Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation.

BACKGROUND: Depression is often accompanied by changes in behavior, including dietary behaviors. The relationship between dietary behaviors and depression has been widely studied, yet previous research has relied on self-reported data which is subject to recall bias. Electronic device-based behavioral monitoring offers the potential for objective, real-time data collection of a large amount of continuous, long-term behavior data in naturalistic settings. OBJECTIVE: The study aims to characterize digital dietary behaviors in depression, and to determine whether these behaviors could be used to detect depression. METHODS: A total of 3310 students (2222 healthy controls [HCs], 916 with mild depression, and 172 with moderate-severe depression) were recruited for the study of their dietary behaviors via electronic records over a 1-month period, and depression severity was assessed in the middle of the month. The differences in dietary behaviors across the HCs, mild depression, and moderate-severe depression were determined by ANCOVA (analyses of covariance) with age, gender, BMI, and educational level as covariates. Multivariate logistic regression analyses were used to examine the association between dietary behaviors and depression severity. Support vector machine analysis was used to determine whether changes in dietary behaviors could detect mild and moderate-severe depression. RESULTS: The study found that individuals with moderate-severe depression had more irregular eating patterns, more fluctuated feeding times, spent more money on dinner, less diverse food choices, as well as eating breakfast less frequently, and preferred to eat only lunch and dinner, compared with HCs. Moderate-severe depression was found to be negatively associated with the daily 3 regular meals pattern (breakfast-lunch-dinner pattern; OR 0.467, 95% CI 0.239-0.912), and mild depression was positively associated with daily lunch and dinner pattern (OR 1.460, 95% CI 1.016-2.100). These changes in digital dietary behaviors were able to detect mild and moderate-severe depression (accuracy=0.53, precision=0.60), with better accuracy for detecting moderate-severe depression (accuracy=0.67, precision=0.64). CONCLUSIONS: This is the first study to develop a profile of changes in digital dietary behaviors in individuals with depression using real-world behavioral monitoring. The results suggest that digital markers may be a promising approach for detecting depression.

Home-based video assessment of children's autism-related behaviours: Psychometric analysis and linkages with parental responsiveness and context.

OBJECTIVES: Assessment of autism-related behaviours (ARBs) in children has generally been limited to direct observations in clinical settings or informant-based reports. The widespread availability of video-streaming devices has made home observations of children's ARBs feasible. This approach could enable assessment of the generalization and durability of interventions and may be able to overcome methodological limitations of predominant current assessment approaches (response biases, limited sensitivity to treatment). DESIGN AND METHODS: Forty-four autistic children and their families participated in a repeated-measures study with a correlational design. Approximately 10 hr of unprompted behaviour at home were videorecorded over the course of a week (2 hr per day) for each participant. Gold standard measures of ARBs were also administered (ADOS-2 and ADI-R). Two home-based observational measures of ARBs utilizing streaming video were developed and evaluated: the ARCHER and the CHEERS. Trained independent evaluators made ratings on the ARCHER, CHEERS and an observational measure of parental responsiveness. RESULTS: Correlations with the ADOS-2 and ADI-R were .47 and .34 for ARCHER scores and .51 and .48 for CHEERS scores, respectively. In linear mixed models, more responsive parenting was associated with fewer ARBs on a daily basis. Children spent their afternoons engaged in many typical activities including electronics, homework and games with family members, and ARBs were more prominent in some of these contexts (e.g., electronics) than others (e.g., family games). CONCLUSIONS: Home-based observational assessment of ARBs may be useful for clinical and descriptive research.

Newborn behavioral observations system in rural Pakistan: A feasibility and acceptability study.

The Newborn Behavioral Observations (NBO) system is a relationship-based tool that helps parents recognize their infant's competencies and learn their behavioral cues, with the goals of enhancing parental responsiveness and satisfaction in the infant-parent relationship. In our study, a pediatrician integrated the NBO into 44 pediatric health care visits of infants in rural Pakistan villages, under the remote guidance of two U.S.-based child psychiatrists. A clinician then gave the mothers a survey about their experience of the NBO and found that the mothers were highly satisfied, reporting greater appreciation of their infant's strengths, greater understanding of their infant's behavioral cues, stronger attachment to their infant, and greater self-confidence as a mother. In their consideration of these results, the authors explore cultural reasons for the mothers' responses and generate hypotheses to inform an outcome study of a similar intervention. This was a feasibility and acceptability study and was not randomized, had no control group, and did not use objective measures of outcome.

El sistema de Observaciones de Comportamiento del Recién Nacido (NBO)™ es una herramienta basada en la relación que se enfoca en ayudar a los padres a reconocer las competencias de su infante y aprender sus señales de comportamiento, con el propósito de mejorar la sensibilidad y satisfacción del progenitor en la relación infante-progenitor. En este estudio de probabilidad y aceptabilidad, un clínico pediatra integró el NBO dentro de 44 visitas de cuidado de salud pediátricas a infantes en aldeas rurales en Pakistán, bajo la guía remota de dos siquiatras infantiles con base en los Estados Unidos. Entonces, un clínico les dio a las madres una encuesta acerca de su experiencia con el NBO y se encontró que las madres estaban altamente satisfechas, reportando un mayor aprecio por los puntos fuertes de sus infantes, una mayor comprensión de las señalas de comportamiento de sus infantes, una más fuerte afectividad hacia sus infantes, así como una mayor auto confianza como madre. En sus consideraciones de estos resultados, los autores exploran razones culturales para las respuestas de las madres y generan hipótesis como información para un estudio de resultado de una intervención similar.

Le système d'observation comportementale du nouveau-né (Newborn Behavioral Observations, abrégé selon l'anglais NBO system™) est un outil basé sur la relation se concentrant sur l'aide aux parents à reconnaître les compétences de leur nourrisson et à comprendre leurs signaux de comportement, se donnant pour but de mettre en valeur la réaction parentale et la satisfaction dans la relation nourrisson-parent. Dans cette étude de faisabilité et d'acceptabilité un clinicien en pédiatrie a intégré le NBO dans 44 visites de santé pédiatrique de nourrissons dans des villages du Pakistan rural, sous l'orientation professionnelle à distance de deux psychiatres de l'enfance basés aux Etats-Unis d'Amérique. Un clinicien a ensuite donné aux mères un questionnaires sur leur expérience du NBO et a trouvé que les mères étaient très satisfaites, faisant état d'une plus grande appréciation des forces de leurs nourrissons, d'une plus grande compréhension des signaux de comportement de leur nourrisson, d'un attachement plus fort à leur nourrisson et d'une plus grande confiance en soi en tant que mère. Dans leur considération de ces résultats les auteurs explorent les raisons culturelles expliquant les réponses des mères et génèrent des hypothèses pour informer une étude d'une intervention similaire.

Gait analysis as a robust pain behavioural endpoint in the chronic phase of the monoiodoacetate-induced knee joint pain in the rat.

The monoiodoacetate-induced rat model of osteoarthritis knee pain is widely used. However, there are between-study differences in the pain behavioural endpoints assessed and in the dose of intraarticular monoiodoacetate administered. This study evaluated the robustness of gait analysis as a pain behavioural endpoint in the chronic phase of this model, in comparison with mechanical hyperalgesia in the injected (ipsilateral) joint and development of mechanical allodynia in the ipsilateral hind paws. Groups of Sprague-Dawley rats received a single intraarticular injection of monoiodoacetate at 0.5, 1, 2 or 3 mg or vehicle (saline) into the left (ipsilateral) knee joint. An additional group of rats were not injected (naïve group). The pain behavioural methods used were gait analysis, measurement of pressure algometry thresholds in the ipsilateral knee joints, and assessment of mechanical allodynia in the ipsilateral hind paws using von Frey filaments. These pain behavioural endpoints were assessed premonoiodoacetate injection and for up to 42-days postmonoiodoacetate injection in a blinded manner. Body weights were also assessed as a measure of general health. Good general health was maintained as all rats gained weight at a similar rate for the 42-day study period. In the chronic phase of the model (days 9-42), intraarticular monoiodoacetate at 3 mg evoked robust alterations in multiple gait parameters as well as persistent mechanical allodynia in the ipsilateral hind paws. For the chronic phase of the monoiodoacetate-induced rat model of osteoarthritis knee pain, gait analysis, such as mechanical allodynia in the ipsilateral hind paws, is a robust pain behavioural measure.

Healthy Eating Index-2015 Scores Among Adults Based on Observed vs Recalled Dietary Intake.

BACKGROUND: The construct and predictive validity of the Healthy Eating Index (HEI) have been demonstrated, but how error in reported dietary intake may affect scores is unclear. OBJECTIVE: These analyses examined concordance between HEI-2015 scores based on observed vs reported intake among adults. DESIGN: Data were from two feeding studies (Food and Eating Assessment STudy, or FEAST, I and II) in which true intake was observed for three meals on 1 day. The following day, participants completed an unannounced 24-hour dietary recall. PARTICIPANTS/SETTING: FEAST I (2012) included 81 men and women, aged 20 to 70 years, living in the Washington, DC, area. FEAST II (2016) included 302 women, aged 18 years or older, with low household incomes and living in the Washington, DC, area. In FEAST I, recalls were completed independently using the Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24-2011) or interviewer-administered using the Automated Multiple-Pass Method. In FEAST II, recalls were completed using ASA24-2016, independently or in a small group setting with assistance. MAIN OUTCOME MEASURES: HEI-2015 scores were calculated using the population ratio method. STATISTICAL ANALYSES PERFORMED: T-tests determined whether differences between scores based on observed and reported intake were different from zero. FEAST I data were stratified by sex, and in FEAST II, analyses were repeated by education and body mass index (BMI). RESULTS: Differences in total HEI-2015 scores between observed and reported intake ranged from -1.3 to 5.8 points among those completing ASA24 independently in both studies, compared with -2.5 points in the small group setting. For interviewer-administered recalls, the differences were -1.1 for men and 2.3 for women. In FEAST II, total HEI-2015 scores derived from observed intake were lower than scores derived from reported intake among those who had completed high school or less (-3.2, SE 1.1, P<0.01) and those with BMI ≥ 30 (-2.8, SE 1.1, P = 0.01). CONCLUSIONS: HEI-2015 scores based on 24-hour dietary recall data are generally well estimated.

fMRI-based detection of alertness predicts behavioral response variability.

Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.

Using web-based technologies to increase reach, inclusion, and generalizability in behavioral observation research.

Social scientists have long utilized observations of human behavior in research designs. For researchers studying couples, observation of romantic partners has led to important discoveries about how such behavior is associated with physical, mental, and family health. Historically, these methods have been used in in-person laboratory paradigms that place notable limitations on reach and inclusion. This has, in turn, restricted the generalizability of such research to couples who may not attend an in-person laboratory assessment. Transferring the observational laboratory into an online format has the potential to expand the capabilities of these methods to include more diverse couples. This article presents two empirical studies that used online methods to conduct observational behavioral research with sexual and gender minority couples, populations that could be difficult to reach using traditional methods in many places. We demonstrate that we were able to reach, recruit, and enroll diverse couples that more closely resemble the population of same-sex couples in the United States than likely would have been reached in-person. Further, we show that the quality of the observational data collected via the internet allowed for over 94% of collected data to be coded, with acceptable interrater reliabilities and convergent validity. These studies provide a proof-of-concept of online observational methods, accompanied by a tutorial for using such methods. We discuss possible extensions of these online methods, their limitations, and the potential to help further the field of close relationships by reaching more diverse relationships and increasing the generalizability of our research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Higher Depression of Patients with Alzheimer's Disease During than Before the Lockdown.

We assessed depression in 72 patients with Alzheimer's disease (AD) who live in retirement homes during the COVID-19-related lockdown. We invited caregivers of 72 patients with AD who live in retirement homes to rate depression in the patients both before and during the lockdown. Analysis demonstrated increased depression in the patients during the lockdown. We attribute this increased depression to the restrictive measures on activities, visits, and physical contact between patients with AD and family members during the lockdown.

Dietary Aspects to Incorporate in the Creation of a Mobile Image-Based Dietary Assessment Tool to Manage and Improve Diabetes.

Diabetes is the seventh leading cause of death in United States. Dietary intake and behaviors are essential components of diabetes management. Growing evidence suggests dietary components beyond carbohydrates may critically impact glycemic control. Assessment tools on mobile platforms have the ability to capture multiple aspects of dietary behavior in real-time throughout the day to inform and improve diabetes management and insulin dosing. The objective of this narrative review was to summarize evidence related to dietary behaviors and composition to inform a mobile image-based dietary assessment tool for managing glycemic control of both diabetes types (type 1 and type 2 diabetes). This review investigated the following topics amongst those with diabetes: (1) the role of time of eating occasion on indicators of glycemic control; and (2) the role of macronutrient composition of meals on indicators of glycemic control. A search for articles published after 2000 was completed in PubMed with the following sets of keywords "diabetes/diabetes management/diabetes prevention/diabetes risk", "dietary behavior/eating patterns/temporal/meal timing/meal frequency", and "macronutrient composition/glycemic index". Results showed eating behaviors and meal macronutrient composition may affect glycemic control. Specifically, breakfast skipping, late eating and frequent meal consumption might be associated with poor glycemic control while macronutrient composition and order of the meal could also affect glycemic control. These factors should be considered in designing a dietary assessment tool, which may optimize diabetes management to reduce the burden of this disease.

Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection.

Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107. Two previously validated ASD logistic regression classifiers, evaluated against parent-reported diagnoses, were used to assess the accuracy of the trusted crowd's ratings of unstructured home videos. A representative balanced sample (N = 50 videos) of videos were evaluated with and without face box and pitch shift privacy alterations, with AUROC and AUPRC scores > 0.98. With both privacy-preserving modifications, sensitivity is preserved (96.0%) while maintaining specificity (80.0%) and accuracy (88.0%) at levels comparable to prior classification methods without alterations. We find that machine learning classification from features extracted by a certified nonexpert crowd achieves high performance for ASD detection from natural home videos of the child at risk and maintains high sensitivity when privacy-preserving mechanisms are applied. These results suggest that privacy-safeguarded crowdsourced analysis of short home videos can help enable rapid and mobile machine-learning detection of developmental delays in children.

Reflection on the Fly: Development of the Team Reflection Behavioral Observation (TuRBO) System for Acute Care Teams.

PURPOSE: Acute care teams work in dynamic and complex environments and must adapt to changing circumstances. A team process that helps teams process information and adapt is in-action team reflection (TR), defined as concurrent collective reflection on group objectives, strategies, or processes during an ongoing care event. However, the health care field lacks a means for systematically observing and ultimately training in-action TR in acute care teams. To bridge this gap, the authors developed a theoretically and empirically informed framework, Team Reflection Behavioral Observation (TuRBO), for measuring in-action TR. METHOD: In 2018 at ETH, Zurich, Switzerland, the authors developed a theoretical framework based on the literature and theory. They then conducted exploratory reviews of preexisting videos of acute care teams training simulated emergencies. The authors adapted observation codes using an iterative approach. Using the developed coding framework, they coded 23 video recordings of acute care teams and provided validity evidence from the 3 sources: content, internal structure (interrater reliability), and relations to other variables. RESULTS: The final TuRBO framework consists of 3 general dimensions-seeking information, evaluating information, and planning-that are further specified in 7 subcodes. Interrater agreement of the coding was substantial (κ = 0.80). As hypothesized, the data showed a positive relationship between in-action TR and team performance. Also, physicians spent significantly more time on in-action TR than nurses. CONCLUSIONS: The TuRBO framework for assessing in-action TR in acute care teams provides positive validity evidence of the data. TuRBO integrates different team communication and calibration processes under the overarching concept of in-action TR and provides descriptive behavioral markers. TuRBO taps into powerful cultural and normative components of patient safety. This tool can augment team training that allows all team members to serve as an important resource for flexible, resilient, and safer patient care.

Protocol for quantitative assessment of social cooperation in mice.

Social cooperation in rodents was recently validated in rats, and we recently successfully applied a modified automated analysis to mice. Here, we describe a detailed procedure for using this paradigm in mice that relies on reward-based mutual communication that is automatically detected by a software algorithm embedded in the custom-made equipment. We also describe exemplary results of analyses in mice as a guide to broader neuroscience research applications employing transgenic knockout mice modeling neuropsychiatric disorders and mice of various ages. For complete details on the use and execution of this protocol, please refer to Han et al. (2020).

Comparing video observation to electronic topography device as a method for measuring cigarette puffing behavior.

BACKGROUND: Smoking topography, or puffing behavior, is an important measure of how consumers may use tobacco products. However, numerous issues may prevent collection of this data via in-person, electronic topography device (e.g., CReSS). This study compared cigarette topography measures collected by video observation and electronic device. METHODS: Laboratory smoking sessions were video recorded and scored for 96 cigarettes collected from 34 daily, adult non-treatment-seeking smokers (73.5 % male, 82.4 % White). Participants smoked three of their preferred brand cigarettes using an electronic topography device, providing carbon monoxide (CO) samples before and after each cigarette. Analyses compared measures from both assessment methods and examined associations with device-obtained total puff volume and CO boost. RESULTS: Agreement analyses indicated robust similarity between methods for measures of puff count and total interpuff interval (Intraclass Correlation Coefficient [ICC]'s > 0.96,p's < 0.001; Bland-Altman [B-A] plotted differences within a priori limit of clinical significance) but diverged on total duration (ICC's > .93, p's < .001, yet B-A plots outside a priori limits). Regardless of assessment method, total duration and puff count (but not total interpuff interval) predicted total puff volume (p's < .001). None predicted CO boost (p's = .07-.90)." CONCLUSIONS: Although some topography outcomes (e.g., total puff volume) cannot be assessed via video observation, video-observed measures of puff count, total duration, and total interpuff interval are generally interchangeable with their topography device-obtained counterparts. Thus, video observation is likely a sufficient substitute method for assessing cigarette topography when using an electronic device is not possible.

On the way to systematize habituation: a protocol to minimize the effects of observer presence on wild groups of Leontocebus lagonotus.

Habituation is used in most field research with primates to minimize observer effects on their behavior. Despite its importance, there is little published on the methods used to habituate different taxa of primates or how these methods vary in different habitat types. We assessed changes in behavior and space use of two groups of Leontocebus lagonotus in the Ecuadorian Amazon in order to document this process. Although the subjects had not been studied before, visitors and researchers were more frequently in the home range of Group 1 than of Group 2. We followed both groups for 2 months, collecting behavioral data through scan sampling and recording the use of space (ground, understory, subcanopy, and canopy) and the routes along which we followed the groups. We then divided our data into two equivalent stages, randomized the data for each stage and looked for significant differences using Wilcoxon tests. Our results show a significant decrease in submissive behaviors toward the observer for both groups and a significant increase in resting and foraging for Group 1. In addition, Group 2 used the subcanopy significantly less and the understory more during the second stage. The routes the animals used were significantly longer in the second stage for Group 1, but not for Group 2. We conclude that our methodology is adequate to advance in the habituation of L. lagonotus in less than 2 months and that a group will habituate more quickly if it has had some previous neutral exposure to humans.

Emotion recognition from posed and spontaneous dynamic expressions: Human observers versus machine analysis.

The majority of research on the judgment of emotion from facial expressions has focused on deliberately posed displays, often sampled from single stimulus sets. Herein, we investigate emotion recognition from posed and spontaneous expressions, comparing classification performance between humans and machine in a cross-corpora investigation. For this, dynamic facial stimuli portraying the six basic emotions were sampled from a broad range of different databases, and then presented to human observers and a machine classifier. Recognition performance by the machine was found to be superior for posed expressions containing prototypical facial patterns, and comparable to humans when classifying emotions from spontaneous displays. In both humans and machine, accuracy rates were generally higher for posed compared to spontaneous stimuli. The findings suggest that automated systems rely on expression prototypicality for emotion classification and may perform just as well as humans when tested in a cross-corpora context. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Development and validation of a behavioural video coding scheme for detecting mental workload in manual assembly.

Manual assembly in the future Industry 4.0 workplace will put high demands on operators' cognitive processing. The development of mental workload (MWL) measures therefore looms large. Physiological gauges such as electroencephalography (EEG) show promising possibilities, but still lack sufficient reliability when applied in the field. This study presents an alternative measure with a substantial ecological validity. First, we developed a behavioural video coding scheme identifying 11 assembly behaviours potentially revealing MWL being too high. Subsequently, we explored its validity by analysing videos of 24 participants performing a high and a low complexity assembly. Results showed that five of the behaviours identified, such as freezing and the amount of part rotations, significantly differed in occurrence and/or duration between the two conditions. The study hereby proposes a novel and naturalistic method that could help practitioners to map and redesign critical assembly phases, and researchers to enrich validation of MWL-measures through measurement triangulation. Practitioner summary: Current physiological mental workload (MWL) measures still lack sufficient reliability when applied in the field. Therefore, we identified several observable assembly behaviours that could reveal MWL being too high. The results propose a method to map MWL by observing specific assembly behaviours such as freezing and rotating parts. Abbreviations: MWL: mental workload; EEG: electroencephalography; fNIRS: functional near infrared spectroscopy; AOI: area of interest; SMI: SensoMotoric Instruments, ETG: Eye-Tracking Glasses; FPS: frames per second; BORIS: Behavioral Observation Research Interactive Software; IRR: inter-rater reliability; SWAT: Subjective Workload Assessment Technique; NASA-TLX: National Aeronautics and Space Administration Task Load Index; EL: emotional load; DSSQ: Dundee Stress State Questionnaire; PHL: physical load; SBO: Strategisch Basis Onderzoek.

Development of the Observation Schedule for Children with Autism-Anxiety, Behaviour and Parenting (OSCA-ABP): A New Measure of Child and Parenting Behavior for Use with Young Autistic Children.

Co-occurring emotional and behavioral problems (EBPs) frequently exist in young autistic children. There is evidence based on parental report that parenting interventions reduce child EBPs. More objective measures of child EBPs should supplement parent reported outcomes in trials. We describe the development of a new measure of child and parenting behavior, the Observation Schedule for Children with Autism-Anxiety, Behaviour and Parenting (OSCA-ABP). Participants were 83 parents/carers and their 4-8-year-old autistic children. The measure demonstrated good variance and potential sensitivity to change. Child and parenting behavior were reliably coded among verbal and minimally verbal children. Associations between reports from other informants and observed behavior showed the measure had sufficient convergent validity. The measure has promise to contribute to research and clinical practice in autism mental health beyond objective measurement in trials.

Behavioural and emotional profile of children in residential care in Greece.

Previous research has shown the harmful effects that out of home care can have on children. Specifically, institutionalized children experience high rates of developmental and psychological problems, and therefore special attention is needed so that a fast intervention can be achieved and further complications can be prevented. This paper focuses on building the psychological and behavioural profile of children living in four residential care units in western Greece, in respect to gender, age and nationality. 153 children (88 children in residential care and 65 children rearing in their families) participated in the study. The children age ranged from 6 to 18 years. Children's behavioural profile was assessed through Child Βehaviour Checklist 6-18 (CBCL 6-18) and was afterwards analyzed with respect to variables such as age, gender and nationality. Children in residential care had higher rates of clinical/borderline range symptoms in Internalizing, Externalizing and Total Problems scale than their counterparts rearing at home. Specifically, they were more withdrawn/ depressed and tended to indicate more rule-breaking behaviour. Both genders showed vulnerability in Internalizing behaviour scale, but girls presented higher rates than boys in the clinical range in Externalizing behaviour scale (22% vs 12%) and Total Problems scale (24% vs 5%). Finally, adolescents in residential care exhibit more internalizing symptoms in clinical range than younger children (22% vs 0%), whereas children of Greek nationality were more vulnerable than children of other nationalities, especially in externalizing behavioural symptoms (28% vs 6%). Our study suggests that children in residential care are at high risk for developing mental health issues. The finding that children are more withdrawn and depressed underlines the possible difficulties in establishing confident relations with peers and adults and can destruct their orientation towards social reality, exhibit mistrust to other people and cause insecurity for their future. There is a lack of longitudinal studies investigating children who have lived in institutions in Greece. Such studies would possibly reveal protective or aggravating parameters that have a positive or negative impact on the development of those children and the transition to adult life.

Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm.

Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the 'ground truth'). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.

Socioeconomic and behavioral determinants of cardiovascular diseases among older adults in Belgium and France: A longitudinal analysis from the SHARE study.

Despite advances in the healthcare system, cardiovascular diseases (CVDs) are still an important public health problem with disparities in the burden within and between countries. Studies among the adult population documented that socioeconomic and environmental factors play a role in the incidence and progression of CVDs. However, evidence is scarce on the socioeconomic determinants and the interplay with behavioral risks among older adults. Therefore, we identified socioeconomic and behavioral determinants of CVDs among older adults. Our sample consisted of 14,322 people aged 50 years and above from Belgium and France who responded to the waves 4, 5, 6 and/or 7 of the Survey of Health Ageing and Retirement in Europe. The effect of determinants on the occurrence of CVD was examined using a Generalized Estimating Equation (GEE) approach for binary longitudinal data. The overall rate of heart attack was 8.3%, which is 7.6% in Belgium and 9.1% in France. Whereas, 2.6% and 2.3% in Belgium and France, respectively, had experienced stroke. In the multivariable GEE model, older age [AOR: 1.057, 95%CI: 1.055-1.060], living in large cities [AOR: 1.14, 95%CI: 1.07-1.18], and retirement [AOR: 1.21, 95%CI: 1.16-1.31] were associated with higher risk of CVD. Furthermore, higher level of education [AOR: 0.82, 95%CI: 0.79-0.90], upper wealth quantile [AOR: 0.82, 95%CI: 0.76-0.86] and having social support [AOR: 0.81, 95%CI: 0.77-0.84] significantly lowers the odds of having CVD. A higher hand grip strength was also significantly associated with lower risk of CVD [AOR: 0.987, 95%CI: 0.984-0.990]. This study demonstrated that older adults who do not have social support, live in big cities, belong to the lowest wealth quantile, and have a low level of education have a higher likelihood of CVD. Therefore, community-based interventions aimed at reducing cardiovascular risks need to give more emphasis to high-risk retired older adults with lower education, no social support and those who live in large cities.