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Kermott, Cindy A.

https://orcid.org/0000-0003-1925-6949

Mayo Clinic

Mayo Clinic

Citations 4

Abstract 13189: CV-Ecosystem Model: Workplace Wellness With Individualized Medicine
Krittanawong, Chayakrit , Kermott, Cindy A. , Zhang, Hongju, Marysia, Tweet S, Wang, Zhen , Chirapongsathorn, Sakkarin , Hayes, Sharonne N.

Circulation | July 3, 2021 | Volume: 132 | Issue: suppl_3 | Pages: No page available

10.1161/circ.132.suppl_3.13189 Get It

Introduction: At-work stress is rapidly becoming a significant contributory cause of occupational disease, while sedentary work and dietary indiscretion, taken together, have contributed to cardiovascular disease, known to be associated with substantial morbidity and mortality. The aim of the CV-Ecosystem Model is to provide an individual approach by systematic environmental modification of the work site. Methods and Results: Our model which we term the CV-Ecosystem Model includes two basic tenets: 1) Workstation technologic design 2) Flexible physiologic activity. Workstation technologic design utilizes the services of a certified Ergonomist to engineer a comfortable, safe, and effective work environment. It is tailored to the unique physiologic needs of the worker while enhancing productivity within different environments. Computer aided design software would evaluate ergonomics to maximally mitigate the risk and incidence of eye strain, musculoskeletal disorders, repetitive movement disorders such as Carpal Tunnel Syndrome, etc unique to that work activity. Flexible physiologic activity would permit a worker to actively focus on basic needs during work time, on site; this would include exercise activity and food intake, channeled into healthy options. Exercise time would provide a sense of calm during stressful projects and enhance emotional well-being. In this regard, a three hour period of time, weekly, could be allocated to fitness or meditation. Conveniently located food stations could provide a variety of healthy, plant-based, fresh food options. Both would come with flexible, relative control and scheduling of these activities, within a social environment and context. Conclusions: The CV Ecosystem Model, due to its simple technology and relative low cost, could be successfully implemented in a number of enterprises, including all healthcare facilities, with benefit to employee health, wellbeing, and favorable to the business bottom line.
Validation of a population coronary disease predictive system: the CASSANDRA model
Kermott, Cindy A. , Bailey, Kent R. , Olson, Janet E.

Journal of Epidemiology and Community Health | May 10, 2014 | Volume: 68 | Issue: 11 | Pages: 1009-1009

10.1136/jech-2014-204165 Get It

Self-Rated Stress Is Noncontributory to Coronary Artery Disease in Higher Socioeconomic Strata
Kermott, Cindy A. , Cha, Stephen S., Hagen, Philip T., Behrenbeck, Thomas, Cha, Stephen S.

Population Health Management | March 28, 2013 | Volume: 16 | Issue: 5 | Pages: 332-340

10.1089/pop.2012.0105 Get It

Stress and its attendant psychosocial and lifestyle variables have been associated with coronary artery disease (CAD), yet the contribution of socioeconomic status (SES) has not been addressed. The aim of this study is to determine if stress assessment is associated with CAD independent of SES, and is incremental to the Framingham Score. The study group consisted of 325 executive patients undergoing comprehensive health assessment. Stress was assessed utilizing the validated “Self-Rated Stress” (SRS) instrument. Coronary artery calcification (CAC) served to assess the degree of atherosclerosis, a CAD equivalent and risk assessment tool. The relationship between SRS and CAC was assessed, with adjustment by potential confounders. CAC was modeled by a variety of cut points (>0, ≥5, ≥100, ≥200) for the test of trend across stress levels per Mantel-Haenszel chi-square (1 df) with nonsignificant P values of 0.9960, 0.5242, 0.1692, 0.3233, respectively. A logistic regression model with SRS as a categorically ranked and continuous variable to predict binary outcome of calcification yielded P values of 0.2366 and 0.9644; this relationship, further adjusted by age, fruit and vegetable consumption, exercise, and education, yielded no statistically significant association. No improvement of fit was observed for the established Framingham Score to CAC relation utilizing SRS. The study concluded that SRS did not play a role in early CAD when focusing on a population in higher socioeconomic strata, and SRS did not add predictive value beyond patient age or calculated Framingham risk. Future studies should focus on additional validated instruments of stress to differentiate between subtypes of stress for varying SES strata. ( Population Health Management 2013;16:332–340)
Adherence to Preventive Recommendations: Experience of a Cohort Presenting for Executive Health Care
Shippee, Nathan D. , Mullan, Rebecca J., Nabhan, Mohammed , Kermott, Cindy A. , Hagen, Philip T., Rhodes, Deborah J. , Montori, Víctor M. , Murad, M. Hassan , Murad, M Hassan , Montori, Victor M , Shippee, Nathan

Population Health Management | November 17, 2011 | Volume: 15 | Issue: 2 | Pages: 65-70

10.1089/pop.2011.0029 Get It

Adherence to recommended preventive services and immunizations in adults is suboptimal and often associated with socioeconomic status, race, and access to care. The aim of this study is to evaluate adherence in a cohort without these barriers to ascertain realistically optimal adherence rates and to examine remaining barriers among relatively advantaged individuals. Specifically, it employed a sample of 6889 patients presenting for executive health care from 2005 to 2009. Adherence varied across colorectal cancer screening (79%), mammography (89%), cervical cancer screening (91%), tetanus immunization (82%), and pneumococcal vaccination (62%). Multivariate logistic regressions revealed that age, education, alcohol use concerns, and being married were positively associated with adherence to certain services. Individuals without the usual barriers to care have variable, less-than-ideal rates of adherence to preventive services, which correlate with some health behaviors and demographics. Understanding the predictors of adherence may inform quality improvement processes aimed at optimizing disease prevention. ( Population Health Management 2012;15:65–70)