Fit Into College II: Physical Activity and Nutrition Behavior Effectiveness and Programming Recommendations

Purpose: To determine whether residency (living on campus versus off campus) was related to the effects of Fit into College on students’ health behaviors, and to understand interns’ perceptions of their roles in mentoring their trainees. Design: Pre-experimental, one-group, pretest-posttest design and a posttest focus group interview. Setting: University-offered health and internship courses. Subjects: Twenty-four students (trainees) participated in the intervention, nine of whom lived on campus. Five student-interns served as their mentors. Intervention: Fit into College was a 14-week intervention in which trainees teamed up with an intern to improve and/or maintain healthy nutrition and physical activity behaviors. Measures: Trainees’ nutrition and physical activity behaviors and perceptions were quantitatively assessed through surveys at preintervention and postintervention. Interns’ mentoring perceptions were qualitatively assessed through a focus group interview after the intervention. Analysis: Two-factor repeated measure ANOVAs and qualitative theme identification. Results: Regardless of their residency location, the trainees’ perceptions of the health benefits of eating fruits and vegetables improved during the intervention. However, for trainees living on campus, the intervention was not effective in increasing the number of fruits and vegetables consumed or the planning for food preparation. The interns perceived that they did not have adequate access to healthy foods, the knowledge or skills to prepare healthy foods, or the competency to teach food preparation strategies to their trainees. For trainees living on campus, the intervention was more effective in decreasing perceived exercise barriers than trainees living off campus. Conclusion: Future iterations of Fit into College may focus on 1) improving college students’ planning and preparation of healthy foods, 2) segmenting trainees into more homogeneous groups for the interns to tailor their areas of expertise (campus vs. off-campus and/or freshman vs. upperclass students), and 3) collaborating with university-partners to improve environmental conditions to promote physical activity and healthy nutrition

Fit Into College: A Program to Improve Physical Activity and Dietary Intake Lifestyles Among College Students

The purpose of this study was to determine whether a 10-week program could improve physical activity, physical fitness, body weight, dietary intake, and perceptions of exercise and diet among college 30 healthy college freshmen. Outcomes were measured at baseline, and following the 10-week program. The weekly sessions incorporated constructs of the Transtheoretical Model of Health Behavior Change and were administered by fitness interns who were junior or senior college students enrolled in health-related majors. The participants presented with low physical activity, physical fitness, and poor dietary intake, and 50% were overweight/obese (BMI \u3e 25). Participants demonstrated gains in their physical fitness and their perceived benefits to engaging in exercise and decreased their perceived barriers to engaging in exercise and a healthy diet. College freshmen presented with low levels of physical activity, poor dietary intake, and excess body weight. A peer-administered program can improve these measures and favorably change perceptions of exercise and diet

Indocyanine Green (ICG) Lymphography Is Superior to Lymphoscintigraphy for Diagnostic Imaging of Early Lymphedema of the Upper Limbs

BACKGROUND: Secondary lymphedema causes swelling in limbs due to lymph retention following lymph node dissection in cancer therapy. Initiation of treatment soon after appearance of edema is very important, but there is no method for early diagnosis of lymphedema. In this study, we compared the utility of four diagnostic imaging methods: magnetic resonance imaging (MRI), computed tomography (CT), lymphoscintigraphy, and Indocyanine Green (ICG) lymphography. PATIENTS AND METHODS: Between April 2010 and November 2011, we examined 21 female patients (42 arms) with unilateral mild upper limb lymphedema using the four methods. The mean age of the patients was 60.4 years old (35-81 years old). Biopsies of skin and collecting lymphatic vessels were performed in 7 patients who underwent lymphaticovenous anastomosis. RESULTS: The specificity was 1 for all four methods. The sensitivity was 1 in ICG lymphography and MRI, 0.62 in lymphoscintigraphy, and 0.33 in CT. These results show that MRI and ICG lymphography are superior to lymphoscintigraphy or CT for diagnosis of lymphedema. In some cases, biopsy findings suggested abnormalities in skin and lymphatic vessels for which lymphoscintigraphy showed no abnormal findings. ICG lymphography showed a dermal backflow pattern in these cases. CONCLUSIONS: Our findings suggest the importance of dual diagnosis by examination of the lymphatic system using ICG lymphography and evaluation of edema in subcutaneous fat tissue using MRI

A CREDENCE Trial Substudy

Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.OBJECTIVES: The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). BACKGROUND: Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations. METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).proofepub_ahead_of_prin

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Publisher Copyright: © 2022 The AuthorsObjectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had 400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.publishersversionpublishe

Standard versus prosocial online support groups for distressed breast cancer survivors: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>The Internet can increase access to psychosocial care for breast cancer survivors through online support groups. This study will test a novel prosocial online group that emphasizes both opportunities for getting and giving help. Based on the helper therapy principle, it is hypothesized that the addition of structured helping opportunities and coaching on how to help others online will increase the psychological benefits of a standard online group.</p> <p>Methods/Design</p> <p>A two-armed randomized controlled trial with pretest and posttest. Non-metastatic breast cancer survivors with elevated psychological distress will be randomized to either a standard facilitated online group or to a prosocial facilitated online group, which combines online exchanges of support with structured helping opportunities (blogging, breast cancer outreach) and coaching on how best to give support to others. Validated and reliable measures will be administered to women approximately one month before and after the interventions. Self-esteem, positive affect, and sense of belonging will be tested as potential mediators of the primary outcomes of depressive/anxious symptoms and sense of purpose in life.</p> <p>Discussion</p> <p>This study will test an innovative approach to maximizing the psychological benefits of cancer online support groups. The theory-based prosocial online support group intervention model is sustainable, because it can be implemented by private non-profit or other organizations, such as cancer centers, which mostly offer face-to-face support groups with limited patient reach.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01396174">NCT01396174</a></p

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm\u27s diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had \u3c50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (\u3c350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, \u3e400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient\u27s BMI or heart rate at time of scan affect the software\u27s diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters

Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Objective: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). Methods: This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (\u3c50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age \u3c65 and ≥65 years. Results: The cohort was 64.4±10.2 years and 29% women. Overall, patients \u3e65 had more PV and CP than patients \u3c65. On a lesion level, patients \u3e65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p\u3c0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p\u3c0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p\u3c0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. Conclusion: AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment