exercisecoachtools 0.1.2

Tools for exercise physiologists and coaches.

Exercise Coach Tools

Exercise Coach Tools is an open source library of tools for fitness/exercise coaches, fitness specialists, exercise physiologists, and personal trainers. The objective of Exercise Coach Tools is to help these people be more efficient and spend more time focusing on their clients instead of the equations/formulas needed to make analyses.

Table of Contents

1. Getting Started
2. ACSM Risk Stratification
3. Percentage Calculators
4. Metabolic Rate Calculators
5. Testing

Getting Started

Prerequisites

This package requires >= Python 3.7.4

Installing

pip install exercisecoachtools

ACSM Risk Stratification

This is a tool for determining risk of cardiovascular disease based on ACSM’s Guidelines for Exercise Testing and Prescription-8th ed. Philadelphia: Lippincott Williams & Wilkins

This tool is not a substitute for advice from a doctor and is used to help fitness specialists refer potential clients to physicians due to possible cardiovascular health risk.

Using the Stratification

A Patient object must be created to use methods.

Patient takes the following parameters in the order listed:

• sex (str male or female)
• age (int positive only)
• smoker (str yes or no)
• sedentary (str yes or no)
• bmi (str yes or no ; input 0 if no available value but waist_girth value is available )
• waist_girth (int positive only ; input 0 if no available value but bmi value is available)
• male_family_death_before_55 (str yes or no)
• female_family_death_before_65 (str yes or no)
• systolic (int positive only)
• diastolic (int positive only)
• hypertensive (str yes or no)
• ldl (int positive only)
• hdl (int positive only)
• using_lipid_lowering_medication (str yes or no)
• cholesterol (int positive only)
• fasting_glucose (int positive only ; input 0 if no available value but oral_glucose_tolerance value is available)
• oral_glucose_tolerance (int positive only ; input 0 if no available value but fasting_glucose value is available)

Useful functions:

• risk_classification(Patient)
  • Returns patient's risk classification based off of the ACSM Guidelines. Classifications range from low, moderate, and high.
• total_risk_factors(Patient)
  • Returns patient's net total risk factors. This total is based off of ACSM's algorithm.

Examples

from risk.risk_stratification import Patient, risk_classification, total_risk_factors

steve_buscemi = Patient('male', 61, 'no', 'yes', 25, 0, 'yes', 'no', 119, 78, 'no', 100, 70, 'no', 100, 60, 0)

risk_classification(steve_buscemi)
# 'Your risk total is 2. You are at a moderate risk for cardiovascular disease. Medical check-up recommended for participation in vigorous physical activity.'

total_risk_factors(steve_buscemi)
# returns 2

Percentage Calculators

Useful functions:

• percentage_value(desired_percentage, 1_rep_max_weight)
  • Returns weight value associated with desired percentage. Can use kg or lbs
• one_rep_max_estimation(reps completed, weight)
  • Returns dictionary of estimated 1-rep max using Brzycki, Epley, Mayhew, Lander, and Lombardi formulas
  • Note: Reps completed must be > 1

Examples

from percentage_calculators.percentage_calculators import percentage_value

percentage_value(80, 100)
# returns 80.0

from percentage_calculators.one_rep_max_estimation import one_rep_max_estimation

one_rep_max_estimation(2, 100)
# returns {'brzycki_formula': 102.85949393128985, 'epley_formula': 106.66666666666667, 'mayhew_formula': 106.41304152560984, 'lander_formula': 104.21275910157765, 'lombardi_formula': 107.17734625362931}

Metabolic Rate Calculators

A MetabolicClient object must be created to use methods.

MetabolicClient takes the following parameters in the order listed:

• sex (str male or female)
• age (int positive only)
• weight (pounds - float positive only)
• height (inches - float positive only)

Useful functions:

• bmr_results(MetabolicClient)

  • Returns client's estimated BMR using Harris-Benedict and Mifflin formulas

• harris_benedict_bmr(MetabolicClient)

  • Returns client's estimated BMR using Harris-Benedict formula

• mifflin_bmr(MetabolicClient)

  • Returns client's estimated BMR using Mifflin St Jeor formula

• get_calories_by_activity(activity_level, bmr)

  • Returns client's daily metabolic rate based on activity level
  • Activity level options:
    • Sedentary — desk job and little to no exercise
    • Lightly Active — light exercise/sports 1–3 days/week
    • Moderately Active — moderate exercise/sports 3–5 days/week
    • Very Active — hard exercise/sports 6–7 days/week
    • Extremely Active — hard daily exercise/sports and physical job or training

Examples

from kcal.metabolic_rate_prediction import MetabolicClient, bmr_results, harris_benedict_bmr, mifflin_bmr
from kcal.daily_kcal_by_lifestyle import get_calories_by_activity

sue_bird = MetabolicClient('female', 39, 150, 69)

bmr_results(sue_bird)
# returns {'harris_benedict_bmr': 974.5799999999999, 'mifflin_st_jeor_bmr': 826.6300000000001}

sue_bird_bmr = harris_benedict_bmr(sue_bird)
# returns 974.5799999999999

mifflin_bmr(sue_bird)
# returns 826.6300000000001

get_calories_by_activity('very active', sue_bird_bmr)
# returns 1681.1505 (kcal/day)

Running the tests

python setup.py test

Authors

chandojo