Search

Amazon Mechanical Turk (MTurk) is one of the most popular crowdsourcing platforms for online research. Although the recent MTurk data quality crisis has raised serious concerns regarding its utility, this issue can be mitigated by including quality control methods. To date, only a few studies have compared the effectiveness of the methods in preserving an experimental effect. To provide empirical guidance on which quality control methods to use for MTurk research, this study compared different methods on their effectiveness in preserving the magnitude effect in delay discounting, a robust finding in the behavioral economics literature. Two groups of MTurkers, whose HIT approval rate (%) was between 95 and 98 and 99 and 100, were recruited in October 2021. They completed a delay discounting task evaluating the magnitude effect and questions from different quality control methods. The results showed that the group with higher HIT approval rates (i.e., 99%--100%) showed a greater magnitude effect and were more likely to pass the quality control methods than those with lower rates. In addition, the majority of the quality control methods were able to preserve the magnitude effect at the expense of sample sizes. The best combinations of quality control methods that optimally maintained the effect were explored with a discussion of their implications. Although improving data quality is imperative, caution should be exercised as the quality control methods may potentially bias the sample in an undesirable manner (e.g., excluding individuals who show steep delay discounting). Keywords MTurk * Data quality * Online research * Delay discounting * Magnitude effect, Introduction The use of online crowdsourcing platforms to collect behavioral data has become popular in social science research in recent decades. These platforms provide evident scientific benefits, including efficiency and [...]

Abstract Alcohol Use Disorder (AUD) contributes significantly to global mortality. GLP-1 (Glucagon-like peptide-1) and GLP-1/GIP (Glucose-dependent Insulinotropic Polypeptide) agonists, FDA-approved for managing type 2 diabetes and obesity, where the former has shown to effectively reduce the consumption of alcohol in animal models but no reports exist on the latter. In this report, we conducted two studies. In the first study, we conducted an analysis of abundant social media texts. Specifically, a machine-learning based attribution mapping of ~ 68,250 posts related to GLP-1 or GLP-1/GIP agonists on the Reddit platform. Secondly, we recruited participants (n = 153; current alcohol drinkers; BMI ≥ 30) who self-reported either taking Semaglutide (GLP-1 agonist), Tirzepatide (the GLP-1/GIP combination) for ≥ 30 days or, as a control group; no medication to manage diabetes or weight loss for a within and between subject remote study. In the social media study, we report 8 major themes including effects of medications (30%); diabetes (21%); and Weight loss and obesity (19%). Among the alcohol-related posts (n = 1580), 71% were identified as craving reduction, decreased desire to drink, and other negative effects. In the remote study, we observe a significantly lower self-reported intake of alcohol, drinks per drinking episode, binge drinking odds, Alcohol Use Disorders Identification Test (AUDIT) scores, and stimulating, and sedative effects in the Semaglutide or Tirzepatide group when compared to prior to starting medication timepoint (within-subjects) and the control group (between-subjects). In summary, we provide initial real-world evidence of reduced alcohol consumption in people with obesity taking Semaglutide or Tirzepatide medications, suggesting potential efficacy for treatment in AUD comorbid with obesity.

ObjectiveDissociation is a conscious state characterized by alterations in sensation and perception and is thought to arise from traumatic life experiences. Previous research has demonstrated that individuals with high levels of dissociation show impairments in cognitive-emotional processes. Therefore, using the Competing Neurobehavioral Decisions System (CNDS) theory, we used statistical modeling to examine whether dissociative experience and trauma symptoms are independently predicted by impulsivity, risk-seeking, affective state (i.e., anxiety, depression, stress, and negative affect), and trauma history.MethodIn this cross-sectional study design, data were collected via Amazon Mechanical Turk from a total of n = 557 English-speaking participants in the United States. Using Qualtrics, participants answered a series of self-reported questionnaires and completed several neurocognitive tasks. Three independent multiple linear regression models were conducted to assess whether impulsivity, risk seeking, affective state, and trauma history predict depersonalization, trauma symptoms, and PTSD symptoms.ResultsAs hypothesized, we found that depersonalization and other trauma symptoms are associated with heightened impulsivity, increased risk-seeking, impaired affective states, and a history of traumatic experiences.ConclusionWe demonstrate that an imbalanced CNDS (i.e., hyperimpulsive/hypoexecutive), as evidenced by decreased future valuation, increased risk seeking, and impaired affective states, predicts heightened depersonalization and other trauma and PTSD symptomatology. This is the first time that dissociation has been connected to delay discounting (i.e., the tendency to place more value on rewards received immediately compared to farther in the future). Interventions that positively impact areas of the CNDS, such as episodic future thinking or mindfulness meditation, may be a target to help decrease dissociative symptoms.

Background Sacubitril/Valsartan has been highly efficacious in randomized trials of heart failure with reduced ejection fraction (HFrEF). However, the effectiveness of sacubitril/valsartan in older patients hospitalized for HFrEF in real-world US practice is unclear. Methods and Results This study included Medicare beneficiaries age ≥65 years who were hospitalized for HFrEF ≤40% in the Get With The Guidelines-Heart Failure registry between October 2015 and December 2018, and eligible for sacubitril/valsartan. Associations between discharge prescription of sacubitril/valsartan and clinical outcomes were assessed after inverse probability of treatment weighting and adjustment for other HFrEF medications. Overall, 1551 (10.9%) patients were discharged on sacubitril/valsartan. Of those not prescribed sacubitril/valsartan, 7857 (62.0%) were prescribed an angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker. Over 12-month follow-up, compared with a discharge prescription of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, sacubitril/valsartan was independently associated with lower all-cause mortality (adjusted hazard ratio [HR], 0.82; 95% CI, 0.72-0.94; P=0.004) but not all-cause hospitalization (adjusted HR, 0.97; 95% CI, 0.89-1.07; P=0.55) or heart failure hospitalization (adjusted HR, 1.04; 95% CI, 0.91-1.18; P=0.59). Patients prescribed sacubitril/valsartan versus those without a prescription had lower risk of all-cause mortality (adjusted HR, 0.69; 95% CI, 0.60-0.79; P

Despite being a major threat to health, vaccine hesitancy (i.e., refusal or reluctance to vaccinate despite vaccine availability) is on the rise. Using a longitudinal cohort of young adults (N = 1260) from Los Angeles County, California we investigated the neurobehavioral mechanisms underlying COVID-19 vaccine hesitancy. Data were collected at two time points: during adolescence (12th grade; fall 2016; average age = 16.96 (±0.42)) and during young adulthood (spring 2021; average age = 21.33 (±0.49)). Main outcomes and measures were delay discounting (DD; fall 2016) and tendency to act rashly when experiencing positive and negative emotions (UPPS-P; fall 2016); self-reported vaccine hesitancy and vaccine beliefs/knowledge (spring 2021). A principal components analysis determined four COVID-19 vaccine beliefs/knowledge themes: Collective Responsibility, Confidence and Risk Calculation, Complacency, and Convenience. Significant relationships were found between themes, COVID-19 vaccine hesitancy, and DD. Collective Responsibility (β = -1.158[-1.213,-1.102]) and Convenience (β = -0.132[-0.185,-0.078]) scores were negatively associated, while Confidence and Risk Calculation (β = 0.283[0.230,0.337]) and Complacency (β = 0.412[0.358,0.466]) scores were positively associated with COVID-19 vaccine hesitancy. Additionally, Collective Responsibility (β = -0.060[-0.101,-0.018]) was negatively associated, and Complacency (β = -0.063[0.021,0.105]) was positively associated with DD from fall 2016. Mediation analysis revealed immediacy bias during adolescence, measured by DD, predicted vaccine hesitancy 4 years later while being mediated by two types of vaccine beliefs/knowledge: Collective Responsibility (β = 0.069[0.022,0.116]) and Complacency (β = 0.026[0.008,0.044]). These findings provide a further understanding of individual vaccine-related decision-making among young adults and inform public health messaging to increase vaccination acceptance.

IntroductionWe describe the development and pilot testing of the experimental tobacco and nicotine product marketplace (ETM)-a method for studying tobacco and nicotine product (TNP) choices and use behavior in a standardized way.Aims and methodsThe ETM resembles an online store populated with TNPs. Surveillance activities and data from a US representative survey and consumer reports were used to determine the most popular TNPs for inclusion in the ETM. Standardized information and videos demonstrating how to use the TNPs were provided. To test the feasibility of using the ETM, smokers (n = 119) underwent monitoring of usual brand cigarette smoking and other TNP use (Baseline Phase) followed by access to the ETM (ETM Phase) that included their usual brand cigarettes, e-cigarettes, moist snuff, snus, and nicotine replacement therapy. During the ETM Phase, participants were provided points based on their baseline TNP consumption to exchange for TNPs in the ETM. Participants were advised to exchange points for enough TNPs to last until their next visit and to refrain from using TNPs not obtained in the ETM. A subset of the participants (n = 62) completed a survey on their experience with the ETM.ResultsThe majority of the participants stated they were comfortable with navigating the ETM (97%), it was easy to determine product characteristics (89%), and they were satisfied with the products included in the marketplace (85%).ConclusionsThe ETM was well received by the vast majority of the participants and can be utilized by researchers to investigate a variety of TNP policy and regulatory science research questions.ImplicationsPatterns of TNP use are complex due to greater availability, marketing, and promotion of a diverse array of TNPs. Innovative methods are needed to experimentally study TNP choices and patterns. Through describing the development of the ETM, we provide researchers with a tool that can be readily adapted to studying a variety of phenomena challenging public health.

This systematic review aims to characterize the utility of machine learning to identify the predictors of smoking cessation outcomes and identify the machine learning methods applied in this area. In the current study, multiple searches occurred through December 9, 2022 in MEDLINE, Science Citation Index, Social Science Citation Index, EMBASE, CINAHL Plus, APA PsycINFO, PubMed, Cochrane Central Register of Controlled Trials, and the IEEE Xplore were performed. Inclusion criteria included various machine learning techniques, studies reporting cigarette smoking cessation outcomes (smoking status and the number of cigarettes), and various experimental designs (e.g., cross-sectional and longitudinal). Predictors of smoking cessation outcomes were assessed, including behavioral markers, biomarkers, and other predictors. Our systematic review identified 12 papers fitting our inclusion criteria. In this review, we identified gaps in knowledge and innovation opportunities for machine learning research in the field of smoking cessation.

Background The angiotensin-receptor/neprilysin inhibitor ( ARNI ) sacubitril/valsartan reduces hospitalization and mortality for patients with heart failure with reduced ejection fraction. However, adoption of ARNI into clinical practice has been slow. Factors influencing use of ARNI have not been fully elucidated. Using data from the Get With The Guidelines-Heart Failure registry, Hospital Compare, Dartmouth Atlas, and the American Hospital Association Survey, we sought to identify hospital characteristics associated with patient-level receipt of an ARNI prescription. Methods and Results We analyzed patients with heart failure with reduced ejection fraction who were eligible for ARNI prescription (ejection fraction≤40%, no contraindications) and hospitalized from October 1, 2015 through December 31, 2016. We used logistic regression to estimate the associations between hospital characteristics and patient ARNI prescription at hospital discharge, accounting for clustering of patients within hospitals using generalized estimating equation methods and adjusting for patient-level covariates. Of 16 674 eligible hospitalizations from 210 hospitals, 1020 patients (6.1%) were prescribed ARNI at discharge. The median hospital-level proportion of patients prescribed ARNI was 3.3% (Q1, Q3: 0%, 12.6%). After adjustment for patient-level covariates, for-profit hospitals had significantly higher odds of ARNI prescription compared with not-for-profit hospitals (odds ratio, 2.53; 95% CI , 1.05-6.10; P=0.04), and hospitals located in the Western United States had lower odds of ARNI prescription compared with those in the Northeast (odds ratio, 0.33; 95% CI , 0.13-0.84; P=0.02). Conclusions Relatively few hospital characteristics were associated with ARNI prescription at hospital discharge, in contrast to what has been observed in early adoption in other disease areas. Additional evaluation of barriers to implementing new evidence into heart failure practice is needed.

BackgroundTranslating repetitive transcranial magnetic stimulation (rTMS) into evidence-based clinical applications relies on research volunteers with different perspectives on the burden of study participation. Additionally, clinical applications of rTMS require multiple visits over weeks or months, the impact of research burden is an important component for these studies and translation of these findings to clinical practice. High frequency rTMS has significant potential to be developed as an evidence-based treatment for smoking cessation, however, the optimal rTMS dosing strategies have yet to be determined. Participant burden is an important component of determining optimal dosing strategy for rTMS as a treatment for long-term smoking cessation.MethodsIn this double-blinded, sham-controlled, randomized design, the effects of treatment duration, intensity, and active/sham assignment of rTMS on research burden were examined.ResultsOverall level of perceived research burden was low. Experienced burden (M = 26.50) was significantly lower than anticipated burden (M = 34.12). Research burden did not vary by race or income.ConclusionsOverall research burden was relatively low. Contrary to our hypotheses, we found little evidence of added significant burden for increasing the duration or intensity of rTMS, and we found little evidence for differences in research burden by race or income.Clinical Trial Registrationidentifier NCT03865472.

Episodic Future Thinking (EFT) reduces delay discounting and may have the potential as a clinical tool to increase the likelihood of health-promoting behaviors. However, evaluations of EFT in clinical settings require control conditions that match the effort and frequency of cue generation, as well as participants' expectations of improvement. The Health Information Thinking (HIT) control addresses these issues, but how this control affects delay discounting in individuals with diabetes and obesity when utilizing diabetes-management specific health-information vignettes is unknown. Moreover, little research has explored whether EFT reduces delay discounting in individuals with type 2 diabetes. To this end, we examined the impact of EFT, HIT, and a secondary no-cue control condition (NCC; assessments as usual) on delay discounting in 434 adults with self-reported type 2 diabetes and obesity recruited using Amazon Mechanical Turk. After completing an initial screening questionnaire, eligible participants reported demographics, then were randomized to EFT, HIT, or NCC conditions. Following the generation of seven EFT or HIT cues, participants assigned to EFT or HIT conditions completed a delay discounting task while imagining EFT or HIT cues; no-cue participants completed the task without cues. EFT participants demonstrated significantly lower delay discounting levels than HIT or NCC participants; no differences in delay discounting between HIT and NCC participants were observed. These results suggest that engaging in EFT, but not diabetes-specific HIT, results in lower delay discounting in adults with type 2 diabetes and obesity. This provides further evidence for the appropriateness of the HIT control for clinical trials examining the effect of EFT on delay discounting in adults with self-reported type 2 diabetes.

The Monetary Choice Questionnaire (MCQ) is a widely used behavioral task that measures the rate of delay discounting (i.e., k), the degree to which a delayed reward loses its present value as a function of the time to its receipt. Both 21- and 27-item MCQs have been extensively validated and proven valuable in research. Different methods have been developed to streamline MCQ scoring. However, existing scoring methods have yet to tackle the issue of missing responses or provide clear guidance on imputing such data. Due to this lack of knowledge, the present study developed and compared three imputation approaches that leverage the MCQ's structure and prioritize ease of implementation. Additionally, their performance was compared with mode imputation. A Monte Carlo simulation was conducted to evaluate the performance of these approaches in handling various missing responses in each observation across two datasets from prior studies that employed the 21- and 27-item MCQs. One of the three approaches consistently outperformed mode imputation across all performance measures. This approach involves imputing missing values using congruent non-missing responses to the items corresponding to the same k value or introducing random responses when congruent answers are unavailable. This investigation unveils a straightforward method for imputing missing data in the MCQ while ensuring unbiased estimates. Along with the investigation, an R tool was developed for researchers to implement this strategy while streamlining the MCQ scoring process.