Effects of Movement Behaviors on Overall Health and Appetite Control: Current Evidence and Perspectives in Children and Adolescents

Purpose of Review To present the definitions and recommendations for movement behaviors in children and adolescents, including physical activity (PA), sedentary behaviors (SB), and sleep, and to provide an overview regarding their impact on health and obesity outcomes from childhood to adulthood, as well as interactions with appetite control. Recent Findings PA represents a variable proportion of daily energy expenditure and one can be active with high SB or vice versa. Studies have described movements across the whole day on a continuum from sleep to SB to varying intensities of PA. More PA, less SB (e.g., less screen time) and longer sleep are positively associated with indicators of physical health (e.g., lower BMI, adiposity, cardiometabolic risk) and cognitive development (e.g., motor skills, academic achievement). However, less than 10% of children currently meet recommendations for all three movement behaviors. Movement behaviors, adiposity, and related cardiometabolic diseases in childhood track into adolescence and adulthood. Furthermore, low PA/high SB profiles are associated with increased energy intake. Recent studies investigating energy balance regulation showed that desirable movement behavior profiles are associated with better appetite control and improved eating habits. Summary Early identification of behavioral phenotypes and a comprehensive approach addressing all key behaviors that directly affect energy balance will allow for individual strategies to prevent or treat obesity and its comorbidities. Investigating exercise as a potential “corrector” of impaired appetite control offers a promising weight management approach.

Impact of Window-to-Wall Ratio on Heating Demand and Thermal Comfort When Considering a Variety of Occupant Behavior Profiles

Energy consumption and thermal comfort in residential buildings are highly influenced by occupant behavior, which exhibits a high level of day-to-day and dwelling-to-dwelling variance. Although occupant behavior stochastic models have been developed in the past, the analysis or selection of a building design parameter is typically based on simulations that use a single “average” occupant behavior schedule which does not account for all possible profiles. The objective of this study is to enhance the understanding of how window-to-wall ratio (WWR) of a residential unit affects heating demand and thermal comfort when considering occupant behavior diversity through a parametric analysis. To do so, a stochastic occupant behavior model generates a high number of possible profiles, which are then used as input in an energy simulation of the dwelling. As a result, one obtains probability distributions of energy consumption and comfort for different WWR values. The paper shows that the shape of the probability distributions is affected by WWR and dwelling orientation, and that the influence of different occupant behavior aspects on performance also varies with WWR. This work could help designers to better assess the impact of WWR for a large spectrum of possible occupant behavior profiles.

Driver Speed Profiles Index Warning Signs of MCI

Goal Use driver behavior profiles to screen and index early warnings of cognitive decline and Alzheimer’s disease (AD). Hypothesis: Real-world driver speed behavior profiles discriminate mild cognitive impairment (MCI). Methods Sensors were installed in personal vehicles of 74 legally-licensed, active drivers (age: 65-90 years, μ = 75.85) who completed 2, 3-month real-world driving assessments, including demographic and cognitive assessments, 1 year apart (244,564 miles driven). MCI status was indexed using 8 neuropsychological tests (spanning executive function, visuospatial skills, processing speed, and memory), relevant to MCI and driving. Driving environment was indexed from state speed limit (SL; roadway type: residential, commercial, interstate) and sunrise-sunset databases (time of day: day vs. night). Models: Data were randomly split into training (66%) and validation (33%) sets. An optimal mixed effects logistic regression model was determined from validation data AUC values. Results MCI drivers drove slower with optimal discrimination (estimated for every 5 mph decrease in speeding) in 1) residential roads (SL 25-35 mph; MCI odds increased by 6% [95% CI: 2-11%]), 2) interstate roads (SL >55 mph; MCI odds increased by 14% [95% CI: 8-20%]), and 3) night environments (MCI odds increased by 7% [95% CI: 2-12%]). Conclusion Quantitative indices of real-world driver data provide “ground truth” for screening and indexing phenotypes of cognitive decline, in line with ongoing efforts to link driver behavior with age-related cognitive decline and AD biomarkers. Behavioral biomarkers for diagnosing early warnings of dementia could ultimately bolster our ability to detect and intervene in early AD.

A new measure between sets of probability distributions with applications to erratic financial behavior

This paper introduces a new framework to quantify distance between finite sets with uncertainty present, where probability distributions determine the locations of individual elements. Combining this with a Bayesian change point detection algorithm, we produce a new measure of similarity between time series with respect to their structural breaks. First, we demonstrate the algorithm’s effectiveness on a collection of piecewise autoregressive processes. Next, we apply this to financial data to study the erratic behavior profiles of 19 countries and 11 sectors over the past 20 years. Our measure provides quantitative evidence that there is greater collective similarity among sectors’ erratic behavior profiles than those of countries, which we observe upon individual inspection of these time series. Our measure could be used as a new framework or complementary tool for investors seeking to make asset allocation decisions for financial portfolios.

Are American Indian/Alaska Native Adolescent Health Behaviors Different? A Review of AI/AN Youth Involved in Native STAND Curriculum, 2014–2017 United States

Objectives To explore health behavior profiles of AI/AN youth involved in native students together against negative decisions (STAND), a national culture-based curriculum. Methods We analyzed data from 1236 surveys conducted among AI/AN youth at 40 native STAND implementation sites located in 16 states throughout the US from 2014 to 2017. Health profiles included demographics, sexual orientation, sexual activity, STI testing, cigarette use, and suicide attempts in the past 12-months. We used t-tests and chi square tests of independence to compare risk behavior prevalence among the sample. Results Health behavior profiles of AI/AN youth indicate that 45.6% of youth did not use condoms the last time they had sex, and 82.7% have never been tested for STIs. Differences in cigarette smoking were observed in questioning youth (questioning: 80.3%, straight/heterosexual: 63.8%, LGBTQ2S + : 49.9%, p = 0.03). Conclusions for Practice Health behaviors related to sex, substance, violence and self-harm, are at least as common for AI/AN youth as those observed in other US teens. Future research should consider similarities and differences in health profiles of AI/AN youth when designing interventions that affect them. Further, our findings underscore the need for culturally-relevant curricula like native STAND, not because their health behavior is different, but because their socio-ecologic environment is different.

A-172 Behavioral Survey of Traits: Reliability with a Neurotypical Sample

Objective Behavioral Survey of Traits (BeST; Andrews & Robins, 2010) is a measure used to detect behavior profiles consistent with prenatal alcohol exposure in children. This study sought to create a control group of neurotypical individuals over the age of 18, to assess reliability of adapted BeST Adult-Self-Report (BeST-ASR) and Adult-Other (BeST-AO). Methods A sample (n = 51, paired sample n = 23) of neurotypical adults were recruited to participate in a cross-sectional study. Participants (ages 20–60, M = 33.6, SD = 14.4) included 13 women and 10 men with 82.6% identifying as White/European-American, 8.7% Black or African American, and 8.7% Mixed-Race. Each participant provided demographics and completed a BeST-ASR for themselves, and asked another individual who knew them well to complete BeST-AO. Results For the paired sample, a total score for BeST-ASR (M = 49.5, SD = 12.9) and the BeST-AO (M = 23.5, SD =10.8) were calculated. No significant differences were found for age or gender on the screeners. BeST-ASR and BeST-AO total scores were significantly different (ASR M = 51.3, AO M = 23). Measures of internal consistency produced a Cronbach’s Alpha (α = 0.84) for the BeST-ASR and a (α = 0.89) for the BeST-AO. Conclusion The BeST is an established measure used to screen behavior profiles consistent with FASD. The adult adapted screeners were found to have high internal consistency with a neurotypical sample. Differences evaluated between the self and other needs further study, which is consistent in a probation sample (Mushlitz, 2019). Overall, high internal consistency is encouraging and warrants further study to understand scores in a neurotypical adult sample.

Psychiatric Characteristics Across Individuals With PTEN Mutations

Germline heterozygous PTEN mutations have been associated with high prevalence of autism spectrum disorder (ASD) and elevated rates and severity of broadly defined behavioral problems. However, limited progress has been made toward understanding whether PTEN mutation is associated with specific psychiatric co-morbidity profiles when compared to idiopathic ASD. The current study aimed to utilize a cross-measure approach to compare concurrent psychiatric characteristics across children and adolescents with PTEN mutation with (PTEN-ASD; n = 38) and without ASD (PTEN-No ASD; n = 23), and ASD with macrocephaly but no PTEN mutation (macro-ASD; n = 25) using the Child Behavior Checklist (CBCL) and the Aberrant Behavior Checklist (ABC). There were significant group effects for the CBCL Internalizing and Externalizing broad symptom score, the majority of specific CBCL syndrome scores, and all ABC subscale scores. Post-hoc comparisons revealed greater behavioral symptoms in the ASD groups (PTEN-ASD and macro-ASD) compared to the PTEN-no ASD group on nearly all subtest scores examined. There were no statistically significant differences between the PTEN-ASD and macro-ASD groups; however, there was a trend for the macro-ASD group showing higher levels of aggressive behaviors. Our findings provide evidence of specific behavior profiles across PTEN-No ASD, PTEN-ASD, and macro-ASD groups and highlight the importance of early identification of behavioral vulnerabilities in individuals with PTEN mutations in order to provide access to appropriate evidence-based interventions.

Pragmatics to Reveal Intent in Social Media Peer Interactions: Mixed Methods Study (Preprint)

BACKGROUND Online health communities (OHCs) have emerged as the leading venues for behavior change and health-related information seeking. The soul and success of these digital platforms lie in their ability to foster social togetherness and a sense of community by providing personalized support. However, we have a minimal understanding of how conversational posts in these settings lead to collaborative societies and ultimately result in positive health changes through social influence. OBJECTIVE Our objective is to develop a content-specific and intent-sensitive methodological framework for analyzing peer interactions in OHCs. METHODS We developed and applied a mixed-methods approach to understand the manifestation of expressions in peer interactions in OHCs. We applied our approach to describe online social dialogue in the context of two online communities, QuitNet (QN) and the American Diabetes Association (ADA) support community. A total of 3011 randomly selected peer interactions (n=2005 from QN, n=1006 from ADA) were analyzed. Specifically, we conducted thematic analysis to characterize communication content and linguistic expressions (speech acts) embedded within the two data sets. We also developed an empirical user persona based on their engagement levels and behavior profiles. Further, we examined the association between speech acts and communication themes across observed tiers of user engagement and self-reported behavior profiles using the chi-square test or the Fisher test. RESULTS Although social support, the most prevalent communication theme in both communities, was expressed in several subtle manners, the prevalence of emotions was higher in the tobacco cessation community and assertions were higher in the diabetes self-management (DSM) community. Specific communication theme-speech act relationships were revealed, such as the social support theme was significantly associated (<i>P</i>&lt;.05) with 9 speech acts from a total of 10 speech acts (ie, assertion, commissive, declarative, desire, directive, expressive, question, stance, and statement) within the QN community. Only four speech acts (ie, commissive, emotion, expressive, and stance) were significantly associated (<i>P</i>&lt;.05) with the social support theme in the ADA community. The speech acts were also significantly associated with the users’ abstinence status within the QN community and with the users’ lifestyle status within the ADA community (<i>P</i>&lt;.05). CONCLUSIONS Such an overlay of communication intent implicit in online peer interactions alongside content-specific theory-linked characterizations of social media discourse can inform the development of effective digital health technologies in the field of health promotion and behavior change. Our analysis revealed a rich gradient of expressions across a standardized thematic vocabulary, with a distinct variation in emotional and informational needs, depending on the behavioral and disease management profiles within and across the communities. This signifies the need and opportunities for coupling pragmatic messaging in digital therapeutics and care management pathways for personalized support.

Transaction Fraud Detection (Anomaly detection) using Machine Learning

Now a day’s online shopping crazy thing for peoples. As well as many people uses Online transaction for many purposes. Transaction fraud is growing seriously. Therefore, the study on fraud detection is interesting and significant and we can say necessary. An important way of detecting fraud is to extract the behavior profiles (BPs) of users based on their historical transaction records, and then to verify if an incoming transaction is a fraud or not in view of Their BPs. Also we apply SVM, Adaboost, and Neural Network machine learning algorithm to see which one is giving the best result.