Advances in wireless devices and mobile technology offer many opportunities for delivering just-in-time adaptive interventions (JITAIs)-suites of interventions that adapt over time to an individual's changing status and circumstances with the goal to address the individual's need for support, whenever this need arises. A major challenge confronting behavioral scientists aiming to develop a JITAI concerns the selection and integration of existing empirical, theoretical and practical evidence into a scientific model that can inform the construction of a JITAI and help identify scientific gaps. The purpose of this paper is to establish a pragmatic framework that can be used to organize existing evidence into a useful model for JITAI construction. This framework involves clarifying the conceptual purpose of a JITAI, namely, the provision of just-in-time support via adaptation, as well as describing the components of a JITAI and articulating a list of concrete questions to guide the establishment of a useful model for JITAI construction. The proposed framework includes an organizing scheme for translating the relatively static scientific models underlying many health behavior interventions into a more dynamic model that better incorporates the element of time. This framework will help to guide the next generation of empirical work to support the creation of effective JITAIs.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Ed 6-19 yr with at least four 10-h days of PA measured by accelerometry were included (n = 3106). Outcomes included mean counts per minute and minutes spent in moderate-to-vigorous PA (MVPA). Results: Among the groups, the 6- to 11-yr-olds spent more time (88 min·d−1) in MVPA than the 12- to 15-yr-olds (33 min·d−1) and 16- to 19-yr-olds (26 min·d−1; P < 0.001 for both). Females spent fewer minutes per day in MVPA than males (P < 0.001). Overall, obese youth spent 16 fewer minutes per day in MVPA than normal-weight youth. However, non-Hispanic white males spent three to four fewer minutes per day in vigorous PA than Mexican American (MA; P = 0.004) and non-Hispanic black (P < 0.001) males but had lower obesity rates and obese 12- to 15-yr-old MA recorded similar minutes in MVPA per day as normal-weight MA (P > 0.050). There was a significant three-way age-body mass index-race/ethnicity interaction for mean minutes per day in MVPA (P < 0.001). Adjustment for total energy intake did not qualitatively alter these results. Conclusions: Females and older youth were the least active groups. Obese youth were generally less active, but this did not hold uniformly across race/ethnic groups. Cultural or biological factors could moderate the association between PA and obesity in youth....

Background: The prevalence of obesity in American children is currently estimated to be 25%. Poor nutritional habits during childhood have been directly related to pediatric obesity. Objective: Our objective was to evaluate the relation between mothers' child-feeding practices and children's adiposity in a sample of boys and girls from 2 ethnic groups. Design: A total of 74 white (25 boys and 49 girls) and 46 African American (22 boys and 24 girls) children (x̄ ± SD age: 11 ± 1.7 y) and their mothers participated in this study. The children's body composition was assessed by dual-energy X-ray absorptiometry. The mothers' child-feeding practices were assessed with the Child Feeding Questionnaire. Dietary intake data were based on three 24-h dietary recalls conducted by use of the multiple-pass technique. Results: Two subscales of the Child Feeding Questionnaire, pressure to eat and concern for child's weight, explained 15% of the variance in total fat mass in both African American and white boys and girls (P < 0.001) after correction for total lean mass and energy intake (which explained 5% of the variance in total fat mass). Ethnicity, sex, and socioeconomic status did not contribute significantly to variance in total fat mass. Conclusions: Child-feeding practices are key behavioral variables that explain more of the variance in total fat mass than does energy intake in a biethnic population of boys and girls. These findings have important implications for the prevention of obesity in children because they suggest that prevention programs need to focus on the feeding behaviors of parents in addition to the macronutrient and energy intakes of children.

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static “snapshots” of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing “gold standard” measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a “knowledge commons,” which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.