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CJSM July Blog Post Journal Club — A Physical Activity Vital Sign


Our Jr. Associate Editor and Journal Club Author, Dr. Jason Zaremski, sporting the contemporary COVID-era look

Our July 2020 issue has just published, and it’s full of many important new position statements and original research publications.

Among the latter is an investigation of a physical activity ‘vital sign’ and its association with cardiometabolic disease.

As always, our Jr. Associate Editor Jason Zaremski, MD will walk us through the study in this edition of the CJSM Blog Post Journal Club.

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Jason Zaremski, MD — Jr. Associate Editor, CJSM

Title: Nelson VR, Masocol RV, Ewing JA, Johnston S, Hale A, Widederman M, Asif IM. Association Between a Physical Activity Vital Sign and Cardiometabolic Disease in High-Risk Patients. Clinical Journal of Sport Medicine: July 2020 – Volume 30 – Issue 4 – p 348-352. doi: 10.1097/JSM.0000000000000588.

Introduction:  The challenges posed by the COVID pandemic are legion.  A less publicized aspect of ‘stay home’ or ‘shelter-in-place’ orders has been the reduction in physical activity in all ages. This new publication by Nelson VR et al. examining a physical activity vital sign (PVS) arrives in the pages of CJSM at just the right time.

Physical inactivity is known to be associated with increased rates of cardiovascular disease, cancer, and increased mortality rates. The ability of physicians to potentially screen patients using efficient means during clinical encounters could be extremely helpful to improve measures of all cause morbidity and mortality of patients.

This month’s journal club focuses on this interesting new study assessing the use of a PAVS and its correlation with cardiometabolic markers and disease in medically complex patient population in a large family medicine clinic in South Carolina.

Purpose: To determine the association between the PAVS and markers of cardiometabolic disease (such as [body mass index (BMI), hemoglobin A1c (A1c), Systolic Blood Pressure (SBP), and low-density lipoprotein (LDL)] in patient’s with chronic disease and comorbidities.

Methods/Design:

Participants: n = 2270 patients, ages 18-96, average 50.2 (SD = 16.8)); 66.0% female, 34.0% male; 49.5% white/Caucasian, 44.7% Black/African American, 2.7% Hispanic, 3.0% remainder. Patient cohort from of a high-risk family medicine residency–based clinic in Greenville, SC. Based on the Milliman Advanced Risk Adjusters score of 5.24, there is a high degree of medical complexity (given a normal score is 1.0) in this patient cohort. Data were obtained from October 1, 2015, to October 31, 2016.

Thirty-one patients (1.3%) were excluded based on BMI criteria for a BMI <18.0 kg/m2. Body mass index (BMI) values were available for 2290 patients (99.5%), BP values for 2301 patients (100%), A1c values for 1008 patients (43.8%), and LDL values for 1026 patients (44.4%).

Medical complexity was driven by hypertension and type 2 diabetes as the most common clinic diagnoses. Patients 18 years or older with at least one PAVS measurement were included.

Inclusion Criteria:

  • Patients 18 years or older
  • At least one PAVS measurement

Exclusion Criteria:

  • Participants having an underweight BMI (<18.0 kg/m2)

Data Acquisition

Demographics: Age, race, ethnicity, and sex were obtained from the electronic medical record (EMR) via self-reporting.

PAVS:

Via intake by nursing staff team, patients provided activity days per week and minutes per day of moderate physical activity (PA). To obtain PA data the nursing team verbally read two questions:

  1. How many days a week of moderate to strenuous exercise (such as a brisk walk)?”
  2. “On average, how many minutes do you exercise per day?”

The authors stated that physicians were aware of the PAVS within the electronic record but were not instructed to alter their standard medical practice during this study.

Cardiometabolic Markers (Blood Pressure, BMI, Hemoglobin A1c and LDL):

BP and BMI were obtained at each office visit.  Hemoglobin A1c and LDL were obtained at the discretion of the treating physician when appropriate. The most recent laboratory values were used for the study.

Definitions:

Weight & BMI

Normal Weight: 18.0-24.9

Overweight: 25.0-29.9

Obese: ≥30.0

PA Levels

Inactive:  0 minutes per week

Underactive: 1-149 minutes per week

Active: ≥150 minutes per week

Statistical Measures/Analysis:  As stated by the authors, each cardiometabolic disease biomarker was tested for correlation with the PAVS using one-way analyses of variance. BMI was evaluated on a continuum as well as stratified into three categories as note above. Statistical analysis was completed with SPSS.

Results/Outcomes:

  • BMI values were available for 99.5%, BP values for 100%, A1c values for 43.8%, and LDL values for 44.4% of total patients
  • Average values for BMI, Hemoglobin A1C, and LDL were: BMI 32.6 kg/m2 (SD = 9.7), 7.0% (SD = 2.0), and 103.5 mg/dL (SD = 37.3) respectively.
  • Mean PAVS was 97.9 (SD = 149.4) min/week of exercise
  • 1% reported physical inactivity and 24.3% participated >150 minutes per week of PA
  • Younger individuals (P < 0.001) and men (P < 0.05) reported more PA than older individuals and women, respectively
  • An average age of 45.4 years of patients that participated in more than 150 min/week of PA. This is lower than the mean age for underactive and inactive patients (P < 0.001)
  • 80% of all patients were overweight or obese (BMI ≥30.0)
  • Patients who reported being physically active were less likely to be overweight (P < 0.05) or obese (P < 0.05) compared with inactive and underactive individuals
  • BMI 18-24.9 reported a mean PAVS of 132.7 (SD = 178.1) versus those with a BMI ≥30.0 averaged 78.9 min/wk (SD = 118.9). Overweight patients had a mean PAVS of 99.5 (SD = 145.3)
  • 2% of active patients had hypertensive BP values compared with inactive (24.4%) or underactive (23.9%) populations
  • Increased PA levels were associated with lower BP
  • There was no association between A1c and reported PAVS
  • There was no significant association between LDL and PAVS

Strengths:

  • This study revealed that this two-question tool is a time- and cost-efficient instrument to correlate with objective measures of physical activity.
  • Large study with diverse patient population

Weaknesses:

  • Underactive PA is 1-149 minutes per week. However, that is a large range and it would be important to stratify PAVS and cardiometabolic markers with smaller increments of amount of PA per week (maybe consider 30 minute increments).
  • PAVS is a self-reporting measure and patients may not be accurate in their reporting to nurses and providers of PA
  • The location of this study took place in South Carolina, a warm weather state. If walking is one possible type of aerobic activity, the challenge for providers who practice in northern/cold weather climates in underserved communities will be to find locations for their patients (such as indoor tracks at local High Schools) to allow for community exercise during the winter time.
  • Lastly, as stated by the authors, this study did not include patients under the age of 18. It would be important replicate this study in a high school or younger age population.

Conclusion: Based upon the data in this study, higher levels of PA are positively associated with improved cardiometabolic markers. This study also suggests that earlier identification, through efficient and cost-effective means, of patients at greater risk for morbidity and mortality will potentially provide an opportunity for providers to intervene earlier rather than later in the disease process.

Clinical Relevance: PAVS is a simple, cost-effective, and efficient instrument that may be implemented in primary care (and potentially other) clinical settings to provide data that may suggest increased cardiometabolic disease risk earlier rather than later. This data may allow providers to dialog with their patients and their family members and to introduce possible strategies to reduce cardiometabolic risk factors for morbidity and mortality before cardiometabolic diseases progress.





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