library(foreign)
#install.packages("ltm") # required to calculate Cronbach's alpha
library(ltm)
## Loading required package: MASS
## Loading required package: msm
## Loading required package: polycor
# read data and assign to data frame
df = read.spss("C:/Users/User/OneDrive/Рабочий стол/Quantitative Research/tmp/ESS11.sav", to.data.frame = T)

Intriduction

Depression is a significant public health issue with far-reaching consequences, affecting individuals’ emotional and physical well-being. Understanding the social determinants of depression is essential for developing effective interventions. This study utilizes the CES-D8 Depression Scale, a concise and reliable measure, to operationalize depression as the dependent variable and explores its relationship with various social factors. A key focus of this analysis is physical activity, measured as the number of days in the past week an individual engaged in sports or other physical activities. Physical activity is widely recognized for its mental health benefits, yet its interaction with other social determinants remains underexplored. Additionally, this study examines alcohol consumption as a potential risk factor, hypothesizing that higher alcohol intake is associated with increased depressive symptoms. By analyzing data from the European Social Survey (ESS) 11, this study investigates how variables such as socioeconomic status, social support, and lifestyle behaviors predict depressive symptoms. Focusing on Iceland, the analysis employs quantitative methods to assess the predictive power of these determinants. The findings aim to provide empirical evidence for targeted interventions and inform public health strategies. By highlighting actionable pathways, this research contributes to the broader understanding of mental health determinants and underscores the importance of addressing social factors in combating depression.

# Select country: Iceland
# Subset data and describe long ordinal variable by frequency distribution
# Alternative 1
# variable: State of depression in country nowadays (fltdpr [1-4])
table(df$fltdpr[df$cntry=="Iceland"])
## 
## None or almost none of the time                Some of the time 
##                             510                             276 
##                Most of the time   All or almost all of the time 
##                              36                              15
#redefine data 
df=df[df$cntry=="Iceland", ]

Literature

Sport is widely recognized as one of the most effective forms of physical activity, offering significant benefits for mental health. Participation in organized sports is associated with higher levels of psychological and social well-being, as it fosters social connections, enhances self-esteem, and increases overall life satisfaction. These factors collectively contribute to a reduction in stress, anxiety, and depression. Moreover, engagement in moderate-to-vigorous physical activity has been shown to lower the risk of mental health issues among both children and adults compared to those who remain physically inactive (Tahira, 2023). Beyond its psychological benefits, physical activity may exert an antidepressant effect through physiological mechanisms. Regular exercise has been linked to an increase in interleukin-10, an anti-inflammatory marker, while simultaneously reducing interleukin-6, a pro-inflammatory marker, and serum thiobarbituric acid-reactive substances (TBARS), an indicator of oxidative stress. Additionally, participation in group sports facilitates social interaction, strengthening social bonds and further mitigating depressive symptoms. The relationship between physical activity and depression management is also reflected in clinical recommendations. According to the National Institute for Health and Care Excellence (NICE) guidelines, engaging in group-based sports for 45–60 minutes, up to three times per week over a period of 10–14 weeks, is advised as an effective strategy for reducing symptoms of depression. Various forms of exercise—including aerobic (cardiorespiratory), anaerobic (high-intensity, short-duration activities), and flexibility-based exercises (such as stretching)—have demonstrated positive effects on depression. Furthermore, research highlights a significant correlation between professional exercise interventions and improved outcomes in depression treatment (Machaczek et al., 2021). Alcohol consumption and depressive disorders are interconnected through both genetic predisposition and environmental influences, contributing to the development and progression of both conditions. Furthermore, these two issues tend to reinforce one another, creating a cyclical pattern that exacerbates their severity (Kuria et al., 2012b). Research indicates that individuals struggling with alcohol use disorders are twice as likely to experience depression compared to those without such issues. Studies conducted among 4 veterans with alcohol dependency suggest that reducing alcohol intake has a beneficial effect on depressive symptoms. This finding underscores the complex interaction between alcohol consumption and mental health, highlighting the potential for symptom improvement through behavioral changes. Clinical trials involving antidepressants have further demonstrated that individuals suffering from both depression and alcohol use disorders report a reduction in depressive symptoms even when receiving a placebo. This suggests that a decrease in alcohol consumption itself contributes to mood improvement. Additionally, patients who participated in counseling sessions aimed at reducing alcohol intake exhibited notable enhancements in both their alcohol-related behaviors and overall mental well-being. These findings reinforce the detrimental impact of alcohol on mental health and emphasize the importance of limiting alcohol consumption as a critical component of effective depression treatment (Nunes, 2023).

Methods

This study is based on the large ESS11 data set. Various variables were selected to analyze emotional well-being. o Depression (fltdpr) o perceived effort (flteeff) o restless sleep (slprl) o happiness (wrhpp) o loneliness (fltlnl) o joie de vivre (enjlf) o sadness (fltsd) o Starting difficulties (cldgng) For a continuous analysis, these variables were numerically transformed, whereby the scales for happiness (d23) and joie de vivre (d25) had to be reversed to align them with the direction of the other variables. To establish internal consistency between the emotional variables (fltdpr, flteef, slprl, wrhpp, fltlnl, enjlf, fltsd, cldgng) Cronbach’s alpha and the mean were calculated. The dependent variable depression was analyzed using descriptive statistics, including the mean, median, quartiles and histogram. To establish a relationship between the dependent variable depression and the comparison variables, pairwise correlation coefficients were calculated and evaluated using Pearson’s correlation. Two hypotheses were tested regarding the relationship between the depression score and sports participation on the one hand and the depression score and the frequency of alcohol consumption on the other. An analysis of variance (ANOVA) was carried out to test the hypotheses. To assess the relationship between alcohol consumption and depression, both as a stand-alone model and in a multivariate framework, a Pearson’s correlation was used to examine the relationships between the variables.

Results

4.2 Bivariate Associations 1 st Hypothesis: The more sports people do, the less depressed they are. An analysis of the Pearson correlation between the depression score and the frequency of sporting activity revealed the following: r = - 0.188 t = - 5.51 , df = 826 p < 0.001 95% Confidence Interval: – 0.253, - 0.122 Hypothesis number one is that the more sport people do, the less depressed they are. Running a correlation analysis between the dependent variable depression and the longscaled variable doing sport revealed a negative correlation. The correlation coefficient of r = - 0.188 demonstrates that a higher level of physical activity is associated with a lower level of depression. Even if the correlation is not extremely high it still is significant. Furthermore, its confidence interval confirms that this effect is probably not due to chance.

# convert to numbers 1-8
df$d20 = as.numeric(df$fltdpr)
df$d21 = as.numeric(df$flteeff)
df$d22 = as.numeric(df$slprl)
df$d23 = as.numeric(df$wrhpp)
df$d24 = as.numeric(df$fltlnl)
df$d25 = as.numeric(df$enjlf)
df$d26 = as.numeric(df$fltsd)
df$d27 = as.numeric(df$cldgng)


# Reverse scales of d23 and d25 (differently poled than )

df$d23 = 5 - df$d23
df$d25 = 5 - df$d25


# Calculate Cronbach's alpha to check internal consistency ("reliability") of tolerance items
cronbach.alpha(df[,c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")], na.rm=T)
## 
## Cronbach's alpha for the 'df[, c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")]' data-set
## 
## Items: 8
## Sample units: 842
## alpha: 0.82
# score = mean of items row wise = sum of item values / number of items 
df$depres = rowSums(df[,c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")]) / 8

# Done. Show descriptives:
summary(df$depres)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   1.250   1.500   1.630   1.875   3.875      11
hist(df$depres)

table(df$depres)
## 
##     1 1.125  1.25 1.375   1.5 1.625  1.75 1.875     2 2.125  2.25 2.375   2.5 
##    56    84    99    92   116    77    70    57    42    36    31    12    12 
## 2.625  2.75 2.875     3 3.125  3.25 3.375   3.5 3.625  3.75 3.875 
##    13    15     3     3     6     1     2     1     1     1     1
# Hypothesis 1: The more sports people do the less depressed they are
# we have to calculate correlation coefficient 

# pairwise correlation 
df$dosprt = as.numeric(df$dosprt)
cor(df[,c("depres", "dosprt")], use="complete.obs")
##            depres     dosprt
## depres  1.0000000 -0.1882115
## dosprt -0.1882115  1.0000000
# ((cor(df$depres, df$dosprt, )))
cor.test(df$depres, df$dosprt)
## 
##  Pearson's product-moment correlation
## 
## data:  df$depres and df$dosprt
## t = -5.5077, df = 826, p-value = 4.858e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2530975 -0.1216399
## sample estimates:
##        cor 
## -0.1882115

2 nd Hypothesis: The more alcohol people consume, the more depressed they are. r = 0.08 p = 0.0163 Analyses were conducted to test the hypothesis that high alcohol consumption is associated with a higher risk of depression. In this case, a correlation coefficient of r = 0.08 was found. Performing a correlation analysis between the dependent variable depression and the long-scale variable alcohol consumption revealed a slightly positive correlation. Given this very low, almost negligible correlation in this data set, one can assume that other factors have a stronger influence on depression than alcohol.

One-way ANOVA Results: F-statistics = 5.79 p = 0.0163 A more in-depth analysis had to be applied to compare the mean depression scores to different alcohol consumption categories. Here, an ANOVA analysis technique was used. This resulted in a significant difference being found between the groups. Depression scores were highest among daily drinkers, with a mean score of 2.09, whereas the respondents who consume alcohol in moderation have a lower value of 1.5. However, an unexpected twist here is that those who never consume alcohol have a slightly higher risk of depression than moderate drinkers.

# Transform variable into numeric
df$alcfreq_n = as.numeric(df$alcfreq)
table(df$alcfreq_n)
## 
##   1   2   3   4   5   6   7 
##   7 103 128 167  93 166 177
# pairwise correlation 
df$alcfreq = as.numeric(df$alcfreq)
cor(df[,c("depres", "alcfreq")], use="complete.obs")
##             depres    alcfreq
## depres  1.00000000 0.08328278
## alcfreq 0.08328278 1.00000000
# ((cor(df$depres, df$alcfreq, )))


# remove missing data
anova_data <- df[!is.na(df$alcfreq) & !is.na(df$depres), ]


# ANOVA
one.way <- aov(depres ~ alcfreq_n, data = anova_data)
summary(one.way)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## alcfreq_n     1   1.34   1.343    5.79 0.0163 *
## Residuals   829 192.31   0.232                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lm(depres ~ alcfreq_n, data = df)
## 
## Call:
## lm(formula = depres ~ alcfreq_n, data = df)
## 
## Coefficients:
## (Intercept)    alcfreq_n  
##     1.52052      0.02323
# show group means of depres by alcfreq
by(df$depres, df$alcfreq, mean, na.rm=T) 
## df$alcfreq: 1
## [1] 2.089286
## ------------------------------------------------------------ 
## df$alcfreq: 2
## [1] 1.637376
## ------------------------------------------------------------ 
## df$alcfreq: 3
## [1] 1.503906
## ------------------------------------------------------------ 
## df$alcfreq: 4
## [1] 1.57803
## ------------------------------------------------------------ 
## df$alcfreq: 5
## [1] 1.59375
## ------------------------------------------------------------ 
## df$alcfreq: 6
## [1] 1.727896
## ------------------------------------------------------------ 
## df$alcfreq: 7
## [1] 1.676006
# test if at least one depression mean differs from all others
model1 = oneway.test(df$depres ~ df$alcfreq) 
model1
## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  df$depres and df$alcfreq
## F = 3.8929, num df = 6.000, denom df = 80.513, p-value = 0.001832

Multivariate Model Estimation For testing multiple influences on depression, a multiple regression model was used to examine the combined effects of doing sports, alcohol consume, gender, and religion on depression. Model 1: Promotion of depression by active sports participation and alcohol consumption - Doing sports (β = -0.044, p < 0.001): Higher sports engagement reduces depression. - Alcohol consumption (β = 0.016, p = 0.235): No significant effect. - Adjusted R-squared = 0.042, explaining ~4.2% of variance. Model 2: Gender and Religion added - Doing sports β = -0.043 → The regression coefficient suggests a small effect of engaging in sports associated with depression p < 0.001 → A low p-value indicates a strong statistical significance dosprt = Significant - Alcohol consumption β = 0.018 → Indicates a slightly increase of depression p = 0.197 → A p-value higher than 0.05 makes the result not statistically significant alkfreq = There is no valid distinction that alcohol has an significant impact on depression - Gender β = -0.020 → A small negative effect can be recognized; this means that it possibly has a little impact. 10 p = 0.674 → A high p-value indicates no statistical significance gndr = No meaningful impact on the dependent variable. - Religion β = -0.050 → A small negative effect that might lead to a decrease in depression p = 0.293 → A high p-value indicates no statistical significance rlgblge = Religion does not significantly influence depression - Adjusted R-squared = 0.041→ indicating no substantial improvement.

# Group means
tapply(df$depres, df$alcfreq, mean, na.rm=T)
##        1        2        3        4        5        6        7 
## 2.089286 1.637376 1.503906 1.578030 1.593750 1.727896 1.676006
oneway.test(depres ~ alcfreq, data = df)
## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  depres and alcfreq
## F = 3.8929, num df = 6.000, denom df = 80.513, p-value = 0.001832
# Additional analyses: Gender and Religion
# Convert gender and religion variables to numeric (replace gndr_var, rlgblge_var with actual variable names)
df$gndr_n = as.numeric(df$gndr)  
df$rlgblge_n = as.numeric(df$rlgblge)  # Replace with actual religion variable name

# Remove missing values for correlation and ANOVA
df_clean = df[!is.na(df$gndr_n) & !is.na(df$rlgblge_n) & !is.na(df$depres), ]

# Correlation for gender and depression
cor(df_clean[,c("depres", "gndr_n")], use="complete.obs")
##             depres      gndr_n
## depres 1.000000000 0.007692411
## gndr_n 0.007692411 1.000000000
# Correlation for religion and depression
cor(df_clean[,c("depres", "rlgblge_n")], use="complete.obs")
##                depres   rlgblge_n
## depres     1.00000000 -0.05560401
## rlgblge_n -0.05560401  1.00000000
# ANOVA for religion and depression
anova_rlgblge <- aov(depres ~ rlgblge_n, data = df_clean)
summary(anova_rlgblge)
##              Df Sum Sq Mean Sq F value Pr(>F)
## rlgblge_n     1   0.36  0.3649    1.42  0.234
## Residuals   458 117.66  0.2569
# Ensure same dataset for linear models
df_model = df[complete.cases(df[, c("depres", "dosprt", "alcfreq", "gndr_n", "rlgblge_n")]), ]

# Linear models
model1 <- lm(depres ~ dosprt + alcfreq, data = df_model)
summary(model1)
## 
## Call:
## lm(formula = depres ~ dosprt + alcfreq, data = df_model)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7847 -0.3671 -0.0898  0.2395  2.1227 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.820638   0.082887  21.965  < 2e-16 ***
## dosprt      -0.043770   0.009748  -4.490 9.03e-06 ***
## alcfreq      0.015892   0.013362   1.189    0.235    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4966 on 456 degrees of freedom
## Multiple R-squared:  0.04644,    Adjusted R-squared:  0.04226 
## F-statistic:  11.1 on 2 and 456 DF,  p-value: 1.956e-05
model2 <- lm(depres ~ dosprt + alcfreq + gndr_n + rlgblge_n, data = df_model)
summary(model2)
## 
## Call:
## lm(formula = depres ~ dosprt + alcfreq + gndr_n + rlgblge_n, 
##     data = df_model)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.80454 -0.36128 -0.09275  0.23450  2.13739 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.919080   0.125577  15.282  < 2e-16 ***
## dosprt      -0.043396   0.009798  -4.429 1.19e-05 ***
## alcfreq      0.017518   0.013549   1.293    0.197    
## gndr_n      -0.019860   0.047209  -0.421    0.674    
## rlgblge_n   -0.049738   0.047208  -1.054    0.293    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.497 on 454 degrees of freedom
## Multiple R-squared:  0.04908,    Adjusted R-squared:  0.0407 
## F-statistic: 5.858 on 4 and 454 DF,  p-value: 0.0001327
# Compare results
anova(model1, model2)
## Analysis of Variance Table
## 
## Model 1: depres ~ dosprt + alcfreq
## Model 2: depres ~ dosprt + alcfreq + gndr_n + rlgblge_n
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    456 112.46                           
## 2    454 112.15  2   0.31169 0.6309 0.5326

Our analysis reveals a clear relationship between physical activity, alcohol consumption, and depression. The findings demonstrate that engaging in sports has a significant antidepressant effect, likely driven by physiological mechanisms such as the regulation of inflammatory markers. Specifically, increased levels of the anti-inflammatory marker interleukin-10 and decreased levels of the pro-inflammatory marker interleukin-6 and serum thiobarbituric acid-reactive substances appear to contribute to improved mental wellbeing. These results highlight sport as a viable method for reducing depressive symptoms. Additionally, our data show a strong correlation between participation in group sports and lower levels of depression. The social connections fostered through team-based physical activity play a key role in enhancing psychological well-being, reinforcing the idea that physical activity is not only a complementary intervention but, in some cases, a primary strategy for managing depression. Regarding alcohol consumption, our findings confirm its association with increased depressive symptoms. The analysis of clinical trials, particularly among veterans, indicates that reducing alcohol intake leads to notable improvements in mental health. Even in a placebo-controlled setting, decreased alcohol consumption corresponded with reduced depression levels, reinforcing the importance of limiting alcohol as part of effective depression management. Overall, our analysis confirms that increased physical activity correlates with lower depressive symptoms, while higher alcohol consumption is linked to worsening depression. These findings emphasize the critical role of lifestyle choices in mental health and suggest that both increased physical activity and reduced alcohol consumption should be considered in depression treatment strategies.



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