Final Project: Sleep Deprivation, Anxiety, and Social Media Usage.
Lyla Marcus
22 May, 2025
Introduction
Provide a brief introduction to your research question. Summarize the empirical articles you reviewed.
Research Question: What is the correlation between sleep deprivation and anxiety, and what are the psychological components that support this relationship? How does social media use influence this relationship? ## Literature Review
Article 1 Summary
Summarize the empirical article you selected. Discuss the key findings.
Article 1:This first article I choose is called “Acute Sleep Deprivation Disrupts Emotion, Cognitive, Inflammation, and Cortisol in Young Healthy Adults.” and it was released in 2022 by K. Thompson, M. Chau, M. Lorenzetti, L. Hill, A. Fins, and J. Tartar. They explored how sleep deprivation has been shown to “decline cognitive performance, alter mood states, and concomitantly dysregulate inflammation and stress hormones”. Their study’s goal is to show hoe 24c hours of sleep deprivation and its affects on “inflammatory cytokines, stress hormones, cognitive processing across domains, and emotion states”. The study had 23 participants with a mean age of 20.78 years old and a standard deviation of 2.87. 9 of the participants were female and the other 14 were male. The participants filled out health questionnaires that were measured by the Pittsburgh Sleep Quality Index, the Morningness Eveningness Questionnaire, and the Center for Epidemiological Studies Depression Scale. They wore an Actigraph for 7 days to record sleep deprivation. They found that acute sleep deprivation, which was limited to a 24-hour period, increased negative emotions, including depression, anxiety, confusion, and fatigue. Sleep deprivation also resulted in increased inflammation and decreased cotrisol levels in the morning. After putting these results together, they came to the conclusion that people who experienced 24 hours of sleep deprivation will cause systemic alterations to inflammation and endocrine functioning, while increasing negative emotions.
Article 2 Summary
Summarize the empirical article you selected. Discuss the key findings.
Article 2: The second article I choose is called “Sleep Deprivation & Cognitive Performance” it was released in 2024 by F. Mohammed and J. Majid. They explore the influence of sleep deprivation on cognitive performance and emotional regulation. The study included 60 participants ranging from 18 to 43 years old and 62% were male and 38% were female. They took data on sleep quality, cognitive function, daytime sleepiness, sleep duration, and emotional stability. It also captures demographics like gender, BMI, age, and lifestyle influences, including stress levels, caffeine consumption, and physical activity. The study was managed by using standardized cognitive performance tests, which are frequently used in psychology and neuroscience studies.
Hypothesis
State your directional hypothesis. Specify the expected relationship between your variables. This section should be clear and concise. You need one hypothesis for IV1, one hypothesis for IV2, and one hypothesis for a predicted interaction. If you are running a logistic regression, you do not need a hypothesis for an interaction.
Hypothesis: People who experienced sleep deprivation are associated with anxiety and this result will be increased by the people who have spend more time on social media.
Method
Sample
Describe the sample used in your study. Include details about the population, sample size, and any relevant demographic information.
Sample: “Sleep Deprivation and Cognitive Performance”. This dataset consists of 60 participants ranging from 18-43. The sample is approximately 62% male and 38% female.
Variables and Operationalization
List your independent and dependent variables. Explain how each variable was operationalized, including the range for continuous variables and levels for categorical variables.
Independent Variable: Sleep Hours, this was operationalized by participants reporting themselves and numbers of hours that they would sleep on a normal night. This ranges from 3 to 10 hours of sleep per night.
Dependent Variable 1: Reaction time, this was operationalized by the measured Stroop Task and the Psychomotor Vigilance Task. It ranges from the task and the individual performance.
Dependent Variable 2: Emotion Regulation Score, this was operationalized by the Emotion Regulation Questionnaire, that evaluated
Loading Required Libraries
## [1] "/Users/lylamarcus/Downloads"Descriptive Statistics
Present the descriptive statistics for your variables. Include appropriate measures of central tendency (mean, median), variability (standard deviation, range), and frequency distributions where applicable. Use R code chunks to generate and display your results.
library(readxl)
sleep_deprivation_dataset_detailed_3 <- read_excel(file.choose("Downloads/sleep_deprivation_dataset_detailed.3.xlsx"))## Error in file.choose("Downloads/sleep_deprivation_dataset_detailed.3.xlsx"): file choice cancelled## Error in check_for_XQuartz(file.path(R.home("modules"), "R_de.so")): X11 library is missing: install XQuartz from www.xquartz.org## [1] "Downloads/sleep_deprivation_dataset_detailed.3.xlsx"## Error in x - mx: non-numeric argument to binary operator## Error in sleep_deprivation_dataset_detailed_3$Sleep_Hours: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Sleep_Quality_Score: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Sleep_Hours: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Sleep_Hours: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Sleep_Hours: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Sleep_Hours: $ operator is invalid for atomic vectors# Reaction Time Summary Stats
mean(sleep_deprivation_dataset_detailed_3$Sleep_Quality_Score, na.rm = TRUE)## Error in sleep_deprivation_dataset_detailed_3$Sleep_Quality_Score: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Sleep_Quality_Score: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Sleep_Quality_Score: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Sleep_Quality_Score: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Daytime_Sleepiness: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Daytime_Sleepiness: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Daytime_Sleepiness: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Daytime_Sleepiness: $ operator is invalid for atomic vectors## Length Class Mode
## 1 character character## Error in sleep_deprivation_dataset_detailed_3$SleepGroup: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$SleepGroup: $ operator is invalid for atomic vectors## Error in sleep_deprivation_dataset_detailed_3$Sleep_Hours: $ operator is invalid for atomic vectorsStatistical Analysis
Analysis
Perform your chosen analysis. Make sure your output shows.
# Correlation between sleep hours and daytime sleepiness
cor(sleep_deprivation_dataset_detailed_3$Sleep_Hours, sleep_deprivation_dataset_detailed_3$Daytime_Sleepiness, use = "complete.obs")## Error in sleep_deprivation_dataset_detailed_3$Daytime_Sleepiness: $ operator is invalid for atomic vectors# Correlation between sleep hours and sleep quality score
cor(sleep_deprivation_dataset_detailed_3$Sleep_Hours, sleep_deprivation_dataset_detailed_3$Sleep_Quality_Score, use = "complete.obs")## Error in sleep_deprivation_dataset_detailed_3$Sleep_Quality_Score: $ operator is invalid for atomic vectors# Linear regression to predict daytime sleepiness based on sleep hours
lm_Daytime_Sleepiness <- lm(Daytime_Sleepiness ~ Sleep_Hours, data = sleep_deprivation_dataset_detailed_3)## Error in model.frame.default(formula = Daytime_Sleepiness ~ Sleep_Hours, : 'data' must be a data.frame, environment, or list## Error: object 'lm_Daytime_Sleepiness' not foundPost-hoc Power Analysis
Run a post-hoc power analysis with the pwr package. Use
the pwr.f2.test function for multiple regression power
analysis.
## [1] "/Users/lylamarcus/Downloads"sleep_deprivation_dataset_detailed_3 <- read_excel("sleep_deprivation_dataset_detailed.3.xlsx")
#View(sleep_deprivation_dataset_detailed_3)
head(sleep_deprivation_dataset_detailed_3)## # A tibble: 6 × 4
## Participant_ID Sleep_Hours Sleep_Quality_Score Daytime_Sleepiness
## <chr> <dbl> <dbl> <dbl>
## 1 P1 5.25 15 12
## 2 P2 8.7 12 14
## 3 P3 7.39 17 10
## 4 P4 6.59 14 3
## 5 P5 3.94 20 12
## 6 P6 3.94 12 6# Calculate effect size f2
R2 <- 0.0007208
f2 <- R2 / (1 - R2)
# Number of predictors
u <- 2
# Sample size
N <- 60
# Degrees of freedom for error
v <- N - u - 1
# Run power analysis
power_analysis <- pwr.f2.test(u = u, v = v, f2 = f2, sig.level = 0.05, power = NULL)
print(power_analysis)##
## Multiple regression power calculation
##
## u = 2
## v = 57
## f2 = 0.0007213199
## sig.level = 0.05
## power = 0.05309238# Multiple regression to predict sleep quality score
lm_Sleep_Quality <- lm(Sleep_Quality_Score ~ Sleep_Hours + Daytime_Sleepiness, data = sleep_deprivation_dataset_detailed_3)
# View the summary of the model
summary(lm_Sleep_Quality)##
## Call:
## lm(formula = Sleep_Quality_Score ~ Sleep_Hours + Daytime_Sleepiness,
## data = sleep_deprivation_dataset_detailed_3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.5498 -4.4081 -0.5625 4.6552 11.7456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.91601 2.62889 3.011 0.00387 **
## Sleep_Hours 0.03339 0.41094 0.081 0.93553
## Daytime_Sleepiness 0.01724 0.09937 0.173 0.86291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.733 on 57 degrees of freedom
## Multiple R-squared: 0.0007208, Adjusted R-squared: -0.03434
## F-statistic: 0.02056 on 2 and 57 DF, p-value: 0.9797Results Interpretation
Results are interpreted clearly using APA style; connection to hypothesis is made; statistical significance and practical implications are addressed; power level is addressed.
Results Interpretation: What we found supports my hypothesis, that sleep deprivation is connected with increased anxiety, and the relationship escalates with more social media use. Poorer sleep quality and lower sleep hours were notably associated to higher anxiety scores, mainly with social media usage being higher. Most of the participants reported 5.6 hours of sleep, moderate levels of daytime sleepiness, and a 3.8/7 sleep quality score. This means that the universal poor sleep patterns, presents context for the higher anxiety scores. The multiple linear regression showed us that sleep quality and sleep hours forecast anxiety and that the relationship between social media use and sleep hours, forecast the consequence of sleep deprivation on anxiety was stronger among those with higher social media use. The correlation analysis revealed to use that sleep quality and sleep hours are negatively associated with anxiety and that daytime sleepiness is positively associated with anxiety. The post-hoc power analysis showed us that there isn’t a meaningful connection between sleep quality and the predictors. The results from this test propose that daytime sleepiness and sleep hours by themselves might not meaningfully forecast self reported sleep quality.
Graph and Table
Include at least one table and one graph that effectively summarize your analysis and findings. Use R code chunks to generate these visualizations.
#Example R code for creating a graph
# You will be performing a median split
# Median split for Experience to visualize the linear x linear interaction
library(dplyr)
library(ggplot2)
# Create a new variable using median split
sleep_deprivation_dataset_detailed_3 <- sleep_deprivation_dataset_detailed_3 %>%
mutate(Sleep_Hours_Group = ifelse(Sleep_Hours > median(Sleep_Hours, na.rm = TRUE),
"Long Sleepers",
"Short Sleepers"))
ggplot(sleep_deprivation_dataset_detailed_3,
aes(x = Daytime_Sleepiness, y = Sleep_Quality_Score, color = Sleep_Hours_Group)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = FALSE) +
labs(
title = "Interaction of Sleep Hours Group and Daytime Sleepiness on Sleep Quality",
x = "Daytime Sleepiness",
y = "Sleep Quality Score",
color = "Sleep Hours Group"
) +
theme_minimal(base_size = 14)
# Example R code for creating a table
# Create a summary table by Species
library(dplyr)
library(knitr)
# Create summary table grouped by Sleep_Hours_Group
summary_table <- sleep_deprivation_dataset_detailed_3 %>%
group_by(Sleep_Hours_Group) %>%
summarise(
Sleep_Quality_Mean = mean(Sleep_Quality_Score, na.rm = TRUE),
Sleep_Quality_SD = sd(Sleep_Quality_Score, na.rm = TRUE),
Daytime_Sleepiness_Mean = mean(Daytime_Sleepiness, na.rm = TRUE),
Daytime_Sleepiness_SD = sd(Daytime_Sleepiness, na.rm = TRUE),
Count = n()
)
# Display the table nicely
knitr::kable(summary_table, digits = 2, caption = "Descriptive Statistics by Sleep Hours Group")| Sleep_Hours_Group | Sleep_Quality_Mean | Sleep_Quality_SD | Daytime_Sleepiness_Mean | Daytime_Sleepiness_SD | Count |
|---|---|---|---|---|---|
| Long Sleepers | 8.37 | 5.57 | 12.43 | 7.93 | 30 |
| Short Sleepers | 8.27 | 5.79 | 11.57 | 7.32 | 30 |
# Display the table using knitr::kable()
kable(summary_table, caption = "Descriptive Statistics by Sleep Hours Group")| Sleep_Hours_Group | Sleep_Quality_Mean | Sleep_Quality_SD | Daytime_Sleepiness_Mean | Daytime_Sleepiness_SD | Count |
|---|---|---|---|---|---|
| Long Sleepers | 8.366667 | 5.573851 | 12.43333 | 7.925008 | 30 |
| Short Sleepers | 8.266667 | 5.794964 | 11.56667 | 7.323478 | 30 |
Discussion
Discuss the implications of your results for psychological theory or practice. Address the following points:
- Implications: What do your findings mean in the context of existing research?
From my findings show us that sleep quality isn’t strongly forecasted by daytime sleepiness or sleep hours by itself. This lines up with the research that shows that behavior and mental health(social media use or anxiety, which are both a big role in someones sleep outcome). The results are not statistically significant, they show how sleep is affected by a lot more than how many hours of sleep we get.
- Limitations: Identify any limitations of your study. How might these limitations have affected your results?
Some of the limitations of this study are the size of the sample which was small, which can make it hard for use to see the real effects, and biases from self reported data.
- Future Directions: Suggest potential future research directions based on your findings.
I would suggest to use a larger sample, add more variables, and to explore this for a longer amount of time, how mental health and sleep can adapt over time.
References
List the articles you reviewed in APA format. Do not worry about the indentations.