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Funder: University of Cambridge, Due to increasing ease of use and ability to quickly collect large samples, online behavioural research is currently booming. With this popularity, it is important that researchers are aware of who online participants are, and what devices and software they use to access experiments. While it is somewhat obvious that these factors can impact data quality, the magnitude of the problem remains unclear. To understand how these characteristics impact experiment presentation and data quality, we performed a battery of automated tests on a number of realistic set-ups. We investigated how different web-building platforms (Gorilla v.20190828, jsPsych v6.0.5, Lab.js v19.1.0, and psychoJS/PsychoPy3 v3.1.5), browsers (Chrome, Edge, Firefox, and Safari), and operating systems (macOS and Windows 10) impact display time across 30 different frame durations for each software combination. We then employed a robot actuator in realistic set-ups to measure response recording across the aforementioned platforms, and between different keyboard types (desktop and integrated laptop). Finally, we analysed data from over 200,000 participants on their demographics, technology, and software to provide context to our findings. We found that modern web platforms provide reasonable accuracy and precision for display duration and manual response time, and that no single platform stands out as the best in all features and conditions. In addition, our online participant analysis shows what equipment they are likely to use.

Purpose of Review The objective of this scoping review was to examine the range of published evidence on recruitment approaches and outcomes of US adolescents and young adults (AYA) ages (18–29 years) into human immunodeficiency virus (HIV)–related behavioral research studies during the past 10 years. Recent Findings Implementation of effective behavioral research strategies among HIV at-risk and infected AYA is key to ending the HIV epidemic and necessitates successful recruitment strategies. Summary A comprehensive search was executed across four electronic databases. Of the 1697 identified studies, seven met inclusion criteria with six of these seven directed to HIV prevention. Most studies used online recruitment as part of a hybrid strategy, and combined field-based/in-person and online methods. Recruitment strategies and outcomes, resources and compensation, procedures for consent, and timelines varied among all seven studies. Our results highlight the need for development of recruitment models in alignment with behavioral strategies aimed to treat and prevent HIV among US AYA.

Improving HIV testing rates and increasing early detection among men who have sex with men (MSM) are critical strategies for enhancing overall health and decreasing HIV transmission. Remote testing and phone delivery of HIV test results may reduce barriers such as geographic isolation or HIV-related stigma. In 2018-19, 50 MSM completed qualitative interviews about their experience receiving a positive HIV test result via phone through their participation in a research study that included remote HIV testing. Interview topics included the acceptability of, and concerns about, phone delivery of HIV results, as well as suggestions for improvement. Interviews were transcribed, coded, and analysed using an inductive thematic approach. Overall, participants reported high acceptability of phone delivery of HIV-positive results. Participants praised the support and information provided by study staff. Benefits identified included increased convenience compared to in-person medical visits, allowing participants to emotionally process their test results privately, as well as receiving the results from supportive and responsive staff members. A few participants indicated drawbacks to phone-based HIV test result delivery, such as logistical concerns about receiving a phone call during the day (e.g., while at work), reduced confidentiality, and the lack of in-person emotional support. Overall, participants described phone delivery of positive HIV-results as acceptable. At-home testing with phone delivery has the potential to increase HIV testing access, especially to geographically isolated or medically underserved patients.

A crisis looms as research and clinical programs have not kept pace with dramatic increases in the number of older adults with schizophrenia. This article provides an overview of the advances in the conceptualization and study of schizophrenia in later life. Theoretic and clinical models in psychiatry and gerontology are integrated. Specifically, recovery is examined in the context of aging, how clinical dimensionality affects diagnoses in older adults, how various features of schizophrenia are implicated in models of accelerated and paradoxic aging, and how outcome in later life is a more dynamic and heterogeneous than assumed previously.

Latinos represent a critical resource of talent that could be cultivated to expand the HIV research workforce. However, their rapid growth, as the largest and fastest growing ethnic minority group in the US population, has yet to translate into a significant increase in Latino health academic researchers. Historically, strategies to build a diverse research workforce have grouped together individuals from underrepresented minority populations obscuring significance between and within group differences. This limits ap­proaches that are responsive to the diversity of needs and experiences of emerging investigators from underrepresented groups.In this article, we discuss challenges associ­ated with heterogeneity of Latinos and bar­riers that impede research independence/ career success in the context of a review of Latino-investigator targeted mentorship approaches on the behavioral-social science of HIV infection. Mentorship workforce strategies could benefit from a personal­ized framework emphasizing individualized and tailored approaches to address the limitations and gaps in knowledge regard­ing Latino research development. This perspective encourages increased emphasis on organizational and structural processes to aid in overcoming institutional-level barriers that impede research and career develop­ment. Recommendations are proposed for features and components of effective mentorship programs that will lead to robust outcomes for strengthening the Latino research workforce in the HIV research field and elsewhere.Ethn Dis. 2020;30(2):305- 312; doi:10.18865/ed.30.2.305.

Aims Amazon Mechanical Turk (MTurk) provides a crowdsourcing platform for the engagement of potential research participants with data collection instruments. This review (1) provides an introduction to the mechanics and validity of MTurk research; (2) gives examples of MTurk research; and (3) discusses current limitations and best practices in MTurk research. Methods We review four use cases of MTurk for research relevant to addictions: (1) the development of novel measures, (2) testing interventions, (3) the collection of longitudinal use data to determine the feasibility of longer-term studies of substance use and (4) the completion of large batteries of assessments to characterize the relationships between measured constructs. We review concerns with the platform, ways of mitigating these and important information to include when presenting findings. Results MTurk has proved to be a useful source of data for behavioral science more broadly, with specific applications to addiction science. However, it is still not appropriate for all use cases, such as population-level inference. To live up to the potential of highly transparent, reproducible science from MTurk, researchers should clearly report inclusion/exclusion criteria, data quality checks and reasons for excluding collected data, how and when data were collected and both targeted and actual participant compensation. Conclusions Although on-line survey research is not a substitute for random sampling or clinical recruitment, the Mechanical Turk community of both participants and researchers has developed multiple tools to promote data quality, fairness and rigor. Overall, Mechanical Turk has provided a useful source of convenience samples despite its limitations and has demonstrated utility in the engagement of relevant groups for addiction science.

Recent advances in wearable sensor technology and smartphones enable simple and affordable collection of personal analytics. This paper reflects on the lessons learned in the SWELL project that addressed the design of user-centered ICT applications for self-management of vitality in the domain of knowledge workers. These workers often have a sedentary lifestyle and are susceptible to mental health effects due to a high workload. We present the sense–reason–act framework that is the basis of the SWELL approach and we provide an overview of the individual studies carried out in SWELL. In this paper, we revisit our work on reasoning: interpreting raw heterogeneous sensor data, and acting: providing personalized feedback to support behavioural change. We conclude that simple affordable sensors can be used to classify user behaviour and heath status in a physically non-intrusive way. The interpreted data can be used to inform personalized feedback strategies. Further longitudinal studies can now be initiated to assess the effectiveness of m-Health interventions using the SWELL methods.

Purpose of Review Behavioral therapies are proven treatments for many neurologic conditions. However, the COVID-19 pandemic has posed significant challenges for conducting behavioral research. This article aims to (1) highlight the challenges of running behavioral clinical trials during the pandemic, (2) suggest approaches to maximize generalizability of pandemic-era studies, and (3) offer strategies for successful behavioral trials beyond the pandemic. Recent Findings Thousands of clinical trials have been impacted by the COVID-19 pandemic, from undergoing protocol revisions to suspension altogether. Furthermore, for ongoing trials, recruitment of diverse populations has suffered, thereby exacerbating existing inequities in clinical research. Patient adherence and retention have been affected by a myriad of pandemic-era restraints, and medical, psychiatric, and other complications from the pandemic have the potential to have long-term effects on pandemic-era study results. Summary In the development of post-pandemic study protocols, attention should be given to designing studies that incorporate successful aspects of pre-pandemic and pandemic-era strategies to (1) broaden recruitment using new techniques, (2) improve access for diverse populations, (3) expand protocols to include virtual and in-person participation, and (4) increase patient adherence and retention.

Given its popularity among youth ages 13–17, social media is a promising avenue for engaging and retaining historically hard-to-reach youth in longitudinal research. Social media use in longitudinal research involving youth, however, has preceded development of best practices for ethical use. This article describes the ethical challenges and considerations of using social media to engage and retain youth within the context of a randomized controlled trial of a group-based adolescent substance use intervention. Best practices for addressing ethical challenges are also provided using the Belmont Principle as a guiding framework. As social media becomes more commonly used to engage and retain youth in clinical research studies, researchers must address emerging ethical concerns within project protocols.

T his article provides an overview to a special section highlighting new directions in obesity research. It includes research on gene by environment interactions, interventions to enhance health equity among specific at-risk populations, and work using creative stimuli and constructs.

This article highlights key health issues, contemporary research topics, and opportunities for advancing aging research with sexual and gender minority (SGM) older adults, or individuals who identify as lesbian, gay, bisexual, transgender, queer or questioning, intersex, non-binary, or who exhibit attractions and behaviors that do not align with heterosexual or traditional gender norms. Using a life course perspective, we provide a sociohistorical context and highlight the social, psychological, and physical health, and healthcare experiences of SGM older adults in the U.S. Additionally, we present an overview of SGM aging research disseminated in the Journals of Gerontology: Series B over the past five years. After reviewing these topics, we discuss limitations and gaps in the current research and provide recommendations for future research with SGM aging populations.

It is estimated that behaviors such as poor diet, alcohol consumption, tobacco use, sedentary behavior, and excessive ultraviolet exposure account for nearly one-half of all cancer morbidity and mortality. Accordingly, the behavioral, social, and communication sciences have been important contributors to cancer prevention and control research, with methodological advances and implementation science helping to produce optimally effective interventions. To sustain these contributions, it is vital to adapt to the contemporary context. Efforts must consider ancillary effects of the 2019 coronavirus disease pandemic, profound changes in the information environment and public understanding of and trust in science, renewed attention to structural racism and social determinants of health, and the rapidly increasing population of cancer survivors. Within this context, it is essential to accelerate reductions in tobacco use across all population subgroups; consider new models of energy balance (diet, physical activity, sedentary behavior); increase awareness of alcohol as a risk factor for cancer; and identify better communication practices in the context of cancer-related decisions such as screening and genetic testing. Successful integration of behavioral research and cancer prevention depends on working globally and seamlessly across disciplines, taking a multilevel approach where possible. Methodological and analytic approaches should be emphasized in research training programs and should use new and underused data sources and technologies. As the leadership core of the National Cancer Institute’s Behavioral Research Program, we reflect on these challenges and opportunities and consider implications for the next phase of behavioral research in cancer prevention and control.

Background Covid-19 triggered the rapid roll-out of mass social distancing behavioural measures for infection control. Pregnant women were categorised as ‘at risk’ requiring extra vigilance with behavioural guidelines. Their understanding and ability to adhere to recommendations was unknown. Objectives To complete a behavioural analysis of the determinants of recommended social distancing behaviour in pregnant women, according to the ‘capability, opportunity, motivation and behaviour’ (‘COM-B’) model to inform the development of recommendations/materials to support pregnant women in understanding and adhering to behavioural guidelines. Design Qualitative interview study with pregnant women in the Bristol area (UK). Methods Semi-structured telephone/videoconference interviews were conducted following a topic guide informed by the COM-B model, transcribed verbatim and subjected to framework analysis. Infographic materials were iteratively produced with stakeholder consultation, to support pregnant women. Results Thirty-one women participated (selected for demographic range). Women reported adhering to social distancing recommendations and intended to continue. COM-B analysis identified gaps in understanding around risk, vulnerability, and the extent of required social distancing, as well as facilitators of social distancing behaviour (e.g. social support, motivation to stay safe, home environment/resources). Additional themes around detrimental mental health effects and changes to maternity healthcare from the social distancing measures were identified. Infographic resources (plus midwife report) addressing women’s key concerns were produced and disseminated. Conclusions The COM-B model provided useful details of determinants of pregnant women’s adherence to social distancing behaviours. The confusion of what being ‘at risk’ meant and varying interpretation of what was expected indicates a need for greater clarity around categories and guidance. The loss of maternity care and negative mental health effects of social distancing suggest a growing area of unmet health needs to be addressed in future.

Open science practices have potential to enhance the rigor, reproducibility, and reach of the work being conducted in behavioral medicine.

When comparing multilevel models (MLMs) differing in fixed and/or random effects, researchers have had continuing interest in using R-squared differences to communicate effect size and importance of included terms. However, there has been longstanding confusion regarding which R-squared difference measures should be used for which kind of MLM comparisons. Furthermore, several limitations of recent studies on R-squared differences in MLM have led to misleading or incomplete recommendations for practice. These limitations include computing measures that are by definition incapable of detecting a particular type of added term, considering only a subset of the broader class of available R-squared difference measures, and incorrectly defining what a given R-squared difference measure quantifies. The purpose of this paper is to elucidate and resolve these issues. To do so, we define a more general set of total, within-cluster, and between-cluster R-squared difference measures than previously considered in MLM comparisons and give researchers concrete step-by-step procedures for identifying which measure is relevant to which model comparison. We supply simulated and analytic demonstrations of limitations of previous MLM studies on R-squared differences and show how application of our step-by-step procedures and general set of measures overcomes each. Additionally, we provide and illustrate graphical tools and software allowing researchers to automatically compute and visualize our set of measures in an integrated manner. We conclude with recommendations, as well as extensions involving (a) how our framework relates to and can be used to obtain pseudo-R-squareds, and (b) how our framework can accommodate both simultaneous and hierarchical model-building approaches.

The University of São Paulo Twin Panel (Painel USP de Gêmeos), based at the Institute of Psychology of the University of São Paulo, started formally in 2017. Our registry is new, but in only two years of formal existence, it comprises a volunteer sample of 4826 registered individuals (98% twins and 2% higher-order multiples), recruited at the University of São Paulo and by social media campaigns. Our main aim is to conduct and promote research with twins on psychological processes and behavior. The University of São Paulo is the largest higher education and research institution in South America, and the Painel USP de Gêmeos has great potential for fostering research on twin-related issues from a psychological perspective in Brazil and South America.

We investigate the relationship between Bayesian inference networks (BayesNets) and diagnostic classification models (DCMs). Specifically, we demonstrate and empirically examine the equivalency of parameterizations between BayesNets and DCMs. Then, we propose a model-comparison framework for testing the model fit of BayesNets, in which we show how BayesNets are nested within the saturated DCM structural models. Additionally, we show when attributes feature a linear hierarchy, the Hierarchical DCM is nested within both BayesNets and saturated DCMs. The usefulness of proposed framework and model-fit testing strategy was supported by the results of analyzing both simulated and empirical data.

A general modeling framework of response accuracy and response times is proposed to track skill acquisition and provide additional diagnostic information on the change of latent speed in a learning environment. This framework consists of two types of models: a dynamic response model that captures the response accuracy and the change of discrete latent attribute profile upon factors such as practice, intervention effects, and other latent and observable covariates, and a dynamic response time model that describes the change of the continuous response latency due to change of latent attribute profile. These two types of models are connected through a parameter, describing the change rate of the latent speed through the learning process, and a covariate defined as a function of the latent attribute profile. A Bayesian estimation procedure is developed to calibrate the model parameters and measure the latent variables. The estimation algorithm is evaluated through several simulation studies under various conditions. The proposed models are applied to a real data set collected through a spatial rotation diagnostic assessment paired with learning tools.

In longitudinal/developmental studies, individual growth trajectories are sometimes bounded by a floor at the beginning of the observation period and/or a ceiling toward the end of the observation period (or vice versa), resulting in inherently nonlinear growth patterns. If the trajectories between the floor and ceiling are approximately linear, such longitudinal growth patterns can be described with a linear piecewise (spline) model in which segments join at knots. In these scenarios, it may be of specific interest for researchers to examine the timing when transition occurs, and in some occasions also to examine the levels of the floors and/or ceilings if they are not known and fixed. In the current study, we propose a reparameterized piecewise latent growth curve model so that a direct estimation of the random knots (and, if needed, a direct estimation of random floors and ceilings) is possible. We derive the model reparameterization using a 4-step structured latent curve modeling approach. We provide two illustrative examples to demonstrate how the proposed reparameterized models can be fitted to longitudinal growth data using the popular SEM software Mplus and we supply the full coding for applied researchers' reference.

Exploratory mediation analysis via regularization, or XMed, is a recently developed technique that allows one to identify potential mediators of a process of interest. However, as currently implemented, it can only be applied to continuous outcomes. We extend this method to allow application to dichotomous outcomes, including both mediators and dependent variables. Simulation results show that XMed can achieve the same sensitivity as more conventional methods for mediation analysis such as the Sobel test, percentile bootstrap, and bias-corrected bootstrap, but in general requires only half the sample size to do so. We demonstrate the implementation of this approach using an illustrative example examining the relationship between youth behavioral/emotional problems and alcohol use.

A dynamical system is a system of variables that show some regularity in how they involve over time. Change concepts described in most dynamical systems models are by no means novel to social and behavioral scientists, but most applications of dynamic modeling techniques in these disciplines are grounded on a narrow subset of — typically linear — theories of change. I provide practical guidelines, recommendations, and software code for exploring and fitting dynamical systems models with linear and nonlinear change functions in the context of four illustrative examples. Cautionary notes, challenges, and unresolved issues in utilizing these techniques are discussed.

Virtual reality (VR) systems offer a powerful tool for human behavior research. The ability to create three-dimensional visual scenes and to measure responses to the visual stimuli enables the behavioral researcher to test hypotheses in a manner and scale that were previously unfeasible. For example, a researcher wanting to understand interceptive timing behavior might wish to violate Newtonian mechanics so that objects can move in novel 3-D trajectories. The same researcher might wish to collect such data with hundreds of participants outside the laboratory, and the use of a VR headset makes this a realistic proposition. The difficulty facing the researcher is that sophisticated 3-D graphics engines (e.g., Unity) have been created for game designers rather than behavioral scientists. To overcome this barrier, we have created a set of tools and programming syntaxes that allow logical encoding of the common experimental features required by the behavioral scientist. The Unity Experiment Framework (UXF) allows researchers to readily implement several forms of data collection and provides them with the ability to easily modify independent variables. UXF does not offer any stimulus presentation features, so the full power of the Unity game engine can be exploited. We use a case study experiment, measuring postural sway in response to an oscillating virtual room, to show that UXF can replicate and advance upon behavioral research paradigms. We show that UXF can simplify and speed up the development of VR experiments created in commercial gaming software and facilitate the efficient acquisition of large quantities of behavioral research data.

Background. Indonesia has reported high prevalence and severity of early childhood caries (ECC). This is at 90% at age 3–5 years with higher mean of decay extraction or filling of teeth (def-t). Preschool children are one of the most vulnerable groups to dental and oral disease because they generally still not have proper behavior with regard to maintaining their oral health. Parental care is an important factor as it serves as the basis of the formation of behaviors that support or do not support childhood oral hygiene. One well-validated theory commonly used to test human attitudes and behaviors is the Theory of Planned Behavior. Objectives. Based on the Theory of Planned Behavior, to describe the behavior of mothers on teaching toothbrushing to preschool children. Material and methods. We undertook a cross-sectional analytic study with sample size 218 of mothers of kindergarten children in Surabaya. We obtained our data by giving a questionnaire to mothers that consists of 35 semi open ended and closed ended questions. Results. The variables of Theory of Planned Behavior which showed significant effect on intention to act were attitude and PBC with p-value < 0.005. Subsequently, attitude, subjective norms, and PBC were tested together to measure the influence to intention – which resulted in a R2 of 0.37. Conclusions. Attitudes and PBC are the most dominant factors in improving the intentions and behavior of mothers towards teaching effective toothbrushing practices to their children.

The Research Centers in Minority Institu­tions (RCMI) program was established by the US Congress to support the develop­ment of biomedical research infrastructure at minority-serving institutions granting doctoral degrees in the health professions or in a health-related science. RCMI institu­tions also conduct research on diseases that disproportionately affect racial and ethnic minorities (ie, African Americans/Blacks, American Indians and Alaska Natives, His­panics, Native Hawaiians and Other Pacific Islanders), those of low socioeconomic sta­tus, and rural persons. Quantitative metrics, including the numbers of doctoral science degrees granted to underrepresented stu­dents, NIH peer-reviewed research funding, peer-reviewed publications, and numbers of racial and ethnic minorities participat­ing in sponsored research, demonstrate that RCMI grantee institutions have made substantial progress toward the intent of the Congressional legislation, as well as the NIH/NIMHD-linked goals of addressing workforce diversity and health disparities. Despite this progress, nationally, many challenges remain, including persistent disparities in research and career develop­ment awards to minority investigators. The continuing underrepresentation of minority investigators in NIH-sponsored research across multiple disease areas is of concern, in the face of unrelenting national health inequities. With the collaborative network support by the RCMI Translational Research Network (RTRN), the RCMI community is uniquely positioned to address these chal­lenges through its community engagement and strategic partnerships with non-RCMI institutions. Funding agencies can play an important role by incentivizing such col­laborations, and incorporating metrics for research funding that address underrep­resented populations, workforce diversity and health equity.Ethn Dis. 2019;29(Suppl 1):135-144; doi:10.18865/ed.29.S1.135.

OBJECTIVE: The goal of this research is to develop a machine learning supervised classification model to automatically code clinical encounter transcripts using a behavioral code scheme. METHODS: We first evaluated the efficacy of eight state-of-the-art machine learning classification models to recognize patient–provider communication behaviors operationalized by the motivational interviewing framework. Data were collected during the course of a single weight loss intervention session with 37 African American adolescents and their caregivers. We then tested the transferability of the model to a novel treatment context, 80 patient–provider interactions during routine human immunodeficiency virus (HIV) clinic visits. RESULTS: Of the eight models tested, the support vector machine model demonstrated the best performance, achieving a .680 F1-score (a function of model precision and recall) in adolescent and .639 in caregiver sessions. Adding semantic and contextual features improved accuracy with 75.1% of utterances in adolescent and 73.8% in caregiver sessions correctly coded. With no modification, the model correctly classified 72.0% of patient–provider utterances in HIV clinical encounters with reliability comparable to human coders (k = .639). CONCLUSIONS: The development of a validated approach for automatic behavioral coding offers an efficient alternative to traditional, resource-intensive methods with the potential to dramatically accelerate the pace of outcomes-oriented behavioral research. The knowledge gained from computer-driven behavioral research can inform clinical practice by providing clinicians with empirically supported communication strategies to tailor their conversations with patients. Lastly, automatic behavioral coding is a critical first step toward fully automated eHealth/mHealth (electronic/mobile Health) behavioral interventions.

Rapid growth in the availability of behavioral big data (BBD) has outpaced the speed of updates to ethical research codes and regulation of data privacy and human subjects' data collection, storage, and use. The introduction of the European Union's (EU's) General Data Protection Regulation (GDPR) in May 2018 will have far-reaching effects on data scientists and researchers who use BBD, not only in the EU, but around the world. Consequently, many companies are struggling to comply with the Regulation. At the same time, academics interested in research collaborations with companies are finding it more difficult to obtain data. In light of the importance of BBD in both industry and academia, data scientists and behavioral researchers would benefit from a deeper understanding of the GDPR's key concepts, definitions, and principles, especially as they apply to the data science workflow. We identify key GDPR concepts and principles and describe how they can impact the work of data scientists and researchers in this new data privacy regulation era.

Significance People readily categorize things as good or bad, a welcome adaptation that enables action and reduces information overload. The present research reveals an unforeseen consequence: People do not fully appreciate this immediacy of judgment, instead assuming that they and others will consider more information before forming conclusions than they and others actually do. This discrepancy in perceived versus actual information use reveals a general psychological bias that bears particular relevance in today’s information age. Presumably, one hopes that easy access to abundant information fosters uniformly more-informed opinions and perspectives. The present research suggests mere access is not enough: Even after paying costs to acquire and share ever-more information, people then stop short and do not incorporate it into their judgments., A world where information is abundant promises unprecedented opportunities for information exchange. Seven studies suggest these opportunities work better in theory than in practice: People fail to anticipate how quickly minds change, believing that they and others will evaluate more evidence before making up their minds than they and others actually do. From evaluating peers, marriage prospects, and political candidates to evaluating novel foods, goods, and services, people consume far less information than expected before deeming things good or bad. Accordingly, people acquire and share too much information in impression-formation contexts: People overvalue long-term trials, overpay for decision aids, and overwork to impress others, neglecting the speed at which conclusions will form. In today’s information age, people may intuitively believe that exchanging ever-more information will foster better-informed opinions and perspectives—but much of this information may be lost on minds long made up.

Locating a vocalizing animal can be useful in many fields of bioacoustics and behavioral research, and is often done in the wild, covering large areas. In zoos, however, the application of this method becomes particularly difficult, because, on the one hand, the animals are in a relatively small area and, on the other hand, reverberant environments and background noise complicate the analysis. Nevertheless, by localizing and analyzing animal sounds, valuable information on physiological state, sex, subspecies, reproductive state, social status, and animal welfare can be gathered. Therefore, we developed a sound localization software that is able to estimate the position of a vocalizing animal precisely, making it possible to assign the vocalization to the corresponding individual, even under difficult conditions. In this study, the accuracy and reliability of the software is tested under various conditions. Different vocalizations were played back through a loudspeaker and recorded with several microphones to verify the accuracy. In addition, tests were carried out under real conditions using the example of the giant otter enclosure at Dortmund Zoo, Germany. The results show that the software can estimate the correct position of a sound source with a high accuracy (median of the deviation 0.234 m). Consequently, this software could make an important contribution to basic research via position determination and the associated differentiation of individuals, and could be relevant in a long-term application for monitoring animal welfare in zoos.

In this article, we present Procura-PALavras (P-PAL), a Web-based interface for a new European Portuguese (EP) lexical database. Based on a contemporary printed corpus of over 227 million words, P-PAL provides a broad range of word attributes and statistics, including several measures of word frequency (e.g., raw counts, per-million word frequency, logarithmic Zipf scale), morpho-syntactic information (e.g., parts of speech [PoSs], grammatical gender and number, dominant PoS, and frequency and relative frequency of the dominant PoS), as well as several lexical and sublexical orthographic (e.g., number of letters; consonant-vowel orthographic structure; density and frequency of orthographic neighbors; orthographic Levenshtein distance; orthographic uniqueness point; orthographic syllabification; and trigram, bigram, and letter type and token frequencies), and phonological measures (e.g., pronunciation, number of phonemes, stress, density and frequency of phonological neighbors, transposed and phonographic neighbors, syllabification, and biphone and phone type and token frequencies) for 53,000 lemmatized and 208,000 nonlemmatized EP word forms. To obtain these metrics, researchers can choose between two word queries in the application: (i) analyze words previously selected for specific attributes and/or lexical and sublexical characteristics, or (ii) generate word lists that meet word requirements defined by the user in the menu of analyses. For the measures it provides and the flexibility it allows, P-PAL will be a key resource to support research in all cognitive areas that use EP verbal stimuli. P-PAL is freely available at http://p-pal.di.uminho.pt/tools., This study was conducted at the Psychology Research Centre (PSI/01662), University of Minho. It was supported by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Science, Technology, and Higher Education through national funds, and co-financed by FEDER through European funds (Grant POCI-01-0145-FEDER-007653).

Wrist-worn alcohol biosensor technology has developed rapidly in recent years. These devices are light, easy to wear, relatively inexpensive, and resemble commercial fitness trackers. As a result, they may be more suitable for a wide range of clinical and research applications. In this paper, we describe three pilot projects examining the associations between reported drinking behavior and transdermal alcohol concentration (TAC) derived from a new, wrist-worn alcohol biosensor (BACtrack Skyn) in diverse participant groups and settings. Study 1 (N = 3) compared Skyn-derived TAC with that from an ankle-worn alcohol sensor (SCRAM CAM) and breath alcohol concentration (BrAC) in a laboratory setting. Study 2 (N = 10) compared Skyn TAC with BrAC during a naturalistic drinking episode in the field. Study 3 (N = 12) used the Skyn to monitor alcohol use in the field for two weeks. Study 2 and 3 also collected usability and acceptability data from participants. The results of Study 1 showed that the Skyn produced a TAC curve that closely resembled that of the validated SCRAM CAM anklet. In Study 2, Skyn detected drinking for all 10 participants (peak BrAC range: 0.02-0.21) with an average delay of 35.6±10.2 minutes after the start of self-reported drinking. In Study 3, Skyn reliably recorded continuous TAC data showing multiple drinking episodes over the monitoring period. Participants in Studies 2 and 3 both reported Skyn as highly acceptable. Collectively, the results of these pilot studies show that the Skyn was able to reliably detect drinking events in the laboratory and natural environments. We offer suggestions for further refinements of alcohol biosensors and accompanying analytic software that may facilitate adoption of these devices as cost-effective, user-friendly, and reliable tools to passively and accurately assess alcohol use in the field.

Comparative European data using Second Generation Surveillance System (SGSS) are scarce among gay, bisexual and other men who have sex with men. This study evaluated the implementation of Sialon II, a bio-behavioural HIV research combined with targeted HIV prevention in 13 European cities conducted in collaboration with community partners. A mixed-methods process evaluation assessed the project's coverage, outputs, quality, challenges and opportunities for improvement. Data collected through structured questionnaire from 71 data collectors from community-based organisations and semi-structured interviews with 17 managers of participating gay venues were analysed. Overall implementation was successful, achieving 4901 valid behavioural questionnaires and obtaining 4716 biological samples. Challenges in conducting bio-behavioural research in gay venues related to strict research protocols and unfavourable characteristics of venues. Formative research, collaboration with community gay venues, and offering HIV prevention emerged as facilitators. Community researchers' training was crucial for fidelity to research protocols, increased trust amongst communities and enabled data collectors to effectively address practical problems in the field. Scientifically sound SGSS with community participation is feasible and allows for including 'hard-to-reach' populations. Prevention benefits include awareness raising, capacity building and sexual health promotion in gay venues. The findings are beneficial for epidemiological research among other HIV key populations.

Long-standing structural features of the military have created a culture and society that is dramatically different and disconnected from civilian society. Thus, veterans transitioning to civilian society face a number of challenges related to fulfilling basic psychological needs (e.g., need for structure and order, belonging) and civilians’ reliance on stereotypes to understand military veterans. In an attempt to enrich the understanding of these challenges, we integrate social psychological theories and insights with research from sociology, clinical psychology, military psychology, and organizational behavior. Theories of compensatory control, stereotype threat, and stereotyping are drawn on to help explain the psychological challenges that veterans may encounter during their transition to civilian society. We present recent research that leverages these theories to understand issues veterans face. This theoretical integration illustrates the opportunity and potential for psychological researchers to conduct basic and applied research in the context of veterans and for clinicians and managers to draw on basic theory to inform programs and interventions.

Health Psychology has received numerous papers over the past several months on topics related to the COVID-19 pandemic. Many of them concern depression, anxiety, stress, or other forms of distress in the general population or in health care workers. We have received far fewer papers on COVID-related health behaviors and health communications-factors that have played central roles in the spread of the pandemic and that are major topics in health psychology. Our experience is consistent with the published scientific literature on the pandemic. A Medline search that we conducted in late September yielded over 23,000 English-language articles pertaining to COVID-19. Over 1,400 of them concerned topics that are within the scope of Health Psychology. As shown in Table 1, COVID-related mental disorders comprised the largest category. Many other studies concerned other forms of stress or emotional distress. At least 248 articles addressed the profound ethnic and racial disparities in COVID-19 infection and death rates and in access to health care that are accentuating longstanding health inequities; 22 (9%) of these articles addressed behavioral or psychosocial aspects of COVID-19 health disparities. Thus, the literature on the behavioral and psychosocial aspects of the pandemic has been dominated, so far at least, by research on stress or distress. Fewer reports have been published so far on critical COVID-related health behaviors, health communication, or health disparities. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

While challenges in the translation of behavioral medicine knowledge from academic settings to real-world use are well documented, they could potentially be reduced through the use of the Design Sprint process, a roadmap that facilitates progression from an idea to a product in 5 days via agile design and user-centered design principles. The Design Sprint is well aligned with several priorities of and methods common in behavioral medicine, including obtaining end-user feedback and thinking about implementation during intervention development. The Design Sprint could be used by behavioral medicine researchers for the development of research tools, implementation strategies, and behavior change interventions. The process may encourage creative and focused thinking, speed product development, and facilitate early user input. However, translating the Design Sprint to behavioral medicine research may present challenges, such as difficulty getting buy-in from team members, inconsistencies between the Design Sprint timeline, the need to obtain institutional review board approval for user testing, and the lack of traditional research processes (e.g., incorporating scientific research into intervention design). Several solutions and adaptations are possible to overcome these challenges while still maintaining the core features of the Design Sprint process. To demonstrate the use of the Design Sprint and potential adaptations, we present a case study of the development of a patient-facing digital tool for weight management and an accompanying implementation strategy. In conclusion, the Design Sprint offers behavioral medicine researchers an approach to developing new products with numerous potential benefits to researchers and to the individuals and communities we seek to impact.

Objectives Adolescents of color are underrepresented in behavioral health research. Study aims were to quantify the amount and types of outreach effort needed to recruit young Black adolescents with type 1 diabetes and their primary caregiver into a clinical trial evaluating a parenting intervention and to determine if degree of recruitment difficulty was related to demographic, diabetes-related, or family characteristics. Methods Data were drawn from a multi-center clinical trial. Participants (N = 155) were recruited from seven pediatric diabetes clinics. Contact log data were used to quantify both number/type of contacts prior to study enrollment as well as length of time to enrollment. Families were coded as having expedited recruitment (ER) or prolonged recruitment (PR). Baseline study data were used to compare ER and PR families on sociodemographic factors, adolescent diabetes management and health status and family characteristics such as household organization and family conflict. Results Mean length of time to recruit was 6.6 months and mean number of recruitment contacts was 10.3. Thirty-nine percent of the sample were characterized as PR. These families required even higher levels of effort (mean of 9.9 months to recruit and 15.4 contacts). There were no significant between-group differences on any baseline variable for ER and PR families, with the exception of family income. Conclusions Researchers need to make persistent efforts in order to successfully enroll adolescents of color and their caregivers into clinical trials. Social determinants of health such as family resources may differentiate families with prolonged recruitment within such samples.

The relative lack of sensitive and clinically valid tests of rodent behavior might be one of the reasons for the limited success of the clinical translation of preclinical Alzheimer's disease (AD) research findings. There is a general interest in innovative behavioral methodology, and protocols have been proposed for touchscreen operant chambers that might be superior to existing cognitive assessment methods. We assessed and analyzed touchscreen performance in several novel ways to examine the possible occurrence of early signs of prefrontal (PFC) functional decline in the APP/PS1 mouse model of AD. Touchscreen learning performance was compared between APP/PS1-21 mice and wildtype littermates on a C57BL/6J background at 3, 6 and 12 months of age in parallel to the assessment of spatial learning, memory and cognitive flexibility in the Morris water maze (MWM). We found that older mice generally needed more training sessions to complete the touchscreen protocol than younger ones. Older mice also displayed defects in MWM working memory performance, but touchscreen protocols detected functional changes beginning at 3 months of age. Histological changes in PFC of APP/PS1 mice indeed occurred as early as 3 months. Our results suggest that touchscreen operant protocols are more sensitive to PFC dysfunction, which is of relevance to the use of these tasks and devices in preclinical AD research and experimental pharmacology. ispartof: GENES BRAIN AND BEHAVIOR vol:20 issue:1 ispartof: location:England status: published

Recent advances in large-scale data storage and processing offer unprecedented opportunities for behavioral scientists to collect and analyze naturalistic data, including from under-represented groups. Audio data, particularly real-world audio recordings, are of particular interest to behavioral scientists because they provide high-fidelity access to subtle aspects of daily life and social interactions. However, these methodological advances pose novel risks to research participants and communities. In this article, we outline the benefits and challenges associated with collecting, analyzing, and sharing multi-hour audio recording data. Guided by the principles of autonomy, privacy, beneficence, and justice, we propose a set of ethical guidelines for the use of longform audio recordings in behavioral research. This article is also accompanied by an Open Science Framework Ethics Repository that includes informed consent resources such as frequent participant concerns and sample consent forms.

Objective: Because of the COVID-19 pandemic, the implementation of quarantine for returning travellers and the effect this has on people’s mental health has become a topical issue. This article briefly describes the historical context of quarantine, research around its impact on people’s well-being, and the experiences of a clinical psychologist providing support to people in quarantine. Conclusions: Mental health professionals are in a unique position to assist people in quarantine, both in terms of counselling and ongoing research.

With the growing technological advances in autonomous driving, the transport industry and research community seek to determine the impact that autonomous vehicles (AV) will have on consumers, as well as identify the different factors that will influence their use. Most of the research performed so far relies on laboratory-controlled conditions using driving simulators, as they offer a safe environment for testing advanced driving assistance systems (ADAS). In this study we analyze the behavior of drivers that are placed in control of an automated vehicle in a real life driving environment. The vehicle is equipped with advanced autonomy, making driver control of the vehicle unnecessary in many scenarios, although a driver take over is possible and sometimes required. In doing so, we aim to determine the impact of such a system on the driver and their driving performance. To this end road users' behavior from naturalistic driving data is analyzed focusing on awareness and diagnosis of the road situation. Results showed that the road features determined the level of visual attention and trust in the automation. They also showed that the activities performed during the automation affected the reaction time to take over the control of the vehicle., Comment: Paper accepted for submission in The 23rd IEEE International Conference on Intelligent Transportation Systems

Background.Suicide risk in patients is markedly elevated during psychiatric inpatient care, as well as after discharge. However, it is unclear whether, and to what extent, this increased suicide risk varies between sex. Thus, the aim of this study was to analyze sex differences for suicides during and after psychiatric hospitalization in various countries.Methods.National suicide mortality rates and inpatient-related suicide rates (three intervals: during psychiatric inpatient treatment, 1 month, and 1 year after discharge) from 12 countries for 2000–2016 were analyzed, and a logistic model was used to quantify the effect of sex.Results.Persons admitted to or discharged from psychiatric inpatient care exhibited significantly increased rates of suicide compared to those in the general population. Furthermore, increase of suicide risk was significantly higher for females than for males for all investigated time intervals (inpatient suicide odds ratio [OR] 1.85; suicide within 1 month after discharge—OR 1.94; suicide within 1 year after discharge—OR 2.04).Conclusion.Analysis confirmed the time during and after psychiatric inpatient care to be significantly associated with an elevated risk for suicide. Further, a significant sex effect was observed, with females in this population being at a proportionally higher risk for suicide during psychiatric inpatient treatment as well as the year following discharge. Our study implicates that more effective suicide preventive measures during inpatient stay, focusing on female patients, are needed.

Elucidating the complex neural mechanisms that underlie cognition is contingent upon our ability to measure behavioral outputs reliably in animal models. While the development of open-source software has made behavioral science more accessible, behavioral research remains underappreciated and underutilized. One reason is the large real estate necessitated by traditional behavioral setups. Space must be specifically allocated for a controlled testing environment, accommodate the large footprint of mazes used in behavioral research, and allow a contiguous computerized area for data acquisition. Additionally, to achieve the distinct and sometimes incompatible environmental conditions required by different tasks, a suite of testing rooms may be necessary. Because space is a limited resource, this makes behavioral testing impractical for some labs or leads to implementation of suboptimal solutions that compromise the ergonomics of the working space, prevent the adequate control of environmental parameters around the testing setup, and jeopardize experimental reproducibility. Here, we describe a modular, space-saving, self-sufficient, functional, customizable, and cost-efficient setup to allow a large line of behavioral tests in mice within a single, compact room (

Type 2 diabetes is a serious global epidemic that disproportionately affects disadvantaged populations. American Indians and Alaska Natives (AIs/ANs) have the highest rates of diabetes in the nation with a prevalence of 14.7% in 2018, more than twice that of non-Hispanic Whites. AI/AN men have the highest prevalence of diagnosed type 2 diabetes (14.5%) compared to non-Hispanic Black (11.4%), non-Hispanic Asian (10.0%), and non-Hispanic White (8.6%) men. Several landmark clinical trials have shown that lifestyle interventions can effectively prevent or delay the onset of diabetes among those at risk, including in AIs/ANs. Despite positive outcomes for AIs/ANs in these studies, very few were men. To date, there have been no concerted efforts to recruit and retain AI/AN men in interventions that promote weight loss and healthy lifestyles to prevent diabetes, and they remain underrepresented in these types of studies. This article describes the design and methods of the first randomized controlled trial of a diabetes prevention program with a study sample comprised entirely of AI/AN men. Research to date has demonstrated suboptimal patterns of recruitment and retention of AI/AN men, resulting in their virtual absence in health and intervention research. Effective methods to recruit and retain AI/AN men, and potential benefit gained from participation in diabetes prevention research, are unknown for this population who experience a high prevalence of type 2 diabetes. The study design presented in this article offers promising insights to help remedy these important shortcomings in the science of recruitment and retention of AI/AN men in research.

The COVID-19 pandemic has been mitigated primarily using social and behavioral intervention strategies, and these strategies have social and economic impacts, as well as potential downstream health impacts that require further study. Digital and community-based interventions are being increasingly relied upon to address these health impacts and bridge the gap in health care access despite insufficient research of these interventions as a replacement for, not an adjunct to, in-person clinical care. As SARS-CoV-2 testing expands, research on encouraging uptake and appropriate interpretation of these test results is needed. All of these issues are disproportionately impacting underserved, vulnerable, and health disparities populations. This commentary describes the various initiatives of the National Institutes of Health to address these social, behavioral, economic, and health disparities impacts of the pandemic, the findings from which can improve our response to the current pandemic and prepare us better for future infectious disease outbreaks.

Many studies needed to quell this and future pandemics are not being done, and the chance is ebbing away. Many studies needed to quell this and future pandemics are not being done, and the chance is ebbing away.

Chronic prenatal exposure to opioids often causes fetal opioid dependence that leads to neonatal opioid withdrawal syndrome (NOWS) shortly after delivery. Rat models of NOWS often require quantifying neonatal withdrawal behaviors using time-consuming, labor-intensive manual scoring methods. The goal of this study was to automate quantification of opioid withdrawal in neonatal rat pups. Accordingly, we used the animal behavior software Ethovision® XT to analyze archived videos of rat pups subjected to precipitated opioid withdrawal testing on postnatal day 0. We compared results obtained from Ethovision® XT with those previously obtained from manual scoring. Two endpoints reported by Ethovision® XT, Distance Moved (cm) and Movement Duration (s), had strong positive linear relationships with manually derived global withdrawal scores (GWS; R2 > 0.73). Sensitivity and specificity of each endpoint to discriminate presence and absence of low-grade withdrawal were assessed by receiver operator characteristic curve analysis, which indicated that Distance Moved and Movement Duration had excellent accuracy (AUC > 0.90). Finally, we analyzed main and interaction effects of prenatal treatment (with vehicle or mu opioid receptor full agonists) and postnatal challenge (with saline or an opioid receptor antagonist) on each endpoint and determined they were similar for the manual and automated methods. These results show that Ethovision® XT software can reliably quantify opioid withdrawal in neonatal rat pups with non-inferiority to manual scoring even in videos that were not originally purposed and optimized for Ethovision® XT analysis. This faster and less labor-intensive method of analysis is expected to accelerate progress in preclinical studies of NOWS.

Background Chronic kidney disease is an increasingly common health problem for Aboriginal and Torres Strait Islander people. It is associated with multiple concurrent psychosocial stressors frequently resulting in negative impacts on emotional and social wellbeing. There is need for well-designed intervention studies to provide evidence of effective treatment for comorbid depression or other mental illness in this setting. Attention to early phase piloting and development work is recommended when testing complex interventions. This paper documents feasibility testing and adaptation of an existing culturally responsive brief wellbeing intervention, the Stay Strong App, and three commonly used wellbeing outcome measures, in preparation for a clinical trial testing effectiveness of the intervention. Methods The Stay Strong App, which has not been used in the setting of Chronic Kidney Disease before, is reviewed and adapted for people with comorbid wellbeing concerns through expert consensus between research team and an Expert Panel. The outcome measures (Kessler 10, Patient Health Questionnaire 9, and EuroQoL) are valid, reliable, and commonly used tools to assess various aspects of wellbeing, which have also not been used in this context before. Feasibility and acceptability are examined and developed through 3 stages: Pilot testing in a purposive sample of five haemodialysis patients and carers; translation of outcome measures through collaboration between the Aboriginal Interpreter Service, Aboriginal and Torres Strait Islander research officers and the research team; and conversion of translated outcome measures to electronic format. Results Research team and expert panel consensus led to adaptation of the Stay Strong App for renal patients through selective revision of words and images. Pilot testing identified challenges in delivery of the wellbeing measures leading to word changes and additional prompts, integration of audio translations in 11 local Indigenous languages within an interactive Outcome Measures App, and related research protocol changes. Conclusion Modelling the complex intervention prior to full-scale testing provided important information about the design of both the outcome measures and the intervention. These changes are likely to better support success in conduct of the clinical trial and future implementation of the intervention in clinical settings.

This narrative review of the literature provides a summary and discussion of 25 years of research into the complex links between depression and diabetes. Systematic reviews have shown that depression occurs more frequently in people with type 1 or type 2 diabetes compared with people without diabetes. Currently, it remains unclear whether depression is also more common in people with impaired glucose metabolism or undiagnosed type 2 diabetes compared with people without diabetes. More prospective epidemiological research into the course of depression and an exploration of mechanisms in individuals with diabetes are needed. Depression in diabetes is associated with less optimal self-care behaviours, suboptimal glycaemic control, impaired quality of life, incident micro- and macrovascular diseases, and elevated mortality rates. Randomized controlled trails concluded that depression in diabetes can be treated with antidepressant medication, cognitive-behavioural therapy (individual, group-based or web-based), mindfulness-based cognitive therapy and stepped care. Although big strides forward have been made in the past 25 years, scientific evidence about depression in diabetes remains incomplete. Future studies should investigate mechanisms that link both conditions and test new diabetes-specific web- or app-based interventions for depression in diabetes. It is important to determine whether treatment or prevention of depression prevents future diabetes complications and lowers mortality rates.

Overweight and obese men were recruited to a 6-month, randomized controlled weight loss trial, which compared the Gutbusters weight loss program alone to the Gutbusters program with incentives for successful weight loss. The intervention was delivered primarily online, with weekly in-person weight collections. Gutbusters was designed using a template from the REFIT intervention and encouraged participants ( N = 102, 47.0 ± 12.3 years, 32.5 kg/m2) to make six 100-calorie changes to their typical daily diet for a total of 42 changes per week. Weight loss was significantly greater in the Gutbusters+Incentive group compared to the Gutbusters alone group at both 12 and 24 weeks ( p’s = < .01). The Gutbusters+Incentive group’s a mean weight loss was 9.9 pounds at 12 weeks (95% CI: 6.9, 12.9) and 8.4 pounds at 24 weeks (95% CI: 3.9, 13.0). The Gutbusters alone group mean weight loss was 3.7 pounds at 12 weeks (95% CI: –.06, 7.5) and 3.4 pounds at 24 weeks (95% CI: –2.2, 9.0). This study adds to the literature of behavioral weight programs that are designed for men. Unlike the majority of previous male weight loss interventions, which were designed with an intervention comparison to a no treatment or waitlist control, Gutbusters was implemented as a comparative effectiveness trial, which will help bolster the evidence base for real-world application.