Weight Loss Program Analysis
Sunny Ahmed
2025-03-19 11:29:13.421215
The purpose of the study:
This study is aims to the impact of lifestyle modifications, particularly diet adherence, exercise intensity, and sleep quality on weight loss outcomes over 12-week weight loss program.
Using a dataset of 35 participants, this research seeks to determine which lifestyle factors have the strongest correlation with successful weight reduction and how demographic variables influence these results. The study aims to generate evidence-based recommendations for improving weight loss programs and personalizing intervention strategies.
Packages
Loading required packages:
library(ggplot2)
library(dplyr)
library(ggstatsplot)
library(knitr)
library(kableExtra)
library(broom)
library(car)
library(lmtest)
library(gtsummary)
library(gt)Data
Loading the Data_WeightLoss data:
Data_Weight <- readxl::read_excel("Data/Data_WeightLoss.xlsx")#Summarize Descriptive Tables
summary(Data_Weight)## Participant Gender Age BaselineWeight
## Min. : 1.0 Length:35 Min. :26.00 Min. :70.00
## 1st Qu.: 9.5 Class :character 1st Qu.:31.50 1st Qu.:78.45
## Median :18.0 Mode :character Median :37.00 Median :84.30
## Mean :18.0 Mean :38.09 Mean :84.43
## 3rd Qu.:26.5 3rd Qu.:46.50 3rd Qu.:89.85
## Max. :35.0 Max. :52.00 Max. :99.50
##
## DietAdherence ExerciseIntensity SleepQuality WeightLoss
## Min. :6.200 Min. :5.100 Min. :5.800 Min. : 6.600
## 1st Qu.:7.250 1st Qu.:6.200 1st Qu.:6.600 1st Qu.: 8.150
## Median :8.400 Median :6.900 Median :7.600 Median : 9.400
## Mean :8.243 Mean :7.129 Mean :7.689 Mean : 9.314
## 3rd Qu.:9.200 3rd Qu.:8.050 3rd Qu.:8.400 3rd Qu.:10.200
## Max. :9.900 Max. :9.300 Max. :9.900 Max. :12.000
## NA's :1
## AfterWeight
## Min. :60.90
## 1st Qu.:68.60
## Median :74.00
## Mean :75.12
## 3rd Qu.:81.65
## Max. :91.60
## library(gtsummary)
Data_Weight %>% tbl_summary(
by = Gender,
missing = "no",
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)"
),
digits = all_continuous() ~ 3
)| Characteristic | Female N = 161 |
Male N = 191 |
|---|---|---|
| Participant | 18.813 (10.809) | 17.316 (9.995) |
| Age | 37.875 (7.848) | 38.263 (8.109) |
| BaselineWeight | 83.944 (8.934) | 84.847 (7.490) |
| DietAdherence | 8.138 (1.235) | 8.332 (1.142) |
| ExerciseIntensity | 6.975 (1.236) | 7.267 (1.114) |
| SleepQuality | 7.606 (1.320) | 7.758 (1.250) |
| WeightLoss | 8.888 (1.413) | 9.674 (1.183) |
| AfterWeight | 75.056 (8.969) | 75.174 (8.210) |
| 1 Mean (SD) | ||
Scatter Plot
##Baseline Weight vs. Percent_Weight_Loss ####
Data_Weight <-Data_Weight %>% #insert a column of Percent_Weight_Loss
mutate(Percent_Weight_Loss = (WeightLoss/BaselineWeight)*100)
ggplot(data = Data_Weight, aes(x = BaselineWeight, y = Percent_Weight_Loss)) +
geom_point(shape = 16,color= "blue",size =3)+
labs( title = "Baseline Weight vs. Percent_Weight_Loss ",
x = "Baseline Weight (kg)",
y = "Percent_Weight_Loss")+
theme_minimal()
##Exercise Intensity vs. Percent_Weight_Loss####
ggplot(data = Data_Weight, aes(x = ExerciseIntensity, y = Percent_Weight_Loss)) +
geom_point(shape = 16,color= "blue",size =3,na.rm=TRUE)+
labs( title = "Exercise Intensity vs. Percent_Weight_Loss ",
x = " Exercise Intensity",
y = "Percent_Weight_Loss")+
theme_minimal()
##Diet Adherence vs. Percent_Weight_Loss####
ggplot(data = Data_Weight, aes(x = DietAdherence, y = Percent_Weight_Loss)) +
geom_point(shape = 16,color= "blue",size =3)+
labs( title = " Diet_Adherence vs. Percent_Weight_Loss ",
x = " Diet_Adherence ",
y = "Percent_Weight_Loss")+
theme_minimal()
##Sleep_Quality vs. Percent_Weight_Loss####
ggplot(data = Data_Weight, aes(x = SleepQuality, y = Percent_Weight_Loss)) +
geom_point(shape = 16,color= "blue",size =3)+
labs( title = "Sleep_Quality vs. Percent_Weight_Loss ",
x = "Sleep_Quality",
y = "Percent_Weight_Loss")+
theme_minimal()
Histrogram
##Distribution of Weight Loss####
ggplot(Data_Weight, aes(x = WeightLoss)) +
geom_histogram(binwidth = 1, fill = "lightblue", color = "black",alpha = 1) +
labs(title = "Distribution of Weight Loss", x = "Weight Loss (kg)", y = "Frequency")
Bar plot
##Average Weight Loss by Gender####
#Calculate average weight loss by gender
avg_weight_loss<- Data_Weight%>%
group_by(Gender)%>%
summarise(AvgWeightloss =mean(WeightLoss,na.rm=TRUE))%>%
arrange(desc(AvgWeightloss))
avg_weight_loss## # A tibble: 2 × 2
## Gender AvgWeightloss
## <chr> <dbl>
## 1 Male 9.67
## 2 Female 8.89#Create the bar plot:
ggplot(avg_weight_loss,aes(x = Gender, y=AvgWeightloss,fill =Gender))+
geom_bar(stat = "identity")+
geom_text(aes(label = round(AvgWeightloss, 2)), # Add text labels
vjust = 1, # Position the text slightly above the bars
color = "black", # Text color
size = 5) + # Text size
scale_x_discrete(limits =c("Male","Female"))+
labs(
title = "Average Weight Loss by Gender",
x = "Gender",
y = "Average Weight Loss (kg)"
) +
theme_minimal()
# Cluster Bar plot
##Average Weight Loss by Age Group and Gender####
# Create AgeGroup column####
Data_Weight<-Data_Weight%>%
mutate(AgeGroup = case_when(
Age < 35 ~ "Young",
Age >= 35 & Age <= 50 ~ "Middle",
Age > 50 ~ "Old"
))
# Calculate average weight loss by age group and gender
avg_weight_loss_age_gender <- Data_Weight %>%
group_by(AgeGroup, Gender) %>%
summarise(AvgWeightLoss = mean(WeightLoss, na.rm = TRUE))## `summarise()` has grouped output by 'AgeGroup'. You can override using the
## `.groups` argument.# Create the grouped bar plot
ggplot(avg_weight_loss_age_gender, aes(x = AgeGroup, y = AvgWeightLoss, fill = Gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(
title = "Average Weight Loss by Age Group and Gender",
x = "Age Group",
y = "Average Weight Loss (kg)",
fill = "Gender"
) +
theme_minimal()
Box Plot
##Weight Loss Distribution by Gender####
ggplot(Data_Weight, aes(x = Gender,y = WeightLoss,fill =Gender))+
geom_boxplot() +
geom_jitter()+
labs(title = "Weight Loss Distribution by Gender",
x = "Gender",
y = "Weight Loss (kg)") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.3, face = "bold", size = 16),
axis.title = element_text(size = 14),
legend.position = "right")
##Age Group and Exercise Intensity Level####
ggplot(Data_Weight, aes(x = AgeGroup, y = WeightLoss, fill = ExerciseIntensity)) +
geom_boxplot(position = position_dodge(0.8)) + # Grouped box plot
geom_jitter()+
labs(
title = "Weight Loss Distribution by Age Group and Exercise Intensity Level",
x = "Age Group",
y = "Weight Loss (kg)",
fill = "Exercise Intensity Level"
) +
theme_minimal()+
theme(plot.title = element_text(hjust = 0.1, face = "bold", size = 10),
axis.title = element_text(size = 14),
legend.position = "right")## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
Violin Plot of Weight Loss by Gender
ggplot(Data_Weight, aes(x = Gender, y = WeightLoss, fill = Gender)) +
geom_violin() +
geom_boxplot(width = 0.1, color = "black", outlier.shape = 16, outlier.size = 4)+
labs(
title = "Violin Plot of Weight Loss by Gender",
x = "Gender",
y = "Weight Loss (kg)"
) +
theme_minimal()
Correlation Matrix of Variables
ggcorrmat(
data = Data_Weight,
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlation of Variables ",
matrix.type = "lower",
type = "parametric", pch = "")+
theme(plot.title = element_text(hjust = 1, face = "bold", size = 10),
axis.title = element_text(angle =45,hjust=1,size = 1),
legend.position = "right") 
Regression Analysis
model <- lm(WeightLoss ~ Age + Gender + DietAdherence + ExerciseIntensity + SleepQuality, data = Data_Weight)
summary_table <- tidy(model)
summary_table %>%
kable(format = "html", caption = "Regression Summary") %>%
kable_styling(full_width = FALSE)%>%
row_spec(0, bold = TRUE, background = "lightblue")| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -3.5935325 | 1.1083398 | -3.242266 | 0.0030590 |
| Age | -0.0330968 | 0.0098484 | -3.360648 | 0.0022603 |
| GenderMale | 0.3597334 | 0.1530682 | 2.350151 | 0.0260441 |
| DietAdherence | 0.6634641 | 0.0651334 | 10.186236 | 0.0000000 |
| ExerciseIntensity | 0.7343135 | 0.0687395 | 10.682560 | 0.0000000 |
| SleepQuality | 0.4225209 | 0.0653858 | 6.461970 | 0.0000005 |
Residuals vs Fitted
plot(model$fitted.values,resid(model),
main = "Residuals vs Fitted", ylab= "Residuals",pch=19)
abline(h=0 ,col= "red")#Reference line
VIF
vif(model)%>%
kable(format = "html", caption = " Variance Inflation Factors") %>%
kable_styling(full_width = FALSE)%>%
row_spec(0, bold = TRUE, background = "lightblue")| x | |
|---|---|
| Age | 1.055455 |
| Gender | 1.031889 |
| DietAdherence | 1.017241 |
| ExerciseIntensity | 1.099412 |
| SleepQuality | 1.118344 |
Breusch-Pagan Test
lmtest::bptest(model)##
## studentized Breusch-Pagan test
##
## data: model
## BP = 7.4295, df = 5, p-value = 0.1906QQ Plot
qqnorm(resid(model))
qqline(resid(model),col ="red")
Shapiro Test
shapiro.test(resid(model))##
## Shapiro-Wilk normality test
##
## data: resid(model)
## W = 0.98808, p-value = 0.9662