580 research outputs found

    Assessing Depression and Attributional Styles as Determinants of Engagement in Digital Behavior Change Interventions

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    Digital behavior change interventions are capable of promoting significant change in health behaviors, but often suffer high disengagement and nonusage dropout. The purpose of this dissertation was to determine if depressive symptoms or pessimistic attributional styles negatively influenced proximal engagement behaviors among users of study websites or smartphone apps. Three different interventions were assessed across the three Aims. Aim One used structural equation modeling to determine if CES-D scores were indirectly associated with 6-month weight change outcomes through mediating latent constructs for engagement and adherence among adults (N=338) in a 12-month eHealth intervention. CES-D scores were negatively associated with both engagement and adherence, which were positively associated with 6-month weight loss, contributing to a significant negative indirect effect. Additionally, CES-D scores predicted significantly higher risk of users disengaging from the website over time. Aim Two applied mixed effects modeling to estimate participants’ (N=52) likelihood of viewing messages as a function of the number of goals they were currently failing in an mHealth microrandomized pilot, and a generalization of log-linear regression analysis to assess the likelihood of reading consecutive program messages following receipt of goal-discrepant messages, and if these relationships were moderated by CES-D scores, net of other covariates. The more goals participants were failing, the less likely they were to read any program messages sent. Additionally, receipt of goal discrepant messages was associated with a significantly lower likelihood of participants reading the next program message sent, compared to neutral messages or no message; however, these relationships did not appear to be influenced by CES-D scores. Aim Three compared the CES-D indicating depressive symptoms and the DAQ indicating pessimistic attributional styles for their potential to predict lower engagement following goal-discrepant feedback using mixed effects regression among a subsample of participants (N=132) in an ongoing mHealth intervention. Both scales were associated with lower odds of reading the next program message sent following receipt of goal-discrepant feedback, and higher odds of disengagement, but no effects on app page views between messages. These results provide some support for tailoring message content based on psychological indicators to reduce negative influences on program engagement.Doctor of Philosoph

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

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    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!

    A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

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    The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory

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    Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination
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