Methodologies for optimizing behavioral interventions: introduction to special section

Exploring research team members' and trial participants' perceptions of acceptability and implementation within one videoconference-based supportive care program for individuals affected by systemic sclerosis during COVID-19: a qualitative interview study

The SPIN-CHAT Program was designed to support mental health among individuals with systemic sclerosis (SSc; commonly known as scleroderma) and at least mild anxiety symptoms at the onset of COVID-19. The program was formally evaluated in the SPIN-CHAT Trial. Little is known about program and trial acceptability, and factors impacting implementation from the perspectives of research team members and trial participants. Thus, the propose of this follow-up study was to explore research team members' and trial participants' experiences with the program and trial to identify factors impacting acceptability and successful implementation. Data were collected cross-sectionally through one-on-one, videoconference-based, semi-structured interviews with 22 research team members and 30 purposefully recruited trial participants (Mage = 54.9, SD = 13.0 years). A social constructivist paradigm was adopted, and data were analyzed thematically. Data were organized into seven themes: (i) getting started: the importance of prolonged engagement and exceeding expectations; (ii) designing the program and trial: including multiple features; (iii) training: research team members are critical to positive program and trial experiences; (iv) offering the program and trial: it needs to be flexible and patient-oriented; (v) maximizing engagement: navigating and managing group dynamics; (vi) delivering a videoconference-based supportive care intervention: necessary, appreciated, and associated with some barriers; and (vii) refining the program and trial: considering modification when offered beyond the period of COVID-19 restrictions. Trial participants were satisfied with and found the SPIN-CHAT Program and Trial to be acceptable. Results offer implementation data that can guide the design, development, and refinement of other supportive care programs seeking to promote psychological health during and beyond COVID-19.

Effects of Desk Pedaling Work Rate on Concurrent Work Performance among Physically Inactive Adults: A Randomized Experiment

PURPOSE

Under-desk pedaling devices could help reduce health risks associated with the global decline in work-related energy expenditure. However, the optimal pedaling work rate to facilitate concurrent work performance among physically inactive adults is unclear. We examined the effects of two light-intensity pedaling work rates on physically inactive adults' work performance.

METHODS

We recruited equal numbers of older (45-65 yr) versus younger (20-44 yr), male versus female, and overweight/obese (body mass index [BMI], 25-35 kg·m -2 ) versus normal weight (BMI, 18.5-24.9 kg·m -2 ) participants. Using a Graeco-Latin square design, participants ( n = 96) completed a laboratory experiment to evaluate the effects of using an under-desk pedaling device at two seated light-intensity work rates (17 and 25 W), relative to a seated nonpedaling condition on objectively measured typing, reading, logical reasoning, and phone task performance. Ergonomic comfort under each pedaling work rate was also assessed. Equivalence tests were used to compare work performance under the pedaling versus nonpedaling conditions.

RESULTS

Treatment fidelity to the 17- and 25-W pedaling work rates exceeded 95%. Mean work performance scores for each pedaling and nonpedaling condition were equivalent under alpha = 0.025. Age, sex, and BMI did not significantly moderate the effect of pedaling on work performance. Participants reported greater ergonomic comfort while completing work tasks at the 17-W relative to the 25-W work rate.

CONCLUSIONS

Physically inactive adults obtained similar work performance scores under the 17- and 25-W pedaling and the nonpedaling conditions, suggesting that either pedaling work rate could help reduce health risks of sedentary work time. The 17-W work rate yielded greater ergonomic comfort and may be an appropriate starting point for introducing diverse inactive workers to under-desk pedaling.

The Barriers and Facilitators of eHealth-Based Lifestyle Intervention Programs for People With a Low Socioeconomic Status: Scoping Review

Background

Promoting health behaviors and preventing chronic diseases through a healthy lifestyle among those with a low socioeconomic status (SES) remain major challenges. eHealth interventions are a promising approach to change unhealthy behaviors in this target group.

Objective

This review aims to identify key components, barriers, and facilitators in the development, reach, use, evaluation, and implementation of eHealth lifestyle interventions for people with a low SES. This review provides an overview for researchers and eHealth developers, and can assist in the development of eHealth interventions for people with a low SES.

Methods

We performed a scoping review based on Arksey and O’Malley’s framework. A systematic search was conducted on PubMed, MEDLINE (Ovid), Embase, Web of Science, and the Cochrane Library, using terms related to a combination of the following key constructs: eHealth, lifestyle, low SES, development, reach, use, evaluation, and implementation. There were no restrictions on the date of publication for articles retrieved upon searching the databases.

Results

The search identified 1323 studies, of which 42 met our inclusion criteria. An update of the search led to the inclusion of 17 additional studies. eHealth lifestyle interventions for people with a low SES were often delivered via internet-based methods (eg, websites, email, Facebook, and smartphone apps) and offline methods, such as texting. A minority of the interventions combined eHealth lifestyle interventions with face-to-face or telephone coaching, or wearables (blended care). We identified the use of different behavioral components (eg, social support) and technological components (eg, multimedia) in eHealth lifestyle interventions. Facilitators in the development included iterative design, working with different disciplines, and resonating intervention content with users. Facilitators for intervention reach were use of a personal approach and social network, reminders, and self-monitoring. Nevertheless, barriers, such as technological challenges for developers and limited financial resources, may hinder intervention development. Furthermore, passive recruitment was a barrier to intervention reach. Technical difficulties and the use of self-monitoring devices were common barriers for users of eHealth interventions. Only limited data on barriers and facilitators for intervention implementation and evaluation were available.

Conclusions

While we found large variations among studies regarding key intervention components, and barriers and facilitators, certain factors may be beneficial in building and using eHealth interventions and reaching people with a low SES. Barriers and facilitators offer promising elements that eHealth developers can use as a toolbox to connect eHealth with low SES individuals. Our findings suggest that one-size-fits-all eHealth interventions may be less suitable for people with a low SES. Future research should investigate how to customize eHealth lifestyle interventions to meet the needs of different low SES groups, and should identify the components that enhance their reach, use, and effectiveness.

Adaptive Goals and Reinforcement Timing to Increase Physical Activity in Adults: A Factorial Randomized Trial

INTRODUCTION

Potent lifestyle interventions to increase moderate-to-vigorous physical activity are urgently needed for population-level chronic disease prevention. This trial tested the independent and joint effects of a mobile health system automating adaptive goal setting and immediate financial reinforcement for increasing daily walking among insufficiently active adults.

STUDY DESIGN

Participants were randomized into a 2 (adaptive versus static goal setting) X 2 (immediate versus delayed financial incentive timing) condition factorial trial to increase walking.

SETTINGS/PARTICIPANTS

Participants (N=512 adults) were recruited between 2016 and 2018 and were 64.5% female, aged 18-60 years, 18.8% Hispanic, 6.1% African American, and 83% White.

INTERVENTION

Principles of reinforcement and behavioral economics directed intervention design.

MAIN OUTCOME MEASURES

Participants wore accelerometers daily (133,876 day-level observations) that remotely measured moderate-to-vigorous physical activity bout minutes of ≥3 minutes/day for 1 year. Primary outcomes were between-condition differences in (1) engaging ≥1 bout of moderate-to-vigorous physical activity on each day and (2) on days with ≥1 bout, daily total moderate-to-vigorous physical activity minutes.

RESULTS

Mixed-effects hurdle models tested treatment group X phase (time) interactions using an intent-to-treat approach in 2021. Engaging in any ambulatory moderate-to-vigorous physical activity was greater for Adaptive than for Static Goal groups (OR=2.34, 95% CI=2.10, 2.60 vs OR=1.66, 95% CI=1.50, 1.84; p<0.001) and for Immediate than for Static Reinforcement groups (OR=2.16 95% CI=1.94, 2.40 vs OR=1.77, 95% CI=1.59, 1.97; p<0.01). The Immediate Reinforcement group increased by 16.54 moderate-to-vigorous physical activity minutes/day, whereas the Delayed Reinforcement group increased by 9.91 minutes/day (p<0.001). The combined Adaptive Goals + Immediate Reinforcement group increased by 16.52 moderate-to-vigorous physical activity minutes/day, significantly more than that of either Delayed Reinforcement group.

CONCLUSIONS

This study offers automated and scalable-behavior change strategies for increasing walking among adults most at-risk for chronic diseases attributed to sedentary lifestyles.

TRIAL REGISTRATION

This study is registered at www.clinicaltrials.gov (ClinicalTrials.gov Identifier: NCT02717663).

A technology-based intervention to improve safety, mental health and empowerment outcomes for immigrant women with intimate partner violence experiences: it's weWomen plus sequential multiple assignment randomized trial (SMART) protocol

BACKGROUND

Intimate partner violence (IPV) disproportionately affects immigrant women, an understudied and underserved population in need for evidence-based rigorously evaluated culturally competent interventions that can effectively address their health and safety needs.

METHODS

This study uses a sequential, multiple assignment, randomized trial (SMART) design to rigorously evaluate an adaptive, trauma-informed, culturally tailored technology-delivered intervention tailored to the needs of immigrant women who have experienced IPV. In the first stage randomization, participants are randomly assigned to an online safety decision and planning or a usual care control arm and safety, mental health and empowerment outcomes are assessed at 3-, 6- and 12-months post-baseline. For the second stage randomization, women who do not report significant improvements in safety (i.e., reduction in IPV) and empowerment from baseline to 3 months follow up (i.e., non-responders) are re- randomized to safety and empowerment strategies delivered via text only or a combination of text and phone calls with trained advocates. Data on outcomes (safety, mental health, and empowerment) for early non-responders is assessed at 6 and 12 months post re-randomization.

DISCUSSION

The study's SMART design provides an opportunity to implement and evaluate an individualized intervention protocol for immigrant women based on their response to type or intensity of intervention. The findings will be useful for identifying what works for whom and characteristics of participants needing a particular type or intensity level of intervention for improved outcomes. If found to be effective, the study will result in an evidence-based trauma-informed culturally tailored technology-based safety decision and planning intervention for immigrant survivors of IPV that can be implemented by practitioners serving immigrant women in diverse settings.

TRIAL REGISTRATION

This trial was registered with ClinicalTrials.gov as NCT04098276 on September 13, 2019.

Identifying Content-Based Engagement Patterns in a Smoking Cessation Website and Associations With User Characteristics and Cessation Outcomes: A Sequence and Cluster Analysis

INTRODUCTION

Using WebQuit as a case study, a smoking cessation website grounded in Acceptance and Commitment Therapy, we aimed to identify sequence clusters of content usage and examine their associations with baseline characteristics, change to a key mechanism of action, and smoking cessation.

METHODS

Participants were adult smokers allocated to the WebQuit arm in a randomized controlled trial (n = 1,313). WebQuit contains theory-informed content including goal setting, self-monitoring and feedback, and values- and acceptance-based exercises. Sequence analysis was used to temporally order 30-s website usage segments for each participant. Similarities between sequences were assessed with the optimal matching distance algorithm and used as input in an agglomerative hierarchical clustering analysis. Associations between sequence clusters and baseline characteristics, acceptance of cravings at 3 months and self-reported 30-day point prevalence abstinence at 12 months were examined with linear and logistic regression.

RESULTS

Three qualitatively different sequence clusters were identified. "Disengagers" (576/1,313) almost exclusively used the goal-setting feature. "Tryers" (375/1,313) used goal setting and two of the values- and acceptance-based components ("Be Aware," "Be Willing"). "Committers" (362/1,313) primarily used two of the values- and acceptance-based components ("Be Willing," "Be Inspired"), goal setting, and self-monitoring and feedback. Compared with Disengagers, Committers demonstrated greater increases in acceptance of cravings (p = .01) and 64% greater odds of quit success (ORadj = 1.64, 95% CI = 1.18, 2.29, p = .003).

DISCUSSION

WebQuit users were categorized into Disengagers, Tryers, and Committers based on their qualitatively different content usage patterns. Committers saw increases in a key mechanism of action and greater odds of quit success.

IMPLICATIONS

This case study demonstrates how employing sequence and cluster analysis of usage data can help researchers and practitioners gain a better understanding of how users engage with a given eHealth intervention over time and use findings to test theory and/or to improve future iterations to the intervention. Future WebQuit users may benefit from being directed to the values- and acceptance-based and the self-monitoring and feedback components via reminders over the course of the program.

Childhood Obesity Evidence Base Project: Methods for Taxonomy Development for Application in Taxonomic Meta-Analysis

Meta-analysis has been used to examine the effectiveness of childhood obesity prevention efforts, yet traditional conventional meta-analytic methods restrict the kinds of studies included, and either narrowly define mechanisms and agents of change, or examine the effectiveness of whole interventions as opposed to the specific actions that comprise interventions. Taxonomic meta-analytic methods widen the aperture of what can be included in a meta-analysis data set, allowing for inclusion of many types of interventions and study designs. The National Collaborative on Childhood Obesity Research Childhood Obesity Evidence Base (COEB) project focuses on interventions intended to prevent childhood obesity in children 2-5 years old who have an outcome measure of BMI. The COEB created taxonomies, anchored in the Social Ecological Model, which catalog specific outcomes, intervention components, intended recipients, and contexts of policies, initiatives, and interventions conducted at the individual, interpersonal, organizational, community, and societal level. Taxonomies were created by discovery from the literature itself using grounded theory. This article describes the process used for a novel taxonomic meta-analysis of childhood obesity prevention studies between the years 2010 and 2019. This method can be applied to other areas of research, including obesity prevention in additional populations.

Childhood Obesity Evidence Base Project: A Rationale for Taxonomic versus Conventional Meta-Analysis

Introduction: There is a great need for analytic techniques that allow for the synthesis of learning across seemingly idiosyncratic interventions. Objectives: The primary objective of this paper is to introduce taxonomic meta-analysis and explain how it is different from conventional meta-analysis. Results: Conventional meta-analysis has previously been used to examine the effectiveness of childhood obesity prevention interventions. However, these tend to examine narrowly defined sections of obesity prevention initiatives, and as such, do not allow the field to draw conclusions across settings, participants, or subjects. Compared with conventional meta-analysis, taxonomic meta-analysis widens the aperture of what can be examined to synthesize evidence across interventions with diverse topics, goals, research designs, and settings. A component approach is employed to examine interventions at the level of their essential features or activities to identify the concrete aspects of interventions that are used (intervention components), characteristics of the intended populations (target population or intended recipient characteristics), and facets of the environments in which they operate (contextual elements), and the relationship of these components to effect size. In addition, compared with conventional meta-analysis methods, taxonomic meta-analyses can include the results of natural experiments, policy initiatives, program implementation efforts and highly controlled experiments (as examples) regardless of the design of the report being analyzed as long as the intended outcome is the same. It also characterizes the domain of interventions that have been studied. Conclusion: Taxonomic meta-analysis can be a powerful tool for summarizing the evidence that exists and for generating hypotheses that are worthy of more rigorous testing.

Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions

BACKGROUND

Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions.

OBJECTIVE

The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions.

OVERVIEW

We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step.

IMPLICATIONS

Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.

Systematic, Multimethod Assessment of Adaptations Across Four Diverse Health Systems Interventions

Background

Many health outcomes and implementation science studies have demonstrated the importance of tailoring evidence-based care interventions to local context to improve fit. By adapting to local culture, history, resources, characteristics, and priorities, interventions are more likely to lead to improved outcomes. However, it is unclear how best to adapt evidence-based programs and promising innovations. There are few guides or examples of how to best categorize or assess health-care adaptations, and even fewer that are brief and practical for use by non-researchers.

Materials and methods

This study describes the importance and potential of assessing adaptations before, during, and after the implementation of health systems interventions. We present a promising multilevel and multimethod approach developed and being applied across four different health systems interventions. Finally, we discuss implications and opportunities for future research.

Results

The four case studies are diverse in the conditions addressed, interventions, and implementation strategies. They include two nurse coordinator-based transition of care interventions, a data and training-driven multimodal pain management project, and a cardiovascular patient-reported outcomes project, all of which are using audit and feedback. We used the same modified adaptation framework to document changes made to the interventions and implementation strategies. To create the modified framework, we started with the adaptation and modification model developed by Stirman and colleagues and expanded it by adding concepts from the RE-AIM framework. Our assessments address the intuitive domains of Who, How, When, What, and Why to classify and organize adaptations. For each case study, we discuss how the modified framework was operationalized, the multiple methods used to collect data, results to date and approaches utilized for data analysis. These methods include a real-time tracking system and structured interviews at key times during the intervention. We provide descriptive data on the types and categories of adaptations made and discuss lessons learned.

Conclusion

The multimethod approaches demonstrate utility across diverse health systems interventions. The modified adaptations model adequately captures adaptations across the various projects and content areas. We recommend systematic documentation of adaptations in future clinical and public health research and have made our assessment materials publicly available.

Engineering Online and In-Person Social Networks for Physical Activity: A Randomized Trial

BACKGROUND

Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity.

PURPOSE

The purpose of this study was to conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively measured outcomes.

METHODS

Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3 % male, 83.4 % overweight/obese) were randomized to one of three groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking as well as prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline.

RESULTS

Participants increased their MVPA by 21.0 min/week, 95 % CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences.

CONCLUSIONS

Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions.

TRIAL REGISTRATION NUMBER

The trial was registered with the ClinicalTrials.gov (NCT01142804).

Agile science: creating useful products for behavior change in the real world

Evidence-based practice is important for behavioral interventions but there is debate on how best to support real-world behavior change. The purpose of this paper is to define products and a preliminary process for efficiently and adaptively creating and curating a knowledge base for behavior change for real-world implementation. We look to evidence-based practice suggestions and draw parallels to software development. We argue to target three products: (1) the smallest, meaningful, self-contained, and repurposable behavior change modules of an intervention; (2) “computational models” that define the interaction between modules, individuals, and context; and (3) “personalization” algorithms, which are decision rules for intervention adaptation. The “agile science” process includes a generation phase whereby contender operational definitions and constructs of the three products are created and assessed for feasibility and an evaluation phase, whereby effect size estimates/casual inferences are created. The process emphasizes early-and-often sharing. If correct, agile science could enable a more robust knowledge base for behavior change.

Key Components in eHealth Interventions Combining Self-Tracking and Persuasive eCoaching to Promote a Healthier Lifestyle: A Scoping Review

BACKGROUND

The combination of self-tracking and persuasive eCoaching in automated interventions is a new and promising approach for healthy lifestyle management.

OBJECTIVE

The aim of this study was to identify key components of self-tracking and persuasive eCoaching in automated healthy lifestyle interventions that contribute to their effectiveness on health outcomes, usability, and adherence. A secondary aim was to identify the way in which these key components should be designed to contribute to improved health outcomes, usability, and adherence.

METHODS

The scoping review methodology proposed by Arskey and O'Malley was applied. Scopus, EMBASE, PsycINFO, and PubMed were searched for publications dated from January 1, 2013 to January 31, 2016 that included (1) self-tracking, (2) persuasive eCoaching, and (3) healthy lifestyle intervention.

RESULTS

The search resulted in 32 publications, 17 of which provided results regarding the effect on health outcomes, 27 of which provided results regarding usability, and 13 of which provided results regarding adherence. Among the 32 publications, 27 described an intervention. The most commonly applied persuasive eCoaching components in the described interventions were personalization (n=24), suggestion (n=19), goal-setting (n=17), simulation (n=17), and reminders (n=15). As for self-tracking components, most interventions utilized an accelerometer to measure steps (n=11). Furthermore, the medium through which the user could access the intervention was usually a mobile phone (n=10). The following key components and their specific design seem to influence both health outcomes and usability in a positive way: reduction by setting short-term goals to eventually reach long-term goals, personalization of goals, praise messages, reminders to input self-tracking data into the technology, use of validity-tested devices, integration of self-tracking and persuasive eCoaching, and provision of face-to-face instructions during implementation. In addition, health outcomes or usability were not negatively affected when more effort was requested from participants to input data into the technology. The data extracted from the included publications provided limited ability to identify key components for adherence. However, one key component was identified for both usability and adherence, namely the provision of personalized content.

CONCLUSIONS

This scoping review provides a first overview of the key components in automated healthy lifestyle interventions combining self-tracking and persuasive eCoaching that can be utilized during the development of such interventions. Future studies should focus on the identification of key components for effects on adherence, as adherence is a prerequisite for an intervention to be effective.

Toward Usable Evidence: Optimizing Knowledge Accumulation in HCI Research on Health Behavior Change

Over the last ten years, HCI researchers have introduced a range of novel ways to support health behavior change, from glanceable displays to sophisticated game dynamics. Yet, this research has not had as much impact as its originality warrants. A key reason for this is that common forms of evaluation used in HCI make it difficult to effectively accumulate-and use-knowledge across research projects. This paper proposes a strategy for HCI research on behavior change that retains the field's focus on novel technical contributions while enabling accumulation of evidence that can increase impact of individual research projects both in HCI and the broader behavior-change science. The core of this strategy is an emphasis on the discovery of causal effects of individual components of behavior-change technologies and the precise ways in which those effects vary with individual differences, design choices, and contexts in which those technologies are used.

Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential

As more behavioral health interventions move from traditional to digital platforms, the application of evidence-based theories and techniques may be doubly advantageous. First, it can expedite digital health intervention development, improving efficacy, and increasing reach. Second, moving behavioral health interventions to digital platforms presents researchers with novel (potentially paradigm shifting) opportunities for advancing theories and techniques. In particular, the potential for technology to revolutionize theory refinement is made possible by leveraging the proliferation of "real-time" objective measurement and "big data" commonly generated and stored by digital platforms. Much more could be done to realize this potential. This paper offers proposals for better leveraging the potential advantages of digital health platforms, and reviews three of the cutting edge methods for doing so: optimization designs, dynamic systems modeling, and social network analysis.

The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research

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.

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

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

Using social and mobile tools for weight loss in overweight and obese young adults (Project SMART): a 2 year, parallel-group, randomised, controlled trial

BACKGROUND

Few weight loss interventions are evaluated for longer than a year, and even fewer employ social and mobile technologies commonly used among young adults. We assessed the efficacy of a 2 year, theory-based, weight loss intervention that was remotely and adaptively delivered via integrated user experiences with Facebook, mobile apps, text messaging, emails, a website, and technology-mediated communication with a health coach (the SMART intervention).

METHODS

In this parallel-group, randomised, controlled trial, we enrolled overweight or obese college students (aged 18-35 years) from three universities in San Diego, CA, USA. Participants were randomly assigned (1:1) to receive either the intervention (SMART intervention group) or general information about health and wellness (control group). We used computer-based permuted-block randomisation with block sizes of four, stratified by sex, ethnicity, and college. Participants, study staff, and investigators were masked until the intervention was assigned. The primary outcome was objectively measured weight in kg at 24 months. Differences between groups were evaluated using linear mixed-effects regression within an intention-to-treat framework. Objectively measured weight at 6, 12, and 18 months was included as a secondary outcome. The trial is registered with ClinicalTrials.gov, number NCT01200459.

FINDINGS

Between May 18, 2011, and May 17, 2012, 404 individuals were randomly assigned to the intervention (n=202) or control (n=202). Participants' mean (SD) age was 22·7 (3·8) years. 284 (70%) participants were female and 125 (31%) were Hispanic. Mean (SD) body-mass index at baseline was 29·0 (2·8) kg/m(2). At 24 months, weight was assessed in 341 (84%) participants, but all 404 were included in analyses. Weight, adjusted for sex, ethnicity, and college, was not significantly different between the groups at 24 months (-0·79 kg [95% CI -2·02 to 0·43], p=0·204). However, weight was significantly less in the intervention group compared with the control group at 6 months (-1·33 kg [95% CI -2·36 to -0·30], p=0·011) and 12 months (-1·33 kg [-2·30 to -0·35], p=0·008), but not 18 months (-0·67 kg [95% CI -1·69 to 0·35], p=0·200). One serious adverse event in the intervention group (gallstones) could be attributable to rapid and excessive weight loss.

INTERPRETATION

Social and mobile technologies did not facilitate sustained reductions in weight among young adults, although these approaches might facilitate limited short-term weight loss.

FUNDING

The National Heart, Lung, and Blood Institute of the National Institutes of Health (U01 HL096715).

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

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