Depression has been linked to a significant burden of disease, loss of life, and decreased efficacy of treatment for its comorbidities. Empirical evidence has mounted over the past several decades supporting health benefits from vinegar ingestion, including improvements in blood glucose management, blood cholesterol levels, and inflammation indicators. To date, there have not been any studies in human populations that explore the potential relationship between daily vinegar ingestion and changes in depression indicators and blood metabolomics. This blinded, randomized controlled trial examined the impact of twice-daily vinegar ingestion on mental health measures in healthy young adults recruited from a metropolitan setting. Participants (n=28; aged 25.8±7.0 y; body mass index [BMI] >23 kg/m2) were stratified by age, gender, and BMI and randomly assigned to the liquid (VIN) or pill (CON) groups. VIN participants ingested 2 tablespoons of red wine vinegar (~1550 mg acetic acid; Pompeian Inc., Appendix J) diluted in 8-12 ounces of water, consumed with food at mealtimes twice daily (total ~3100mg acetic acid daily). CON participants consumed 1 vinegar pill daily (22.5 mg acetic acid; Spring Valley, Appendix J). The study lasted four weeks, and anthropometric measurements were conducted in a fasted state at weeks 0 and 4. Study adherence varied slightly (90±17% and 100±14% for VIN and CON respectively, p=0.084); hence, adherence was controlled for in all subsequent analyses. Changes in L-tryptophan (p=0.777, η2=0.003), peripheral serotonin levels (p=0.348, η2=0.035), GABA (p=0.403, η2=0.028), and gut-mediated short-chain fatty acids acetic acid (p=0.355, η2=0.034), and propionic acid (p=0.383, η2=0.031), did not differ significantly between VIN and CON groups respectively with the exception of isobutyric acid (p=0.0374, q=0.0473). However, Patient Health Questionnaire (PHQ-9) depression scores improved significantly for VIN participants in comparison to CON participants (-0.5±1.3 vs. +0.7±2.4 cm [p=0.026] and -0.4±0.7 vs. +0.3±1.0% [p=0.045]. Although these differences between groups are modest, which would be expected given the short study duration, changes were not driven by pharmacological or lifestyle interventions, suggesting the benefits of vinegar ingestion on mental health symptomology and the blood metabolome.

When questions about a person’s mental state arise in court, psychologists are often called in to help. Psychological assessment tools are routinely included in these evaluations to inform legal decision making. In accordance with the Daubert standard, which governs the admissibility of expert testimony, courts are obligated to exclude evidence that relies on poor scientific practice, including assessment tools. However, prior research demonstrates that psychological assessment tools with weak psychometric properties are routinely admitted in court, rarely challenged on the basis of their reliability, and if a challenge is indeed raised, often still admitted (Neal et al., 2019). Is neuropsychological assessment evidence in particular vulnerable to the same pitfalls? The present research aimed to 1) quantify the quality of neuropsychological assessment evidence used in court, 2) evaluate whether courts are calibrated to the quality of these tools through the rate and success of legal admissibility challenges raised, and 3) compare forensic mental health evaluators’ experiences and practices with regard to the quality of neuropsychological versus non-neuropsychological assessment tools. Neuropsychological tools appeared to perform worse than non-neuropsychological tools in terms of psychometric quality. However, in a case law analysis, significantly fewer challenges were observed to the legal admissibility of neuropsychological tools than to non-neuropsychological tools. To protect the legitimacy of the legal system and prevent wrongful decisions, it is critical that the evidence on which psychologists’ expert opinions are formed is scientifically valid, and that judges and attorneys adequately scrutinize the quality of evidence introduced in court.

As threats emerge and change, the life of a police officer continues to intensify. To better support police training curriculums and police cadets through this critical career juncture, this thesis proposes a state-of-the-art framework for stress detection using real-world data and deep neural networks. As an integral step of a larger study, this thesis investigates data processing techniques to handle the ambiguity of data collected in naturalistic contexts and leverages data structuring approaches to train deep neural networks. The analysis used data collected from 37 police training cadetsin five different training cohorts at the Phoenix Police Regional Training Academy. The data was collected at different intervals during the cadets’ rigorous six-month training course. In total, data were collected over 11 months from all the cohorts combined. All cadets were equipped with a Fitbit wearable device with a custom-built application to collect biometric data, including heart rate and self-reported stress levels. Throughout the data collection period, the cadets were asked to wear the Fitbit device and respond to stress level prompts to capture real-time responses. To manage this naturalistic data, this thesis leveraged heart rate filtering algorithms, including Hampel, Median, Savitzky-Golay, and Wiener, to remove potentially noisy data. After data processing and noise removal, the heart rate data and corresponding stress level labels are processed into two different dataset sizes. The data is then fed into a Deep ECGNet (created by Prajod et al.), a simple Feed Forward network (created by Sim et al.), and a Multilayer Perceptron (MLP) network for binary classification. Experimental results show that the Feed Forward network achieves the highest accuracy (90.66%) for data from a single cohort, while the MLP model performs best on data across cohorts, achieving an 85.92% accuracy. These findings suggest that stress detection is feasible on a variate set of real-world data using deepneural networks.

Stress is one of the critical factors in daily lives, as it has a profound impact onperformance at work and decision-making processes. With the development of IoT technology, smart wearables can handle diverse operations, including networking and recording biometric signals. Also, it has become easier for individual users to selfdetect stress with recorded data since these wearables as well as their accompanying smartphones now have data processing capability. Edge computing on such devices enables real-time feedback and in turn preemptive identification of reactions to stress. This can provide an opportunity to prevent more severe consequences that might result if stress is unaddressed. From a system perspective, leveraging edge computing allows saving energy such as network bandwidth and latency since it processes data in proximity to the data source. It can also strengthen privacy by implementing stress prediction at local devices without transferring personal information to the public cloud. This thesis presents a framework for real-time stress prediction using Fitbit and machine learning with the support from cloud computing. Fitbit is a wearable tracker that records biometric measurements using optical sensors on the wrist. It also provides developers with platforms to design custom applications. I developed an application for the Fitbit and the user’s accompanying mobile device to collect heart rate fluctuations and corresponding stress levels entered by users. I also established the dataset collected from police cadets during their academy training program. Machine learning classifiers for stress prediction are built using classic models and TensorFlow in the cloud. Lastly, the classifiers are optimized using model compression techniques for deploying them on the smartphones and analyzed how efficiently stress prediction can be performed on the edge.

Human Immunodeficiency Virus (HIV) remains a persistent problem around the world, even though antiretroviral therapy has shown to be effective in reducing viral load and limiting transmission of the virus. Due to HIV’s infectious nature, visibility, the populations at risk, and its connections to race, class, and sexuality, it is more stigmatized than any other illness. HIV stigma has been associated with increased depression, social isolation, and poor psychological adjustment. HIV stigma can influence disclosure and care-seeking behavior. Internet-based interventions have shown to be effective in increasing knowledge on STIs and HIV, however, researchers have tested strategies that include educating participants on HIV to reduce stigma and have found that informational approaches alone are not effective. There is evidence that emotional intelligence and empathy are associated with prosocial behavior and influence attitudes towards stigmatized groups. Thus, this thesis aims to test an online intervention using an informational video from the Center for Disease Control and Prevention (CDC) in combination with an empathy-generating component to reduce stigma. It was hypothesized that the online intervention would increase HIV knowledge scores (H1), but stigma will only be reduced in the group introduced to the empathy-inducing component (H2) and those with high emotional intelligence would show the greatest reduction in stigmatizing attitudes (H3). Results did not support these hypotheses, suggesting that the CDC’s video does not significantly increase HIV knowledge in the general public. Further, the video intended to generate empathy and reduce stigma was also ineffective. These findings stress the need for further research and questions the effectiveness of empathy-generating interventions (e.g., FACES OF HIV, HIV Justice Network) to increase knowledge and reduce stigma. Future researchers should test the effectiveness of personalized interventions to reduce HIV-related stigma.

This study was been influenced from the perspective of clinical psychology. The main research question was: What personality traits and/or characteristics (in this case emotional characteristics) can influence dating violence? Aspects such as gender, age, sexual orientation, and current relationship status were considered. Given the evolving culture of relationship dominance, it has been difficult to detect when, and if, people can become potential victims of relationship violence.
Results of the dating violence assessments were reported as well as the results of a personality assessment. The comparisons between the three relationship assessments were inconclusive. This research should be taken as a guidance into the factors of dating violence by taking into consideration the characteristics and personalities of potential victims. It can also be seen as a snapshot of the current time period on the topic of relationship violence and its ideas and its prevalence.
The research conducted was at Arizona State University in three psychology classes. The results included participants relationships, abuse screening scores, and personality assessments. The True Colors personality test showed that the majority of the participants were associated with being emotion driven.

Background: In the United States, approximately 50,000 teens with Autism Spectrum Disorder (ASD) age into adulthood every year (Shattuck et al., 2012). A hallmark symptom of ASD includes pronounced difficulties in social interactions and verbal and nonverbal communication (Lai, Lombardo, & Baron-Cohen, 2014). These social cognition difficulties consist of difficulties interpreting social cues, employing appropriate adaptive behavioral responses in various social contexts, as well as the ability to interpret emotions and mental states of others, known as theory of mind (TOM; Premack & Woodruff, 1978). In neurotypical (NT) adults, women perform better on social cognition tasks and difficulties become more prevalent with age, however little is known how sex differences and aging may impact social cognition in adults with ASD (Carstensen, Fung, & Charles 2003).

Objective: This research intended to characterize the influence of sex and age on social cognition in adults with ASD using an adult sample. We hypothesized Reading the Mind in the Eyes (RME) scores would be lower in adults with ASD, with a stronger relationship between decreasing performance aging effects compared to NTs. Additionally, we hypothesized deficits would be more severe in in males with ASD compared to females with ASD.

Methods: The RME task was administered to 181 adults to quantify ToM abilities. The participants consisted of 100 adults with ASD (69 males, 32 females; age range: 18-71, mean=39.45±1.613) and matched 81 NT adults (47 males, 34 females; age range: 18-70, mean=41.51±1.883). Multiple regression analyses examined interactions between diagnosis and age, diagnosis and sex, and diagnosis by age by sex. Exploratory within group analyses assessed 1) sex differences using ANCOVA, and 2) associations with age using Pearson correlation in SPSS.

Results: We found that NT adults performed better on the RME task than adults with ASD. Worse performance on the RME task correlated with greater age for the NT, but not ASD. Additionally, no influence of sex on RME scores was identified.

Discussion: These results are consistent with other studies indicate social cognition deficits in adults with ASD compared to NT adults. Additionally, we replicated findings that suggest ToM performance declines with age in NT adults. Fewer social relationships, smaller social networks, and reduced social engagement have been associated with aging in both NTs and individuals with ASD (Pratt & Norris, 1994). However, our cross-sectional sample suggests ToM abilities may not decline with age in adults with ASD as hypothesized. Longitudinal studies are needed to corroborate these findings. Further developments in this line of research may inform novel interventions tailored toward the growing population of adults with ASD. Ultimately, our research aims to improve quality of life across the lifespan for an already vulnerable population.

Mood disorders are highly prevalent, especially in adolescent populations. One potential cause of the widespread nature of these disorders is the formation of stigma around emotionality. Emotion research, while extensive, has not expanded to capture how a parent’s emotion regulation and expression may lead to stigmatized behaviors in their child affecting that child’s mental health into adulthood. The current thesis aimed to investigate the relevance of this novel concept – emotionality stigma – in the relationship between parental emotionality and adult-child mental health. Using social learning theory, parental emotionality was predicted to influence a child’s emotionality into adulthood. Specifically, this thesis investigated if parental emotion over- and under-expression (dysregulation) would influence adult-children to perceive a stigma around emotionality leading to worse mental health, whereas well-regulated parental emotion expression would relate to adult-child emotional intelligence, leading to better mental health. Moreover, it was predicted that these relationships would differ depending on parent and child gender. To examine these ideas, data was collected from 1,136 college and community individuals through a university survey system and Amazon’s Mechanical Turk. Using a combination of linear regression, PROCESS, and Structural Equation Modeling (SEM) models, the results supported the proposed hypotheses. As predicted, parental dysregulation in childhood predicted impaired adult-child mental health, whereas parental regulation in childhood predicted lower levels of adult-child depression and anxiety. Additionally, emotionality stigma and emotional intelligence partially mediated the relationship between parental emotionality and adult-child mental health. Furthermore, results showed interesting gender differences; male participants were more impacted by both maternal and paternal emotionality as compared to female participants. These findings not only build on emotion research, but also have numerous applications in practice including improving parenting classes and family therapy interventions. This study is the first to explore the role of parental emotionality on adult-child mental health through stigma and emotional intelligence.