What would you do? Higher R without interaction terms

I'm working with RStudio. I've searched, but I haven't been able to find anyone with this problem. I'm dealing with a dataset that has many variables, and I've found that the model I've created with interaction terms has a lower R and R squared than the model without the interaction terms. What would you do in this case? Datasets: https://www.kaggle.com/arashnic/fitbit I used sleepDay_merged.csv and dailyActivity_merged.csv

# Installing packages
install.packages("pacman")

pacman::p_load(tidyverse, lubridate, janitor, DataExplorer, venn, e1071, olsrr,ggiraphExtra)

daily_sleep <- read_csv("sleepDay_merged.csv")
head(daily_sleep)

# Cleaning and mutating, data frames, and preparing for merge.
daily_sleep <- daily_sleep %>%
  clean_names() %>% 
  mutate(date = mdy(sleep_day))

daily_activity <- read_csv("dailyActivity_merged.csv")
head(daily_activity)
glimpse(daily_activity)


daily_activity <- daily_activity %>% 
  clean_names() %>% 
  mutate(id = as.factor(id), date = mdy(activity_date), day = weekdays(date))
head(daily_activity)

sleep_activity <- merge(daily_activity, daily_sleep)
sleep_activity <- sleep_activity[c(1,2,4,5,8:10,12:16,17,20,21)]
head(sleep_activity)
str(sleep_activity)
usage <- daily_activity %>%
    group_by(id,date) %>% 
  summarise(day,sum_minutes = sum(lightly_active_minutes)+sum(fairly_active_minutes)+
              sum(very_active_minutes)) %>% 
  mutate(usage_level = case_when(
    sum_minutes >= 0 & sum_minutes <= 183 ~ "Low Usage",
    sum_minutes >138 & sum_minutes <= 358.8 ~ "Moderate Usage",
    sum_minutes > 358.8 | sum_minutes <= 552.0 ~ "High Usage"))
head(usage)
summary(usage)

usage_day <- usage %>% group_by(day) %>% summarise(sum_minutes,usage_level)

usage_day$day <- ordered(usage_day$day, 
                         levels=c("Monday", "Tuesday", "Wednesday", "Thursday", 
                                         "Friday", "Saturday", "Sunday"))

# Transforming sleep_activity data
sleep_activity %>% plot_histogram(ncol = 5, ggtheme = theme_light())
sleep_activity_filtered <- merge(sleep_activity,usage)
sleep_activity_filtered <- sleep_activity_filtered[!(sleep_activity_filtered$sedentary_minutes <5),]
head(sleep_activity_filtered)
# Transforming skewed data with log transformations
sleep_activity_filtered <- sleep_activity_filtered %>% 
  mutate(log_moderately_active_distance = log(moderately_active_distance+1),
         log_very_active_distance = log(very_active_distance+1),
         log_very_active_minutes = log(very_active_minutes+1),
         log_fairly_active_minutes = log(fairly_active_minutes+1))
colnames(sleep_activity_filtered)
sleep_activity_filtered <- sleep_activity_filtered %>%
  select(-c("moderately_active_distance",
            "very_active_minutes",
            "very_active_distance",
            "fairly_active_minutes"))

# Merging final data frames.
new_data <- sleep_activity_filtered %>% 
  select(-c("id"))

Regular multiple linear regression model:

multi_model <- lm(calories ~., data = new_data)
summary(multi_model)
mutli_step <- ols_step_both_p(multi_model, pent = 0.05, prem = 0.1, details = TRUE)
Model Summary                           
-----------------------------------------------------------------
R                       0.923       RMSE                 292.130 
R-Squared               0.851       Coef. Var             12.175 
Adj. R-Squared          0.847       MSE                85339.931 
Pred R-Squared          0.829       MAE                  215.072 

Multiple Linear Regression with Interaction terms:

interaction <- lm(calories ~ date + day + sedentary_minutes + (total_minutes_asleep *
               total_time_in_bed) + (total_steps*total_distance * light_active_distance *
               lightly_active_minutes) + (total_steps * total_distance *
               log_moderately_active_distance *
               log_fairly_active_minutes) +
               (total_steps * total_distance * log_very_active_distance *
               log_very_active_minutes), data = new_data )
summary(interaction)
int_step_model <- ols_step_both_p(interaction, pent = 0.5, prem = 0.1, details = TRUE)
step_model$interaction
plot(int_step_model, interaction, print_plot = TRUE)
Model Summary                            
------------------------------------------------------------------
R                       0.871       RMSE                  373.020 
R-Squared               0.758       Coef. Var              15.546 
Adj. R-Squared          0.750       MSE                139143.659 
Pred R-Squared          0.736       MAE                   293.464 
------------------------------------------------------------------
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