Package: exercisecoachtools
Exercise Coach ToolsExercise 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 ContentsGetting StartedACSM Risk StratificationPercentage CalculatorsMetabolic Rate CalculatorsTestingGetting StartedPrerequisitesThis package requires>= Python 3.7.4Installingpip install exercisecoachtoolsACSM Risk StratificationThis is a tool for determining risk of cardiovascular disease based onACSM’s Guidelines for Exercise Testing and Prescription-8th ed. Philadelphia: Lippincott Williams & WilkinsThis 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 StratificationAPatientobject must be created to use methods.Patienttakes the following parameters in the order listed:sex (strmale or female)age (intpositive only)smoker (stryes or no)sedentary (stryes or no)bmi (stryes or no; input0if no available value but waist_girth value is available )waist_girth (intpositive only; input0if no available value but bmi value is available)male_family_death_before_55 (stryes or no)female_family_death_before_65 (stryes or no)systolic (intpositive only)diastolic (intpositive only)hypertensive (stryes or no)ldl (intpositive only)hdl (intpositive only)using_lipid_lowering_medication (stryes or no)cholesterol (intpositive only)fasting_glucose (intpositive only; input0if no available value but oral_glucose_tolerance value is available)oral_glucose_tolerance (intpositive only; input0if 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.Examplesfrom 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 2Percentage CalculatorsUseful functions:percentage_value(desired_percentage, 1_rep_max_weight)Returns weight value associated with desired percentage. Can use kg or lbsone_rep_max_estimation(reps completed, weight)Returns dictionary of estimated 1-rep max using Brzycki, Epley, Mayhew, Lander, and Lombardi formulasNote: Reps completedmust be> 1Examplesfrom percentage_calculators.percentage_calculators import percentage_value

percentage_value(80, 100)
# returns 80.0from 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 CalculatorsAMetabolicClientobject must be created to use methods.MetabolicClienttakes the following parameters in the order listed:sex (strmale or female)age (intpositive only)weight (pounds - floatpositive only)height (inches - floatpositive only)Useful functions:bmr_results(MetabolicClient)Returns client's estimated BMR using Harris-Benedict and Mifflin formulasharris_benedict_bmr(MetabolicClient)Returns client's estimated BMR using Harris-Benedict formulamifflin_bmr(MetabolicClient)Returns client's estimated BMR using Mifflin St Jeor formulaget_calories_by_activity(activity_level, bmr)Returns client's daily metabolic rate based on activity levelActivity level options:Sedentary — desk job and little to no exerciseLightly Active — light exercise/sports 1–3 days/weekModerately Active — moderate exercise/sports 3–5 days/weekVery Active — hard exercise/sports 6–7 days/weekExtremely Active — hard daily exercise/sports and physical job or trainingExamplesfrom 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 testspython setup.py testAuthorschandojo