"Culture and Well-being: Art as Prescription Therapy ("Art on Prescription"), Promoting Social Cohesion and Harnessing the "Silver Economy"
Phase: NA
Conditions: Mental Health Issue, Anxiety Disorders, Depressive Disorder, Emotional Disorder, Schizophrenia, Autism
Sponsor: KAVADIA ELENI

Pre-registration for quantitative part of Art on Prescription 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Athens 
March, 10, 2024 
 

Design 
Stratified randomised controlled trial with two arms: arts intervention (any of the arts 
interventions, see list), which the investigators call here Active Group (AG) vs waitlist control 
(WL). Stratifying will happen at each site. 
 
 
 
 
 
 
 
 
 
 
 
 
 
REMEMBER the investigators want to measure AG and WL, then WL becomes AG after 3 
months and both the initial AG and the WL → AG will be measured in exactly the same way 
for another 3 months.  
 
AG for 3 months. Then goes into 3 month follow up.  
WL for 3 months. Then this group receives intervention and becomes AG and has 
measurements for 3 months as the AG did.  
There will be a 2-month break between T3 and T4 due to summer vacation 
 
In the beginning, an open invitation to institutions of culture and mental health had been 
sent. Culture institutions i.e. Opera House, Contemporary Art Museum, National Theater etc. 
presented their action plans for interventional courses. Investigators did the connection 
between mental health and culture institutions. 
Baseline - Start of 
Intervention
T1
End of Intervention
T3
Switch
T4
End of Follow Up
T5
AG 
WL 
AG 
WL 
AG 
WL 
2 months 
break 

Potential participants declared their preference for the interventional course such as dance, 
cinema etc. and they listed while an external investigator did the blind randomization into AG 
or WL group. 
 
 
 
 
Measurement 
Participants in both the AG and the WL groups will be measured on the following outcomes. 
Primary Outcome:  Wellbeing Questionnaire (WEMWBS), PHQ-9, GAD-7 
Secondary Outcomes: UCLA-20.  
 
Frequency of Measurement:  
→The primary outcome will be measured at time points (for adults):  
● T0: (before the randomisation; patient’s personal data and demographics) 
● T1: (before the start of the intervention for the active group or the entry into the study 
for the WL group) 
● T2: 6 weeks (after the start of the intervention for the active group or the entry into the 
study for the WL group) 
● T3: 12 weeks (after the start of the intervention for the active group or the entry into 
the study for the WL group) 
The secondary outcomes will be measured at time points 0 (T4) and 12 weeks (T5) for both 
AG and WL. 
 
→The primary outcome will be measured at time points (for children/adolescents):  
● T0: (before the randomisation; patient’s personal data and demographics) 
● T1: (before the start of the intervention for the active group or the entry into the study 
for the WL group) 
● T3: 12 weeks (after the start of the intervention for the active group or the entry into 
the study for the WL group) 
The secondary outcomes will be measured at time points 0 (T4) and 12 weeks (T5) for both 
AG and WL. 
 
 
 
Model 
The investigators will follow standard practice for RCTs and apply an ANCOVA model to 
estimate the effects of treatment. The formalism is: 
 
y ~ b0 + b1xtreatment_group + b2xtime +  b3xtreatment_groupxtime + covariates (1) 
 
Where, y is each outcome, b1-b3 are the regression coefficients and covariates denote 
covariates at randomisation (DETERMINE) 
 
The inference of interest for the model concerns b3, the interaction term, as the investigators 
posit that there will be a difference in slopes between the treatment groups. 
The inference is going to happen via an estimation of the t-statistic, defined as follows” 
 

t = b3/SE (2) 
 
Where SE is the standard error for coefficient b3 
 
t > 1.96 (3) 
 
And 1.96 is the critical value for statistical significance at alpha = 0.05.  
 
 
Target Sample Size per Arm 
Parameters:  
● power = 0.8 
● alpha = 0.05 
● Correlation between time points = 0.6 (a conservative estimate based on 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2222612/ ) 
● Effect size corresponding to d = 0.5  
 
 
 
 
library(tidyverse) # for data wrangling 
library(faux)      # for simulation 
library(broom)     # for tidy analysis results 
 
set.seed(1974) 
 
# I will simulate the following 

# two groups, treatment and WL control.  
# they start off with distributions with the same mean and sd (due to randomisation) 
# the WL control stays the same, the treatment group changes to arrive at a d = 0.4, which i 
# what investigators stipulated in advance 
# the shape of the change should not matter that much for this linear model calculation 
# what should matter are  
# a) sds which I will keep the same as in baseline 
# b) number of measurements, stipulated to be n = 4 
# c) number of people, sample size, which I will vary for the simulation 
 
 
 
# here comes the actual function 
ancova_sim_func <- function(df_means, sd_across, n_subjects, number_sims, 
correlations_across){ 
   
  arts_on_pres_sim <- sim_design( 
    n = n_subjects, # this is the per arm n  
    between = list(group = c("active", "wl")),  
    within = list(time = c("T1", "T2", "T3", "T4")),  
    mu= df_means, 
    sd = sd_across, 
    dv = "score", 
    r = correlations_across, 
    long = TRUE,  
    plot = FALSE, 
    rep = number_sims 
     
  )   
 
 
  temp_results <- lst() 
  temp_results_disp <- lst() 
  temp_p_values <- 0 
   
  for(i in 1:n_sims){ 
     
    temp_results[[i]] <-  aov(score~ time + group + time*group , data = 
arts_on_pres_sim$data[[i]]) 
     
    temp_results_disp[[i]] <-broom::tidy(temp_results[[i]]) 
     
    temp_p_values[i] <-  temp_results_disp[[i]]$p.value[3] 
     
     
  } 
  return(temp_p_values) 
} 

 
 
# here come the parameters that I will pass to this function 
# from https://hqlo.biomedcentral.com/articles/10.1186/1477-7525-10-156 
mean_T1 <- 43.5 # the biggest trial in that list, the PEIP 
sd_T1 <- 10.4 # from that trial 
d <- -0.5 # stipulated effect size of difference 
# to derive the mean needed for the final effect size 
# cohen_d = mean_T1-mean_T4/sd_T1 # using sd_T_1 as I stipulated this to be the same 
throughout 
# which converts to  
mean_T4 <- mean_T1 + d*sd_T1 
 
# to create equally spaced value, my idealised decline of values 
the_treatment_means <- seq(mean_T1, mean_T4, length.out = 4) 
 
# the control means will be the same throughout 
the_control_means <- rep(mean_T1, 4) 
 
# investigators assume a correlation between values from each time point r = 0.6 
within_corr <- 0.5 
 
# now investigators can simulate using faux. 
n_sims <- 500 
 
 
# the way I have created the function above, I need to create this dataframe as an obejct for  
# the faux function 
df_means <-data.frame(T1 = c(the_treatment_means[1], the_control_means[1]), 
                      T2 = c(the_treatment_means[2], the_control_means[2]),  
                      T3 = c(the_treatment_means[3], the_control_means[3]), 
                      T4 = c(the_treatment_means[4], the_control_means[4]), 
                      row.names = c("active", "wl")) 
 
 
# Now I will loop this function over a number of different sample sizes 
 
# this is the list of sample sizes 
n_per_arm <- seq(20,300, by = 20) 
 
# this is the p-values to extract 
p_vals <- lst() 
p_above_criterion <- 0 
for(i in 1:length(n_per_arm)){ 
   
p_vals[[i]] <- ancova_sim_func (df_means = df_means, sd_across = sd_T1,  
                 n_subjects = n_per_arm[i] , number_sims = n_sims, correlations_across 
                 = within_corr) 

 
p_above_criterion[i] <- sum(p_vals[[i]]<0.05)/length(p_vals[[i]]) 
 
} 
 
p_above_criterion 
 
 
# now I can plot the sample sizes 
df_to_plot <- data.frame(p_above_criterion = p_above_criterion, n_per_arm = n_per_arm) 
 
df_to_plot %>%  
  ggplot(aes(y = p_above_criterion, x = n_per_arm))+ 
  geom_point()+ 
  geom_hline(yintercept = 0.8, colour = "red", linetype = "dashed") + 
  scale_x_continuous("sample size", breaks = n_per_arm) 
   
 
# # from https://hqlo.biomedcentral.com/articles/10.1186/1477-7525-10-156 
# mean_T1 <- 43.5 # the biggest trial in that list, the PEIP 
# sd_T1 <- 10.4 # from that trial 
# d <- -0.5 # stipulated effect size of difference 
# # to derive the mean needed for the final effect size 
# # cohen_d = mean_T1-mean_T4/sd_T1 # using sd_T_1 as I stipulated this to be the same 
throughout 
# # which converts to  
# mean_T4 <- mean_T1 + d*sd_T1 
#  
# # to create equally spaced value, my idealised decline of values 
# the_treatment_means <- seq(mean_T1, mean_T4, length.out = 4) 
#  
# # the control means will be the same throughout 
# the_control_means <- rep(mean_T1, 4) 
#  
# # investigators assume a correlation between values from each time point r = 0.6 
# within_corr <- 0.6 
#  
# # now we can simulate using faux. 
# n_sims <- 50 
#  
# n_subj_per_arm <- 100 
#  
#  
#####this is an example  
# arts_on_pres_sim <- sim_design( 
#   n = 10, # this is the per arm n 
#   between = list(group = c("active", "wl")), 
#   within = list(time = c("T1", "T2", "T3", "T4")), 

#   mu= data.frame(T1 = c(the_treatment_means[1], the_control_means[1]), 
#                  T2 = c(the_treatment_means[2], the_control_means[2]), 
#                  T3 = c(the_treatment_means[3], the_control_means[3]), 
#                  T4 = c(the_treatment_means[4], the_control_means[4]), 
#    # sd= sd_T1, 
#   row.names = c("active", "wl")), 
#   sd = sd_T1, 
#   dv = "score", 
#   r = within_corr, 
#   long = TRUE, 
#   rep = 1 
#  
# ) 
#    
#  
# #dim(arts_on_pres_sim$data[[1]]) 
#  
#  
# # test <- aov(response~factor(drug)+Error(factor(patient)), data = df) 
# #  
# #  
# # test <-aov(score~ time + group + time*group , data = arts_on_pres_sim$data[[1]]) 
# # test <-broom::tidy(test) 
# # test$p.value[3] 
#  
# temp_results <- lst() 
# temp_results_disp <- lst() 
# temp_p_values <- 0 
#  
# for(i in 1:n_sims){ 
#    
# temp_results[[i]] <-  aov(score~ time + group + time*group , data = 
arts_on_pres_sim$data[[i]]) 
#  
# temp_results_disp[[i]] <-broom::tidy(temp_results[[i]]) 
#  
# temp_p_values[i] <-  temp_results_disp[[i]]$p.value[3] 
#    
#  
# } 
#  
# sum(temp_p_values<0.05)/length(temp_p_values) 
 
# create a function for all the above ------------------------------------- 
 
# I will simulate the following 
# two groups, treatment and WL control.  
# they start off with distributions with the same mean and sd (due to randomisation) 

# the WL control stays the same, the treatment group changes to arrive at a d = 0.4, which i 
# what investigators stipulated in advance 
# the shape of the change should not matter that much for this linear model calculation 
# what should matter are  
# a) sds which I will keep the same as in baseline 
# b) number of measurements, stipulated to be n = 4 
# c) number of people, sample size, which I will vary for the simulation 
 
 
Brief description of tools: 
 
● Patient’s personal data and demographics (i.e. name, age, nationality, religion, 
educational level, marital status, socio-economic characteristics, medical history, 
mental health problems, medication, therapies). 
 
● The Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS). This tool was developed 
in order to measure mental wellbeing in the general population by Tennant et al. 
(2007). It consists of 14 items responding using a 5-point Likert scale from 1 (never) to 
5 (all the time). The overall score is calculated by summing the responses for every 
item without reversing none. The minimum overall score ranges from 14 to 70. Higher 
scores indicate increased mental wellbeing. The scale has been widely used nationally 
and internationally for monitoring, evaluating projects and programmes and 
investigating the determinants of mental wellbeing. An efficient internal consistency 
was proven using Cronbach's alpha score which was 0.89 for the student sample and 
0.91 for the general population sample. Also, test-retest reliability at one week was 
high (0.83). The Greek validation showed acceptable internal consistency (Cronbach's 
alpha score 0.90).  
[Tennant R, Hiller L, Fishwick R, Platt S, Joseph S, Weich S, Parkinson J, Secker J, 
Stewart-Brown S. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): 
development and UK validation. Health Qual Life Outcomes. 2007 Nov 27;5:63. doi: 
10.1186/1477-7525-5-63. PMID: 18042300; PMCID: PMC2222612. 
 
● The Generalised Anxiety Disorder Assessment (GAD-7) is provided to screen 
symptom severity for the four most common anxiety disorders (generalized anxiety 
disorder, panic disorder, social phobia and posttraumatic stress disorder). GAD-7 
constructed by Spitzer et al (2006) and validated in Greek by Vogazianos et al (2022). 
It consists of 7 items. The GAD-7 score is computed by assigning scores of 0, 1, 2, 
and 3, to the response categories of 'not at all', 'several days', 'more than half the days', 
and 'nearly every day', respectively, and summing together the scores for the seven 
questions (scores range from 0 to 21). Higher levels indicate increased anxiety. There 
are cut-offs for severity of anxiety as: (i) score 0-4: Minimal Anxiety; (ii) score 5-9: Mild 
Anxiety; (iii) score 10-14: Moderate Anxiety; (iv) score greater than 15: Severe Anxiety. 
The internal consistency of the GAD-7 was excellent (Cronbach α = 0.92) and test-
retest reliability was also efficient (intraclass correlation = 0.83) (Spitzer et al., 2006).  
[Spitzer RL, Kroenke K, Williams JB, et al; A brief measure for assessing generalized 
anxiety disorder: the GAD-7. Arch Intern Med. 2006 May 22;166(10):1092-7]. 
[Vogazianos P, Motrico E, Domínguez-Salas S, Christoforou A, Hadjigeorgiou E. 
Validation of the generalized anxiety disorder screener (GAD-7) in Cypriot pregnant 

and postpartum women. BMC Pregnancy Childbirth. 2022 Nov 15;22(1):841. doi: 
10.1186/s12884-022-05127-7].  
 
● The Patient Health Questionnaire-9 (PHQ-9) is a questionnaire which measures 
depression and grade severity of symptoms in general medical and mental health 
settings using nine DSM-5 criteria for major depression within the last two weeks. 
PHQ-9 constructed by Kroenke et al. (2001) and validated in Greek by Hyphantis et 
al. (2011) is using a four-point Likert-type scale. After summing all responses (0=not 
at all, 3=nearly every day), scores range from 0 to 27, with higher levels indicating 
increased symptom severity (0–4 no to minimal; 5–9 mild; 10–14 moderate; 15–19 
moderately severe; 20–27 severe). The internal reliability of the PHQ-9 was excellent, 
with a Cronbach's α of 0.89 in the PHQ Primary Care Study (Kroenke et al., 2001) and 
0.82 in Greek validation (Hyphantis et al., 2011).  
[Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: validity of a brief depression 
severity measure. J Gen Intern Med. 2001;16(9):606–613]. 
[Hyphantis T, Kotsis K, Voulgari PV, Tsifetaki N, Creed F, Drosos AA. Diagnostic 
accuracy, internal consistency, and convergent validity of the Greek version of the 
patient health questionnaire 9 in diagnosing depression in rheumatologic disorders. 
Arthritis Care Res (Hoboken). 2011 Sep;63(9):1313-21. doi: 10.1002/acr.20505. 
PMID: 21618450]. 
 
● The Revised UCLA Loneliness Scale-20 (UCLA-20) is a questionnaire which 
measures level of psychological loneliness experienced by a person. UCLA-20 
constructed by Russel et al. (1980) and validated in Greek by Anderson & Malikiosi-
Loizos (1992). The internal reliability of the UCLA-20 was excellent, with a Cronbach's 
α of 0.94 (Russel et al., 1980) and 0.89 in Greek validation (Anderson & Malikiosi-
Loizos, 1992). This long scale was shortened to 3 questions (UCLA 3-item Loneliness 
Scale) in order to be used in large surveys (Hughes et al., 2004). It is using a 3-point 
Likert-type scale (hard ever, some of the time, often). The scores can be added 
together to give a range of scores from 3 to 9. Higher scores mean most lonely (worse 
outcome).  
[Russel D, Peplau LA, Cutrona CE. (1980). The Revised UCLA Loneliness Scale: 
Concurrent and discriminant validity evidence, Journal of Personality and Social 
Psychology, 39, 472-480.]. 
[Anderson LR, Malikiosi-Loizos M. (1992). Reliability data for a Greek translation of the 
revised UCLA Loneliness Scale: Comparisons with data from the USA. Psychological 
Reports, 71, 665-666. 
Hughes ME, Waite LJ, Hawkley LC, Cacioppo JT. A Short Scale for Measuring 
Loneliness in Large Surveys: Results From Two Population-Based Studies. Res 
Aging. 2004;26(6):655-672. doi: 10.1177/0164027504268574]. 
 
● The Strengths and Difficulties Questionnaire (SDQ) is a brief behavioural screening 
tool for children and adolescents aged 2-17 years. PHQ-9 constructed by Goodman 
(2001) and validated in Greek by Gomez et al. (2021) is using a 3-point Likert-type 
scale (“not true,” “somewhat true”, “certainly true”). SDQ consists of 25 positive and 
negative items. These 25 items are divided between 5 scales: (i) emotional symptoms 
(5 items), (ii) conduct problems (5 items), (iii) hyperactivity/inattention (5 items), (iv) 
peer relationship problems (5 items), (v) prosocial behaviour (5 items). “Somewhat 

true” is always scored as 1 but the scoring of “not true” and “certainly true” varies and 
score 0 or 2. The total difficulties score is generated by summing the cores form all the 
scales except the prosocial scale. The final score can be ranged from 0 to 40. Higher 
scores mean most difficulties (worse outcome). Cronbach’s alpha of the original study 
for all scales ranged from 0.35 to 0.64. 
[Goodman R (2001) Psychometric properties of the strengths and difficulties 
questionnaire. J Am Acad Child & Adolesc Psychiatr 40(11):1337–45]. 
[Gomez R, Motti-Stefanidi F, Jordan S, Stavropoulos V. Greek Validation of the 
Factor Structure and Longitudinal Measurement Invariance of the Strengths and 
Difficulties Questionnaire-Self Report (SDQ-SR): Exploratory Structural Equation 
Modelling. Child Psychiatry Hum Dev. 2021 Oct;52(5):880-890. doi: 10.1007/s10578-
020-01065-7].