A "SMART" Design for Building Individualized Treatment Sequences

The translation of basic behavioral science discoveries into practical strategies represents a promising approach to developing more effective preventive interventions to improve health. Since translational research inevitably involves making use of diverse perspectives from multiple disciplines, it is best conducted as a transdisciplinary enterprise. In this paper, we discuss current strategies used by NIH to support transdisciplinary translational behavioral (TDTB) research, summarize successful efforts, and highlight challenges encountered in conducting such work (ranging from conceptual to organizational to methodological). Using examples from NIH-funded projects we illustrate the potential benefits of, and barriers to, pursuing this type of research and discuss next steps and potential future directions for NIH-supported TDTB research.

Section: Tdtb Methods and Trainingmentioning

confidence: 99%

Transdisciplinary translational behavioral (TDTB) research: opportunities, barriers, and innovations

Czajkowski

Lynch

Hall

et al. 2016

“…An example of an early precursor to the SMART is the Clinical Antipsychotics Trial of Intervention Effectiveness (CATIE) [54] in chronic schizophrenia. Lei et al [23] describe the rationale and scientific questions addressed by four different SMART studies either recently completed or underway in autism, attention-deficit/hyperactivity disorder (ADHD), and for various substance use disorders. The Methodology Center at Penn State University hosts a web page with the description, status, and citations to articles of various different SMART designs completed or currently underway in a variety of health settings [55].…”

Section: Sequential Multiple Assignment Randomized Trialsmentioning

confidence: 99%

“…A SMART DESIGN VARIATION Different SMART designs are suitable for addressing different types of scientific questions [21,23]. …”

Section: Original Researchmentioning

confidence: 99%

“…The Sequential Multiple Assignment Randomized Trial (SMART)-a special type of factorial study design [17]-uses experimental design principles to obtain answers to many of the challenging critical questions around adaptive interventions, such as those raised above [18][19][20]. Developed explicitly for the purpose of building empirically supported adaptive interventions, they are meant to complement the use of theoretical models and expert clinical consensus for developing optimal adaptive interventions [21][22][23].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research

Almirall

Nahum‐Shani

Sherwood

et al. 2014

337

274

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 highquality 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. KEYWORDS

“…[21] Several alternative approaches for efficacy testing exist, although these have received relatively limited attention. For example, regressionincontinuity, stepped-wedge designs, SMART trials [22], and pragmatic randomized controlled trials have been proposed as study designs that could efficiently yield some efficacy data and be more amenable to handling changes in technology features [21]. In this issue, Cobb and Poirer conduct a pragmatic controlled trial that demonstrates the ability to recruit, retain, intervene, and make data-driven intervention enhancements at low cost and on a rapid timeline [23].…”

Section: How To Strike a Balance Between The Pace Of Research And Thementioning

confidence: 99%

How behavioral science can advance digital health

Pagoto

Bennett

2013

132

102

The field of behavioral science has produced myriad data on health behavior change strategies and leveraged such data into effective human-delivered interventions to improve health. Unfortunately, the impact of traditional health behavior change interventions has been heavily constrained by patient and provider burden, limited ability to measure and intervene upon behavior in real time, variable adherence, low rates of implementation, and poor third-party coverage. Digital health technologies, including mobile phones, sensors, and online social networks, by being available in real time, are being explored as tools to increase our understanding of health behavior and to enhance the impact of behavioral interventions. The recent explosion of industry attention to the development of novel health technologies is exciting but has far outpaced research. This Special Section of Translational Behavioral Medicine, Smartphones, Sensors, and Social Networks: A New Age of Health Behavior Change features a collection of studies that leverage health technologies to measure, change, and/or understand health behavior. We propose five key areas in which behavioral science can improve the impact of digital health technologies on public health. First, research is needed to identify which health technologies actually impact behavior and health outcomes. Second, we need to understand how online social networks can be leveraged to impact health behavior on a large scale. Third, a team science approach is needed in the developmental process of health technologies. Fourth, behavioral scientists should identify how a balance can be struck between the fast pace of innovation and the much slower pace of research. Fifth, behavioral scientists have an integral role in informing the development of health technologies and facilitating the movement of health technologies into the healthcare system. KEYWORDS Digital health, mHealth, Social networksThe efficacy of traditional health behavior change interventions has long been constrained by expense, patient burden, and variable adherence. Current models of health care which limit our access to patients to face-to-face visits reduce our ability to gain an accurate understanding of the antecedents and consequences of behavior and to intervene in moments when patients most need help. Digital health technologies, including mobile phones, sensors, and online social networks, by being available in real time, are being explored as ways to enhance our ability to understand health behavior and intervene upon it more effectively. mHealth, a segment of the digital health market that leverages mobile technologies to solve health problems, has reached its tipping point. A rapidly growing body of evidence demonstrates the efficacy of mHealth approaches across a wide range of conditions, populations, and settings. mHealth has also attracted a explosion of industry attention. An extremely diverse group of companies are capitalizing on the mHealth market, which is projected to reach over $23 billion in r...