Past Behavior Research Articles

Eating disorder risk among Australian youth starting a diet in the community

Dieting is a potent risk factor for eating disorder (ED) symptoms and development, which typically occur in late adolescence. However, as diets are often motivated by body image concerns (another core ED risk factor), dieters may already carry heightened ED risk. Thus, the current study aimed to document ED risk among young people starting a diet in the community. Young people (16–25 years) starting or intending to start a self-initiated diet (N = 727) provided data via a screener questionnaire, assessing containing sociodemographic factors, past and current ED symptoms and behaviours. Over a third (36.9%) screened using a validated instrument were found to be at-risk of a current ED, with 10% above the clinical cut-off. Consistent with this finding, over 10% of the sample self-reported experiencing a lifetime ED, while nearly a quarter reported symptoms consistent with an ED diagnosis with no reported formal diagnosis. Findings suggest a high level of ED risk among young people starting a diet in the community and point to the need for more proactive measures targeted at this cohort (e.g., screening, monitoring). Further education on the risks of dieting and encouragement for help-seeking in young people is indicated.

Determinants of tweens’ saving intentions: a cross-sectional study

PurposeChildren are beginning to socialize as consumers earlier than ever, highlighting the importance of their saving behavior as an effective form of consumer protection. The paper explored the influence of parents, peers, attitudes, knowledge, past behavior, allowance and self-efficacy on saving intention.Design/methodology/approachWith the aim to explore a range of determinants of adolescent saving and to specify the potential mechanisms through which different determinants operate, we adopted a multitheoretical approach based on theories of planned behavior, consumer and financial socialization, and self-efficacy. The paper investigates the formation of the saving intentions on a sample of 1,476 children 10–15 years old in Croatia.FindingsThe results indicate strong importance of parental influence and self-efficacy, implying that saving intention among tweens requires a supportive family structure as well as beliefs in the tweens themselves that they are able to save money and face difficulties.Originality/valueThis paper investigates the very nature of saving intention formation at a crucial developmental stage; it investigates the interplay of mechanisms through which determinants of savings operate at that developmental stage; and it explores the age-variance of the mechanism and the interplay of relevant variables, shedding light on the nature of the mechanism of development.

Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults: Study of Domain Adaptation and Deep Learning.

Cognitive impairment and dementia pose a significant challenge to the aging population, impacting the well-being, quality of life, and autonomy of affected individuals. As the population ages, this will place enormous strain on health care and economic systems. While computerized cognitive training programs have demonstrated some promise in addressing cognitive decline, adherence to these interventions can be challenging. The objective of this study is to improve the accuracy of predicting adherence lapses to ultimately develop tailored adherence support systems to promote engagement with cognitive training among older adults. Data from 2 previously conducted cognitive training intervention studies were used to forecast adherence levels among older participants. Deep convolutional neural networks were used to leverage their feature learning capabilities and predict adherence patterns based on past behavior. Domain adaptation (DA) was used to address the challenge of limited training data for each participant, by using data from other participants with similar playing patterns. Time series data were converted into image format using Gramian angular fields, to facilitate clustering of participants during DA. To the best of our knowledge, this is the first effort to use DA techniques to predict older adults' daily adherence to cognitive training programs. Our results demonstrated the promise and potential of deep neural networks and DA for predicting adherence lapses. In all 3 studies, using 2 independent datasets, DA consistently produced the best accuracy values. Our findings highlight that deep learning and DA techniques can aid in the development of adherence support systems for computerized cognitive training, as well as for other interventions aimed at improving health, cognition, and well-being. These techniques can improve engagement and maximize the benefits of such interventions, ultimately enhancing the quality of life of individuals at risk for cognitive impairments. This research informs the development of more effective interventions, benefiting individuals and society by improving conditions associated with aging.

Using machine learning to predict investors’ switching behaviour

Individual investors’ decisions to switch investments very often lead to significantly lower investment returns so having an effective predictive model of these switches would be of value to clients, advisors and investment managers. A random forest algorithm was applied to a new dataset of over 20 million observations relating to 95,685 clients on Momentum Investments’ platform between 2018 and 2024. It identified a combination of investor characteristics (number of holdings, past switching behaviour, total assets) and external features (past returns, macroeconomic variables) as the key features of investor switch behaviour. This model exceeds commercially accepted standards in respect of the AUC and Gini metrics showcasing the model’s strength in its ranking capability. It can thus provide a useful basis for client segmentation and engagement by financial advisors.

Evaluation of rheo-viscoelasticity of magnesium-silicate-hydrate mixes incorporating different superplasticizers

This study investigated rheo-viscoelastic behaviors of MgO-SiO2 pastes with three phosphate additives (sodium hexametaphosphate (SHMP), sodium trimetaphosphate (STMP), and sodium orthophosphate (SOP)) as superplasticizers. Static yield stress, dynamic yield stress, median differential viscosity, thixotropic loop area, structural recovery rate, and non-destructive structural build-up were analyzed. Furthermore, assessment of hydration and particle dispersion stability was facilitated by employing isothermal calorimetry, XRD, DTG, and zeta potential techniques. SHMP significantly decreased the static and dynamic rheology, thixotropy, and viscoelastic evolution. Samples incorporating 2 wt% SOP exhibited enhanced rheological parameters, particularly static yield stress, owing to increased brucite formation and a denser microstructure in the fresh paste. Adsorption of phosphate additives on MgO surfaces inhibited formation of brucite and improved interparticle electrostatic repulsion, thereby increasing suspension stability. High-efficiency adsorption of SHMP on MgO surfaces, coupled with the retardation of brucite nucleation-and-growth, and enhancement in suspension stability, greatly restrained the rheo-viscoelastic evolution of MgO-SiO2 system.

Alteration of cement rheological properties and setting-hardening: Novel insight into the impact of polyaluminum sulfate

The accelerator significantly influences the setting and hardening performance of shotcrete, in order to develop novel setting components, this study investigates the underlying mechanisms through which polyaluminum sulfate (PAS) facilitates the setting of cement paste. QXRD, Isothermal calorimetry, Zeta potential and Rheological test were employed to analyze the effects of PAS addition on the setting and hardening behavior, hydration kinetics, and rheological properties of cement paste. When the dosage of PAS is 10 %, the initial and final setting times are reduced by 54.39 % and 38.57 % respectively, meanwhile the compressive strength after 6 hours increases by 659.09 %. The influence of PAS on cement setting acceleration and rheological properties were analyzed using the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory and the Water Film Thickness (WFT) theory. PAS leads to an acceleration in particle aggregation rate and an increase in shear stress and plastic viscosity of the cement paste. Furthermore, the accelerated hydration effect of PAS promotes the formation of additional hydration products, strengthens the interparticle connectivity, and facilitates the formation of a rigid network. This study highlights a novel pathway for understanding the rapid setting mechanism of aluminum-based accelerators.

A Fully Connected Neural Network (FCNN) Model to Simulate Karst Spring Flowrates in the Umbria Region (Central Italy)

In the last decades, climate change has led to increasingly frequent drought events within the Mediterranean area, creating an urgent need of a more sustainable management of groundwater resources exploited for drinking and agricultural purposes. One of the most challenging issues is to provide reliable simulations and forecasts of karst spring discharges, whose reduced information, as well as the hydrological processes involving their feeding aquifers, is often a big issue for water service managers and researchers. In order to plan a sustainable water resource exploitation that could face future shortages, the groundwater availability should be assessed by continuously monitoring spring discharge during the hydrological year, using collected data to better understand the past behaviour and, possibly, forecast the future one in case of severe droughts. The aim of this paper is to understand the factors that govern different spring discharge patterns according to rainfall inputs and to present a model, based on artificial neural network (ANN) data training and cross-correlation analyses, to evaluate the discharge of some karst spring in the Umbria region (Central Italy). The model used is a fully connected neural network (FCNN) and has been used both for filling gaps in the spring discharge time series and for simulating the response of six springs to rainfall seasonal patterns from a 20-year continuous daily record, collected and provided by the Regional Environmental Protection Agency (ARPA) of the Umbria region.

The effects of substance use on non-communicable diseases among older adults aged 60 and above in the North-eastern States of India.

The North-eastern region of India has a relatively higher prevalence of substance use, which together with poor dietary practices and a lack of physical activity is one of the key risk factors for NCDs among older adults in the region. Understanding the prevalence of NCDs and their relationship to substance use can help develop preventive strategies and sensitization in North-eastern India. To assess the prevalence of NCDs and the strength of the association of substance abuse among the geriatric population of North-eastern states in India, for the development of preventive strategies. Data from the Longitudinal Ageing Study in India (LASI Wave-I, 2017-18) were drawn to develop this paper. The bi-variate and binary logistic regression analyses were carried out to predict the association between non-communicable diseases and substance use adjusting select socio-demographic characteristics. The paper revealed the prevalence of NCDs among urban people (61.45%) is higher than among rural people (42.45%). Hypertension (37.29%) can be seen as the most prevalent disease among the following given NCDs followed by Diabetes (8.94%). The chances of having Cancer are nineteen times higher (OR = 19.8; C.I. = 18.82-20.83) if an individual has past smoking behaviour after controlling for socio-demographic and physical activity variables. Since, the high prevalence of hypertension correlated with the high level of substance abuse, require immediate attention to develop appropriate intervention strategies for its control (substance abuse) and prevention of hypertension. In a lower middle-income country like India, preventive measures, rather than curative measures will be cost-effective and helpful.

Effect and mechanisms of type, content, and dispersion method of flash graphene on the rheological behaviors of fresh cement pastes

Flash Joule heating provides a facile, low-carbon, and cost-effective method for upscale graphene production, benefiting the application of graphene in modifying cement-based materials. Understanding the effect and mechanisms of flash graphene (FG) type, content, and dispersion method on the rheological behaviors of cement-based materials is essential for designing and controlling cement-based materials in different application scenarios. Therefore, this study explored the rheological behaviors of cement pastes with 2 different types of flash graphene at 4 different content levels (namely 0.1 wt%, 0.2 wt%, 0.3 wt%, and 0.4 wt% of cement mass) using 3 different dispersion methods (namely water reducer dispersion with ultrasonic treatment, dry mixing with water reducer, and mechanical dispersion with water reducer). The results indicated that the addition of FG generally increases the yield stress and plastic viscosity of fresh cement pastes. The water reducer dispersion with ultrasonic treatment and nano-boron doping shows a good effect in dispersing FG. The influence mechanisms of FG on rheological behaviors of fresh cement pastes are a competition between exert replacement, agglomeration, lubrication, and adsorption effects.

“You need to speak up”: Agency in the cancer pre-diagnosis phase

This study analyzes patient agency within the cancer pre-diagnosis phase: the space between the onset of concerning symptoms but before a positive or negative diagnosis is received. Through two phases of data collection, fifty-five interviews were conducted with people who had gone through a cancer diagnosis process. Interview transcripts were analyzed using thematic analysis. Thematic analysis revealed that patients illustrated four tenets of human agency: intentionality, forethought, self-reactiveness, and self-reflectiveness. Patients expressed agency by accelerating testing timelines, seeking out medical information, and advocating for further testing. There were notable agency constraints when healthcare providers did not communicate pertinent information. Additionally, some patients intentionally chose to be passive or regretted past passive behaviors. These findings enhance understanding of agency expressions in the pre-diagnosis phase.

Mental Health and Social Correlates of Reincarceration of Youths as Adults.

The rise in the U.S. prison population over the past 40 years has heightened scrutiny of the incarceration of children and adolescents. Correlates of later reincarceration in this group, especially correlates relating to psychiatric and substance use disorders, are understudied in the U.S. population. We aimed to establish the prevalence and correlates of the reincarceration as adults of people incarcerated before age 18. Data were derived from clinical interviews and from validated diagnostic and psychometric instruments. They were obtained as part of a cross-sectional representative survey of the civilian U.S. population, the National Epidemiological Survey on Alcohol and Related Conditions (NESARC-III). We identified 1,543 adults (4.3% of the NESARC sample) who had been incarcerated before they were 18. Of these, 55.9 percent had subsequently been incarcerated as adults. In addition to variables that have been repeatedly identified in criminological research (less education, past antisocial behavior, and parental imprisonment), substance use disorder, bipolar disorder, and longer childhood incarceration were independently associated with incarceration as an adult. The possibility that psychiatric treatment could reduce reincarceration in this group warrants longitudinal and experimental research.

Contextualising Experiences of Co-Occurring Mental Ill-Health and Substance Use Among Trans, Non-Binary, and Gender Diverse Young People: Implications for Tailored Harm Reduction Approaches.

Though significant research highlights higher rates of mental ill-health and substance use among trans, non-binary and gender diverse (henceforth 'trans') young people, little research has considered patterns, contextual characteristics, and correlates of co-occurring experiences of mental ill-health and substance use among trans young people. Using data from the Trans Pathways study, we used prevalence ratios and age- and gender-adjusted logistic regression models to examine prevalence and differences of co-occurring substance use (past six-month cigarette use, alcohol use, and other drug use) and contextual characteristics of substance use (past six-month solitary alcohol and/or drug use, substance use for coping) by mental ill-health (depression disorder, anxiety disorder, past 12-month self-harm thoughtsand behaviours,suicidal thoughts, planning, and attempt/s). Age- and gender-adjusted models assessed associations between co-occurring depressive and anxiety disorders and recent cigarette, alcohol, and other drug use (six co-occurring items total) and 18 interpersonal stressors. Significantly increased odds of smoking or recentuse of cannabis or sedatives was observed among trans young people reporting depressive disorder, anxiety disorder (aORs ranging 1.8-3.1). Trans young people who reported recent smoking or use of cannabis, inhalants, or sedatives, had 40% to 80% reduced odds of past 12-month self-harm thoughts, self-harm behaviours,suicidal thoughts, and suicide attempt/s (aORs ranging 0.2-0.6). On the other hand, solitary alcohol and/or other drug use and substance use for coping was significantly associated with increased odds of all mental ill-health outcomes. Issues with school, secure housing, and intimate partner abuse were themost robust correlates of co-occurring mental ill-health and substance use. Trans young people using substances, especially cigarettes, cannabis, and sedatives, often so do with co-occurring experiences of depression and anxiety though limited substance use in more 'social' contexts may confer benefits for preventingself-harm and suicide thoughts and behaviours. Continued research in partnership with trans young people is warranted to conceptualise more nuanced and precise conceptualparameters of trans-affirming substance useharm reduction approaches.

An Upper Paleolithic horse mandible with an embedded lithic projectile: Insights into 16,500 cal BP hunting strategies through a unique case of bone injury from Cantabrian Spain

AbstractEmbedded artifacts in osteoarchaeological remains may be key to approaching hunting strategies and other behavioral‐related issues such as technological development. However, that kind of evidence is not common within the archaeological record and often not well‐characterized, especially for faunal remains from prehistoric sites. Here, we present and discuss a unique case of a horse (Equus caballus) mandible with an embedded lithic remains from the Upper Paleolithic (ca. 17,300–16,200 cal BP) from La Garma cave in Cantabria, Spain. Our macro‐ and microscopic faunal and lithic integrated analysis suggests that the case presented here is a potential perimortem hunting lesion, representing an uncommon hunting strategy during the Magdalenian period. Furthermore, this study, representing the first case of its kind in the Iberian Peninsula, emphasizes the importance of the taphonomic analysis of bone surfaces to approach the understanding of past human behaviors.

Coping behaviors to reduce anxiety and depressive symptoms: A prospective repeated assessment study

IntroductionHealth institutions provide general recommendations to cope with global crises such as pandemics or geopolitical tensions. However, these recommendations are mainly based on cross-sectional evidence. The preregistered Repeated Assessment of Behaviors and Symptoms in the Population (RABSYPO) study sought to establish prospective longitudinal evidence from a cohort with a demographic distribution similar to that of the Spanish population to provide evidence for developing solid universal recommendations to reduce anxiety and depressive symptoms during times of uncertainty. Material and methodsWe first recruited via social networks a pool of Spanish individuals willing to participate and then randomly selected some within each stratum of age×gender×region×urbanicity to conduct a one-year-long bi-weekly online follow-up about the frequency of ten simple potential coping behaviors as well as anxiety (GAD-7) and depressive symptoms (PHQ-9). Mixed-effects autoregressive moving average models were used to analyze the relationship between past behaviors’ frequency and subsequent symptom changes across the twenty-seven time points. ResultsAmong the 1049 who started the follow-up, 942 completed it and were included in the analyses. Avoiding excessive exposure to distressing news and maintaining a healthy/balanced diet, followed by spending time outdoors and physical exercise, were the coping behaviors most strongly associated with short and long-term reductions of anxiety and depressive symptoms. Engaging in relaxing activities and drinking water to hydrate were only associated with short-term symptom reductions. Socializing was associated with symptom reductions in the long term. ConclusionsThis study provides compelling prospective evidence that adopting a set of simple coping behaviors is associated with small but significant reductions in anxiety and depressive symptoms during times of uncertainty. It also includes a layman's summary of this evidence to help develop general recommendations that serve as universal tools for enhancing mental health and well-being.

Segmenting and profiling seaweed consumers: A cross-cultural comparison of Australia, the United Kingdom and Croatia

Seaweed is increasingly recognised as a highly nutritious and sustainable food source. While it has a long tradition in many Asian countries, it still only plays a minor role in the Western diet. The aim of this study was to identify and describe consumer segments that are more likely to intend to consume seaweed. An online survey was conducted with 1,621 participants from three Western countries (Australia, the United Kingdom and Croatia) with different cultures, culinary traditions and levels of development in the seaweed industry.Two consumer segments were identified: Food Enthusiasts and Food Conservatives. The profiling of these segments is based on factors oriented to consumers’ food innovativeness, food neophobia, food involvement, food responsibility, health consciousness, and symbolic value. The segments differ significantly in terms of food innovativeness, food neophobia, food involvement, eating and dietary style, past behaviour around seaweed consumption, future intention to eat seaweed and socio-demographic characteristics. These results provide valuable insights for the seaweed industry to develop effective marketing strategies that will meet the demands of different Western consumers.

Effectiveness of Iterative Learning Control for Vision-Based Tracking in Repeated Tasks Under Varying Lighting Conditions

Among available techniques for lateral tracking in autonomous driving, none possesses the capability to learn from past behaviors and progressively reduce lateral errors. In contrast, our proposed iterative learning control (ILC) scheme significantly enhances tracking performance in vision-based classical control, particularly in challenging environmental conditions. Our vision-based control system incorporates real-time semantic segmentation using the ENet model for unconstructed road area extraction. Linear regression estimates steering adjustments, while a visual PID controller maintains the vehicle’s position at the road’s centerline. This control system’s performance varies with lighting conditions, notably in dense shade, where the vehicle tends to deviate from the desired path. To assess ILC’s potential in error reduction over successive trials, we examined various ILC structures and compared them with a pure PID controller. Despite challenges in directly comparing PID and ILC designs due to changing lighting conditions during experiments, ILC consistently reduced tracking errors and improved path alignment with each iteration. Notably, the degree of error reduction became more pronounced with a greater number of learning gains in the ILC design. Our experimental results underscore the overall effectiveness of ILC in tracking the desired path in autonomous driving scenarios, particularly in varying environmental conditions.

Consumer Privacy Expectations and the Impact on Buying Intentions

Based on past research and attitudinal theories, this study intends to investigate the effect of attitudes about data privacy protection and disclosure and the perception of corporate data privacy performance on future purchase intention. Additionally, it seeks to understand the relationship between purchase behavior intensity, personal data privacy concerns and privacy expectations (attitude). A total of 271 usable surveys were gathered from United States consumers and analyzed by two multiple linear regression models and correlation analysis. The results show that attitude (data privacy protection and disclosure responsibility) and perceptions about corporate data privacy performance predict purchase intention. Furthermore, past purchase behavior intensity and personal data privacy concerns influence corporate data privacy responsibility attitudes. This study extends the current data privacy literature by understanding how data privacy expectations (attitude) and data privacy performance perceptions impact purchase intent and are influenced by personal data privacy concerns and purchase behavior.

Predicting the Number of Visitors with Artificial Neural Networks to Support Strategic Decision-Making for Science Centers

Accurately predicting visitor attendance has become increasingly vital for science centers to optimize operations, improve visitor experiences, and stay competitive in attracting and engaging global audiences. As the demand for advanced predictive analytics grows, this study explores the use of artificial neural networks (ANNs) to forecast visitor numbers at science centers. In order to achieve this objective, data pertaining to the number of visitors to the Konya Science Centre was utilized. By analyzing a dataset of ten input factors, such as weather conditions and past visitor behavior, the study develops predictive models capable of accurately estimating future attendance patterns. The best-performing model, utilizing Bayesian regularization, 0.91444 for the training set, 0.25119 for the test set, and 0.91342 overall. These findings underscore the transformative potential of predictive analytics in science center management. Leveraging machine learning techniques, the study provides valuable insights into visitor preferences and behavior. This knowledge can empower science centers to make data-driven decisions, optimize resource allocation, and adapt their offerings to meet the evolving needs of their target audience. Ultimately, the study highlights how predictive analytics can enhance the long-term sustainability and global competitiveness of science center operations.

Rental subsidies and take‐up barriers in an era of expensive rents: An extended version of the theory of planned behaviour

AbstractDespite increasing rent price burdens throughout Europe, many eligible households for rental subsidies do not use them. Understanding determinants of low take‐up is crucial for evaluating program effectiveness and anticipating fiscal implications. To identify those determinants, we built an extended version of the theory of planned behaviour, incorporating dimensions from the claiming costs scale. In an online survey, participants comprising renting households likely eligible for the housing subsidy from Germany (n = 862) and Spain (n = 1032) completed measures of behavioural intention, rental cost burdens, theory of planned behaviour constructs (attitudes, subjective norms, perceived behavioural control, and past behaviour), and claiming costs constructs (information costs, process costs, and stigma). Results supported a multi‐group model, showing different paths for Germany and Spain. Several similar paths were identified for both countries, such as the indirect association of stigma with intention to apply for rental subsidies via attitude, the positive and strong association of subjective norms, and the positive association between perceived behavioural control and intention. The indirect relationship involving information costs was observed for Spain but not Germany, and rental cost burdens operated differently as well. These findings suggest that multiple dimensions, both psychological and policy‐related, should be considered when investigating claiming behaviours for rental subsidies, while emphasizing the need to acknowledge the unique characteristics of each country. This evidence is also relevant to policymakers, offering insights into leveraging established policy tools. Please refer to the Supplementary Material Section to find this article's Community and Social Impact Statement.

God, witchcraft, and beliefs about illness in Mauritius

ABSTRACT Why do people use supernatural concepts to explain and treat illness? In a Mauritian sample, we examined how uncertainty around the cause of symptoms, illness severity, and knowledge about past moral behavior, influenced participants’ tendency to attribute illnesses to God and/or witchcraft. We employed a preregistered vignette-based experiment to manipulate these variables in four illnesses and a combination of scaled and open-ended freelist questions about the causes and cures for each illness. Participants (N = 530) gave supernatural causes and cures for all illnesses. High uncertainty around the cause of the symptoms increased participants’ claims that the illness was caused by God. When the sick person had a history of immoral behavior, participants were more willing to attribute their illness to God and witchcraft and offered up 4 times more supernatural causes in freelists compared with a person with no such history. We found no evidence that severity influenced participants’ likelihood of suggesting supernatural causes or cures. Finally, when participants gave a supernatural cause, they were more likely to also indicate that the illness needed a supernatural cure (e.g., consulting a spiritual healer), suggesting that supernatural causes increase the need for supernatural cures.