text messages was a concern. Therefore, a fully automated push notification system was developed to facilitate sending tailored daily messages to participants to support weight loss. Conclusions Following the completion of specifying the conceptual model and the feasibility pilot study, the message intervention went through a final iteration. Theory and feasibility pilot study results during the preparation phase informed critical decisions about automation, frequency, triggers, and content before inclusion as a treatment component in a factorial optimization trial. Trial Registration ClinicalTrials.gov NCT01814072; https://clinicaltrials.gov/ct2/show/NCT01814072

Wearable sensors can provide reliable, automated measures of health behaviors in free-living populations. However, validation of these measures is impossible without observable confirmation of behaviors. Participants have expressed discomfort during the use of ego-centric wearable cameras with first-person view. We argue that mounting the camera on different body locations with a different lens orientation, gives a device recording affordance that has the effect of reducing surveillance and social discomfort compared to ego-centric cameras. We call these types of cameras "activity-oriented" because they are designed to capture a particular activity, rather than the field of view of the wearer. We conducted an experiment of three camera designs with 24 participants, collecting qualitative data on participants' experience while wearing these devices in the wild. We provide a model explaining factors that lead to an increase in social presence and social stigma, which, therefore, create social and surveillance discomfort for the wearer. Wearers' attempts to reduce this discomfort by modifying their behavior or abandoning the device threatens the validity of observations of authentic behaviors. We discuss design implications and provide recommendations to help reduce social presence and stigma in order to improve the validity of observations with cameras in the wild.

Objective Intensive behavioral obesity treatments face scalability challenges, but evidence is lacking about which treatment components could be cut back without reducing weight loss. The Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity (Opt-IN) study applied the Multiphase Optimization Strategy to develop an entirely remotely delivered, technology-supported weight-loss package to maximize the amount of weight loss attainable for ≤$500. Methods Six-month weight loss was examined among adults (N = 562) with BMI ≥ 25 who were randomly assigned to conditions in a factorial experiment crossing five dichotomous treatment components set to either low/high (12 vs. 24 coaching calls) or off/on (primary care provider reports, text messaging, meal replacements, and buddy training). Results About 84.3% of participants completed the final assessment. The treatment package yielding maximum weight loss for ≤$500 included 12 coaching calls, buddy training, and primary care provider progress reports; produced average weight loss of 6.1 kg, with 57.1% losing ≥5% and 51.8% losing ≥7%; and cost $427 per person. The most expensive candidate-treatment component (24 vs. 12 coaching calls) was screened out of the optimized treatment package because it did not increase weight loss. Conclusions Systematically testing each treatment component's effect on weight loss made it possible to eliminate more expensive but less impactful components, yielding an optimized, resource-efficient obesity treatment for evaluation in a randomized controlled trial.

Objective To determine the effects on weight loss of three abbreviated behavioral weight loss interventions with and without coaching and mobile technology. Methods A randomized controlled efficacy study of three 6-month weight loss treatments was conducted in 96 adults with obesity: 1) self-guided (SELF), 2) standard (STND), or 3) technology-supported (TECH). STND and TECH received eight in-person group treatment sessions. SELF and STND used paper diaries to self-monitor diet, activity, and weight; TECH used a smartphone application with social networking features and wireless accelerometer. Results Weight loss was greater for TECH and STND than SELF at 6 months (−5.7 kg [95% confidence interval: −7.2 to −4.1] vs. −2.7 kg [95% confidence interval: −5.1 to −0.3], P

Objective How a healthy lifestyle intervention changes the frequency and duration of daily moderate-vigorous physical activity and sedentary behavior has not been well characterized. Secondary analyses of data from the Make Better Choices randomized controlled trial were conducted to evaluate how interventions to increase physical activity or reduce leisure screen time affected the frequency and duration of these behaviors during treatment initiation and follow-up. Method Participants were 202 adults who exhibited insufficient physical activity, excessive screen time and poor diet during a 14-day baseline screening period. The design was a randomized controlled trial with a 3-week intervention period followed by eight 3- to 7-day bursts of data collection over the 6-month follow-up period after intervention termination. Participants self-reported on their physical activity and screen time at the end of each day. Results A 2-part multilevel model indicated that, relative to baseline levels, the physical activity intervention increased the odds of daily moderate-vigorous intensity physical activity (frequency) but not the duration of activity during the intervention period and these effects persisted (albeit somewhat more weakly) during the follow-up period. The screen time intervention reduced both the frequency and duration of daily screen time from the beginning of the intervention through the follow-up period. Conclusions A 3-week intervention increased daily physical activity frequency but not duration, and reduced both the frequency and duration of daily leisure screen time. These effects were maintained over 20 weeks following the end of the intervention. (PsycINFO Database Record

BACKGROUND Mobile messaging is often used in behavioral weight loss interventions, yet little is known as to the extent to which they contribute to weight loss when part of a multicomponent treatment package. The multiphase optimization strategy (MOST) is a framework that researchers can use to systematically investigate interventions that achieve desirable outcomes given specified constraints. OBJECTIVE This study describes the use of MOST to develop a messaging intervention as a component to test as part of a weight loss treatment package in a subsequent optimization trial. METHODS On the basis of our conceptual model, a text message intervention was created to support self-regulation of weight-related behaviors. We tested the messages in the ENLIGHTEN feasibility pilot study. Adults with overweight and obesity were recruited to participate in an 8-week weight loss program. Participants received a commercially available self-monitoring smartphone app, coaching calls, and text messages. The number and frequency of text messages sent were determined by individual preferences, and weight was assessed at 8 weeks. RESULTS Participants (n=9) in the feasibility pilot study lost 3.2% of their initial body weight over the 8-week intervention and preferred to receive 1.8 texts per day for 4.3 days per week. Researcher burden in manually sending messages was high, and the cost of receiving text messages was a concern. Therefore, a fully automated push notification system was developed to facilitate sending tailored daily messages to participants to support weight loss. CONCLUSIONS Following the completion of specifying the conceptual model and the feasibility pilot study, the message intervention went through a final iteration. Theory and feasibility pilot study results during the preparation phase informed critical decisions about automation, frequency, triggers, and content before inclusion as a treatment component in a factorial optimization trial. CLINICALTRIAL ClinicalTrials.gov NCT01814072; https://clinicaltrials.gov/ct2/show/NCT01814072

Objective This paper examines how and why to improve care systems for disease management and health promotion for the growing population of cancer survivors with cardiovascular multi-morbidities. Method We reviewed research characterizing cancer survivors' and their multiple providers' common sense cognitive models of survivors' main health threats, preventable causes of adverse health events, and optimal coping strategies. Results Findings indicate that no entity in the health care system self-identifies as claiming primary responsibility to address longstanding unhealthy lifestyle behaviors that heighten survivors' susceptibility to both cancer and cardiovascular disease (CVD) and whose improvement could enhance quality of life. Conclusions To address this gap, we propose systems-level changes that integrate health promotion into existing survivorship services by including behavioral risk factor vital signs in the electronic medical record, with default proactive referral to a health promotionist (a paraprofessional coach adept with mobile technologies and supervised by a professional expert in health behavior change). By using the patient's digital tracking data to coach remotely and periodically report progress to providers, the health promotionist closes a gap, creating a connected care system that supports, reinforces, and maintains accountability for healthy lifestyle improvement. No comparable resource solely dedicated to treatment of chronic disease risk behaviors (smoking, obesity, physical inactivity, treatment nonadherence) exists in current models of integrated care. Integrating health promotionists into care delivery channels would remove burden from overtaxed PCPs and instantiate a comprehensive, actionable systems-level schema of health risks and coping strategies needed to have preventive impact with minimal interference to clinical work flow. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Although current funding announcements increasingly call for research that involves collaboration across several scholarly disciplines, many senior scientists were trained in an era when team-based cross-disciplinary collaboration was uncommon and even discouraged. The need for team science professional development opportunities that cross disciplinary backgrounds and experience levels is great, but the resources available to provide such training are few. Having previously created teamscience.net, a suite of free interactive online learning modules about team science, we illustrate how the modules were paired with an in-person workshop to facilitate the launch of a newly funded, interdisciplinary, highly geographically distributed team. The Mobile Big Data to Knowledge (MD2K) study is a Big Data Center of Excellence funded by the National Institutes of Health to develop and study the use of novel wearable sensors to prevent two adverse health events: relapse to smoking among recently quit smokers, and re-hospitalization among recently discharged heart failure patients. Team-based learning principles were applied to facilitate cross-talk among the 68 health professionals and computer scientists from 11 institutions who attended the first annual MD2K all-hands meeting. This chapter includes the protocol, timeline, and workshop materials and provides observations about implementing the training. We suggest that team science training is feasible and valued by scientists from different academic backgrounds who are trying to conduct cross-disciplinary research. We also propose a need for training resources that are tailored to meet the changing professional development needs of teams as they evolve from team assembly, through team launch, to maturation.

Behavioral medicine is devoting increasing attention to the topic of participant engagement and its role in effective mobile health (mHealth) behavioral interventions. Several definitions of the term "engagement" have been proposed and discussed, especially in the context of digital health behavioral interventions. We consider that engagement refers to specific interaction and use patterns with the mHealth tools such as smartphone applications for intervention, whereas adherence refers to compliance with the directives of the health intervention, independent of the mHealth tools. Through our analysis of participant interaction and self-reported behavioral data in a college student health study with incentives, we demonstrate an example of measuring "effective engagement" as engagement behaviors that can be linked to the goals of the desired intervention. We demonstrate how clustering of one year of weekly health behavior self-reports generate four interpretable clusters related to participants' adherence to the desired health behaviors: healthy and steady, unhealthy and steady, decliners, and improvers. Based on the intervention goals of this study (health promotion and behavioral change), we show that not all app usage metrics are indicative of the desired outcomes that create effective engagement. As such, mHealth intervention design might consider eliciting not just more engagement or use overall, but rather, effective engagement defined by use patterns related to the desired behavioral outcome.

IMPACT: Our research identifies key opportunities for increased cross-CTSA collaboration, as a means to improve community-research cooperation and better CBPR practices. OBJECTIVES/GOALS: Currently, team science training prioritizes developing the collaborative competencies of interdisciplinary scientists to work with each other and, more recently, with communities. Community-facing team science resources are scarce but present among some CTSAs, suggesting that capacity gaps might be remedied through cross-hub collaboration. METHODS/STUDY POPULATION: We reviewed online information provided by the 62 current CTSAs to identify: (1) which hubs engage in community research, and (2) what resources the hubs utilize to orient, train, and support community stakeholders as research partners. We then examined the capacities of the collectively available CTSA resources to address needed knowledge, skills, and attitudes that community-engaged researchers have identified as essential for community-based stakeholders to partner equally in research. Finally, we explored practical challenges in team-based dynamics (e.g., interpersonal difficulties, expertise gaps, resource management) that may facilitate or hinder communities’ research endeavors, and suggest resources that CTSAs might implement to facilitate team science dynamics. RESULTS/ANTICIPATED RESULTS: Hubs (n=59) have community engagement programs, 12 of which provide community-based participatory research toolkits. Toolkits vary from basic checklists to fully developed modules. Some hubs also offer consultation services and partner match-making. Learning objectives include: outcome definition, logic models, and goal-setting. Learning resources remain underdeveloped to help communities appreciate the benefits of research engagement and convince academic partners of the value of real-world knowledge and community improvement relative to scientific advancement. Also lacking is easily accessible support to understand the research process, build verifiable trust, maintain bidirectional knowledge and assets, and implement consistent, best practice methodological and reporting protocols. DISCUSSION/SIGNIFICANCE OF FINDINGS: Gaps between current hub offerings and community needs suggest prioritizing creation of resources whose learning objectives highlight the benefits of research engagement for community partners; foster mutual values affirmation between partners; and offer tools that build warranted community-researcher rapport.

Background Cardiovascular disease (CVD) remains the leading cause of death globally. Seven health factors are associated with ideal cardiovascular health: being a non-smoker; not overweight; physically active; having a healthy diet; and normal blood pressure; fasting plasma glucose and cholesterol. Whereas approximately half of U.S. youth have ideal levels in at least 5 of the 7 components of cardiovascular health, this proportion falls to 16% by adulthood. Objective We will evaluate whether the NUYou cardiovascular mHealth intervention is more effective than an active comparator to promote cardiovascular health during the transition to young adulthood. Methods 302 incoming freshmen at a midwest university will be cluster randomized by dormitory into one of two mHealth intervention groups: 1) Cardiovascular Health (CVH), addressing behaviors related to CVD risk; or 2) Whole Health (WH), addressing behaviors unrelated to CVD. Both groups will receive smartphone applications, co-designed with students to help them manage time, interact with other participants via social media, and report health behaviors weekly. The CVH group will also have self-monitoring features to track their risk behaviors. Cardiovascular health will be assessed at the beginning of freshman year and the end of freshman and sophomore years. Linear mixed models will be used to compare groups on a composite of the seven cardiovascular-related health factors. Significance This is the first entirely technology-mediated multiple health behavior change intervention delivered to college students to promote cardiovascular health. Findings will inform the potential for primordial prevention in young adulthood. Trial Registration Number: clinicaltrials.gov # NCT02496728

OBJECTIVES/GOALS: A new researcher-facing module to support community engaged research has been added to the updated COALESCE website and user traffic was tracked since last reporting. We describe the process of development and the features of the new module, past 2-year traffic, and plans to develop a community facing module. METHODS/STUDY POPULATION: We monitored the number of unique users of COALESCE (teamscience.net) between 2017 and 2019 to determine if traffic slowed, stayed the same, or increased since teamscience.net was physically updated to function on mobile devices. In December 2019, a new module was launched to introduce researchers to the stages of team science community engaged research. To develop the module, we collaborated with academic partners at University of Illinois-Chicago to identify 3 local historic research case studies in and to characterize how each exemplified a team science stage: assembly, launch, or maturation. After interviewing key team members from each study, we iterated storyboards and scripts in collaboration with community engaged research experts and case study team members. The module was built, tested, and launched. RESULTS/ANTICIPATED RESULTS: In the 6 years between 2011 through 2017, the site attracted 16,016 unique visitors (approximately 2699 per year). In 2 years from 2017 through 2019, since the modernization of the website, user traffic has held steady or grown, attracting 6992 unique visitors (approximately 3496 per year). Our newly posted researcher-facing module highlights team assembly in the case a task force charged with reducing disparity in breast cancer outcomes in Chicago, team launch in a study to improve asthma management in a local FQHC, and team maturation in a study comparing clinic-based to public school-based treatment of disruptive behavior. We will soon create a companion community-facing module and resources to address identified needs for community partners engaging in research with academic institutions. DISCUSSION/SIGNIFICANCE OF IMPACT: COALESCE (teamscience.net) remains the first and only open-access, online training in team science for health professionals. Recent updates have improved usability and expanded available resources. We launched a comprehensive module for academics interested in community engaged research; future work will develop parallel community facing resources.

BACKGROUND Prevalent co-occurring poor diet and physical inactivity convey chronic disease risk to the population. Large magnitude behavior change can improve behaviors to recommended levels, but multiple behavior change interventions produce small, poorly maintained effects. OBJECTIVE The Make Better Choices 2 trial tested whether a multicomponent intervention integrating mHealth, modest incentives, and remote coaching could sustainably improve diet and activity. METHODS Between 2012 and 2014, the 9-month randomized controlled trial enrolled 212 Chicago area adults with low fruit and vegetable and high saturated fat intakes, low moderate to vigorous physical activity (MVPA) and high sedentary leisure screen time. Participants were recruited by advertisements to an open-access website, screened, and randomly assigned to either of two active interventions targeting MVPA simultaneously with, or sequentially after other diet and activity targets (N=84 per intervention) or a stress and sleep contact control intervention (N=44). They used a smartphone app and accelerometer to track targeted behaviors and received personalized remote coaching from trained paraprofessionals. Perfect behavioral adherence was rewarded with an incentive of US $5 per week for 12 weeks. Diet and activity behaviors were measured at baseline, 3, 6, and 9 months; primary outcome was 9-month diet and activity composite improvement. RESULTS Both simultaneous and sequential interventions produced large, sustained improvements exceeding control (P CONCLUSIONS Multicomponent mHealth diet and activity intervention involving connected coaching and modest initial performance incentives holds potential to reduce chronic disease risk. CLINICALTRIAL ClinicalTrials.gov NCT01249989; https://clinicaltrials.gov/ct2/show/NCT01249989 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT01249989).

Objective To examine within-person variation in dietary self-monitoring during a 6-month technology-supported weight loss trial as a function of time-varying factors including time in the study, day of the week, and month of the year. Methods Smartphone self-monitoring data were examined from 31 obese adults (aged 18–60 years) who participated in a 6-month technology-supported weight loss program. Multilevel regression modeling was used to examine within-person variation in dietary self-monitoring. Results Participants recorded less as time in the study progressed. Fewer foods were reported on the weekends compared with weekdays. More foods were self-monitored in January compared with October; however, a seasonal effect was not observed. Conclusions and Implications The amount of time in a study and day of the week were associated with dietary self-monitoring but not season. Future studies should examine factors that influence variations in self-monitoring and identify methods to improve technology-supported dietary self-monitoring adherence.

Background: Recruitment and retention strategy investigations in mHealth clinical trials are rare. Technology presents an opportunity to intensely and remotely evaluate recruitment, use of mobile apps, and retention, leading to new insights for continuous improvement of mHealth trials. The objective of this paper is to present a case study in which a trial evaluated and changed strategies during a clinical trial to improve recruitment, adherence to study protocols, and retention in the mHealth trial. Methods: In Fall 2015, the NUYou trial enrolled 150 college freshmen in an mHealth protocol. Three months after study initiation, NUYou struggled to meet recruitment goals and maintain anticipated usage levels of the study smartphone application. Two sets of data were collected to improve recruitment and retention: a survey about recruitment was sent to the target population and surveys regarding usability of the app was sent to the study sample. Survey results informed improvements in recruitment strategies, the study retention protocol, and the smartphone application. Results: Survey results revealed several insights including misunderstanding components of the trial by potential participants, low perceived usefulness of the app, and little recall or impact of the incentive structure. After implementation of user-centered improvements, the second cohort of NUYou recruitment in the fall of 2016 produced an equal sample size in 4 weeks less time. Winter quarter of 2016 compared to 2017 demonstrated an improvement in retention via app use and completion of weekly in-app surveys. Conclusions: Recruitment and retention in clinical trials continues to be a critical challenge and mHealth trials may present both unique challenges and opportunities. To our knowledge, this is the first study to describe a systematic evaluation followed by changes and further evaluation to recruitment, use of the mHealth application, adherence to study protocol, and retention during an mHealth clinical trial. Future work should adopt and explicitly study these processes to optimize both enrollment and retention in these types of trials to preserve validity and reliability of research results.

Lower cost alternatives are needed for the traditional in-person behavioral weight loss programs to overcome challenges of lowering the worldwide prevalence of overweight and obesity. Smartphones have become ubiquitous and provide a unique platform to aid in the delivery of a behavioral weight loss program. The technological capabilities of a smartphone may address certain limitations of a traditional weight loss program, while also reducing the cost and burden on participants, interventionists, and health care providers. Awareness of the advantages smartphones offer for weight loss has led to the rapid development and proliferation of weight loss applications (apps). The built-in features and the mechanisms by which they work vary across apps. Although there are an extraordinary number of a weight loss apps available, most lack the same magnitude of evidence-based behavior change strategies typically used in traditional programs. As features develop and new capabilities are identified, we propose a conceptual model as a framework to guide the inclusion of features that can facilitate behavior change and lead to reductions in weight. Whereas the conventional wisdom about behavior change asserts that more is better (with respect to the number of behavior change techniques involved), this model suggests that less may be more because extra techniques may add burden and adversely impact engagement. Current evidence is promising and continues to emerge on the potential of smartphone use within weight loss programs; yet research is unable to keep up with the rapidly improving smartphone technology. Future studies are needed to refine the conceptual model’s utility in the use of technology for weight loss, determine the effectiveness of intervention components utilizing smartphone technology, and identify novel and faster ways to evaluate the ever-changing technology.

Energy balance is one component of weight management, but passive objective measures of caloric intake are non-existent. Given recent success of actigraphy as a passive objective measure of the physical activity construct that relieves participants of the burden of biased self-report, researchers are aiming to find a passive objective measure of caloric intake to improve understanding of problematic eating behaviors in obese and non-obese participants. Passive sensing food intake systems have failed to go beyond the lab and into behavioral research in part due to low adherence to wearing passive monitoring systems. This paper presents preliminary results in obese and non-obese participants performing structured and unstructured eating experiments to understand wearable adherence as affected by: 1) perceived data privacy; 2) stigma of wearing devices; 3) comfort. Wearables examined include neck- and wrist-worn sensors, and video camera-based systems.

Energy balance is one component of weight management, but passive objective measures of caloric intake are non-existent. Given the recent success of actigraphy as a passive objective measure of the physical activity construct that relieves participants of the burden of biased self-report, computer scientists and engineers are aiming to find a passive objective measure of caloric intake. Passive sensing food intake systems have failed to go beyond the lab and into behavioral research in part due to low adherence to wearing passive monitoring systems. While system accuracy and battery lifetime are sine qua non to a successfully deployed technology, they come second to adherence, since a system does nothing if it remains unused. This paper focuses on adherence as affected by: 1) perceived data privacy; 2) stigma of wearing devices; 3) comfort. These factors highlight new challenges surrounding participant informed consent and Institutional Review Board (IRB) risk assessment. The wearables examined include neck- and wrist-worn sensors, and video camera-based systems. Findings support the potential for adherence using wrist- and shoulder-based video cameras, and personalized style-conscious neck-worn sensors. The feasibility of detecting fine-grained eating gestures to validate the machine learning models is shown, improving the potential of translation of this technology.

Characterizing eating behaviors to inform and prevent obesity requires nutritionists, behaviorists and interventionists to disrupt subjects' routine with questionnaires and unfamiliar eating environments. Such a disruption may be necessary as a means of self-reflection, however, prevents researchers from capturing problematic eating behaviors in a free-living environment. An automated system alleviates many of these disruptions; however, success in automating sensing of eating habits has proven to be a challenge due to high within-subject variability in people's eating habits. Given a positive correlation between eating duration and caloric intake, along with the fact that many problematic eaters spend time alone, this paper presents a passive sensing system designed with the following three goals: detecting eating episodes through data analytics of passive sensors, detecting time spent alone while eating, and designing a passive sensing system that people will adhere to wearing in the field, without disrupting regular activity or behavior. A real-time coarse multi-layered classification approach is proposed to detect challenging eating episodes with confounding factors using data from piezoelectric, audio, and inertial sensors. The system was tested on 7 participants with 14 eating episodes, resulting in an 80.8%, and 91.3% average F-measure for detection of eating and alone time, respectively. Additionally, results of a survey highlights the importance of user-customization to increase adherence to neck-worn sensors.

Previous research has begun to delineate the complicated reactions experienced by bone marrow and stem cell donors. The purpose of this study was to examine the influence of the donor-recipient relationship on the related donor's emotional reactions. Twenty-eight adult stem cell donors completed questionnaires before donation, 30 days post stem cell infusion, and 1 year after infusion. Questionnaires addressed the donor-recipient relationship, depression, mood, guilt and responsibility, self-esteem, ambivalence about donation and reactions to the donation itself. Results indicated that most donors reported little ambivalence about donation, and their reactions to the donation itself were generally positive. Closer and more positive donor-recipient relationships were associated with less anticipated guilt and responsibility if the transplant did not work. The relationships between the donor and the recipient did not change over time. Mood disturbance and depression were low overall, not related to the donor-recipient relationship, and did not significantly change over time. These results indicate that related stem cell donors are generally without significant emotional distress, and are comfortable with the donation process. Further, a more positive relationship with the recipient may help donors to avoid feeling guilty and responsible if the transplant does not work.

Background Primary care is well positioned to facilitate cardiovascular risk improvement and reduce future cardiovascular disease (CVD) burden. Methods The efficacy of risk factor screening, behavioural counselling, and pharmacological treatment to lower CVD risk was assessed via a prospective pre- and postintervention health risk assessment, individualized intervention with behaviour modification, risk factor treatment, and linkage to community programs, with 1-year follow-up and final health risk assessment. Primary outcome was the proportion of subjects with moderate and high baseline Framingham Risk Score (FRS) reducing their risk by 10% and 25%, respectively; the secondary end point was the proportion dropping ≥ 1 risk category. Results Patients were enrolled (N = 1509) from March 2006 through October 2008 and 72% completed the study. This analysis focuses on 563 subjects with moderate or high baseline FRS, and excluded 325 low-risk patients and 205 with established CVD or diabetes mellitus. Median age was 56 years, 57.7% were female. The primary outcome was achieved in 31.8% (N = 112; 95% confidence interval [CI], 26.9%-36.6%) of moderate risk FRS participants and 47.9% (N = 101; 95% CI, 41.2%-54.6%) of high-risk participants. The secondary outcome was achieved by 37.2% (N = 210; 95% CI, 33.2%-41.2%). Prevalence of metabolic syndrome fell from 79.2% (N = 446; 95% CI, 75.9%-82.6%) at entry to 52.8% (N = 303; 95% CI, 48.7%-56.9%) at study end. Significant improvements in all modifiable risk factors occurred through lifestyle modification. Conclusions Global cardiovascular risk can be effectively decreased via lifestyle changes informed by readiness to change assessment and individualized counselling targeting specific behaviours. Trial registration: ClinicalTrials.gov number NCT01620996.

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

BackgroundA 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. ObjectiveMyBehavior, 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. MethodsMyBehavior 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. ResultsIn 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