VisualizeTailoredExercisePlan {T2DFitTailor}

Tailor Exercise Plan for T2D Patients

Description

This function generates a tailored exercise plan for T2D (Type 2 Diabetes)

Arguments

input_df

A data frame containing patient data necessary for generating a tailored exercise plan. Each column in the dataframe should be as follows (All data must be numeric.):

Age

Patient's age in years.

Sex

Patient's sex, where 0 might denote female and 1 denotes male.

BMI

Body Mass Index, a measure of body fat based on height and weight.

WHtR

Patient's ratio of waist and height.

PCS

Patient's Physical Component Summary (PCS) score, a metric derived from the SF-12 (Short Form Health Survey) which measures health-related quality of life. The PCS score is calculated based on responses to questions in the SF-12 survey, reflecting physical functioning, bodily pain, general health perceptions, and physical roles. A higher score indicates better physical health. Users can refer to the PCS_Calculation function within this package for detailed calculations based on SF-12 survey responses.

Duration_T2D (year)

Duration of Type 2 Diabetes Mellitus in years. It reflects how long the patient has been living with Type 2 diabetes.

Total cholesterol (mmol/L)

Total cholesterol levels in mmol/L. This measurement includes LDL, HDL, and other lipid components. Higher overall cholesterol levels can increase the risk of cardiovascular disease, although the balance of different types of lipids (HDL, LDL, others) also plays a critical role.

HDL (mmol/L)

High-Density Lipoprotein cholesterol levels in mmol/L. HDL is considered the "good" cholesterol, and higher levels are often associated with a lower risk of heart disease.

LDL (mmol/L)

Low-Density Lipoprotein cholesterol levels in mmol/L. LDL is often referred to as the "bad" cholesterol because high levels can lead to plaque buildup in arteries and increase the risk of heart disease.

VO2_Max

Patient's maximum oxygen uptake.

Lung_capacity

Patient's lung volume or capacity.

BACK_Scratch_Test

Assessment of the patient's upper limb flexibility and shoulder range of motion.

Note: It is crucial that the data for each of these columns is correctly formatted and accurately represents the patient's health information for the exercise plan to be effectively tailored.

Value

A list containing two data frames: ⁠$RecommendedExercisePlan⁠: This data frame includes exercise plans that are recommended based on the criterion that the intervention leads to a positive reduction in HbA1c levels, and ⁠$AllExercisePlan⁠ which includes all received plans.

Examples

#Create a demo dataframe
set.seed(5)
df <- data.frame(
  Age = sample(39:77, 8, replace = TRUE),
  Sex = sample(0:1, 8, replace = TRUE),
  BMI = sample(18:31, 8, replace = TRUE),
  WHtR = sample(0.4:0.6, 8, replace = TRUE),
  PCS = sample(27:54, 8, replace = TRUE),
  Duration_T2D = sample(1:26, 8, replace = TRUE),
  Total_cholesterol = sample(7.4:14.1, 8, replace = TRUE),
  HDL = sample(1:1.7, 8, replace = TRUE),
  LDL = sample(2.2:4.7, 8, replace = TRUE),
  VO2_Max = sample(13:45, 8, replace = TRUE),
  Lung_capacity = sample(1900:4600, 8, replace = TRUE),
  Back_Scratch_Test = sample(-30:8, 8, replace = TRUE))

names(df) <- c('Age', 'Sex', 'BMI', 'WHtR', 'PCS', 'Duration_T2D (year)',
               'Total cholesterol (mmol/L)', 'HDL (mmol/L)', 'LDL (mmol/L)',
               'VO2_Max (ml/kg/min)', 'Lung_capacity (ml)', 'Back_Scratch_Test (cm)')
rownames(df) <- c('Sample1', 'Sample2', 'Sample3', 'Sample4',
                  'Sample5', 'Sample6', 'Sample7', 'Sample8')

# Run the TailorExercisePlan function
demo_result <- TailorExercisePlan(df)
# View the structure of the returned list
str(demo_result)