Human Behavior Patterns Research Articles

34831 Applications of behavioral economics in dermatology

Behavioral economics applies the principles of psychology and economics to explain patterns in human decision making. When applied to health care settings, behavioral economics explains how heuristics and cognitive biases may lead patients to make counterproductive choices that undermine their health and wellbeing. These maladaptive decisions lead to noncompliance and suboptimal treatment, which contributes to disease progression and increased utilization of medical resources. An understanding of patterns in human behavior can be used to counter noncompliance and improve the efficacy of health promotion interventions.

Research of autoencoder-based user biometric verification with motion patterns

In the current research, we continue our previous study regarding motion-based user biometric verification, which consumes sensory data. Sensory-based verification systems empower the continuous authentication narrative – as physiological biometric methods mainly based on photo or video input meet a lot of difficulties in implementation. The research aims to analyze how various components of sensor data from an accelerometer affect and contribute to defining the process of unique person motion patterns and understanding how it may express the human behavioral patterns with different activity types. The study used the recurrent long-short-term-memory autoencoder as a baseline model. The choice of model was based on our previous research. The research results have shown that various data components contribute differently to the verification process depending on the type of activity. However, we conclude that a single sensor data source may not be enough for a robust authentication system. The multimodal authentication system should be proposed to utilize and aggregate the input streams from multiple sensors as further research.

Sports Participation and Anti-Epidemic: Empirical Evidence on the Influence of Regular Physical Activity on the COVID-19 Pandemic in Mainland China.

This study aims to investigate the effects and influencing mechanisms of regular physical activity (RPA) on the COVID-19 pandemic. Daily data from 279 prefecture-level cities in mainland China were collected from 1 January to 17 March 2020. A two-way fixed-effects model was used to identify the causal relationship between physical activity and COVID-19, while also considering factors such as patterns of human behavior and socioeconomic conditions. The instrumental variable (IV) approach was applied to address potential endogeneity issues for a more accurate causal identification, and the mediating effect model was applied to examine the mechanisms of the influence of physical activity on the epidemic. We found that regular physical activity significantly improves individual immunity, which, in turn, leads to a reduction in the probability of being infected with COVID-19. Furthermore, we investigated the heterogeneity of the influence, finding that the negative impact of physical activity on the pandemic is more pronounced in the absence of adequate medical resources, strong awareness of prevention among residents, and fully implemented public health measures. Our results provide empirical evidence for the mechanisms of influence of physical activity on the pandemic. We would suggest that not only should physical activity be actively practiced during the pandemic, but also long-term regular exercise habits should be consciously cultivated to improve the ability of the individual immune system to better cope with sudden outbreaks of emerging infectious diseases.

Turning data into better mental health: Past, present, and future.

In this mini-review, we discuss the fundamentals of using technology in mental health diagnosis and tracking. We highlight those principles using two clinical concepts: (1) cravings and relapse in the context of addictive disorders and (2) anhedonia in the context of depression. This manuscript is useful for both clinicians wanting to understand the scope of technology use in psychiatry and for computer scientists and engineers wishing to assess psychiatric frameworks useful for diagnosis and treatment. The increase in smartphone ownership and internet connectivity, as well as the accelerated development of wearable devices, have made the observation and analysis of human behavior patterns possible. This has, in turn, paved the way to understand mental health conditions better. These technologies have immense potential in facilitating the diagnosis and tracking of mental health conditions; they also allow the implementation of existing behavioral treatments in new contexts (e.g., remotely, online, and in rural/underserved areas), and the possibility to develop new treatments based on new understanding of behavior patterns. The path to understand how to best use technology in mental health includes the need to match interdisciplinary frameworks from engineering/computer sciences and psychiatry. Thus, we start our review by introducing bio-behavioral sensing, the types of information available, and what behavioral patterns they may reflect and be related to in psychiatric diagnostic frameworks. This information is linked to the use of functional imaging, highlighting how imaging modalities can be considered “ground truth” for mental health/psychiatric dimensions, given the heterogeneity of clinical presentations, and the difficulty of determining what symptom corresponds to what disease. We then discuss how mental health/psychiatric dimensions overlap, yet differ from, psychiatric diagnoses. Using two clinical examples, we highlight the potential agreement areas in assessment/management of anhedonia and cravings. These two dimensions were chosen because of their link to two very prevalent diseases worldwide: depression and addiction. Anhedonia is a core symptom of depression, which is one of the leading causes of disability worldwide. Cravings, the urge to use a substance or perform an action (e.g., shopping, internet), is the leading step before relapse. Lastly, through the manuscript, we discuss potential mental health dimensions.

The relationship between novelty-seeking traits and behavior: Establishing construct validity for the human Behavioral Pattern Monitor

Novelty seeking is a tendency to approach new situations, putatively driven by the brain's catecholaminergic system. It is traditionally measured via self-report, but a laboratory-based paradigm, the human Behavioral Pattern Monitor (hBPM), quantifies behavior in a novel environment and has utility in cross-species studies of neuropsychiatric disorders. Our primary aim assessed whether self-reported novelty-seeking traits were associated with novelty-seeking behavior in the hBPM. An existing sample of 106 volunteers were categorized as high vs. low novelty seekers using the Temperament and Character Inventory (TCI). Subjects had been randomized to one dose of amphetamine (10 or 20 mg) or modafinil (200 or 400 mg), allowing us to explore whether a pharmacological catecholamine challenge further enhanced novelty-seeking behavior. High TCI novelty-seekers had more hBPM motor activity and novel object interactions. The exploratory analyses, although limited by low power, suggested that amphetamine and modafinil did not markedly moderate novelty-seeking traits. The hBPM demonstrates construct validity as a lab-based measure of novelty seeking and thus useful in translational studies of neuropsychiatric conditions and treatment options. Further research may illuminate whether a biological predisposition towards higher catecholaminergic activity, combined with the novelty-seeking trait, may increase propensity for risky and addictive behaviors.

The New Paradigm of Communication Within the Transdisciplinary Research

This paper aims to postulate a new paradigm of communication within transdisciplinary research, treated as a desirable model of practicing science in the XXI century (Brandt et al. 2013, Balsiger 2004, Wickson et al. 2006, Aenis 2010). The author emphasizes the need for a re-naturalization of transdisciplinary communication within the internally diversified teams, by returning to the natural, unforced patterns of human behavior within a group. Although it may seem to be a manifestation of sentiment for the Renaissance vision of science, combining threads from various, seemingly unconnected, areas of study (e.g., physics, metaphysics, and philosophy in one research approach) - today's necessity to restore transdisciplinary communication is a challenging task, including a need to return to the natural practices of cross-domain communication, using the potential of in-depth expertise in all the scientific areas. A mature transdisciplinary dialogue base on a translation of perspectives (Schuetz 1984, Perinbanayagam 2005) resulting from the natural predilection of a human actor to put himself/herself in the position of the other (interaction partner) to understand his/her point of view, for the set of intersubjectively shared senses and meanings is the starting point for every kind of partnership.

Multi-Level Transformer-Based Social Relation Recognition.

Social relationships refer to the connections that exist between people and indicate how people interact in society. The effective recognition of social relationships is conducive to further understanding human behavioral patterns and thus can be vital for more complex social intelligent systems, such as interactive robots and health self-management systems. The existing works about social relation recognition (SRR) focus on extracting features on different scales but lack a comprehensive mechanism to orchestrate various features which show different degrees of importance. In this paper, we propose a new SRR framework, namely Multi-level Transformer-Based Social Relation Recognition (MT-SRR), for better orchestrating features on different scales. Specifically, a vision transformer (ViT) is firstly employed as a feature extraction module for its advantage in exploiting global features. An intra-relation transformer (Intra-TRM) is then introduced to dynamically fuse the extracted features to generate more rational social relation representations. Next, an inter-relation transformer (Inter-TRM) is adopted to further enhance the social relation representations by attentionally utilizing the logical constraints among relationships. In addition, a new margin related to inter-class similarity and a sample number are added to alleviate the challenges of a data imbalance. Extensive experiments demonstrate that MT-SRR can better fuse features on different scales as well as ameliorate the bad effect caused by a data imbalance. The results on the benchmark datasets show that our proposed model outperforms the state-of-the-art methods with significant improvement.

Foodborne zoonotic trematode infections in Yen Bai, Vietnam: a situational analysis on knowledge, attitude, and practice (KAP) and risk behaviors.

SummaryIntroductionFoodborne Zoonotic Trematode Infections (FZTi) are neglected tropical diseases of public health concern in Vietnam. The transmission of FZTi is linked to human behavior patterns. The aims to investigate the knowledge, attitude, and practices regarding FZTi among local people.MethodsA cross-sectional study was conducted using a mixed method, which included a baseline survey and in-depth interviews. 375 participants were interviewed face-to-face in the survey, and 27 participants had the in-depth interviews.ResultsThe results showed that 36.3% passed the knowledge assessment, 86.7% passed the attitude assessment, and 24% passed the practical assessment. There were differences in average knowledge scores among gender (men higher than women, p = 0.006), ethnicities (Kinh higher than Dao and Tay, p < 0.001), and educational level (higher education, higher knowledge score, p < 0.001). There were differences in the frequency of eating raw fish between men and women (men higher than women, p < 0.001), and in the average practice score between men and women (women higher than men, p = 0.028). Eating raw fish and/or undercooked fish, raw vegetables, and drinking untreated water from Thac Ba lake in Yen Bai province were identified as FZTi risk behaviors. The occurrence of these risky habits can be explained by the lack of knowledge on FZTi, poor economic conditions and typical social features of local people.ConclusionThe current knowledge of local people in regard to safe eating practices is poor. They keep performing unsafe practices, which lead to infection with FZTi; therefore, an integrated control of FZTi is essential.

Extracting new information from old stones: An analysis of three quarries in the semi-arid Pilbara region, northwest Australia

Quarries are important archaeological sites in Australia. They represent the starting point for conditioning variability in stone artefact assemblages (e.g. raw material availability, accessibility, quality, size and shape) and serve as a basis to track human movement from source to discard. Despite this, there is relatively little published research on quarries. For example, in the Pilbara, Western Australia, where stone artefact assemblages are ubiquitous, the paucity of published quarry research makes it difficult to accurately reconstruct patterns of past human behaviour. This research begins to address these issues through the application of a suite of analytical techniques to three quarry sites located in Puutu Kunti Kurrama and Pinikura (PKKP) Country in the inland Pilbara. Some variability in site use and reduction occurs between the three quarries. However, a clear pattern of assemblage formation involving natural heat fracture, non-intensive reduction, core transport, high proportions of non-intensively used tools and non-economic tool selection, was identified at all sites. The results demonstrate clear planning and manipulation of stone resources by Aboriginal people in the raw material rich Hamersley Range and demonstrate the vital importance of quarries as multi-functional locations in understanding wider systems of Aboriginal interaction with the Pilbara landscape.

Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives

The question regarding the actual usage of built environments is of immense importance in behavioral research. Yet traditional methods of collecting and analyzing data on movements and activities often lack needed accuracy and granularity. Thus, this article reviewed and summarized the applicability of emergent GPS trajectory mining approaches in the field of architecture from geographic, semantic, and quantitative perspectives, respectively. Accordingly, three experiments based on a case study using real GPS trajectory data from visitors to the Palace Museum in China were conducted to examine the usefulness and weakness of the aforementioned approaches. The findings revealed that although all three dimensions of the trajectory mining approaches had the potential to provide useful information for architectural and urban design, the higher the dimensionality in utilizing the data, the more effective the approach was in discovering generalizable knowledge of human behavioral pattern. Furthermore, the results suggested that to gain insights into the typological characteristics of human behaviors related to the built environments, the contribution of trajectory data alone was limited, hence, conventional field surveys and questionnaires which contain information on individual characteristics and spatial features should be used in conjunction. Future research and practical implications were outlined.

Does COVID-19 lockdowns have impacted on global dengue burden? A special focus to India

BackgroundThe world has been battling several vector-borne diseases since time immemorial. Socio-economic marginality, precipitation variations and human behavioral attributes play a major role in the proliferation of these diseases. Lockdown and social distancing have affected social behavioral aspects of human life and somehow impact on the spread of vector borne diseases. This article sheds light into the relationship between COVID-19 lockdown and global dengue burden with special focus on India. It also focuses on the interconnection of the COVID-19 pandemic (waves 1 and 2) and the alteration of human behavioral patterns in dengue cases.MethodsWe performed a systematic search using various resources from different platforms and websites, such as Medline; Pubmed; PAHO; WHO; CDC; ECDC; Epidemiology Unit Ministry of Health (Sri Lanka Government); NASA; NVBDCP from 2015 until 2021. We have included many factors, such as different geographical conditions (tropical climate, semitropic and arid conditions); GDP rate (developed nations, developing nations, and underdeveloped nations). We also categorized our data in order to conform to COVID-19 duration from 2019 to 2021. Data was extracted for the complete duration of 10 years (2012 to 2021) from various countries with different geographical region (arid region, semitropic/semiarid region and tropical region).ResultsThere was a noticeable reduction in dengue cases in underdeveloped (70–85%), developing (50–90%), and developed nations (75%) in the years 2019 and 2021. The dengue cases drastically reduced by 55–65% with the advent of COVID-19 s wave in the year 2021 across the globe.ConclusionsAt present, we can conclude that COVID-19 and dengue show an inverse relationship. These preliminary, data-based observations should guide clinical practice until more data are made public and basis for further medical research.

HASAN LANGGULUNG PARADIGM ABOUT APPRECIATION OF ISLAMIC VALUES IN EDUCATION

Islamic values ??are something that is valuable for the effort that has been given, which has advantages and can be compared with something else. Islamic values ??as a driving force that is inherent in a person's soul so that it becomes a way of life. The pattern of human behavior must follow Islamic values ??accompanied by appreciation in the human being so that certain thoughts, attitudes, and feelings will develop. This study aims to examine more deeply Hasan Langgulung's paradigm of appreciation of Islamic values ??in education. This research uses library research method with descriptive, conceptual, and psychological approaches. The results of this study indicate: appreciation of Islamic values ??is a type of human learning process in life, by following the law in the learning process without violating existing rules. The appreciation of Islamic values ??in oneself is accompanied by a learning process in the presence of a stimulus from humans to practice Islamic values ??in education and various certain values ??so that these actions will be rewarded from human practice. If Islamic values ??have become human guidelines, if the human does not live it in the form of practice, then the human will feel uncomfortable in his life.

Decade-low aerosol levels over the Bohai and Yellow Seas amid the COVID-19 lockdown.

Coronavirus disease 2019 (COVID-19) has crucially influenced anthropogenic activities, which in turn impacts upon the environment. In this study, we investigated the variations on aerosol optical depth (AOD) at 550 nm over the Bohai Sea and Yellow Sea during the COVID-19 lockdown (from February to March in 2020) of China mainland based on Moderate-resolution Imaging Spectroradiometer (MODIS) observation by comparing with historical AOD records (2011–2019). Our results show that with the lockdown implementation, the decade-low AOD levels are achieved in February and March 2020 (0.39 ± 0.18 and 0.37 ± 0.19, respectively), which are 22% and 28% (p < 0.01) lower than the average AOD between 2011 and 2019 (0.50 ± 0.08 and 0.52 ± 0.05, respectively). After the lockdown restrictions were relaxed and industrial production gradually resumed, the AOD in April 2020 rebounded to the historical average level. Besides, compared with historical observations (2011–2019), the AOD temporal variability from February to April 2020 showed different pattern, with the decade-high increase from March to April (+0.11) and decade-low increase from February to March (-0.01). Independent observations and simulation, including fine particulate matter (PM2.5) from ground-based measurements, wind field from Cross-Calibrated Multi-Platform, satellite-derived aerosol type, and back trajectories calculation by Hybird Single Paricle Lagrangian Intergrated Trajectory (HYSPLIT) model, indicated that the above abnormal AOD variation can be attributed to reduction of anthropogenic emissions during the COVID-19 lockdown period. The results of this paper, therefore, indicate that aerosols over the Bohai and Yellow Seas are strongly influenced by human activities, and the public health events such as the epidemic may alter the intensity of human activities and thus the spatio-temporal pattern of aerosol over ocean. With the global spread of the epidemic and the corresponding significant changes in human behavior patterns (restrictions on human activities, etc.), more studies should be carried out in the future about the aerosol variability and its potential impact on the marine environment.

Landscaping the Behavioural Ecology of Primate Stone Tool Use

Ecology is fundamental in the development, transmission, and perpetuity of primate technology. Previous studies on tool site selection have addressed the relevance of targeted resources and raw materials for tools, but few have considered the broader foraging landscape. In this landscape-scale study of the ecological contexts of wild chimpanzee (Pan troglodytes verus) tool use, we investigated the conditions required for nut-cracking to occur and persist in discrete locations at the long-term field site of Bossou, Guinea. We examined this at three levels: selection, frequency of use, and inactivity. We collected data on plant foods, nut trees, and raw materials using transect and quadrat methods, and conducted forest-wide surveys to map the location of nests and watercourses. We analysed data at the quadrat level (n = 82) using generalised linear models and descriptive statistics. We found that, further to the presence of a nut tree and availability of raw materials, abundance of food-providing trees as well as proximity to nest sites were significant predictors of nut-cracking occurrence. This suggests that the spatial distribution of nut-cracking sites is mediated by the broader behavioural landscape and is influenced by non-extractive foraging of perennial resources and non-foraging activities. Additionally, the number of functional tools was greater at sites with higher nut-cracking frequency, and was negatively correlated with site inactivity. Our research indicates that the technological landscape of Bossou chimpanzees shares affinities with the ‘favoured places’ model of hominin site formation, providing a comparative framework for reconstructing landscape-scale patterns of ancient human behaviour. A French translation of this abstract is provided in theelectronic supplementary information: EMS 2.

Patterns of human behaviour in public urban green spaces: On the influence of users' profiles, surrounding environment, and space design

Public urban green spaces (PUGS) play a vital role in the dense urban fabric as places of Nature-Society contact and socialization. Despite some advances in the field, the relationship between the PUGS surroundings and their users' behaviours remains unclear. This study examined the patterns of human behaviour in four PUGS of the city of Porto, on the north Portuguese coast, where the behavioural mapping method was applied. Observational data regarding 979 PUGS users' socio-demographics and behaviours were recorded on a base map and a grid between August and November 2020. The use patterns during different times of the day were disaggregated across behaviours and users' profiles, and their relationship with the surroundings and design elements was assessed with ANOVA, Chi-Square test, and Pearson correlation. The findings presented may pave the way for future studies and inform the urban planning and design of the effectiveness of new interventions, namely providing more accurate aligning between the greenspace design language with users' needs. Furthermore, this study highlights the importance of considering the sociodemographic background of PUGS users and the surroundings to guide designers and planners.

A fusion of a deep neural network and a hidden Markov model to recognize the multiclass abnormal behavior of elderly people

With a rapidly aging population, the health problems of older individuals have attracted increasing attention. Elderly people are exposed to more health risks, and their behavior can often indicate signs of crises and diseases. This paper proposes a method for multiclass abnormal behavior recognition based on the integration of sensors (accelerometer, gyroscope, and orientation sensor) in smartphones with positioning monitoring systems. An attention-convolutional neural network (CNN)-long short-term memory (LSTM) algorithm is introduced for human action recognition, which can handle time-dependent data with multiple features of varying importance. Furthermore, based on the long-term activity data (action and position) of the human body, a hidden Markov model (HMM) of the individual’s daily behavior activity state is constructed. Experimental results show that compared with the existing approaches, the proposed attention-CNN-LSTM algorithm performs better in recognizing different human behaviors, with 94.2% precision, 95.1% recall, and F1-score of 94.6%. The developed daily behavior HMM for an individual has been proven to be able to detect changes in human behavioral patterns and indicate the specific behaviors that have changed. The method proposed in this paper can provide a powerful technical guarantee for the health and care of elderly individuals at home.

Dampak Cyber Bullying Terhadap Kesehatan Mental Remaja Serta Implementasi Nilai-nilai Pancasila Sebagai Upaya Pencegahan Cyber Bullying

The Impact of Cyber Bullying on Adolescent Mental Health and Implementation of Pancasila Values as an Effort to Prevent Cyber Bullying . The growing developments in information technology that follow the advances of the age certainly affect the lives, habits, and patterns of human behavior especially when surfing social networks are influenced by many factors. This rapid social media development has generated some negative effects, one of the more negative effects being bullying behaviors that are initiated from negative comments and occur continuously that are necessary for social media ethics. The study has been conducted to identify the cyber bullying and the implementation of pancasila values in daily life by using qualitative methods with the study of literature tools that study and discuss cyber bullying among the popular youth, and the completion that can be made against the control of pancasila values. The results of this study suggest that applying pancasila values in daily life is able to establish a more controlled pattern of human behavior by holding a grip on prevailing values and creating a better life according to the values and values in the pancasila article

A Cross-Sectional Study on Ethical Buyer Behavior towards Cruelty-Free Cosmetics: What Consequences for Female Leadership Practices?

This study extends the theory of planned behavior model and examines the humane factors (altruism, environmental knowledge, personal appearance concerns, attitude, perceived behavioral control, and subjective norms) that shape attitudes and buyer behavior toward cruelty-free cosmetics and the consumer characteristics that reflect their behavior toward such products. Recent global occurrences have affected human behavioral patterns, namely, the COVID-19 pandemic, which we aim to study. Has behavior changed to become more ethical? A survey was carried out involving a sample of 425 Portuguese participants (a feminine culture), following a convenience- and snowball-sampling procedure. Significant correlations were found between environmental knowledge, subjective norms, and buyer behavior toward cruelty-free cosmetics with attitude and environmental knowledge and buyer behavior. Through structural equation modeling to evaluate the conceptual model, a good model fit was found, being that standardized values in the model are significant except for regressions from perceived behavior control and personal appearance concerns to buyer behavior toward cruelty-free cosmetics. Women present higher values than men on attitude, altruism, environmental knowledge, and buyer behavior, in line with what is expected in a traditional and conservative feminine culture such as that to be found in Portugal. Such a result points to the need to promote increased gender equality, for example, in senior leadership roles, as women are seen to have the desirable qualities required for a more sustainable, cruelty-free, and humane society. This is an alert for human-resource managers in the region.

A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

AbstractIn recent years, understanding human behavior has become one of the most important topics in the field of computer vision research. The reason for this growing attention is the wide range of applications that can benefit from the results of this field of research. The human behavior analysis (HBA) includes a wide range of research areas from the detection of human motion and action. Datasets created through actions and detection of human activities make it possible to compare different detection methods with the same input data. Data mining and big data approaches are very popular for analyzing data related to human behavior and can be used to address the challenges of fast processing. This article provides a systematic survey of data mining and big data in the HBA. We focus on current datasets and models related to the detection of human behavior patterns in the literature. The purpose of this survey is to assist researchers in select appropriate datasets and models to evaluate algorithms, as well as to identify research gaps for future work. To achieve this goal, articles published between 2010 and 2021 have been reviewed. These articles fall into five general categories in terms of dataset focus: object detection, motion, action, activity, and behavior. This article provides a summary of data mining and big data models in the HBA, as well as related datasets based on these categories, to highlight promising research avenues for future work.

Social Robots Acceptance and Marketability in Italy and Germany: A Cross-National Study Focusing on Assisted Living for Older Adults

In the last years, social robots have become a trending topic. Indeed, robots which communicate with us and mimic human behavior patterns are fascinating. However, while there is a massive body of research on their design and acceptance in different fields of application, their market potential has been rarely investigated. As their future integration in society may have a vast disruptive potential, this work aims at shedding light on the market potential, focusing on the assistive health domain. A study with 197 persons from Italy (age: M = 67.87; SD = 8.87) and Germany (age: M = 62.15; SD = 6.14) investigates cultural acceptance, desired functionalities, and purchase preferences. The participants filled in a questionnaire after watching a video illustrating some examples of social robots. Surprisingly, the individual perception of health status, social status as well as nationality did hardly influence the attitude towards social robots, although the German group was somewhat more reluctant to the idea of using them. Instead, there were significant correlations with most dimensions of the Almere model (like perceived enjoyment, sociability, usefulness and trustworthiness). Also, technology acceptance resulted strongly correlated with the individual readiness to invest money. However, as most persons consider social robots as “Assistive Technological Devices” (ATDs), they expected that their provision should mirror the usual practices followed in the two Countries for such devices. Thus, to facilitate social robots’ future visibility and adoption by both individuals and health care organisations, policy makers would need to start integrating them into official ATDs databases.