Margaret M. McCarthy headshot

Margaret McCarthy


Assistant Professor

1 212 992 5796

433 First Avenue
Room 404
New York, NY 10010
United States

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Professional overview

Margaret McCarthy, PhD, RN, FNP-BC, FAHA is both a family nurse practitioner and an exercise physiologist.
Her research focus is in promoting exercise in populations at risk for cardiovascular disease. She has conducted research in adults with both type 1 and type 2 diabetes. Her future research goal is to develop interventions to promote exercise in these populations.


PhD - New York University
MS, Family Nursing Practitioner - Pace University
MA, Exercise Physiology - Adelphi University
BSN - Binghamton University

Honors and awards

NYU College of Nursing PhD Overall Distinguished Student (2013)
Fellow of the American Heart Association (2017)
Fellow of the New York Academy of Medicine (2018)


Non-communicable disease
Adult health

Professional membership

American Heart Association
Heart Failure Society of America
Society of Behavioral Medicine
Eastern Nursing Research Society



Type 1 Diabetes Self-Management From Emerging Adulthood Through Older Adulthood

McCarthy, M. M., & Grey, M. (2018). Diabetes Care. 10.2337/dc17-2597
OBJECTIVE: The purpose of this study of adults with type 1 diabetes was to analyze patterns of diabetes self-management behaviors and predictors of glycemic control across the adult life span.RESEARCH DESIGN AND METHODS: This study was a secondary cross-sectional analysis of data from of 7,153 adults enrolled in the Type 1 Diabetes Exchange clinic registry who were divided into four developmental stages (emerging, young, middle-aged, and older adults). Data were collected by questionnaire and medical record review at enrollment. Statistical analyses compared sociodemographic, clinical, and diabetes-related factors across groups. Logistic regressions were conducted for each group to identify factors associated with hemoglobin A1c ≥7%.RESULTS: The sample was divided according to adult developmental stage: emerging adults, age 18 to <25 years (n = 2,478 [35%]); young adults, age 25 to <45 years (n = 2,274 [32%]); middle-aged adults, age 45 to <65 years (n = 1,868 [26%]; and older adults, age ≥65 years (n = 533 [7%]). Emerging adults had the highest mean hemoglobin A1c level (8.4 ± 1.7% [68 mmol/mol]), whereas older adults had the lowest level (7.3 ± 0.97% [56 mmol/mol]; P < 0.0001). Emerging adults were less likely to use an insulin pump (56%) or a continuous glucose monitor (7%), but were more likely to miss at least one insulin dose per day (3%) and have had an episode of diabetic ketoacidosis in the past year (7%) (all P < 0.0001). Different factors were associated with hemoglobin A1c ≥7% in each age group, but two factors were noted across several groups: the frequency of blood glucose checks and missed insulin doses.CONCLUSIONS: When discussing diabetes self-management, providers may consider a patient's developmental stage, with its competing demands, such as work and family; psychosocial adjustments; and the potential burden of comorbidities.

Arthritis-related limitations predict insufficient physical activity in adults with prediabetes identified in the NHANES 2011-2014

Strauss, S. M., & McCarthy, M. (2017). Diabetes Educator, 43(2), 163-170. 10.1177/0145721717691849
Purpose The purpose of the study was to determine the extent to which arthritis-related limitations are salient in predicting less than the recommended amount of time for adults with prediabetes to spend on moderate or vigorous physical activity. Methods Data from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) in the United States were used to identify the predictors of insufficient physical activity in a large sample of adults with prediabetes 20 years of age and older (n = 2536). Results When extrapolated to more than 45 million adults in the United States at least 20 years of age with prediabetes, 42.7% had insufficient physical activity. Having arthritis- related functional limitations was a significant predictor of insufficient physical activity, even after accounting for the statistically significant contributions of female sex, older age, lower education level, higher body mass index, and depression. Conclusion When educating and counseling adults with prediabetes, diabetes educators should assess for arthritis-related functional limitations when examining factors that may affect prediabetes progression. Recommendations for physical activity for those with mobility and other limitations need to be individualized within a tailored exercise program to accommodate their specific limitations.

Physical inactivity and cardiac events: An analysis of the Detection of Ischemia in Asymptomatic Diabetics (DIAD) study

McCarthy, M., Wackers, F. J., Davey, J., & Chyun, D. (2017). Journal of Clinical and Translational Endocrinology, 9, 8-14. 10.1016/j.jcte.2017.05.005
Aims Diabetes affects 29 million adults, and the majority have type 2 diabetes (T2D). Coronary artery disease (CAD) is the leading cause of death, and physical inactivity is an important risk factor. The aims of this study were to examine the contribution of physical inactivity to CAD events, and to identify the independent predictors of CAD events in a sample of older adults with T2D. Method A secondary data analysis of the prospective randomized screening trial “Detection of Ischemia in Asymptomatic Diabetics (DIAD)” study. Cox proportional hazard modeling was used to examine the outcome of CAD events. Results During the five years of follow-up, the CAD event rate for all subjects (n = 1119) was 8.4% (n = 94). In unadjusted analysis, physical inactivity was significantly associated with development of a CAD event. In the final model, nine baseline variables were significant predictors (p < 0.05) of a CAD: physical inactivity, race, diabetes duration, hemoglobin A1c (HbA1c), peripheral numbness, insulin use, increasing waist-to-hip ratio, family history of premature CAD, and a higher pulse pressure. In men only, there were five predictors (p < 0.05) of a CAD event: diabetes duration, peripheral numbness, HbA1c, increasing waist-to-hip ratio, and higher pulse pressure. The final model in women included three independent predictors (p < 0.05) of a CAD event: diabetes duration, a family history of premature CAD, and higher pulse pressure. Conclusion Several variables predicted CAD events in this sample of older adults with T2D. Understanding baseline characteristics that heighten risk may assist providers in intervening early to prevent its occurrence.

Self-management of physical activity in adults with type 1 diabetes

McCarthy, M., Whittemore, R., Gholson, G., & Grey, M. (2017). Applied Nursing Research, 35, 18-23. 10.1016/j.apnr.2017.02.010

Cardiovascular health in adults with type 1 diabetes

McCarthy, M., Funk, M., & Grey, M. (2016). Preventive Medicine, 91, 138-143. 10.1016/j.ypmed.2016.08.019
Adults with type 1 diabetes (T1D) are at risk for cardiovascular (CV) disease. Managing CV risk is an important prevention strategy. The American Heart Association has defined 7 factors for ideal CV health. The purpose of this 2016 secondary analysis was to assess the prevalence of 6 CV health factors in a sample of adults ≥ 18 (n = 7153) in the T1D Exchange Clinic registry. CV health factors include: hemoglobin A1c (HbA1c) < 7%, BMI < 25 kg/m2, blood pressure < 120/80 mm Hg, total cholesterol < 200 mg/dL, non-smoking, and physical activity ≥ 150 min/week. HbA1c < 7% was substituted for the AHA health factor of fasting blood glucose. Frequencies of each factor were tabulated for the total sample and for each gender. Logistic regression examined variables associated with achievement of each CV health factor. The mean age was 37.14 ± 17 years. Mean HbA1c was 7.9 ± 1.5%, and duration was 19.5 ± 13.5 years. The majority (54%) were working full or part-time. Achievement of CV health factors in the whole sample ranged from 27% (HbA1c < 7%) to 94% nonsmoking. Achievement of some factors varied by gender. Common variables associated with several CV health factors included gender, education, employment, and T1D duration. This young sample exhibited low levels of some CV health factors, especially HbA1c and physical activity. Providers need to routinely assess and advise on management of all CV risk factors to prevent this common diabetes complication.

An Exercise Counseling Intervention in Minority Adults with Heart Failure

McCarthy, M., Vaughan Dickson, V., Katz, S. D., & Chyun, D. (2016). Rehabilitation Nursing. 10.1002/rnj.265
Purpose: The primary aim of this study was to assess the feasibility of an exercise counseling intervention for adults of diverse race/ethnicity with heart failure (HF) and to assess its potential for improving overall physical activity, functional capacity, and HF self-care. Design: This study was a quasi-experimental, prospective, longitudinal cohort design. Methods: Twenty adults were enrolled and completed the 6-minute walk and standardized instruments, followed by exercise counseling using motivational interviewing. Each received an accelerometer, hand weights, and a diary to record self-care behaviors. Participants were followed via phone for 12 weeks to collect step-counts, review symptoms, and plan the following week's step-goal. Findings: Results indicate this intervention was feasible for most participants, and resulted in improvements in physical activity, functional capacity, and self-care behaviors. Conclusion/Clinical Relevance: Brief exercise counseling may be an appropriate option to improve outcomes for stable patients with HF, and may be tailored to fit different settings.

Physical Activity in Adults With Type 1 Diabetes

McCarthy, M., Whittemore, R., & Grey, M. (2016). Diabetes Educator, 42(1), 108-115. 10.1177/0145721715620021
Purpose: The purpose of this study was to examine sociodemographic, clinical, and psychological factors associated with engaging in regular physical activity (PA) in adults with type 1 diabetes. Methods: Secondary cross-sectional analysis based on data from the Type One Diabetes Exchange clinic registry was conducted. Adults ≥18 years old enrolled in the clinic registry who had completed PA self-report data (n = 7153) were included in this study. Results: Mean age was 37.14 ± 17 years, and 54% (n = 3840) were men. Type 1 diabetes duration was 19.5 ± 13.5 years, and mean A1C level was 7.9% ± 1.5% (62 mmol/mol). Twelve percent (n = 848) of the sample reported no PA; 55% (n = 3928) reported PA 1 to 4 days per week; and 33% (n = 2377) reported PA ≥5 days per week. Factors that were associated with increased odds of no PA were older age, less-than-excellent general health, increased body mass index, longer duration of diabetes, and increased depressive symptoms. More blood glucose meter checks per day decreased odds of no PA. Factors associated with lower odds of ≥5 days of PA included minority race/ethnicity, education, less-than-excellent general health, presence of a foot ulcer, increased body mass index, and depressive symptoms. Male sex, less-than-full-time employment, and being single increased the odds of ≥5 days of PA. Conclusions: Several demographic, clinical, diabetes-related, and psychosocial factors were related to PA. Potential interventions may target those with depressive symptoms or self-reported poor general health, or they may be tailored to working adults who may find it harder to be physically active.

Motion sensor use for physical activity data: Methodological considerations

McCarthy, M., & Grey, M. (2015). Nursing Research, 64(4), 320-327. 10.1097/NNR.0000000000000098
Background: Physical inactivity continues to be amajor risk factor for cardiovascular disease, and only one half of adults in the United States meet physical activity (PA) goals. PA data are often collected for surveillance or for measuring change after an intervention. One of the challenges in PA research is quantifying exactly how much and what type of PA is taking place-especially because self-report instruments have inconsistent validity. Objective: The purpose is to review the elements to consider when collecting PA data via motion sensors, including the difference between PA and exercise, type of data to collect, choosing the device, length of time to monitor PA, instructions to the participants, and interpretation of the data. Methods: The current literature on motion sensor research was reviewed and synthesized to summarize relevant considerations when using a motion sensor to collect PA data. Results: Exercise is a division of PA that is structured, planned, and repetitive. Pedometer data include steps taken and calculated distance and energy expenditure. Accelerometer data include activity counts and intensity. The device chosen depends on desired data, cost, validity, and ease of use. Reactivity to the device may influence the duration of data collection. Instructions to participantsmay vary depending on the purpose of the study. Experts suggest pedometer data be reported as steps-because that is the direct output-and distance traveled and energy expenditure are estimated values. Accelerometer count data may be analyzed to provide information on time spent in moderate or vigorous activity. Discussion: Thoughtful decision making about PA data collection using motion sensor devices is needed to advance nursing science.

Process evaluation of an exercise counseling intervention using motivational interviewing

McCarthy, M., Vaughan Dickson, V., Katz, S. D., Sciacca, K., & Chyun, D. (2015). Applied Nursing Research, 28(2), 156-162. 10.1016/j.apnr.2014.09.006
Aim: To describe the results of the process evaluation of an exercise counseling intervention using motivational interviewing (MI). Background: Exercise can safely be incorporated into heart failure self-care, but many lack access to cardiac rehabilitation. One alternative is to provide exercise counseling in the clinical setting. Methods: This process evaluation was conducted according to previously established guidelines for health promotion programs. This includes an assessment of recruitment and retention, implementation, and reach. Results: Desired number of subjects were recruited, but 25% dropped out during study. Good fidelity to the intervention was achieved; the use of MI was evaluated with improvement in adherence over time. Dose included initial session plus 12 weekly phone calls. Subjects varied in participation of daily diary usage. Setting was conducive to recruitment and data collection. Conclusions: Evaluating the process of an intervention provides valuable feedback on content, delivery and fidelity.

Predictors of Physical Inactivity in Men and Women With Type 2 Diabetes From the Detection of Ischemia in Asymptomatic Diabetics (DIAD) Study

McCarthy, M., Davey, J., Wackers, F. J. T., & Chyun, D. (2014). Diabetes Educator, 40(5), 678-687. 10.1177/0145721714540055