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Background Given the current worldwide epidemic of obesity , there is a demand for interventions with higher impact, such as those carried out in the primary health care (PHC) setting. Here we evaluate the effect of intervention performed according to the stages of change of the transtheoretical model (TTM) for weight management. Methods This randomized controlled trial in Brazilian PHC offered free physical exercise and nutrition education. The participants were women, aged 20 years or older who were obese or overweight, users in PHC service. The intervention group (IG, n  = 51) received the same orientation as the comparison group (CG, n  = 35) plus individual health counseling based on the TTM aimed at weight loss, which lasted 6 months. The outcome measures were anthropometric, food, and nutrient profiles. Inflammatory parameters were evaluated in a random subsample. The inter-group and intra-group differences were evaluated using interntion-to-treat analysis, and analysis of covariance (ANCOVA) used to assess intervention effectiveness. Results There was a difference between groups of − 1.4 kg (CI95%: − 2.5; − 0.3) in body weight after the intervention. About 97% of women in the IG reported benefits of the intervention and presented positive changes in diet, biochemical markers, and anthropometry. The IG showed better body mass index, resistine, and blood glucose results compared to the CG during follow-up. Conclusion The individualized TTM-based intervention, combined with usual care, was an effective strategy in PHC. These results should encourage the use of interdisciplinary practices; nevertheless, research to identify additional strategies is needed to address barriers to weight maintenance among obese low-income women. Trial registration The trial is registered with Brazilian clinical trials under the code: RBR-8t7ssv , Registration date: 12/12/2017 (retrospectively registered).

Baclofen has been suggested as a potential pharmacotherapy for alcohol use disorder, but the clinical data are conflicting. Here we investigated the biobehavioral effects of baclofen in a sample of anxious alcohol-dependent individuals. This was a randomized, double-blind, placebo-controlled, human laboratory study in non-treatment seeking alcohol-dependent individuals with high trait anxiety ( N =34). Participants received baclofen (30 mg per day) or placebo for at least 8 days, then performed an experimental session consisting of alcohol cue-reactivity followed by alcohol administration procedure (alcohol priming, then alcohol self-administration). Total amount of alcohol self-administered was the primary outcome; alcohol craving, subjective/physiological responses and mood/anxiety symptoms were also evaluated. There was no significant medication effect on the total amount of alcohol consumed during the alcohol self-administration ( P =0.76). Baclofen blunted the positive association between maximum breath alcohol concentration during priming and the amount of alcohol consumption (significant interaction, P =0.03). Ratings of feeling intoxicated were significantly higher in the baclofen group after consuming the priming drink ( P =0.006). During the self-administration session, baclofen significantly increased ratings of feeling high ( P =0.01) and intoxicated ( P =0.01). A significant reduction in heart rate ( P <0.001) and a trend-level increase in diastolic blood pressure ( P =0.06) were also detected in the baclofen group during the alcohol laboratory session. In conclusion, baclofen was shown to affect subjective and physiological responses to alcohol drinking in anxious alcohol-dependent individuals. These results do not support an anti-craving or anti-reinforcing effect of baclofen, but rather suggest that baclofen may act as a substitution medication for alcohol use disorder.

In the eighteenth century, Daniel Bernoulli, Adam Smith and Jeremy Bentham proposed that economic choices rely on the computation and comparison of subjective values 1 . This hypothesis continues to inform modern economic theory 2 and research in behavioural economics 3 , but behavioural measures are ultimately not sufficient to verify the proposal 4 . Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods 5 . Value-encoding cells integrate multiple dimensions 6 – 9 , variability in the activity of each cell group correlates with variability in choices 10 , 11 and the population dynamics suggests the formation of a decision 12 . 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 brain 13 – 15 remains correlational 16 . 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. Direct electrical stimulation of the brain in rhesus monkeys ( Macaca mulatta ) predictably varied subjective valuation and choices, linking valuation and economic decision making to the orbitofrontal cortex.

Wearable activity trackers offer the opportunity to increase physical activity through continuous monitoring. Viewing tracker use as a beneficial health behavior, we explored the factors that facilitate and hinder long-term activity tracker use, applying the transtheoretical model of behavior change with the focus on the maintenance stage and relapse. The aim of this study was to investigate older adults' perceptions and uses of activity trackers at different points of use: from nonuse and short-term use to long-term use and abandoned use to determine the factors to maintain tracker use and prevent users from discontinuing tracker usage. Data for the research come from 10 focus groups. Of them, 4 focus groups included participants who had never used activity trackers (n=17). These focus groups included an activity tracker trial. The other 6 focus groups (without the activity tracker trial) were conducted with short-term (n=9), long-term (n=11), and former tracker users (n=11; 2 focus groups per user type). The results revealed that older adults in different tracker use stages liked and wished for different tracker features, with long-term users (users in the maintenance stage) being the most diverse and sophisticated users of the technology. Long-term users had developed a habit of tracker use whereas other participants made an effort to employ various encouragement strategies to ensure behavior maintenance. Social support through collaboration was the primary motivator for long-term users to maintain activity tracker use. Short-term and former users focused on competition, and nonusers engaged in vicarious tracker use experiences. Former users, or those who relapsed by abandoning their trackers, indicated that activity tracker use was fueled by curiosity in quantifying daily physical activity rather than the desire to increase physical activity. Long-term users saw a greater range of pros in activity tracker use whereas others focused on the cons of this behavior. The results suggest that activity trackers may be an effective technology to encourage physical activity among older adults, especially those who have never tried it. However, initial positive response to tracker use does not guarantee tracker use maintenance. Maintenance depends on recognizing the long-term benefits of tracker use, social support, and internal motivation. Nonadoption and relapse may occur because of technology's limitations and gaining awareness of one's physical activity without changing the physical activity level itself.

The locus coeruleus (LC), the origin of noradrenergic modulation of cognitive and behavioral function, may play an important role healthy ageing and in neurodegenerative conditions. We investigated the functional significance of age-related differences in mean normalized LC signal intensity values (LC-CR) in magnetization-transfer (MT) images from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) cohort - an open-access, population-based dataset. Using structural equation modelling, we tested the pre-registered hypothesis that putatively noradrenergic (NA)-dependent functions would be more strongly associated with LC-CR in older versus younger adults. A unidimensional model (within which LC-CR related to a single factor representing all cognitive and behavioral measures) was a better fit with the data than the a priori two-factor model (within which LC-CR related to separate NA-dependent and NA-independent factors). Our findings support the concept that age-related reduction of LC structural integrity is associated with impaired cognitive and behavioral function. Alterations of locus coeruleus signal intensity have been associated with functional changes in health and disease. Here, the authors tested a pre-registered hypothesis on a large number of subjects as part of the Cam-CAN consortium.

Online shopping behavior has the characteristics of rich granularity dimension and data sparsity and presents a challenging task in e-commerce. Previous studies on user behavior prediction did not seriously discuss feature selection and ensemble design, which are important to improving the performance of machine learning algorithms. In this paper, we proposed an SE-stacking model based on information fusion and ensemble learning for user purchase behavior prediction. After successfully using the ensemble feature selection method to screen purchase-related factors, we used the stacking algorithm for user purchase behavior prediction. In our efforts to avoid the deviation of the prediction results, we optimized the model by selecting ten different types of models as base learners and modifying the relevant parameters specifically for them. Experiments conducted on a publicly available dataset show that the SE-stacking model can achieve a 98.40% F1 score, approximately 0.09% higher than the optimal base models. The SE-stacking model not only has a good application in the prediction of user purchase behavior but also has practical value when combined with the actual e-commerce scene. At the same time, this model has important significance in academic research and the development of this field.

Open research data provide considerable scientific, societal, and economic benefits. However, disclosure risks can sometimes limit the sharing of open data, especially in datasets that include sensitive details or information from individuals with rare disorders. This article introduces the concept of synthetic datasets, which is an emerging method originally developed to permit the sharing of confidential census data. Synthetic datasets mimic real datasets by preserving their statistical properties and the relationships between variables. Importantly, this method also reduces disclosure risk to essentially nil as no record in the synthetic dataset represents a real individual. This practical guide with accompanying R script enables biobehavioural researchers to create synthetic datasets and assess their utility via the synthpop R package. By sharing synthetic datasets that mimic original datasets that could not otherwise be made open, researchers can ensure the reproducibility of their results and facilitate data exploration while maintaining participant privacy. It is becoming increasingly common for scientists to share their data with other researchers. This makes it possible to independently verify reported results, which increases trust in research. Sometimes it is not possible to share certain datasets because they include sensitive information about individuals. In psychology and medicine, scientists have tried to remove identifying information from datasets before sharing them by, for example, adding minor artificial errors. But, even when researchers take these steps, it may still be possible to identify individuals, and the introduction of artificial errors can make it harder to verify the original results. One potential alternative to sharing sensitive data is to create ‘synthetic datasets’. Synthetic datasets mimic original datasets by maintaining the statistical properties of the data but without matching the original recorded values. Synthetic datasets are already being used, for example, to share confidential census data. However, this approach is rarely used in other areas of research. Now, Daniel S. Quintana demonstrates how synthetic datasets can be used in psychology and medicine. Three different datasets were studied to ensure that synthetic datasets performed well regardless of the type or size of the data. Quintana evaluated freely available software that could generate synthetic versions of these different datasets, which essentially removed any identifying information. The results obtained by analysing the synthetic datasets closely mimicked the original results. These tools could allow researchers to verify each other’s results more easily without jeopardizing the privacy of participants. This could encourage more collaboration, stimulate ideas for future research, and increase data sharing between research groups.

During the past forty years, statistical modelling and simulation have come to frame perceptions of epidemic disease and to determine public health interventions that might limit or suppress the transmission of the causative agent. The influence of such formulaic disease modelling has pervaded public health policy and practice during the Covid-19 pandemic. The critical vocabulary of epidemiology, and now popular debate, thus includes R₀, the basic reproduction number of the virus, ‘flattening the curve’, and epidemic ‘waves’. How did this happen? What are the consequences of framing and foreseeing the pandemic in these modes? Focusing on historical and contemporary disease responses, primarily in Britain, I explore the emergence of statistical modelling as a ‘crisis technology’, a reductive mechanism for making rapid decisions or judgments under uncertain biological constraint. I consider how Covid-19 might be configured or assembled otherwise, constituted as a more heterogeneous object of knowledge, a different and more encompassing moment of truth – not simply as a measured telos directing us to a new normal. Drawing on earlier critical engagements with the AIDS pandemic, inquiries into how to have ‘theory’ and ‘promiscuity’ in a crisis, I seek to open up a space for greater ecological, sociological, and cultural complexity in the biopolitics of modelling, thereby attempting to validate a role for critique in the Covid-19 crisis.

Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.

Background Sub-Saharan Africa (SSA) experiences disproportionate burden of cervical cancer incidence and mortality due in part to low uptake of cervical screening, a strategy for prevention and down-staging of cervical cancer. This scoping review identifies studies of interventions to increase uptake of cervical screening among women in the region and uses the Integrated Behavioral Model (IBM) to describe how interventions might work. Methods A systematic search of literature was conducted in PubMed, Web of Science, Embase, and CINAHL databases through May 2019. Screening and data charting were performed by two independent reviewers. Intervention studies measuring changes to uptake in screening among women in SSA were included, with no restriction to intervention type, study setting or date, or participant characteristics. Intervention type and implementation strategies were described using behavioral constructs from the IBM. Results Of the 3704 citations the search produced, 19 studies were selected for inclusion. Most studies were published between 2014 and 2019 (78.9%) and were set in Nigeria (47.4%) and South Africa (26.3%). Studies most often assessed screening with Pap smears (31.6%) and measured uptake as ever screened (42.1%) or screened during the study period (36.8%). Education-based interventions were most common (57.9%) and the IBM construct of knowledge/skills to perform screening was targeted most frequently (68.4%). Willingness to screen was high, before and after intervention. Screening coverage ranged from 1.7 to 99.2% post-intervention, with six studies (31.6%) reporting a significant improvement in screening that achieved ≥60% coverage. Conclusions Educational interventions were largely ineffective, except those that utilized peer or community health educators and mHealth implementation strategies. Two economic incentivization interventions were moderately effective, by acting on participants’ instrumental attitudes, but resulted in screening coverage less than 20%. Innovative service delivery, including community-based self-sampling, acted on environmental constraints, striving to make services more available, accessible, and appropriate to women, and were the most effective. This review demonstrates that intent to perform screening may not be the major determinant of screening behavior, suggesting other theoretical frameworks may be needed to more fully understand uptake of cervical screening in sub-Saharan Africa, particularly for health systems change interventions.