Exploring Refinements in Targeted Behavioral Medicine Intervention to Advance Public Health

King, Abby C.; Ahn, David F.; Atienza, Audie A.; Kraemer, Helena C.

doi:10.1007/s12160-008-9032-0

Cited by 48 publications

(47 citation statements)

References 74 publications

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“…Specifically, our goal was to utilize common data exploration practices from system identification (e.g., data visualization, progressive model building using system identification) (Ljung, 2009, 2011) to generate empirically-based hypotheses (A. C. King, Ahn, Atienza, & Kraemer, 2008) that could inform later a priori N-of-1 experimental testing of key intervention components that may promote improved maintenance of physical activity.…”

Section: Addressing Classical Methodological Challengesmentioning

confidence: 99%

Exploring Behavioral Markers of Long-Term Physical Activity Maintenance

et al. 2013

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Efficacious interventions to promote long-term maintenance of physical activity are not well understood. Engineers have developed methods to create dynamical system models for modeling idiographic (i.e., within-person) relationships within systems. In behavioral research, dynamical systems modeling may assist in decomposing intervention effects and identifying key behavioral patterns that may foster behavioral maintenance. The Active Adult Mentoring Program (AAMP) was a 16-week randomized controlled trial of a group-based, peer-delivered physical activity intervention targeting older adults. Time intensive (i.e., daily) physical activity reports were collected throughout the intervention. We explored differential patterns of behavior among participants who received the active intervention (N=34; 88% women, 64.1±8.3 years of age) and either maintained 150 minutes/week of moderate to vigorous intensity physical activity (MVPA; n=10) or did not (n=24) at 18 months following the intervention period. We used dynamical systems modeling to explore whether key intervention components (i.e., self-monitoring, access to an exercise facility, behavioral initiation training, behavioral maintenance training) and theoretically plausible behavioral covariates (i.e., indoor vs. outdoor activity) predicted differential patterns of behavior among maintainers and non-maintainers. We found that maintainers took longer to reach a steady-state of MVPA. At week 10 of the intervention, non-maintainers began to drop whereas maintainers increased MVPA. Self-monitoring, behavioral initiation training, % outdoor activity, and behavioral maintenance training, but not access to an exercise facility, were key variables that explained patterns of change among maintainers. Future studies should be conducted to systematically explore these concepts within a priori idiographic (i.e., N-of-1) experimental designs.

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Section: Addressing Classical Methodological Challengesmentioning

confidence: 99%

Exploring Behavioral Markers of Long-Term Physical Activity Maintenance

et al. 2013

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“…Third, CART can accommodate continuous predictor variables that are highly skewed and/or multi-modal as well as categorical predictors that may have ordinal or nonordinal structure. The CART approach has been used in marketing [28], public health [29,30,31] and cancer control research [23,32,33]. For example, using CART, Lemon et al [23] identified subgroups of cancer patients whose surgical decisions were driven by similar psychosocial factors.…”

Section: Methodsmentioning

confidence: 99%

Skin Cancer Concerns and Genetic Risk Information-Seeking in Primary Care

Hay

Kaphingst

Baser

et al. 2011

Public Health Genomics

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Background: Genomic testing for common genetic variants associated with skin cancer risk could enable personalized risk feedback to motivate skin cancer screening and sun protection. Methods: In a cross-sectional study, we investigated whether skin cancer cognitions and behavioral factors, sociodemographics, family factors, and health information-seeking were related to perceived importance of learning about how (a) genes and (b) health habits affect personal health risks using classification and regression trees (CART). Results: The sample (n = 1,772) was collected in a large health maintenance organization as part of the Multiplex Initiative, ranged in age from 25–40, was 53% female, 41% Caucasian, and 59% African-American. Most reported that they placed somewhat to very high importance on learning about how genes (79%) and health habits (88%) affect their health risks. Social influence actors were associated with information-seeking about genes and health habits. Awareness of family history was associated with importance of health habit, but not genetic, information-seeking. Conclusions: The investment of family and friends in health promotion may be a primary motivator for prioritizing information-seeking about how genes and health habits affect personal health risks and may contribute to the personal value, or personal utility, of risk information. Individuals who seek such risk information may be receptive to interventions aimed to maximize the social implications of healthy lifestyle change to reduce their health risks.

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“…1, 2 One approach that capitalizes on the large variability in weight outcomes is ‘treatment matching', in which characteristics of individuals are optimally matched to targeted treatments to improve success. 2, 3 Although the goal is tempting, few treatment-matching frameworks exist and are limited in scope. These frameworks primarily focus on matching individuals' initial degree of overweight to stepped-care treatments (self-help manuals, group support, medication, surgery).…”

Section: Status Of Current Researchmentioning

confidence: 99%

Treatment matching for obesity: identifying mediators of psychosocial and behavioral intervention components

Kiernan

2012

Int J Obes Supp

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In light of the limited long-term success of obesity treatments, it is tempting to consider the elusive goal of ‘treatment matching', in which characteristics of individuals are optimally matched to targeted treatments to improve success. Previous frameworks for treatment matching in obesity have primarily focused on basic physiological characteristics, such as initial degree of overweight, and on treatment intensity, such as stepped-care alternatives (self-help manuals, group support, medication and surgery). Few studies have empirically evaluated the success of these frameworks. Given recent advances in genomics, neuroscience and other fields, both the breadth of domains and combinations of individuals' characteristics that could be used for treatment matching have increased markedly. Although the obesity field seems poised to build on these advances, a crucial challenge remains regarding the treatments themselves. Ultimately, the success of treatment matching will rely on identifying treatment intervention components with well-differentiated and empirically supported mediators, that is, clear insights into how intervention components work. Here we examine the scope of this challenge specifically for the design of efficacious psychosocial and behavioral intervention components, and identify areas for future research.

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Exploring Refinements in Targeted Behavioral Medicine Intervention to Advance Public Health

Abstract: The recommended exploratory work can help to make the most efficient use of RCTs to identify the best paths for subsequent RCT development in a resource-constrained era.

Cited by 48 publications

References 74 publications

Exploring Behavioral Markers of Long-Term Physical Activity Maintenance

Exploring Behavioral Markers of Long-Term Physical Activity Maintenance

Skin Cancer Concerns and Genetic Risk Information-Seeking in Primary Care

Treatment matching for obesity: identifying mediators of psychosocial and behavioral intervention components

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