Cancer poses a significant worldwide burden where ongoing efforts are targeted towards improving patient outcomes in which a significant contribution results from cancer screening. Multi-cancer early detection tests have been introduced which measure a series of biomarkers to detect signals that may indicate carcinogenesis in its earliest stages and work in tandem with other diagnostic techniques to localize and verify tumor formation across multiple cancer types. Molecular biomarkers such as autoantibodies are promising candidates for early detection across multiple cancers. This study identifies autoantibodies that are aberrantly expressed across multiple cancer types that may be used to discriminate between healthy individuals and those with cancer from a single serum sample. Multiple datasets are integrated from prior studies to examine 8,200 sera autoantibodies from 5 cancer types including lung adenocarcinoma, basal-like breast cancer, advanced colorectal cancer, ovarian cancer, and HER2+ breast cancer. The diagnostic utility of these autoantibodies is assessed for combined cancer types by meta-receiver operating characteristic (ROC) curve analysis. A meta-analysis data processing pipeline is utilized for processing each biomarker with statistical analysis performed across ROC metrics for each meta-curve including partial area under the curve and sensitivity at a 90% specificity threshold. Results identified 26 autoantibody biomarkers that are useful for multi-cancer detection and may be developed for future clinical applications in cancer screening.

Sticking to healthy behaviors is difficult. The lack of long-term behavior maintenance negatively impacts health outcomes and increases healthcare costs. Current methods for improving behavior maintenance yield varying and often limited results. This dissertation designs and tests quantitative methods for identifying behavioral strategies associated with long-term maintenance the long-term maintenance of three different health behaviors. Data were collected from three settings: mindfulness through a commercial app, walking from a randomized controlled trial, and pill-taking from a commercial app-based intervention. Novel pattern-detection methodologies were employed to measure temporal consistency and identify key behavioral strategies. For mindfulness and walking behaviors, the impact of individual phenotypes on long-term behavior maintenance was analyzed. For medication adherence, the optimal window of time in which pills should be taken was empirically determined, and the impact of consistent timing on long-term medication adherence was analyzed. To perform these analyses, robust and regularized models, panel data models, statistical tests, and clustering algorithms were used. For mindfulness meditation, both consistent and inconsistent phenotypes were associated with long-term engagement. In the walking intervention, those with a consistent phenotype experienced greater increases in walking after the study than inconsistent individuals. However, the effect of consistency was strongest for individuals who either exercised less than 10 or more than 30 minutes per day. Lastly, in the medication adherence incentive program, consistently taking medication within 55 minutes of the goal time had the strongest association with future adherence. This dissertation demonstrates that certain phenotypes are more advantageous than others for long-term maintenance and interventions. Temporal consistency is likely helpful for maintaining behaviors that offer delayed physical benefits, such as regular walking or medicating for chronic illnesses, but less helpful for cognitive behaviors like mindfulness, which can provide more immediate satisfaction. When designing interventions, the nature of the behavior and observable phenotypes should be taken into consideration. Generally, focusing on consistency is likely to contribute to long-term success; however, this is individual and context dependent. Future research should investigate this further by examining the relationship between behavioral phenotypes and psychological measurement tools to gain a deeper understanding of the successful maintenance of healthy behaviors.

The proposed research is motivated by the colon cancer bio-marker study, which recruited case (or colon cancer) and healthy control samples and quantified their large number of candidate bio-markers using a high-throughput technology, called nucleicacid-programmable protein array (NAPPA). The study aimed to identify a panel of biomarkers to accurately distinguish between the cases and controls. A major challenge in analyzing this study was the bio-marker heterogeneity, where bio-marker responses differ from sample to sample. The goal of this research is to improve prediction accuracy for motivating or similar studies. Most machine learning (ML) algorithms, developed under the one-size-fits-all strategy, were not able to analyze the above-mentioned heterogeneous data. Failing to capture the individuality of each subject, several standard ML algorithms tested against this dataset performed poorly resulting in 55-61% accuracy. Alternatively, the proposed personalized ML (PML) strategy aims at tailoring the optimal ML models for each subject according to their individual characteristics yielding best highest accuracy of 72%.

The unprecedented amount and sources of information during the COVID-19 pandemic resulted in an indiscriminate level of misinformation that was confusing and compromised healthcare access and delivery. The World Health Organization (WHO) called this an ‘infodemic’, and conspiracy theories and fake news about COVID-19, plagued public health efforts to contain the COVID-19 pandemic. National and international public health priorities expanded to counter misinformation. As a multi-disciplinary study encompassing expertise from public health, informatics, and communication, this research focused on eliciting strategies to better understand and combat misinformation on COVID-19. The study hypotheses is that 1) factors influencing vaccine-acceptance like socio-demographic factors, COVID-19 knowledge, trust in institutions, and media related factors could be leveraged for public health education and intervention; and 2) individuals with a high level of knowledge regarding COVID-19 prevention and control have unique behaviors and practices, like nuanced media literacy and validation skills that could be promoted to improve vaccine acceptance and preventative health behaviors. In this biphasic study an initial survey of 1,498 individuals sampled from Amazon Mechanical Turk (MTurk) assessed socio-demographic factors, an 18-item test of COVID-19 knowledge, trust in healthcare stakeholders, and measures of media literacy and consumption. Subsequently, using the Positive Deviance Framework, a diverse subset of 25 individuals with high COVID-19 knowledge scores were interviewed to identify these deviants’ information and media practices that helped avoid COVID-19 misinformation. Access to primary care, higher educational attainment and living in urban communities were positive socio-demographic predictors of COVID-19 vaccine acceptance emphasizing the need to invest in education and rural health. High COVID-19 knowledge and trust in government and health providers were also critical factors and associated with a higher level of trust in science and credible information sources like the Centers for Disease Control (CDC) and health experts. Positive deviants practiced media literacy skills that emphasized checking sources for scientific basis as well as hidden bias; cross-checking information across multiple sources and verifying health information with scientific experts. These identified information validation and confirmation practices may be useful in educating the public and designing strategies to better protect communities against harmful health misinformation.

Meditation app usage is associated with decreases in stress, anxiety, and depression symptoms. Many meditation app subscribers, however, quickly abandon or reduce their app usage. This dissertation presents three manuscripts which 1) determined the behavioral, demographic, and socioeconomic factors associated with the abandonment of a meditation app, Calm, during the COVID-19 pandemic, 2) determined which participant characteristics predicted meditation app usage in the first eight weeks after subscribing, and 3) determined if changes in stress, anxiety, and depressive symptoms from baseline to Week 8 predicted meditation app usage from Weeks 8-16. In Manuscript 1, a survey was distributed to Calm subscribers in March 2020 that assessed meditation app behavior and meditation habit strength, and demographic information. Cox proportional hazards regression models were estimated to assess time to app abandonment. In Manuscript 2, new Calm subscribers completed a baseline survey on participants’ demographic and baseline mental health information and app usage data were collected over 8 weeks. In Manuscript 3, new Calm subscribers completed a baseline and Week 8 survey on demographic and mental health information. App usage data were collected over 16 weeks. Regression models were used to assess app usage for Manuscripts 2 and 3. Findings from Manuscript 1 suggest meditating after an existing routine decreased risk of app abandonment for pre-pandemic subscribers and for pandemic subscribers. Additionally, meditating “whenever I can” decreased risk of abandonment among pandemic subscribers. No behavioral factors were significant predictors of app abandonment among the long-term subscribers. Findings from Manuscript 2 suggest men had more days of meditation than women. Mental health diagnosis increased average daily meditation minutes. Intrinsic motivation for meditation increased the likelihood of completing any meditation session, more days with meditation sessions, and more average daily meditation minutes. Findings from Manuscript 3 suggest improvements in stress increased average daily meditation minutes. Improvements in depressive symptoms decreased daily meditation minutes. Evidence from this three-manuscript dissertation suggests meditation cue, time of day, motivation, symptom changes, and demographic and socioeconomic variables may be used to predict meditation app usage.