The biobehavioral family model and the family relational assessment protocol: Map and GPS for family systems training.

The Biobehavioral Family Model (BBFM) was developed and evolved as a heuristic research model to support the investigation of pathways by which family relational function impacts individual family member wellbeing and disorder. Recently, the BBFM and its related assessment approach, the Family Relational Process Assessment Protocol (FRAP), have emerged as tools for clinical practice and training. The BBFM model will be presented, along with definitions of the dimensions constructed in the model, and research evidence in support of the model. To illustrate how the BBFM and FRAP are used in training, instructions for conducting the FRAP will be presented. Then, transcripts from two contrasting families participating in one of the interaction tasks will illustrate how, in training, the FRAP is interpreted through the BBFM lens to illuminate how these patterns of family relationship impact the identified patient. Finally, three applications of this training approach will exemplify the use of the BBFM and FRAP in the context of a Child and Adolescent Psychiatry Fellowship, a Family Medicine Fellowship, and a Family Therapy Training Program in Istanbul, Turkey. Limitations and future directions for the application of the BBFM in the exploration of multicultural aspects of family function for clinical and training purposes will be discussed.

An application of the Biobehavioral Family Model: Examining the impact of maternal depression on child asthma mediated by insecure attachment and child depression.

OBJECTIVE: Child asthma disparities are prevalent in socio-economically stressed single-parent families. Stress impacts childhood asthma mediated by immune and autonomic pathways, but specific family stress pathways are not well established. This study tests the hypothesis, derived from a version of the Biobehavioral Family Model, that single parent maternal depression impacts child asthma mediated by insecure attachment and child depression. METHODS: In a cross-sectional study, children with asthma (age 7-17 years old) from a socio-economically disadvantaged population and their single parent mothers (N = 202) were assessed for depression and attachment security. Child asthma disease activity was assessed by symptom report and lung function tests. Structural equation modeling (SEM) was used to test a model in which caregiver depression impacts child asthma disease activity mediated by insecure attachment and child depression. RESULTS: SEM results indicated that maternal depression statistically predicted child depression (ß = 0.21, p < 0.01) and insecure mother-child attachment (ß = 0.17, p < 0.05). In addition, insecure attachment statistically predicted child depression (ß = 0.50, p < 0.001). Child depression mediated the adverse effects of maternal depression and insecure attachment on child asthma disease activity (ß = 0.43, p < 0.01). There was no direct effect of insecure attachment on child asthma. CONCLUSION: In single-parent families, maternal depression may impact child asthma disease activity, mediated serially by insecure attachment and child depression. Longitudinal and/or intervention studies are needed to establish a causal effect. These findings suggest that caregiver depression and parent-child relationships may be important targets for family intervention. These interventions may help improve child asthma outcomes and reduce health disparities.

Values encoded in orbitofrontal cortex are causally related to economic choices.

In the eighteenth century, Daniel Bernoulli, Adam Smith and Jeremy Bentham proposed that economic choices rely on the computation and comparison of subjective values1. This hypothesis continues to inform modern economic theory2 and research in behavioural economics3, but behavioural measures are ultimately not sufficient to verify the proposal4. Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods5. Value-encoding cells integrate multiple dimensions6-9, variability in the activity of each cell group correlates with variability in choices10,11 and the population dynamics suggests the formation of a decision12. However, it is unclear whether these neural processes are causally related to choices. More generally, the evidence linking economic choices to value signals in the brain13-15 remains correlational16. Here we show that neuronal activity in the OFC is causal to economic choices. We conducted two experiments using electrical stimulation in rhesus monkeys (Macaca mulatta). Low-current stimulation increased the subjective value of individual offers and thus predictably biased choices. Conversely, high-current stimulation disrupted both the computation and the comparison of subjective values, and thus increased choice variability. These results demonstrate a causal chain linking subjective values encoded in OFC to valuation and choice.

Feasibility and acceptability of intensive, real-time biobehavioral data collection using ecological momentary assessment, salivary biomarkers, and accelerometers among middle-aged African Americans.

Perceived racial discrimination is linked to unhealthy behaviors and stress-related morbidities. A compelling body of research indicates that perceived racial discrimination may contribute to health disparities among African Americans (AAs). The purposes of this study were to describe the study protocol including data collection procedures and study measures and to evaluate the feasibility and acceptability of intensive biobehavioral data collection using ecological momentary assessment (EMA), salivary biomarkers, and accelerometers over 7 days among middle-aged AAs with a goal of understanding the relationships between perceived racial discrimination and biobehavioral responses to stress. Twelve AA men and women participated in the feasibility/acceptability study. They completed surveys, anthropometrics, and received in-person training in EMA and saliva sample collection at baseline. Participants were asked to respond to the random prompt text message-based EMA five times a day, wear an accelerometer daily for 7 days, and to self-collect saliva samples four times a day for 4 consecutive days. The EMA surveys included perceived racial discrimination, affective states, lifestyle behaviors, and social and physical contexts. The mean EMA response rate was 82.8%. All participants collected saliva samples four times a day for 4 consecutive days. About 83% of participants wore the accelerometer on the hip 6 out of 7 days. Despite the perception that the intensive nature of assessments would result in high participant burden, the acceptability of the study procedures was uniformly favorable.

Why Class Formation Occurs in Humans but Not among Other Primates : A Primate Coalitions Model.

Most human societies exhibit a distinct class structure, with an elite, middle classes, and a bottom class, whereas animals form simple dominance hierarchies in which individuals with higher fighting ability do not appear to form coalitions to "oppress" weaker individuals. Here, we extend our model of primate coalitions and find that a division into a bottom class and an upper class is inevitable whenever fitness-enhancing resources, such as food or real estate, are exploitable or tradable and the members of the bottom class cannot easily leave the group. The model predicts that the bottom class has a near flat, low payoff and always comprises at least half the society. The upper class may subdivide into one or more middle class(es), resulting in improved payoff for the topmost members (elite). The model predicts that the bottom class on its own is incapable of mounting effective counter-coalitions against the upper class, except when receiving support from dissatisfied members of the middle class(es). Such counter-coalitions can be prevented by keeping the payoff to the lowest-ranked members of the middle classes (through concessions) well above that of the bottom class. This simple model explains why classes are also absent in nomadic hunter-gatherers and predominate in (though are not limited to) societies that produce and store food. Its results also agree well with various other known features of societies with classes.

Use Andersen's behavior model to explain non-urgent visits in emergency department: A single center study in southwest China.

BACKGROUND: Non-urgent ED visit was an important contributor of emergency overcrowding. Previous studies showed that patients make non-urgent ED visits for many reasons and their ED use patterns may be influenced by many factors. However, seldom studies were theory-based research aimed to offer a systemic insight regarding this question. METHODS: This was a cross-sectional study conducted in the ED of a tertiary hospital in China guided by Andersen's behavior model of health service use. Patients triaged as non-urgent were surveyed using a self-administrated questionnaire to investigate the reasons for ED presentation and associated factors influenced their ED use patterns. RESULTS: Perceived severity of illness and urgent treatment need (374, 68.6%), accessible problems to alternative services (144, 26.4%) and referral by medical staffs (134, 24.6%) were most commonly reported reasons for ED presentation. Alternative service attempt before ED presentation was determined by two predisposing factors: education level (OR = 0.638, P < 0.05) and use of nearest medical institution (OR = 1.588, P < 0.05). Prior non-urgent ED use was explained by both predisposing and need factors. They were: nationality (OR = 3.057, P < 0.01), type of health problems (OR = 1.641, P < 0.01) and self-rated health status (OR = 0.769, P < 0.01). CONCLUSION: Patients' perceived need of emergency care played an extremely important role in driving ED use although several factors were identified. Future studies aim to decrease non-urgent ED use in China may firstly gain success from patient education programs.

Multi-domain cognitive assessment of male mice shows space radiation is not harmful to high-level cognition and actually improves pattern separation.

Astronauts on interplanetary missions - such as to Mars - will be exposed to space radiation, a spectrum of highly-charged, fast-moving particles that includes 56Fe and 28Si. Earth-based preclinical studies show space radiation decreases rodent performance in low- and some high-level cognitive tasks. Given astronaut use of touchscreen platforms during training and space flight and given the ability of rodent touchscreen tasks to assess functional integrity of brain circuits and multiple cognitive domains in a non-aversive way, here we exposed 6-month-old C57BL/6J male mice to whole-body space radiation and subsequently assessed them on a touchscreen battery. Relative to Sham treatment, 56Fe irradiation did not overtly change performance on tasks of visual discrimination, reversal learning, rule-based, or object-spatial paired associates learning, suggesting preserved functional integrity of supporting brain circuits. Surprisingly, 56Fe irradiation improved performance on a dentate gyrus-reliant pattern separation task; irradiated mice learned faster and were more accurate than controls. Improved pattern separation performance did not appear to be touchscreen-, radiation particle-, or neurogenesis-dependent, as 56Fe and 28Si irradiation led to faster context discrimination in a non-touchscreen task and 56Fe decreased new dentate gyrus neurons relative to Sham. These data urge revisitation of the broadly-held view that space radiation is detrimental to cognition.

Using Behavioral Analytics to Predict Customer Invoice Payment.

Experiences from various industries show that companies may have problems collecting customer invoice payments. Studies report that almost half of the small- and medium-sized enterprise and business-to-business invoices in the United States and United Kingdom are paid late. In this study, our aim is to understand customer behavior regarding invoice payments, and propose an analytical approach to learning and predicting payment behavior. Our logic can then be embedded into a decision support system where decision makers can make predictions regarding future payments, and take actions as necessary toward the collection of potentially unpaid debt, or adjust their financial plans based on the expected invoice-to-cash amount. In our analysis, we utilize a large data set with more than 1.6 million customers and their invoice and payment history, as well as various actions (e.g., e-mail, short message service, phone call) performed by the invoice-issuing company toward customers to encourage payment. We use supervised and unsupervised learning techniques to help predict whether a customer will pay the invoice or outstanding balance by the next due date based on the actions generated by the company and the customer's response. We propose a novel behavioral scoring model used as an input variable to our predictive models. Among the three machine learning approaches tested, we report the results of logistic regression that provides up to 97% accuracy with or without preclustering of customers. Such a model has a high potential to help decision makers in generating actions that contribute to the financial stability of the company in terms of cash flow management and avoiding unnecessary corporate lines of credit.

Emergence of social inequality in the spatial harvesting of renewable public goods.

Spatially extended ecological public goods, such as forests, grasslands, and fish stocks, are at risk of being overexploited by selfish consumers-a phenomenon widely recognized as the 'tragedy of the commons.' The interplay of spatial and ecological dimensions introduces new features absent in non-spatial ecological contexts, such as consumer mobility, local information availability, and strategy evolution through social learning in neighborhoods. It is unclear how these features interact to influence the harvesting and dispersal strategies of consumers. To answer these questions, we develop and analyze an individual-based, spatially structured, eco-evolutionary model with explicit resource dynamics. We report the following findings. (1) When harvesting efficiency is low, consumers evolve a sedentary consumption strategy, through which the resource is harvested sustainably, but with harvesting rates far below their maximum sustainable value. (2) As harvesting efficiency increases, consumers adopt a mobile 'consume-and-disperse' strategy, which is sustainable, equitable, and gives maximum sustainable yield. (3) A further increase in harvesting efficiency leads to large-scale overexploitation. (4) If costs of dispersal are significant, increased harvesting efficiency also leads to social inequality between frugal sedentary consumers and overexploitative mobile consumers. Whereas overexploitation can occur without social inequality, social inequality always leads to overexploitation. Thus, we identify four conditions that-while being characteristic of technological progress in modern societies-risk social inequality and overexploitation: high harvesting efficiency, moderately low costs of dispersal, high consumer density, and the tendency of consumers to adopt new strategies rapidly. We also show how access to global information-another feature widespread in modern societies-helps mitigate these risks.

Geospatial mapping and resource utilization tool in support of a national smoke-free public housing rule.

OBJECTIVE: To advance public health support for the U.S. Department of Housing and Urban Development's smoke-free rule, the Centers for Disease Control and Prevention collaborated with the Georgia Institute of Technology to develop a geospatial mapping tool. The objective was to create a tool state and local public health agencies could use to tailor smoke-free educational materials and cessation interventions for specific public housing development resident populations. RESULTS: The resulting "Extinguish Tool" includes an interactive map of U.S. public housing developments (PHDs) and healthcare facilities that provides detailed information on individual PHDs, their proximity to existing healthcare facilities, and the demographic characteristics of residents. The tool also estimates the number of PHD residents who smoke cigarettes and calculates crude estimates of the potential economic benefits of providing cessation interventions to these residents. The geospatial mapping tool project serves as an example of a collaborative and innovative public health approach to protecting the health and well-being of the nation's two million public housing residents, including 760,000 children, from the harms of tobacco smoking and secondhand smoke exposure in the places where they live, play, and gather.

Quantifying the relationship between food sharing practices and socio-ecological variables in small-scale societies: A cross-cultural multi-methodological approach.

This article presents a cross-cultural study of the relationship among the subsistence strategies, the environmental setting and the food sharing practices of 22 modern small-scale societies located in America (n = 18) and Siberia (n = 4). Ecological, geographical and economic variables of these societies were extracted from specialized literature and the publicly available D-PLACE database. The approach proposed comprises a variety of quantitative methods, ranging from exploratory techniques aimed at capturing relationships of any type between variables, to network theory and supervised-learning predictive modelling. Results provided by all techniques consistently show that the differences observed in food sharing practices across the sampled populations cannot be explained just by the differential distribution of ecological, geographical and economic variables. Food sharing has to be interpreted as a more complex cultural phenomenon, whose variation over time and space cannot be ascribed only to local adaptation.

The Social Context of Sedentary Behaviors and Their Relationships With Health in Later Life.

This study examined sedentary behaviors among older adults and explored associations with social context and health measures using cross-sectional data from the National Health and Aging Trends Study (N = 1,687). Multivariate models were estimated to explore associations of time in six sedentary behaviors (i.e., television watching, sitting and talking, hobbies, computer use, driving, and resting) with sociodemographic characteristics and level of social engagement and with health status. Results indicated substantial variability in sedentary behaviors, with television watching being the most frequent and resting the least frequent activities. Sedentary behaviors varied by sociodemographic characteristics, including age, race/ethnicity, and education, as well as by level of social engagement. Television watching and resting, but not other behaviors, were associated with poorer health. These findings help to unpack the role of social context in sedentary behaviors and could inform public health interventions aimed at reducing time spent in behaviors that are adversely associated with health.

Development of a suicide index model in general adolescents using the South Korea 2012-2016 national representative survey data.

Suicide is a leading cause of death among adolescents and a major public health concern. Here we developed a risk stratification model for adolescent suicide attempts using sociodemographic characteristics, risk behaviours and psychological variables. Participants were 247,222 subjects in the Korea Youth Risk Behavior Web-based Survey (KYRBS). We developed a suicide index based on the suicide risk estimated in the generalized linear model and proposed the risk stratification model using the R language to measure the probability of suicide attempt among adolescents. Among the study population, the annual rate of suicide attempt was approximately 4%. The model provided good prediction for suicide attempt (AUC = 0.85). The important univariate risk factors for the outcome were dimensional measures of age, sex, breakfast consumption, experience of violence, sleep duration, perceived stress, feeling of sadness, current cigarette smoking, current alcohol drinking, perceived general health, perceived academic record, household economic status and living with biological or adoptive parents. Our suicide index model allowed the identification of adolescents who are at a high risk for suicide. This tool may promote the prevention of adolescent suicide and can be particularly useful in everyday settings where it is difficult to contact mental health professionals immediately.

[Big Data: Technical improvement, degradation or transformation of the solidarity model?] / Big Data : amélioration technique, dégradation ou transformation du modèle de solidarité ?

Big Data, the production of a massive amount of heterogeneous data, is often presented as a means to ensure the economic survival and sustainability of health systems. According to this perspective, Big Data could help save the spirit of our welfare states based on the principles of risks-sharing and equal access to care for all. According to a second perspective, opposed to the first, Big Data would fuel a process of demutualization, transferring to individuals a growing share of responsibility for managing their health. This article proposes to develop a third approach: Big Data does not induce a loss of solidarity but a transformation of the European model of welfare states. These are the data that are now the objects of the pooling. Individual and collective responsibilities are thus redistributed. However, this model, as new as it is, remains liberal in its inspiration; it basically allows the continuation of political liberalism by other means.

The fundamentals of cross-sector collaboration for social change to promote population health.

Cross-sector collaboration is increasingly relied upon to tackle society's pressing and intractable problems. Chief among societal problems are unfavorable structural and social determinants of health. The ability to positively change these health determinants rests on the collaborative processes and structures of governance across diverse sectors in society. The purpose of this article is to present a conceptual framework that sheds light on the basic requirements of cross-sector collaboration for social change to promote the health of populations. A search for theoretical articles on cross-sector collaboration in the fields of public administration and public health was conducted within the journal databases ABI/INFORM Complete and MEDLINE. This search strategy was supplemented by an internet search of the grey literature for high-profile models of cross-sector collaboration. The conceptual framework builds on previous scholarly work by placing emphasis on five essential conditions for collective impact, and on the pivotal role of collective learning. Collective learning, at the basis of planning and taking action, is at the core of effective cross-sector initiatives, specifically because of its critical role in constantly adapting strategies to changing circumstances and unanticipated situations within complex socio-ecological systems.

Determinants of incentive preferences for health behavior change in Japan.

Interest in the use of incentives to promote health behavior change has been growing worldwide. However, to develop an effective incentive program, accurate information regarding individual preferences is essential. Therefore, the aim of this study was to clarify determinants of incentive preferences for health behavior change. A self-completed questionnaire survey regarding incentives for health behavior change was conducted in a Japanese village in 2015. The outcome measures were individual preferences for features of incentives, including item, frequency, type and value. The types of incentives were defined as follows: assured-type (given for participation); effort-type (given if participants make some kind of effort); and outcome-type (given if participants make achievements decided a priori). The associations with respondents' sex, age, presence of lifestyle-related disease and stage in the transtheoretical model of health behavior change were investigated using multivariate analysis. A total of 1469 residents responded to the survey. Significant associations with preference for different incentive features were found as follows: for monetary items, female and elderly; for high frequency, female and maintenance stage; for effort-type, male, action stage and contemplation stage; and for outcome-type, maintenance stage and action stage. Our results appeared to identify determinants of incentive preferences for health behavior change. These findings are expected to promote the development of an incentive program more in tune with individual preferences.

Terrain coefficients for predicting energy costs of walking over snow.

BACKGROUND: Predicting the energy costs of human travel over snow can be of significant value to the military and other agencies planning work efforts when snow is present. The ability to quantify, and predict, those costs can help planners determine if snow will be a factor in the execution of dismounted tasks and operations. To adjust predictive models for the effect of terrain, and more specifically for surface conditions, on energy costs, terrain coefficients (ƞ) have been developed. The physiological demands of foot travel over snow have been studied previously, and there are well established methods of predicting metabolic costs of locomotion. By applying knowledge gained from prior studies of the effects of terrain and snow, and by leveraging those existing dismounted locomotion models, this paper seeks to outline the steps in developing an improved terrain coefficient (ƞ) for snow to be used in predictive modeling. METHODS: Using published data, methods, and a well-informed understanding of the physical elements of terrain, e.g., characterization of snow sinkage (z), this study made adjustments to ƞ-values specific to snow. RESULTS: This review of published metabolic cost methods suggest that an improved ƞ-value could be developed for use with the Pandolf equation, where z = depth (h)*(1 - (snow density (ρ0)/1.186)) and ƞ = 0.0005z3 + 0.0001z2 + 0.1072z + 1.2604. CONCLUSION: While the complexity of variables related to characteristics of snow, speed of movement, and individuals confound efforts to develop a simple, predictive model, this paper provides data-driven improvements to models that are used to predict the energy costs of dismounted movements over snow.

Childhood obesity research at the NIH: Efforts, gaps, and opportunities.

Childhood obesity is a major public health challenge. This article describes an overview of the National Institutes of Health (NIH) behavioral and social sciences childhood obesity research efforts. The overview will highlight five areas of childhood obesity research supported by the NIH: (a) basic behavioral and social sciences; (b) early childhood; (c) policies, programs, and environmental strategies; (d) health disparities; and (e) transagency and public-private collaboration. The article also describes potential gaps and opportunities in the areas of childhood obesity and severe obesity, measurement, and sleep.

Revenue-sharing clubs provide economic insurance and incentives for sustainability in common-pool resource systems.

Harvesting behaviors of natural resource users, such as farmers, fishermen and aquaculturists, are shaped by season-to-season and day-to-day variability, or in other words risk. Here, we explore how risk-mitigation strategies can lead to sustainable use and improved management of common-pool natural resources. Over-exploitation of unmanaged natural resources, which lowers their long-term productivity, is a central challenge facing societies. While effective top-down management is a possible solution, it is not available if the resource is outside the jurisdictional bounds of any management entity, or if existing institutions cannot effectively impose sustainable-use rules. Under these conditions, alternative approaches to natural resource governance are required. Here, we study revenue-sharing clubs as a mechanism by which resource users can mitigate their income volatility and importantly, as a co-benefit, are also incentivized to reduce their effort, leading to reduced over-exploitation and improved resource governance. We use game theoretic analyses and agent-based modeling to determine the conditions in which revenue-sharing can be beneficial for resource management as well as resource users. We find that revenue-sharing agreements can emerge and lead to improvements in resource management when there is large variability in production/revenue and when this variability is uncorrelated across members of the revenue-sharing club. Further, we show that if members of the revenue-sharing collective can sell their product at a price premium, then the range of ecological and economic conditions under which revenue-sharing can be a tool for management greatly expands. These results have implications for the design of bottom-up management, where resource users themselves are incentivized to operate in ecologically sustainable and economically advantageous ways.

Continuous time Markov chain approaches for analyzing transtheoretical models of health behavioral change: A case study and comparison of model estimations.

Continuous time Markov chain models are frequently employed in medical research to study the disease progression but are rarely applied to the transtheoretical model, a psychosocial model widely used in the studies of health-related outcomes. The transtheoretical model often includes more than three states and conceptually allows for all possible instantaneous transitions (referred to as general continuous time Markov chain). This complicates the likelihood function because it involves calculating a matrix exponential that may not be simplified for general continuous time Markov chain models. We undertook a Bayesian approach wherein we numerically evaluated the likelihood using ordinary differential equation solvers available from the gnu scientific library. We compared our Bayesian approach with the maximum likelihood method implemented with the R package MSM. Our simulation study showed that the Bayesian approach provided more accurate point and interval estimates than the maximum likelihood method, especially in complex continuous time Markov chain models with five states. When applied to data from a four-state transtheoretical model collected from a nutrition intervention study in the next step trial, we observed results consistent with the results of the simulation study. Specifically, the two approaches provided comparable point estimates and standard errors for most parameters, but the maximum likelihood offered substantially smaller standard errors for some parameters. Comparable estimates of the standard errors are obtainable from package MSM, which works only when the model estimation algorithm converges.