Doctor of Philosophy

dissertationCardiorespiratory endurance is a major component of health-related fitness testing in physical education. FITNESSGRAM recommends the 1-mile Run/Walk (1-MRW) or the Progressive Aerobic Cardiovascular Endurance Run (PACER) to assess cardiorespiratory endurance by estimating aerobic capacity, or VO2 Peak. No research to date has cross-validated prediction models from both 1-MRW and PACER using current FITNESSGRAM criterion-referenced (CR) standards. Additionally, new prediction models for 1-MRW without a body mass index (BMI) term are needed to attenuate the problems incorporating this index into an aerobic capacity model. The purpose of this dissertation was to cross-validate various prediction models using 1-MRW and PACER and to develop alternative 1-MRW aerobic capacity prediction models for adolescent youth. Participants included 90 students aged 13 to 16 years. Each student completed the 1-MRW and PACER, in addition to a maximal treadmill test to measure VO2 Peak. Multiple correlations among various models with measured VO2 Peak were considered strong (R = 0.74 to 0.78). CR validity, examined using modified kappa (Κq), percentage of agreement (Pa), and phi was considered moderate among all models (Κq = 0.25 to 0.49; Pa = 72% to 79%; phi = 0.38 to 0.65). Two new models were developed from 1-MRW times, one linear and one quadratic model. The linear and quadratic models displayed multiple correlations of R = 0.77 and R = 0.82 with measured VO2 Peak, respectively. CR validity evidence was considered moderate with (Kq = 0.38; Pa = 73%; phi = 0.57) using the linear model and (Kq = 0.34; Pa = 70%; phi = 0.54) using the quadratic model. The accuracy of these models was confirmed using k-fold cross-validation. In conclusion, the prediction models demonstrated strong linear relationships with measured VO2 Peak, acceptable prediction error, and moderate CR agreement with measured VO2 Peak using FITNESSGRAM's CR standards to categorize health groups. The new 1-MRW models displayed good predictive accuracy and moderate CR agreement with measured VO2 Peak without using a BMI predictor. Despite evidence for predictive utility of the new models, they must be externally validated to ensure they can be generalizable to larger populations of students

PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease.Comment: AAAI Conference on Artificial Intelligence 201

Clinical validity assessment of a breast cancer risk model combining genetic and clinical information

_Background:_ The extent to which common genetic variation can assist in breast cancer (BCa) risk assessment is unclear. We assessed the addition of risk information from a panel of BCa-associated single nucleotide polymorphisms (SNPs) on risk stratification offered by the Gail Model.

_Methods:_ We selected 7 validated SNPs from the literature and genotyped them among white women in a nested case-control study within the Women’s Health Initiative Clinical Trial. To model SNP risk, previously published odds ratios were combined multiplicatively. To produce a combined clinical/genetic risk, Gail Model risk estimates were multiplied by combined SNP odds ratios. We assessed classification performance using reclassification tables and receiver operating characteristic (ROC) curves. 

_Results:_ The SNP risk score was well calibrated and nearly independent of Gail risk, and the combined predictor was more predictive than either Gail risk or SNP risk alone. In ROC curve analysis, the combined score had an area under the curve (AUC) of 0.594 compared to 0.557 for Gail risk alone. For reclassification with 5-year risk thresholds at 1.5% and 2%, the net reclassification index (NRI) was 0.085 (Z = 4.3, P = 1.0×10^-5^). Focusing on women with Gail 5-year risk of 1.5-2% results in an NRI of 0.195 (Z = 3.8, P = 8.6×10^−5^).

_Conclusions:_ Combining clinical risk factors and validated common genetic risk factors results in improvement in classification of BCa risks in white, postmenopausal women. This may have implications for informing primary prevention and/or screening strategies. Future research should assess the clinical utility of such strategies.
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Multi-Systemic Biological Risk and Cancer Mortality: The NHANES III Study.

Multi-systemic biological risk (MSBR), a proxy for allostatic load, is a composite index of biomarkers representing dysregulation due to responses to chronic stress. This study examined the association of an MSBR index with cancer mortality. The sample included n = 13,628 adults aged 20-90 from the NHANES III Linked Mortality File (1988-1994). The MSBR index included autonomic (pulse rate, blood pressure), metabolic (HOMAir, triglycerides, waist circumference), and immune (white blood cell count, C-reactive protein) markers. We fit Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CI) of overall cancer mortality risk, according to quartiles (q) of the index. In multivariable models, compared to those in q1, q4 had a 64% increased risk for cancer mortality (HR = 1.64, 95% CI:1.13-2.40). The immune domain drove the association (HR per unit = 1.19, 95% CI:1.07-1.32). In stratified analyses, the HR for those with a BMI ≥ 25 was 1.12 per unit (95% CI:1.05-1.19) and those with a BMI < 25 was 1.04 per unit (95% CI:0.92-1.18). MSBR is positively associated with risk for cancer mortality in a US sample, particularly among those who are overweight or obese. The utilization of standard clinical measures comprising this index may inform population cancer prevention strategies

Making a case for cardiorespiratory fitness surveillance among children and youth

We review the evidence that supports cardiorespiratory fitness (CRF) as an important indicator of current and future health among school-aged children and youth, independent of physical activity levels. We discuss the merit of CRF measurement for population health surveillance and propose the development of CRF guidelines to help support regional, national, and international surveillance efforts

Utility of three anthropometric indices in assessing the cardiometabolic risk profile in children

Objectives: To evaluate the ability of BMI, WC and WHtR to identify increased cardiometabolic risk in pre-adolescents. Methods: This is a cross-sectional study involving 192 children (10.92 ± 0.58 years, 56% female) from the United Kingdom between 2010 and 2013. Receiver operating characteristic curves determined the discriminatory ability of BMI, WC and WHtR to identify individuals with increased cardiometabolic risk (increased clustered triglycerides, HDL-cholesterol, systolic blood pressure, cardiorespiratory fitness and glucose). Results: A WHtR ≥ 0.5 increased the odds by 5.2 (95% confidence interval 2.6, 10.3) of having increased cardiometabolic risk. Similar associations were observed for BMI and WC. Both BMI-z and WHtR were fair predictors of increased cardiometabolic risk although BMI-z demonstrated the best trade-off between sensitivity and specificity, 76.1% and 63.6%, compared to 68.1% and 65.5% for WHtR. Cross-validation analysis revealed that BMI-z and WHtR correctly classified 84% of individuals (kappa score = 0.671, 95% CI 0.55, 0.79). The sensitivity of the cut-points suggests that 89.3% of individuals were correctly classified as being at risk with only 10.7% misdiagnosed whereas the specificity of the cut-points indicated that 77.8% of individuals were correctly identified as being healthy with 22.2% of individuals incorrectly diagnosed as being at risk. Conclusions: Findings suggest that WHtR provides similar cardiometabolic risk estimates to age and sex adjusted BMI