Behavioral Profile Research Articles

Evaluation of the Effects of Diet-Induced Obesity in Zebrafish (Danio rerio): A Comparative Study

Objectives: This study aimed to compare diet-induced obesity (DIO) models in zebrafish and investigate the complications and differences between sexes in biochemical and inflammatory parameters. Methods: Adult animals of both sexes were divided into four groups (n = 50) and fed for eight weeks: control group 1: Artemia sp. (15–30 mg/day/fish); control group 2: commercial fish food (3.5% of average weight); obesity group 1: pasteurized egg yolk powder + soybean oil (5% of average weight); obesity group 2: Artemia sp. (60–120 mg/day/fish). Dietary intake, caloric intake and efficiency, body mass index, biochemical, inflammatory, behavioral, histopathological, and stereological parameters, and inflammation-related gene expression were investigated. Results: Obesity group 1 was the most indicated to investigate changes in the anxious behavioral profile (p < 0.05), triglyceride elevation [52.67 (1.2) mg/dL], adipocyte hypertrophy [67.8 (18.1) µm2; p = 0.0004], and intestinal inflammation. Obesity group 2 was interesting to investigate in terms of weight gain [167 mg; p < 0.0001), changes in fasting glucose [48.33 (4.14) mg/dL; p = 0.003), and inflammatory parameters [IL-6: 4.24 (0.18) pg/mL; p = 0.0015]. Conclusions: Furthermore, both DIO models evaluated in the present study were effective in investigating hepatic steatosis. The data also highlighted that sex influences inflammatory changes and fasting blood glucose levels, which were higher in males (p > 0.05). The results show new metabolic routes to be explored in relation to DIO in zebrafish.

Profiles and motivations of disaster volunteers: a proposed framework in the brazilian context

Purpose: Environmental disasters have caused various human, ecological, and economic damage worldwide. To reduce the potential damage and save lives in the short term, the volunteers are involved in support in disaster situations. Given this scenario, studies on volunteers who work in disaster contexts in Brazil proved to be an interesting and necessary research topic. Design/methodology/approach: This research employed a qualitative methodology, utilizing 16 semi-structured, in-depth interviews to gather data. The process of selecting participants was carried out through the snowball sampling technique, focusing on individuals volunteering in disaster response within the Brazilian setting. Following data collection, a thorough content analysis was undertaken. The motivations uncovered through this analysis were then meticulously compared and contrasted against the Volunteer Functions Inventory (VFI), which is rooted in the psychological functionalist theory of volunteerism. Findings: The findings of this study advance the literature by confirming four motivations for volunteers to act in disasters, as identified in the VFI: values, enhancement, protective, and social. Additionally, this study contributes by bringing to light two new motivations, knowledge, and readiness, which emerged from the content data analysis. The study also advances by clustering the behavior profiles determined from these motivations as expert, sensitive, altruistic, beginner, and social. Originality/value: Based on the results, a Disaster Volunteer Framework was proposed, which serves as a basis for developing public policies for the government and assists institutions that work in disaster management in attracting new volunteers.

Behavioral Risk Profiles of Stroke Survivors Among US Adults: Geographic Differences Between Stroke Belt and Non-Stroke Belt States.

Stroke, a leading cause of illness, death, and long-term disability in the US, presents with significant disparities across the country, most notably in southeastern states comprising the "Stroke Belt." This study intended to identify differences between Stroke Belt states (SBS) and non-Stroke Belt states (NSBS) in terms of prevalence of stroke, sociodemographic and behavioral risk factors, and health-related quality of life (HRQOL). We analyzed data from the 2019 Behavioral Risk Factor Surveillance System to compare demographic characteristics, risk factors, physical activity adherence, functional independence, and HRQOL among stroke survivors in SBS and NSBS. Of 18,745 stroke survivors, 4,272 were from SBS and 14,473 were from NSBS. Stroke was more prevalent in SBS (odds ratio [OR] = 1.39; 95% CI, 1.35-1.44; P < .001), with significant differences by age, sex, and race and ethnicity, except for Hispanic ethnicity. Selected stroke risk factors were more common in every category in SBS. Stroke survivors in SBS were less likely to meet physical activity guidelines for aerobic (OR = 0.77; 95% CI, 0.69-0.86; P < .001) and aerobic and strengthening combined (OR = 0.77; 95% CI, 0.70-0.86; P < .001) activities. Stroke survivors in SBS were more likely to not meet either physical activity guideline (OR = 1.31; 95% CI, 1.22-1.41; P < .001). Living in SBS significantly increased the odds of stroke occurrence. Stroke survivors from SBS reported lower HRQOL and insufficient physical activity as well as lower functional independence. Specific strategies are needed for residents of SBS, with a focus on policies and primary and secondary prevention practices across healthcare professions.

Patterns of dietary quality, physical activity, and sleep duration among cancer survivors and caregivers.

Fruit and vegetable intake (FVI), moderate-to-vigorous physical activity (MVPA), and sleep duration are each independently associated with cancer-related and general health outcomes among cancer survivors. Past research suggests that health behaviors cluster among cancer survivors, with caregivers demonstrating similar patterns. This analysis examined co-occurrence of FVI, MVPA, and sleep duration among cancer survivors and informal cancer caregivers and identified sociodemographic and clinical correlates of health behavior engagement. Using data from the Health Information National Trends Survey (HINTS), an exploratory latent profile analysis (LPA) was conducted among those self-reporting a history of cancer or identifying as a cancer caregiver. The LPA model was fit with daily self-reported FVI (cups/d), MPVA (minutes/d) and sleep duration (hours/d). Multinomial logistic regression models were used to predict profile membership based on sociodemographic and clinical characteristics. Four health behavior profiles were identified (Least Engaged-No MVPA, Least Engaged-Low MVPA, Moderately Engaged, and Highly Engaged). The largest profile membership was Least Engaged-No MVPA, capturing 37% of the sample. Profiles were most distinguished by MVPA, with the lowest variance in sleep duration. Participants reporting higher FVI also often reported greater MVPA and longer sleep duration. Profile membership was significantly associated with age, relationship status, education, income, rurality, alcohol use, self-efficacy, psychological distress, BMI, and cancer type. This study identified four health behaviors patterns and sociodemographic correlates that distinguished those patterns among cancer survivors and caregivers drawn from a nationally representative sample. Results may help identify for whom health behavior interventions could be of greatest benefit.

P2X7 receptors modulate acquisition of cue fear extinction and contextual background memory generalization in male mice

The purinergic P2X7 receptors (P2X7R) are activated by adenosine triphosphate (ATP) in several brain regions, particularly those involved with emotional control and the regulation of fear-related memories. Here, we investigate the role of P2X7R in fear learning memory, specifically in the acquisition and consolidation phases of the cued fear conditioning paradigm. C57Bl/6 wildtype (WT) male mice that received a single i.p. injection of the selective P2X7R antagonist A438079 prior the conditioning session showed generalization of cued fear memory and impaired fear extinction recall in the test session, while those treated prior the extinction session exhibited a similar behavior profile accompanied by resistance in the extinction learning. However, no effects were observed when this drug was administered immediately after the conditioning, extinction, or before the test session. Our results with P2X7R knockout (P2X7 KO) mice showed a behavioral profile that mirrored the collective effects observed across all pharmacological treatment conditions. This suggests that the P2X7R KO model effectively replicates the behavioral changes induced by the pharmacological interventions, demonstrating that we have successfully isolated the role of P2X7R in the fear and extinction phases of memory. These findings highlight the role of P2X7R in the acquisition and recall of extinction memory and supports P2X7R as a promising candidate for controlling abnormal fear processing, with potential applications for stress exposure-related disorders such as post-traumatic stress disorder (PTSD).

Analysis of health recommendations using longitudinal quality of life data: QoL@TbA - A transformer-based approach.

Objective: Health recommendation systems suggest behavioral modifications to improve quality of life. However, current approaches do not facilitate the generation or examination of such recommendations considering the multifeature longitudinal evolution of behaviors. This paper proposes the use of a deep learning transformer-based model that allows the analysis of recommendations for behavior changes. Methods: We adapted a prediction approach, namely Behavior Sequence Transformer (BST), which analyzes temporal human routines and patterns, generating inductive outcomes. The evaluation relied on a case study that employed the behavioral history and profile of the English Longitudinal Study of Ageing (ELSA) participants (n = 2682), predicting their psychological mood (normal, pre-depressed, depressed) according to input recommendations for behavioral changes. Root mean squared error (RMSE) and learning curves were used to track the recommendation accuracy evolution and possible overfitting problems. Results: Experiments demonstrated lower RMSE values for the multifeature model (0.28/0.03) when compared to its single-feature versions (marital status, 0.59/0.001), (high pressure, 0.357/0.04), (diabetes, 0.36/0.01), (sleep quality, 0.57/0.02), (level of physical activity, 0.57/0.01). Conclusions: The results demonstrate the architecture's capability to analyze multifeatured longitudinal data, supporting the generation of suggestions for concurrent modifications across multiple input features. Moreover, these suggestions align with findings in specialized literature.

Behavioral effects of two cannabidiol and cannabigerol-rich formulas on mice

Cannabis sativa L. produces more than 100 specific bioactive compounds, known as cannabinoids. The major non-psychotropic Cannabis constituent is cannabidiol (CBD), which displays beneficial properties in a variety of medical conditions. However, the potential therapeutic role of other minor phytocannabinoids, such as cannabigerol (CBG), and their use in combination with CBD, has remained largely unexplored. In this study, we wanted to assess the in vivo effects of two novel non-psychotropic cannabinoid formulas, both containing relatively high percentages of CBD but differing mainly for CBG content, hereafter called CBG+ and CBG- formulas. We employed different behavioral tests to evaluate the effects of these formulas at three different dosages on mice locomotor activity, anxiety-related behaviors, short-term memory and sociability. We found that these two formulas display unique behavioral profiles: CBG+ formula produced an increase in mice locomotor activity and displayed anxiolytic properties, whereas both formulas improved spatial short-term memory and social interactions. The results obtained suggest that different combinations of phytocannabinoids are able to determine different behavioral effects and highlight the importance of studying the effects of less known phytocannabinoids (like CBG), which used in combination with other phytocannabinoids can change the profile of action of other active compounds (such as CBD).

Sensor-Based and Visual Behavioral Profiling of Dry Holstein Cows Presenting Distinct Median Core Body Temperatures

The consequences of heat stress during the dry period can extend into the postpartum period, affecting health and productivity in the subsequent lactation. We hypothesized that cows with distinct core body temperatures (CBTs) would exhibit disparate behaviors associated with different degrees of heat generation or dissipation. The primary objective was to investigate behavioral differences of dry Holstein cows (n = 50) classified as high-temperature (HT) or low-temperature (LT), based on median CBT during the summer months using visual observations and accelerometer technology. A secondary objective was to investigate the transcriptome of white blood cells (WBCs) collected from a subgroup of HT and LT cows (n = 5; per group). Minor behavior differences were observed during the visual observations (performed for a total of 16h/cow). Based on automated monitoring system (AMS) data, collected 24/7 over a period of 42 days per cow, HT cows displayed higher periods of high activity and lower periods of inactivity prepartum and diminished rumination time postpartum than LT cows. There were 16 differently expressed genes (DEGs) in WBCs of HT compared to LT cows. Several of the identified DEGs have been previously associated with heat stress. The observed trends in the AMS data indicate that CBT and patterns of activity prepartum may serve as valuable predictors for identifying dairy cows with distinct tolerance to heat stress.

Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos

Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this study, a series of representative psychoactive compounds were selected as model pollutants, and a new approach combining zebrafish embryo behavioral phenotyping with deep learning was used to identify and classify water pollutants. Zebrafish embryos were exposed to 17 psychoactive compounds at environmentally relevant concentrations (1 and 10 μg/L), and their locomotor behavior was recorded at 5 and 6 days post-fertilization (dpf). We constructed six distinct zebrafish locomotor track datasets encompassing various observation times and developmental stages and evaluated multiple deep learning models on these datasets. The results demonstrated that the ResNet101 model performed optimally on the 1-min track dataset at 6 dpf, achieving an accuracy of 65.35 %. Interpretability analyses revealed that the model effectively focused on the relevant locomotor track features for classification. These findings suggest that the integration of zebrafish embryo behavioral analysis with deep learning can serve as an environmentally friendly and economical method for detecting water pollutants. This approach offers a new perspective for water quality monitoring and has the potential to assist existing chemical analytical techniques in detection, thereby advancing environmental toxicology research and water pollution control efforts.

Altered Patterns of Maternal Behavior Transitions in Rats Exposed to Limited Bedding and Nesting Material Paradigm.

Maternal care plays a fundamental role in early life, and the alteration of its patterns can negatively affect the developmental course of the offspring in a myriad of domains in both rats and humans. The limited bedding and nesting (LBN) protocol is an extensively used paradigm in rodents to address the impact of altered maternal behavior patterns on infants' neurodevelopment. Here, we explore the altered patterns of maternal care in rats in LBN conditions by describing sequences of transition between maternal behavior components using network analysis. Using this technique, we capture how often maternal behavior transitions take place during the LBN period and which behaviors play central roles in those transitions over time. Female rats and their pups were placed in standard and LBN housing conditions from Postpartum Days 2 to 9, during which maternal behavior was observed during the light and dark phases. We used inferential statistical analysis to compare the maternal behavior profiles of control and LBN dams, and network analysis was used to capture the altered sequence of maternal behavior transitions during the period of LBN. Compared to control dams, LBN dams significantly increased their high crouch nursing posture during light/dark phases (p=0.018), and the number of behavioral transitions increased only during the dark phase (p=0.0004). Network analysis revealed specific altered patterns of behavioral transitions in LBN dams, characterized by the predominance of switches between active nursing postures during the first five days of the LBN protocol. Nursing behavior was the most disrupted component of maternal behavior under the LBN protocol, mainly during the dark phase. Network analysis can complement and extend traditional methods to gain a more thorough understanding of maternal care strategies and behavioral patterns in LBN conditions and potential consequences for the offspring.

The ultrasonic vocalization (USV) syllable profile during neonatal opioid withdrawal and a kappa opioid receptor component to increased USV emissions in female mice.

Opioid use during pregnancy can lead to negative infant health outcomes, including neonatal opioid withdrawal syndrome (NOWS). NOWS comprises gastrointestinal, autonomic nervous system, and neurological dysfunction that manifest during spontaneous withdrawal. Variability in NOWS severity necessitates a more individualized treatment approach. Ultrasonic vocalizations (USVs) in neonatal mice are emitted in isolation as a stress response and are increased during opioid withdrawal, thus modeling a negative affective state that can be utilized to test new treatments. We sought to identify the behavioral and USV profile, brainstem transcriptomic adaptations, and role of kappa opioid receptors in USVs during neonatal opioid withdrawal. We employed a third trimester-approximate opioid exposure model, where neonatal inbred FVB/NJ pups were injected twice-daily with morphine (10mg/kg, s.c.) or saline (0.9%, 20 ul/g, s.c.) from postnatal day(P) 1 to P14. This protocol induces reduced weight gain, hypothermia, thermal hyperalgesia, and increased USVs during spontaneous morphine withdrawal. On P14, there were increased USV emissions and altered USV syllables during withdrawal, including an increase in Complex 3 syllables in FVB/NJ females (but not males). Brainstem bulk mRNA sequencing revealed an upregulation of the kappa opioid receptor (Oprk1), which contributes to withdrawal-induced dysphoria. The kappa opioid receptor (KOR) antagonist, nor-BNI (30 mg/kg, s.c.), significantly reduced USVs in FVB/NJ females, but not males during spontaneous morphine withdrawal. Furthermore, the KOR agonist, U50,488h (0.625 mg/kg, s.c.), was sufficient to increase USVs on P10 (both sexes) and P14 (females only) in FVB/NJ mice. We identified an elevated USV syllable, Complex 3, and a female-specific recruitment of the dynorphin/KOR system in increased USVs associated with neonatal opioid withdrawal severity.

A Corpus-based Behavioral Profile Study of Synonymous Expressions Meaning “to Start”

Synonymy is an extremely significant component in language learning, and the usage of synonymy is rather flexible and difficult to distinguish. In this research, the author employs a corpus-based behavioral profile approach to study the usage of a set of English synonymous expressions meaning “to start”, namely, start, begin, commence, launch, set out. The author first searches in American Contemporary English Corpus to explore the overall usage frequency and specific genre of each synonymous expression, then randomly selects 500 sentences, and makes manual annotation with 12 ID tag and 46 ID tag levels. Later, hierarchical clustering analysis and correspondence analysis have been employed to examine the semantic differences and usage structures of this set of expressions.The results shows that: ① The overall usage frequency varies greatly—from the highest to the lowest are start, begin, launch, set out, commence. ② They are used in various genres. Start is mainly used in informal styles including blogs, while the other four are mainly used in formal styles, including fictions and magazines. ③ Begin and set out have the greatest semantic similarity; start, begin, and set out form one semantic branch, and commence and launch form another, and there is a huge difference between these two branches. ④ There are great differences between the five expressions and their morphological, syntactic and semantic changes.Quantitative analysis method has been used to reveal the different semantic features and usage of a set of synonymous expressions, which is conducive to both promote the study of synonymous relations, and verify the effectiveness and applicability of the behavioral profile approach in semantic study.

Composite Hydrogel of Polyacrylamide/Starch/Gelatin as a Novel Amoxicillin Delivery System.

This study investigates the development and characterization of a novel composite hydrogel composed of polyacrylamide (PAAm), starch, and gelatin for use as an amoxicillin delivery system. The optical properties, swelling behavior, and drug release profile of the composite hydrogel's were studied to evaluate its efficacy and potential applications. UV-visible spectroscopy was employed to determine the optical properties, revealing significant transparency in the visible range, which is essential for biomedical applications. The incorporation of starch and gelatin into the polyacrylamide matrix significantly enhanced the hydrogel's swelling capacity and biocompatibility. Studies on drug delivery demonstrated a sustained release profile of amoxicillin in simulated gastrointestinal fluids, which is essential for maintaining therapeutic levels for a prolonged amount of time. The results indicate that the composite hydrogel of PAAm/starch/gelatin has good swelling behavior, appealing optical characteristics, and a promising controlled drug release mechanism. These results point to this hydrogel's considerable potential as a drug delivery method, providing a viable path toward enhancing the medicinal effectiveness of amoxicillin and maybe other medications.

A novel approach for APT attack detection based on an advanced computing

To enhance the effectiveness of the Advanced Persistent Threat (APT) detection process, this research proposes a new approach to build and analyze the behavior profiles of APT attacks in network traffic. To achieve this goal, this study carries out two main objectives, including (i) building the behavior profile of APT IP in network traffic using a new intelligent computation method; (ii) analyzing and evaluating the behavior profile of APT IP based on a deep graph network. Specifically, to build the behavior profile of APT IP, this article describes using a combination of two different data mining methods: Bidirectional Long Short-Term Memory (Bi) and Attention (A). Based on the obtained behavior profile, the Dynamic Graph Convolutional Neural Network (DGCNN) is proposed to extract the characteristics of APT IP and classify them. With the flexible combination of different components in the model, the important information and behavior of APT attacks are demonstrated, not only enhancing the accuracy of detecting attack campaigns but also reducing false predictions. The experimental results in the paper show that the method proposed in this study has brought better results than other approaches on all measurements. In particular, the accuracy of APT attack prediction results (Precision) reached from 84 to 91%, higher than other studies of over 7%. These experimental results have proven that the proposed BiADG model for detecting APT attacks in this study is proper and reasonable. In addition, those experimental results have not only proven the effectiveness and superiority of the proposed method in detecting APT attacks but have also opened up a new approach for other cyber-attack detections such as distributed denial of service, botnets, malware, phishing, etc.

Health Risk Behaviors of Adolescents in Europe: A Latent Profile Analysis of Health Behavior in School-Aged Children Survey Data From 43 Countries

PurposeCurrent literature on prevention and intervention programs for European adolescents who engage in health risk behaviors is limited. Few studies have utilized latent profile analysis methods and internationally comparative data on adolescents and their health risk behaviors, highlighting the need for more comprehensive research using internationally comparative data. The current study aimed to examine the health risk behavior profiles and identify subgroups of adolescents with similar patterns of health risk behaviors using a representative sample of European countries. Findings will inform better targeted prevention strategies for this population. MethodsThe study used a quantitative approach and implemented secondary data analysis using the Health Behavior in School-Aged Children survey 2017/2018 from 43 European countries to understand the prevalence of health risk behaviors among European adolescents and yield latent profiles regarding their health risk behaviors by sex. ResultsFive latent profiles of health risk behaviors were identified for boys and six for girls. For both boys and girls, low-risk (83.4% and 85.7%), poor lifestyle, smoking (6.2% and 5.1%), balanced lifestyle, aggressive acts (5.4% and 3.7%), poor lifestyle, high substance use (2% and 1%), moderate lifestyle, and moderate substance use (3% and 0.9%) were common profiles. A moderate lifestyle and low substance use profile (3.6%) was only specific to girls. DiscussionThe current study provides important insights regarding European adolescents' health risk behaviors. The findings suggest the need for better targeted prevention programming.

Attachment security, environmental adversity, and fast life history behavioral profiles in human adolescents.

One species-general life history (LH) principle posits that challenging childhood environments are coupled with a fast or faster LH strategy and associated behaviors, while secure and stable childhood environments foster behaviors conducive to a slow or slower LH strategy. This coupling between environments and LH strategies is based on the assumption that individuals' internal traits and states are independent of their external surroundings. In reality, individuals respond to external environmental conditions in alignment with their intrinsic vitality, encompassing both physical and mental states. The present study investigated attachment as an internal mental state, examining its role in mediating and moderating the association between external environmental adversity and fast LH strategies. A sample of 1169 adolescents (51% girls) from 9 countries was tracked over 10 years, starting from age 8. The results confirm both mediation and moderation and, for moderation, secure attachment nullified and insecure attachment maintained the environment-LH coupling. These findings suggest that attachment could act as an internal regulator, disrupting the contingent coupling between environmental adversity and a faster pace of life, consequently decelerating human LH.

The Impacts of Co-Occurring Developmental Language Disorder on the Academic, Interpersonal, and Behavioral Profiles of Children With Attention-Deficit/Hyperactivity Disorder

Purpose: Attention-deficit/hyperactivity disorder (ADHD) is a multifactorial disorder that has been linked to increased risk for behavioral, academic, and interpersonal problems. Developmental language disorder (DLD) has also been linked to poor outcomes in these areas. The presence of co-occurring disorders in children's profiles is generally associated with poorer outcomes relative to profiles consisting of only one disorder. This study investigated the extent to which co-occurring DLD in children already identified with ADHD presented increased risk for severity of their primary ADHD symptoms, the presence of secondary behavioral problems, and/or increased problems in the tertiary areas of academic performance and interpersonal interactions. Method: Data from 90 children from combined community-based and clinically based study samples were used in this retrospective study. Behavioral, academic, and social attributes were assessed from parent ratings on the Child Behavior Checklist. Results: Results indicated primary symptoms of ADHD and related tertiary interpersonal consequences were not increased when co-occurring DLD was present. However, the co-occurrence of DLD on ADHD resulted in increased risk for secondary externalizing problems and deficits in academic performance. Conclusions: Outcomes highlight the need for high-quality collaborations among clinical professionals, school-based professionals, and parents to ensure the complex needs of children with ADHD are adequately addressed. Children with ADHD who have elevated externalizing behaviors and who are struggling academically should be tested for possible co-occurring DLD.

A Multimodal, In Vivo Approach for Assessing Structurally and Phenotypically Related Neuroactive Molecules.

A recently reported behavioral screen in larval zebrafish for phenocopiers of known anesthetics and associated drugs yielded an isoflavone. Related isoflavones have also been reported as GABAA potentiators. From this, we synthesized a small library of isoflavones and incorporated an in vivo phenotypic approach to perform structure-behavior relationship studies of the screening hit and related analogs via behavioral profiling, patch-clamp experiments, and whole brain imaging. This revealed that analogs effect a range of behavioral responses, including sedation with and without enhancing the acoustic startle response. Interestingly, a subset of compounds effect sedation and enhancement of motor responses to both acoustic and light stimuli. Patch clamp recordings of cells with a human GABAA receptor confirmed that behavior-modulating isoflavones modify the GABA signaling. To better understand these molecules' nuanced effects on behavior, we performed whole brain imaging to reveal that analogs differentially effect neuronal activity. These studies demonstrate a multimodal approach to assessing activities of neuroactives.

A comparative analysis of 24-hour movement behaviors features using different accelerometer metrics in adults: Implications for guideline compliance and associations with cardiometabolic health.

Movement behavior features such as time use estimates, average acceleration and intensity gradient are crucial in understanding associations with cardiometabolic health. The aim of this study was to 1) compare movement behavior features processed by commonly used accelerometer metrics among adults (i.e. Euclidian Norm Minus One (ENMO), Mean Amplitude Deviation (MAD) and counts per minute (CPM)), 2) investigate the impact of accelerometer metrics on compliance with movement behavior guidelines, and 3) explore potential variations in the association between movement behavior features and cardiometabolic variables depending on the chosen metric. This cross-sectional study collected movement behavior features (Actigraph GT3X+) and cardiometabolic variables. Accelerometer data were analyzed by four metrics, i.e. ENMO, MAD, and CPM vertical axis and CPM vector magnitude (GGIR). Intraclass correlations and Bland‒Altman plots identified metric differences for time use in single movement behaviors (physical activity, sedentary behavior), average acceleration and intensity gradient. Regression models across the four metrics were used to explore differences in 24-hour movement behaviors (24h-MBs; compositional variable) as for exploration of associations with cardiometabolic variables. Movement behavior data from 213 Belgian adults (mean age 45.8±10.8 years, 68.5% female) differed according to the metric used, with ENMO representing the most sedentary movement behavior profile and CPM vector magnitude representing the most active profile. Compliance rates for meeting integrated 24h-MBs guidelines varied from 0-25% depending on the metric used. Furthermore, the strength and direction of associations between movement behavior features and cardiometabolic variables (body mass index, waist circumference, fat% and HbA1c) differed by the choice of metric. The metric used during data processing markedly influenced cut-point dependent time use estimates and cut-point independent average acceleration and intensity gradient, impacting guideline compliance and associations with cardiometabolic variables. Consideration is necessary when comparing findings from accelerometry studies to inform public health guidelines.

Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias

We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54–8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.