R_Markdown#1 - Final Paper

Literature review

Depression is influenced by multiple social determinants. This section reviews key factors affecting depression levels based on previous research, focusing on age, gender, financial background, social support and diet.

Age relevance in depression

Research indicates that younger individuals experience higher levels of depression compared to older adults. A study by Goodwin et al. (2022) found that 40% of individuals aged 18–39 reported anxiety, while 33% reported depression, whereas in adults aged 60 and older, anxiety and depression rates were significantly lower at 20% and 16%, respectively. Similarly, student mental health data from the Mental Health Barometer 2022 (Zick, 2023) revealed that 45% of Slovenian students rated their mental health as poor or very bad, with 82% reporting high levels of study-related stress. In contrast, older adults (65+) experience significantly lower rates of major depressive episodes and lifetime depression (Goodwin et al., 2022), suggesting a decline in depression prevalence with age.

Gender relevance on depression

Research strongly supports that women are more likely to report higher depression rates than men. Over time, depression prevalence has increased, with women’s rates rising from 9.7% in 2015 to 11.8% in 2020, while men’s rates increased from 4.7% to 6.4% over the same period (Goodwin et al., 2022). Women consistently reported higher depression levels across all study years. Genetic factors also play a role, as differences in gene inheritance and interactions with the environment contribute to the increased likelihood of depression in women (Prelog et al., 2022).

Financial difficulties in childhood

Bøe et al. (2016) examined childhood financial hardship’s long-term effects on depression across 19 European countries. Findings showed early financial stress significantly predicted higher depression in adults aged 25–40 in ten countries. However, its influence declined in older age as social factors, such as marital status and community engagement, became more significant. In Slovenia, improvements in childhood financial conditions may contribute to better mental health outcomes (“First progress report on implementing the European child guarantee in Slovenia 2022-2023,” 2024).

The personal matters

Existing research provides valuable insights into the broader context of social support and mental health in Slovenia (“First progress report on implementing the European child guarantee in Slovenia 2022-2023”, 2024). Examining the impacts COVID-19 had on mental health, a research from 2021 highlights the benefits social support has on individuals well-being (Cugmas et al., 2021). Those with stronger support networks are likely to experience lower rates of depression.

Fruit consumption

Głąbska et al. (2020) and Kirbiš et al. (2025) explored the connection between dietary habits, particularly fruit consumption and mental health outcomes like depression. They highlight that increased fruit consumption, rich in essential nutrients such as vitamins, antioxidants and fiber, is associated with improved mental well-being and a reduction in depression symptoms. While Głąbska et al. (2020) discusses this link in several European countries and Kirbiš et al. (2025) does not directly focus on Slovenians, the findings suggest that regular fruit intake could play a role in lowering depression levels. These studies align with the hypothesis that more frequent fruit consumption is associated with lower levels of depression, emphasizing diet as a significant factor in mental health.

##Method

Data source and sample

This study utilizes data from the 11th round of the European Social Survey (ESS-11), specifically focusing on Slovenian participants. The ESS is a cross-national survey that collects data on social attitudes, behaviors and well-being across Europe. Our sample includes individuals who provided responses on depression levels and key explanatory variables.

#Our selected country is Slovenia.
#df_sl equals to Slovenia to only refer to Slovenian data set
df_sl = df[df$cntry=="Slovenia",]

Participants

Our research is based on a sample of 1248 Slovenian respondents (50% men and 50% women) aged 16 - 90 participating in the 11th round of the ESS-11.

Operationalization of variables

Dependent variable: depression (Card 45):

Depressive symptoms were assessed using the eight-item CES-D8 scale, which categorizes different levels of depression. Respondents reported how frequently they experienced certain emotions and behaviors in the past week, including: feeling depressed, struggling to complete tasks, experiencing restless sleep, feeling happy, feeling lonely, enjoying life, feeling sad and having difficulty getting started. Responses were recorded on a four-point scale: ‘None or almost none of the time’, ‘Some of the time’, ‘Most of the time’ and ‘All or almost all of the time’, with an additional ‘Don’t know’ option. The eight-item scale included two reverse-coded items and responses were summed to create a composite score ranging from 1 to 4. A mean score was assigned only if the respondent completed at least six of the eight items. According to Boe et al. (2017), analysis using ESS data has demonstrated that this version of the CES-D scale is a valid and reliable measure of depression across different age groups and remains consistent across genders.

# Convert CES‐D8 Depression Scale variables into numbers from 1-4
df_sl$d20 = as.numeric(df_sl$fltdpr)
df_sl$d21 = as.numeric(df_sl$flteeff)
df_sl$d22 = as.numeric(df_sl$slprl)
df_sl$d23 = as.numeric(df_sl$wrhpp)
df_sl$d24 = as.numeric(df_sl$fltlnl)
df_sl$d25 = as.numeric(df_sl$enjlf)
df_sl$d26 = as.numeric(df_sl$fltsd)
df_sl$d27 = as.numeric(df_sl$cldgng)
# Reverse scales of d23 and d25 (as they are differently poled than the other depression scales)
df_sl$d23 = 5 - df_sl$d23
df_sl$d25 = 5 - df_sl$d25
#Check polarity of the two variables that need to be reversed
table(df_sl$d23)

## 
##   1   2   3   4 
## 296 760 160  28

table(df_sl$d25)

## 
##   1   2   3   4 
## 312 720 159  48

df_sl$depression = rowSums(df_sl[,c("d20", "d21", "d22", "d23", "d24", "d25", "d26", "d27")]) / 8
#Show descriptives:
summary(df_sl$depression)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   1.250   1.500   1.599   1.875   3.875      28

hist(df_sl$depression, breaks=8)

table(df_sl$depression)

## 
##     1 1.125  1.25 1.375   1.5 1.625  1.75 1.875     2 2.125  2.25 2.375   2.5 
##    90    92   194   140   158   130   107    83    64    40    31    20    14 
## 2.625  2.75 2.875     3 3.125  3.25 3.375   3.5 3.625 3.875 
##    17    11     8     2     6     7     3     1     1     1

#####Independent variables: ######Age (agea): Measured in years (continuous variable).

# H1: Younger individuals tend to report higher levels of depression than older individuals.
# Convert age into numeric scale to create a continuous variable.
age=(df_sl$agea)
lm(depression ~ as.numeric(age), data=df_sl)

## 
## Call:
## lm(formula = depression ~ as.numeric(age), data = df_sl)
## 
## Coefficients:
##     (Intercept)  as.numeric(age)  
##         1.52355          0.00213

plot(df_sl$age, df_sl$depression)

# Saving model to show extended summary
model = lm(depression ~ as.numeric(age), data = df_sl)

######Gender (gndr): Originally coded as 1 = Male, 2 = Female.
Recorded into a binary variable (0 = Male, 1 = Female).

by(as.numeric(as.character(df_sl$age)), df_sl$gndr, mean, na.rm=T)

## df_sl$gndr: Male
## [1] 49.08388
## ------------------------------------------------------------ 
## df_sl$gndr: Female
## [1] 50.41094

######Childhood Financial Difficulties (fnsdfml): - Measures association with financial struggles during childhood.

#Chisq.test to examine association.
chisq.test(df_sl$fnsdfml, df_sl$depression)

## Warning in chisq.test(df_sl$fnsdfml, df_sl$depression): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  df_sl$fnsdfml and df_sl$depression
## X-squared = 190.37, df = 88, p-value = 1.638e-09

######Social Support (inprdsc): Number of people with whom respondents discuss intimate and personal matters. Used as a continuous predictor (higher values indicate stronger social support). Recorded into three categorical groups to assess the relationship between social support and depression: Low Social Support: 0–2 people; Medium Social Support: 3–6 people; High Social Support: 7 or more people.

df_sl$support = factor(NA, levels = c ("Low social support", "Medium social support", "High social support"))
df_sl$support[df_sl$inprdsc == "None"] <- "Low social support"
df_sl$support[df_sl$inprdsc == "1"] <- "Low social support"
df_sl$support[df_sl$inprdsc == "2"] <- "Low social support"
df_sl$support[df_sl$inprdsc == "3"] <- "Medium social support"
df_sl$support[df_sl$inprdsc == "4-6"] <- "Medium social support"
df_sl$support[df_sl$inprdsc == "7-9"] <- "High social support"
df_sl$support[df_sl$inprdsc == "10 or more"] <- "High social support"
table(df_sl$inprdsc, df_sl$support)

##             
##              Low social support Medium social support High social support
##   None                       49                     0                   0
##   1                         183                     0                   0
##   2                         255                     0                   0
##   3                           0                   365                   0
##   4-6                         0                   300                   0
##   7-9                         0                     0                  52
##   10 or more                  0                     0                  32

summary(model)

## 
## Call:
## lm(formula = depression ~ as.numeric(age), data = df_sl)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.68120 -0.33107 -0.08533  0.21777  2.24067 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.5235514  0.0272234  55.965   <2e-16 ***
## as.numeric(age) 0.0021303  0.0006696   3.181   0.0015 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4572 on 1218 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.008241,   Adjusted R-squared:  0.007427 
## F-statistic: 10.12 on 1 and 1218 DF,  p-value: 0.001503

#ANOVA
oneway.test(df_sl$depression ~ df_sl$support)

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  df_sl$depression and df_sl$support
## F = 2.6233, num df = 2.00, denom df = 234.48, p-value = 0.07469

#Regression Model
model = lm(depression ~ support, data = df_sl)

#Chisq.test to check association
chisq.test(df_sl$support, df_sl$depression)

## Warning in chisq.test(df_sl$support, df_sl$depression): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  df_sl$support and df_sl$depression
## X-squared = 48.23, df = 44, p-value = 0.3058

######Fruit Consumption (etfruit): Frequency of fruit consumption measured on a scale from 1 (“Never”) to 7 (“Every day”). Recoded into three categorical groups to simplify analysis: Low Fruit Consumption: Never, Less than once a week; Medium Fruit Consumption: 1–3 times per week; High Fruit Consumption: Once a day or more.

#Categorizing answers in three groups
df_sl$frconsumption = factor(NA, levels = c ("Low fruit consumption", "Medium fruit consumption", "High fruit consumption"))
df_sl$frconsumption[df_sl$etfruit == "Never"] <- "Low fruit consumption"
df_sl$frconsumption[df_sl$etfruit == "Less than once a week"] <- "Low fruit consumption"
df_sl$frconsumption[df_sl$etfruit == "Less than 4 times a week but at least once a week"] <- "Medium fruit consumption"
df_sl$frconsumption[df_sl$etfruit == "Less than once a day but at least 4 times a week"] <- "Medium fruit consumption"
df_sl$frconsumption[df_sl$etfruit == "Once a day"] <- "High fruit consumption"
df_sl$frconsumption[df_sl$etfruit == "Twice a day"] <- "High fruit consumption"
df_sl$frconsumption[df_sl$etfruit == "Three times or more a day"] <- "High fruit consumption"
table(df_sl$etfruit, df_sl$frconsumption)

##                                                    
##                                                     Low fruit consumption
##   Three times or more a day                                             0
##   Twice a day                                                           0
##   Once a day                                                            0
##   Less than once a day but at least 4 times a week                      0
##   Less than 4 times a week but at least once a week                     0
##   Less than once a week                                                41
##   Never                                                                 6
##                                                    
##                                                     Medium fruit consumption
##   Three times or more a day                                                0
##   Twice a day                                                              0
##   Once a day                                                               0
##   Less than once a day but at least 4 times a week                       154
##   Less than 4 times a week but at least once a week                      101
##   Less than once a week                                                    0
##   Never                                                                    0
##                                                    
##                                                     High fruit consumption
##   Three times or more a day                                             93
##   Twice a day                                                          277
##   Once a day                                                           569
##   Less than once a day but at least 4 times a week                       0
##   Less than 4 times a week but at least once a week                      0
##   Less than once a week                                                  0
##   Never                                                                  0

oneway.test(df_sl$depression ~ df_sl$frconsumption)

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  df_sl$depression and df_sl$frconsumption
## F = 2.1172, num df = 2.00, denom df = 109.71, p-value = 0.1253

####Analytical approach - The analysis is conducted using R software, applying statistical models to assess the relationship between depression and the selected social determinants. - Bivariate analysis such as Chi-Square, T-tests, ANOVA are used to describe and examine depression differences across categorical variables. - Regression analysis such as the regression model is applied to explore the combined effects of independent variables on depression scores, controlling for potential confounders. - Statistical significance levels such as by, are reported using p-values and confidence intervals.

##Results

H1: Younger individuals tend to report higher levels of depression than older individuals.

plot(df_sl$age, df_sl$depression)

Description of the regression line If age is 0, depression is estimated 1.5235 on average (unrealistic assumption). An increase of age by 1 leads to 0.00213 additional performance points on average every year. Interpretation: This result supports H1 that younger individuals tend to report lower levels of depression. The relationship is positive (yet the size is small) indicating a very weak association between age and depression.

summary(model)

## 
## Call:
## lm(formula = depression ~ support, data = df_sl)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63298 -0.33302 -0.08302  0.24202  2.24202 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   1.63298    0.02110  77.391   <2e-16 ***
## supportMedium social support -0.04996    0.02765  -1.807    0.071 .  
## supportHigh social support   -0.10135    0.05446  -1.861    0.063 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4574 on 1205 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.004291,   Adjusted R-squared:  0.002639 
## F-statistic: 2.597 on 2 and 1205 DF,  p-value: 0.07494

describing the extended summary of the regression model (lm) As the p-value: 0.001503 and is therefore smaller than 0.05, we conclude that age is significantly associated with depression levels. As the coefficient is positive, the depression scale increases with age.The regression model (lm) has been used to see how depression changes with age. The results have proven a relevance between age and depression.

H2: Women are more likely to report higher depression than men.

Male: 49.08388 Female: 50.41094 The average female is slightly older then the average male, by 1.33 year. The older women are, the likelier they are to be depressed than men.

Does depression differ by gender?

plot(df_sl$gndr, df_sl$depression)

table(df_sl$female)

## < table of extent 0 >

Female show higher depression than male. mean male = 1.53 mean female = 1.66 The difference of means = 1.66 - 1.53 = 0.13

Now, the male/female difference can be expressed by a regression model, too: expected average performance of male applicants = 1.53 + 00.13 = 1.53 expected average performance of female applicants = 1.53 + 10.13 = 1.66 Women report significantly higher depression scores than men.

P-value = 3.707e-07 is above 0.001 which proves a high significant difference in depression scores between men and women. Women report significantly higher depression scores than men. The result strongly supports our hypothesis that women are more likely to report higher level of depression than men.

H3: Individuals who experienced financial difficulties in their childhood are more likely to have higher depression scores.

Slovenians with financial childhood difficulties have a higher depression rate

chisq.test(df_sl$fnsdfml, df_sl$depression)

## Warning in chisq.test(df_sl$fnsdfml, df_sl$depression): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  df_sl$fnsdfml and df_sl$depression
## X-squared = 190.37, df = 88, p-value = 1.638e-09

This leaves us to assume that there is … association between the two categorical variables As the p-value 1.638e-09 is smaller than 0.001, individuals with financial hardships in childhood report higher depression scores. Therefore the p-value is of high significance.

#H4: Depression is lower when having more people to discuss intimate and personal matters.

Running the Annova test, it can be observed that the p-value is above 0.05, the result is not significant. It means there is not a strong evidence that social support affects depression on this specific population (sloveniance). Hence we will conduct a regression model to verify the relevance of the variables for potential linear effects.

summary(model)

## 
## Call:
## lm(formula = depression ~ support, data = df_sl)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63298 -0.33302 -0.08302  0.24202  2.24202 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   1.63298    0.02110  77.391   <2e-16 ***
## supportMedium social support -0.04996    0.02765  -1.807    0.071 .  
## supportHigh social support   -0.10135    0.05446  -1.861    0.063 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4574 on 1205 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.004291,   Adjusted R-squared:  0.002639 
## F-statistic: 2.597 on 2 and 1205 DF,  p-value: 0.07494

The Regression Model cross-checks the Annova-test. Both mark low relevance results. Taken from these two tests, it appears that social support does not affect depression.

A Chisq.test is being run to triple check any possible significance. In this test, the p-value is again above 0.05.

It can be concluded that neither test shows any success and therefore leads us to believe there is no association between the dependent and independent variable.

#H5: More frequent fruit consumption is associated with lower levels of depression.

Running the Chisq.test, following result is observed: X-squared = 55.749, df = 44, p-value = 0.1102

Running the Annova, following result is observed: F = 2.1172, num df = 2.00, denom df = 109.71, p-value = 0.1253

Low fruit consumption: Min: 1.000, Mean:1.681, Max:3.625 Medium fruit consumption: Min: 1.000, Mean:1.644, Max:3.250 High fruit consumption: Min: 1.000, Mean:1.584, Max:3.875

Using the Standard Deviation, the following has been observed: Low fruit consumption: 0.5426128; This group has the highest average depression score and the highest variability in depression scores. Medium fruit consumption: 0.4745989; The average depression score in this group is slightly lower than in the low fruit consumption group. High fruit consumption: 0.44944; Higher fruit consumption seems to be associated with lower depression (as shown by the lowest mean depression score of 1.584 in the high fruit consumption group).

In conclusion for H5, the standard deviation and mean of each fruit consumption group present a minor difference in depression variances, which leads to assume a relevance between the variables. Yet the Chisq.test and Anova test both provide a p-value above the significance value of 0.05. Therefore the hypothesis is not met, and there is no significance within this group of participants.

##Discussion

The empirical findings provide insights into the relationships between various factors and depression levels among Slovenian participants. For H1, a linear regression was conducted treating age as a continuous variable, suggesting that older individuals report slightly higher depression scores. However, the results showed a small positive relationship between age and depression, with each year of age corresponding to a 0.00213 increase in depression scores. While the p-value of 0.001503 is statistically significant, the effect size is minimal, indicating that age is not a strong predictor of depression in this sample. These findings contradict prior research (Goodwin et al., 2022), which found that younger individuals, particularly those aged 18–39, report higher depression levels than older adults. One possible explanation is that this dataset does not fully capture younger populations experiencing stress from academic or work-related pressures. Additionally, the measurement of depression symptoms in older individuals may differ from that of younger adults, as older populations may report physical symptoms rather than emotional distress, leading to potential underreporting in previous studies. Moreover, research suggests that coping mechanisms, life satisfaction, and emotional regulation improve with age, which could explain lower depression prevalence in older adults (Carstensen et al., 2020). However, our findings suggest that age-related factors such as health concerns or social isolation may counterbalance these protective effects, leading to the observed weak but positive relationship between age and depression.

In H2, the hypothesis that women are more likely to report higher depression than men, the empirical results support this claim. The average depression score for women (1.66) was higher than that of men (1.53), with a difference of 0.13. The regression model confirmed that, on average, women report significantly higher depression scores than men. Additionally, the t-test result, with a p-value of 3.707e-07, shows a highly significant difference in depression scores between genders. The findings align with previous research (Goodwin et al., 2022; Prelog et al., 2022), which consistently shows that women have higher depression prevalence. Possible explanations include biological factors (hormonal differences), gendered stress exposure and help-seeking behaviors, which are more common among women. It is important to consider that gender differences in depression may also be influenced by socioeconomic factors, caregiving roles, and access to mental health resources. Future research could examine how employment status, parenting responsibilities, or gender role expectations moderate depression outcomes between men and women.

For H3, the chi-square test revealed a strong association between childhood financial difficulties and higher depression scores, with a p-value of 1.638e-09, which is much smaller than 0.001. This aligns with research by Bøe et al. (2016), which found that individuals who experienced financial instability in childhood were more likely to develop depression in adulthood. The findings indicate a strong relationship between the two variables, confirming that individuals who faced financial hardships during childhood are more likely to report long-term mental health effects like higher levels of depression in adulthood. However, our study does not capture potential mediators such as parental support, education, or resilience, which may buffer the impact of childhood financial difficulties on depression outcomes. Future research could explore whether individuals who experienced financial hardship but had strong social or institutional support report different depression levels compared to those without such protective factors.

H4, however, was not supported. Despite categorizing social support into three levels: low, medium and high social support. The results from various statistical tests, including ANOVA, chi-square tests and a regression model, showed no significant association between social support and depression (p-values: 0.07469 and 0.3058). This contradicts previous research (Cugmas et al., 2021), which found that strong social networks are typically associated with lower depression levels. A possible explanation is that social support alone is not sufficient to mitigate depression. Perhaps other factors like mental health interventions, socioeconomic stability and personality traits may play a larger role. Future studies should examine the quality rather than quantity of social support, which may have varying effects on depression.

Lastly, in testing H5, the mean depression scores were slightly lower for individuals with high fruit consumption (1.584) compared to low (1.681) and medium (1.644) groups. However, chi-square (p = 0.1102) and ANOVA (p = 0.1253) tests found no significant association. This finding contradicts Głąbska et al. (2020), who reported that fruit consumption improves mental well-being due to its nutritional benefits (antioxidants, vitamins, fiber). One possible explanation is diet alone does not significantly impact depression unless combined with other lifestyle factors (exercise, sleep, stress management). Perhaps longitudinal studies could assess better whether sustained fruit consumption over time has more pronounced effects on depression than cross-sectional data can capture. Additionally a combined effect of multiple dietary habits could be explored instead of isolating the fruit consumption alone.

##Limitations

While the study provides meaningful insights, several limitations should be acknowledged: 1. Self-reported data: Depression and predictor variables were collected via self-reports, which can introduce response bias. Participants may underestimate or overestimate their depression levels. 2. Cross-sectional design: The study does not establish causation, only associations and therefore does not explore causal relationships. 3. Sample characteristics: The findings only apply to Slovenian participants. which limits the sample size and does not provide enough impact. 4. Additional variables: While multiple statistical tests were conducted, additional variables like employment status, stress levels or education could have been interesting to improve the analysis, instead of restricting it on the five chosen variables.

##Conclusion

This study examined the relationships between age, gender, childhood financial difficulties, social support and fruit consumption with depression levels in Slovenian participants. Using data from the 11th round of the European Social Survey (ESS), we applied bivariate and multivariate statistical methods to test five hypotheses. The findings confirmed that gender and childhood financial difficulties are significant predictors of depression, with women and individuals with early-life financial struggles reporting higher depression scores. However, age showed a weak positive association with depression, contradicting expectations that younger individuals report higher depression levels. Nevertheless, did social support and fruit consumption not significantly impact depression, challenging prior research that suggests these factors contribute to mental well-being. These findings highlight the complex interplay of social determinants in depression, emphasizing that economic stability and gender-related factors play a stronger role than lifestyle behaviors such as diet and social support. Future research should incorporate longitudinal data and additional variables (e.g., employment status and stress levels) to deepen our understanding of these relationships.

lm(depression~agea+gndr+fnsdfml+support+frconsumption, data=df_sl)

## 
## Call:
## lm(formula = depression ~ agea + gndr + fnsdfml + support + frconsumption, 
##     data = df_sl)
## 
## Coefficients:
##                           (Intercept)                                 agea16  
##                             1.7288339                              0.2476799  
##                                agea17                                 agea18  
##                             0.3088136                              0.1930322  
##                                agea19                                 agea20  
##                             0.4679233                              0.1780754  
##                                agea21                                 agea22  
##                             0.4250404                              0.2348279  
##                                agea23                                 agea24  
##                             0.1808435                              0.0252078  
##                                agea25                                 agea26  
##                             0.1575591                              0.0148466  
##                                agea27                                 agea28  
##                            -0.0715978                              0.2124839  
##                                agea29                                 agea30  
##                             0.2289246                              0.1897835  
##                                agea31                                 agea32  
##                             0.1047432                              0.0440523  
##                                agea33                                 agea34  
##                             0.2570264                              0.1134944  
##                                agea35                                 agea36  
##                             0.1830840                              0.0990776  
##                                agea37                                 agea38  
##                             0.2954555                              0.1610012  
##                                agea39                                 agea40  
##                             0.1809742                              0.0703851  
##                                agea41                                 agea42  
##                             0.0891815                             -0.0132418  
##                                agea43                                 agea44  
##                             0.0661749                              0.0896731  
##                                agea45                                 agea46  
##                             0.0192563                             -0.0932474  
##                                agea47                                 agea48  
##                            -0.0017349                              0.1471875  
##                                agea49                                 agea50  
##                             0.0378267                             -0.0500521  
##                                agea51                                 agea52  
##                            -0.1052482                              0.2937668  
##                                agea53                                 agea54  
##                             0.1261290                              0.3003010  
##                                agea55                                 agea56  
##                             0.1655324                              0.1182404  
##                                agea57                                 agea58  
##                             0.1917165                              0.1464101  
##                                agea59                                 agea60  
##                             0.0498763                             -0.0013279  
##                                agea61                                 agea62  
##                             0.0807733                              0.1330057  
##                                agea63                                 agea64  
##                             0.2482062                             -0.0003316  
##                                agea65                                 agea66  
##                             0.1507980                              0.2802071  
##                                agea67                                 agea68  
##                             0.1708179                              0.0309067  
##                                agea69                                 agea70  
##                             0.1413080                              0.2689939  
##                                agea71                                 agea72  
##                             0.1328392                              0.2468592  
##                                agea73                                 agea74  
##                             0.3871048                              0.1380748  
##                                agea75                                 agea76  
##                             0.0184659                              0.3163267  
##                                agea77                                 agea78  
##                             0.1659110                              0.4154134  
##                                agea79                                 agea80  
##                             0.0373176                              0.3631554  
##                                agea81                                 agea82  
##                             0.4309358                              0.1889187  
##                                agea83                                 agea84  
##                             0.2718253                              0.2274260  
##                                agea85                                 agea86  
##                             0.4812614                              0.3175211  
##                                agea87                                 agea88  
##                             0.7723882                              0.1042570  
##                                agea89                                 agea90  
##                             0.3977727                              0.6824195  
##                            gndrFemale                           fnsdfmlOften  
##                             0.1155900                             -0.1759680  
##                      fnsdfmlSometimes                     fnsdfmlHardly ever  
##                            -0.2528026                             -0.3090019  
##                          fnsdfmlNever           supportMedium social support  
##                            -0.4219585                             -0.0469327  
##            supportHigh social support  frconsumptionMedium fruit consumption  
##                            -0.1168666                              0.0439806  
##   frconsumptionHigh fruit consumption  
##                            -0.0255870