1. ABOUT THE DATASET
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Title:	 Dataset supporting a study of Emotion Regulation, Alexithymia, Apathy and Mental Health in Parkinson’s Disease

Creator(s): 	Caroline Seton, MSc  (0000-0003-3868-6477)

Organisation(s): School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom

Rights-holder(s): Caroline Seton

Publication Year: 2026

Description: This dataset contains anonymised data from 107 individuals diagnosed with Parkinson’s Disease (PD), collected as part of a remote study investigating the relationships between apathy, alexithymia, emotion regulation (ER) strategies, and mental health outcomes. Data include demographic and clinical variables (e.g., age, gender, ethnicity, education, age of diagnosis, illness duration, levodopa equivalent daily dose, motor and non-motor aspects of daily living, and global cognition, and validated questionnaires assessing apathy, alexithymia, anxiety, depression, mental wellbeing and both cognitive and behavioural ER strategies. Data were collected via video call and online survey platforms. The dataset has been fully de-identified and stored in .csv format. It may be of interest to researchers studying interactions between apathy, alexithymia, and ER symptoms in PD.

Cite as: Seton, Caroline (2026): Dataset supporting a study of Emotion Regulation, Alexithymia, Apathy and Mental Health in Parkinson’s Disease. University of Reading. Dataset. https://doi.org/10.17864/1947.001489

Link to Preprint: Seton, C., van Reekum, C. M., & Ho, A. (2026, February 6). Emotion regulation as a transdiagnostic mechanism of mental health in Parkinson’s disease: Linking alexithymia and apathy. Retrieved from osf.io/preprints/psyarxiv/kvzc4_v1    

Contact: 
Caroline Seton
School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
Email: c.seton@pgr.reading.ac.uk  
Carien M. van Reekum
School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
Tel: +44 (0) 118 378 5556 
Email: c.vanreekum@reading.ac.uk

Aileen K. Ho
School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
Tel: +44 (0) 118 378 5550 
Email: a.k.ho@reading.ac.uk 

Acknowledgements: 	We wish to acknowledge all the participants who generously contributed their time and effort to this research. We also wish to acknowledge Parkinson’s UK for their support in facilitating recruitment and promoting awareness of this study.

2. TERMS OF USE
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Copyright 2026 Caroline Seton. This dataset is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/ 

3. PROJECT AND FUNDING INFORMATION
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This dataset was not created during or for a funded project.

4. CONTENTS
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File Listing
All data frame files are in .csv format and all R analyses scripts are in .r format. 
df_subscales.csv – Raw dataset including all individual questionnaire items and task-level responses.
df.csv – Collated dataset with total/subscale scores for all measures, ready for preprocessing.
transformation_summary.csv - Summary of skewness, kurtosis, and Shapiro-Wilk test results across transformation types for each variable.
df_transformed.csv – Dataset with Yeo-Johnson transformed variables.
df_standardised.csv – Transformed and z-standardised variables for analysis.
df2.csv – Final analytic dataset.
Path_Coefficients_Unadjusted.csv - Path coefficients from mediation models without covariates. 
Path_Coefficients_Adjusted.csv - Path coefficients from mediation models with covariates.
data_processing_script.r – R Script for transformation-type comparison of characteristics (skewness, kurtosis, and Shapiro-Wilk p-values) against original distributions. Yeo-Johnson was selected as the default due to consistent improvements in normality across variables. All transformed variables were z-standardised.
1A_Correlations_script.r – R Script for Spearman rank-order correlation analyses examining associations between apathy, alexithymia, mental health outcomes and control variables.
1B_Correlations_script.r – R Script for Spearman rank-order correlation analyses examining associations between apathy, alexithymia, ER strategies, mental health outcomes and control variables.
mediation_unadjusted_script.r – R Script for unadjusted mediation analyses examining ER strategies as mediators of the relationships between Apathy or Alexithymia and mental health outcomes. 
mediation_adjusted_script.r - R Script for adjusted mediation analyses (controlling for covariates; age, education, illness duration and motor aspects of daily living) examining ER strategies as mediators of the relationships between Apathy or Alexithymia and mental health outcomes.
mediation_path_coefficients_script.r – R Script to generate visual diagrams of the mediation paths between predictors (Apathy or Alexithymia), mediators (ER strategies), and mental health outcomes.
moderation_unadjusted_script.r – R Script for unadjusted linear models used to test the effects of interactions between each predictor (Apathy or Alexithymia) and ER strategy on mental health outcomes. 
moderation_adjusted_script.r - R Script for unadjusted linear models used to test the effects of interactions between each predictor (Apathy or Alexithymia) and ER strategy on mental health outcomes, controlling for covariates including age, education, illness duration and motor aspects of daily living.
Opening the data files:
The dataset is provided in .csv format with a semicolon (;) delimiter. Some spreadsheet programs (e.g., Excel) may initially display all values in a single column if the file is opened directly. To ensure correct formatting, open Excel first and then import/open the file from within the program, selecting semicolon as the delimiter (or import using a statistical package such as R, SPSS, or Python, specifying sep=";" where relevant).

Dataset Summary

Number of variables: 33

Number of records (rows): 107 participants

Abbreviations Used

MDS-UPDRS: Movement Disorder Society – Unified Parkinsons Disease Rating Scale 

CERQ: Cognitive Emotion Regulation Questionnaire

BERQ: Behavioural Emotion Regulation Questionnaire

TAS-20: Toronto Alexithymia Scale

ER: Emotion Regulation

Variable Key

Variable Label	Variable Description	Variable Units / Coding	
ID	Participant ID		
Age	Participant Age	Years	
Gender	Gender Identity	1 (man), 2 (woman), 3 (trans woman) 	
Ethnicity 	Self-identified Ethnicity	(1) White British, (2) Any other White background, (3) White - Scottish, (4) White - Irish, (5) Any other ethnic group, (6) White - Northern Irish, (7) Any other Asian, (8) Arab	
Education	Years of Formal Education	Years	
Diagnosed	Age at Diagnosis 	Years	
PD_Duration	Illness Duration	Years	
LEDD	Levodopa Equivalent Daily Dosage	Milligrams (mg)	
Medication_Other	Taking medication for mental health condition	Yes/ No	
MoCA_Total	Telephone Montreal Cognitive Assessment (global cognition)	Total Score	
UPDRS_NonMotor	MDS-UPDRS Non-Motor Aspects of Daily Living Subscale	Total Score on items 
1.1 to 1.13	
UPDRS_Motor	MDS-UPDRS Motor Aspects of Daily Living Subscale	Total Score on items 
2.1 to 2.13	
Apathy	Starkstein Apathy Scale	Total Score	
GAD	Generalised Anxiety Disorder-7	Total Score	
GDS	Geriatric Depression Scale-15	Total Score	
WEMWBS	Warwick Edinburgh Mental Wellbeing Scale	Total Score	
TAS_DDF	TAS-20 Difficulty Describing Feelings Subscale	Total Score on items 2, 4, 11, 12, 17.	
TAS_DIF	TAS-20 Difficulty Identifying Feelings Subscale	Total Score on items 1, 3, 6, 7, 9, 13, 14.	
TAS_EOT	TAS-20 Externally Oriented Thinking Subscale	Total Score on items 5, 8, 10, 15, 16, 18, 19, 20.	
TAS_Total	TAS-20 	Total Score	
CERQ_SelfBlame	CERQ Self-blame Subscale 	Average rating on items 
1 – 4	
CERQ_Acceptance	CERQ Acceptance Subscale 	Average rating on items 
5 – 8	
CERQ_Rumination	CERQ Rumination Subscale	Average rating on items 
9 – 12	
CERQ_Refocus	CERQ Positive Refocusing Subscale 	Average rating on items 
13 – 16	
CERQ_Planning	CERQ Focus on Planning Subscale 	Average rating on items 
17 – 20	
CERQ_Reappraisal	CERQ Positive Reappraisal Subscale 	Average rating on items 
21 – 24	
CERQ_Perspective	CERQ Putting Things into Perspective Subscale	Average rating on items 
25 – 28	
CERQ_Catastrophising	CERQ Catastrophising Subscale	Average rating on  items 
29 – 32	
CERQ_OtherBlame	CERQ Blaming Others Subscale 	Average rating on items 
33 – 36	
BERQ_Distraction	BERQ Seeking Distractions Subscale 	Average rating on items 
1 – 4	
BERQ_Withdrawal	BERQ Withdrawal Subscale 	Average rating on items 
5 – 8	
BERQ_ActivelyApproach	BERQ Actively Approaching 	Average rating on items 
9 – 12	
BERQ_SocialSupport	BERQ Seeking Social Support 	Average rating on items 
13 – 16	
BERQ_Ignoring	BERQ Ignoring Subscale	Average rating on items 
17 – 20	

5. METHODS
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Experimental procedures/protocols:

Participants completed the consent form and an adapted version of the Older Adult Capacity to Consent to Research (Lee, 2010) scale, measuring their understanding of the study. Eligible participants then took part in an online video call session lasting 30–60 minutes, during which the T-MoCA (Pendlebury et al., 2013), the MDS-UPDRS I & II (Goetz et al., 2008) were administered. 

Within one week of the session, participants independently completed the remaining self-report questionnaires online, including the Apathy Scale (Starkstein et al., 1992), TAS-20 (Bagby et al., 1994), WEMWBS (Tennant et al., 2007), CERQ (Garnefski et al., 2007), BERQ (Kraaij & Garnefski, 2019), GDS-15 (Yesavage & Sheikh, 1986), and GAD-7 (Spitzer et al., 2006; Swinson, 2006).

Environmental/experimental conditions:

Participants were recruited through the Parkinson's UK Research Support Network, and the study was conducted remotely. 

Data collection was conducted between 08/01/2024 and 01/07/2024. 

Instruments used:

Online platform: Gorilla.sc (www.gorilla.sc) was used to administer questionnaires remotely.

Hardware: Participants used personal devices (computer/laptop/tablet with keyboard) to complete the study.

Data export and analysis: Raw data were exported from Gorilla.sc in .xlsx format. Data cleaning, distribution checks, transformation, and analysis were conducted using Microsoft Excel, JASP (version 0.18.3) and R (version 4.2.2). Reproducible R scripts are archived alongside the datasets. 

Methods used for processing the data: 

Data distributions (i.e., skewness, kurtosis, and normality) were initially explored in JASP (version 0.18.3) to provide rapid visual inspection of variable characteristics, identify potential outliers, and guide the selection of transformation methods. This included histograms, Q–Q plots, and numerical summaries, which informed subsequent formal distributional testing and transformation procedures conducted in R.

Data Transformations and Standardisation

Raw data were preprocessed in R with the objective of addressing non-normality in continuous variables prior to statistical analyses.

A set of 14 variables were transformed using the Yeo-Johnson method to reduce skewness and improve normality. Variables included demographic (e.g., PD_Duration), clinical (e.g., MDS-UPDRS scores), and self-report outcomes (e.g., GAD-7, GDS-15, CERQ, BERQ subscales).

For each variable, three transformations were compared against original distributions including Yeo-Johnson, Logarithmic (with constant offset for zero handling) and Square Root (with constant offset). Distribution characteristics (skewness, kurtosis, and Shapiro-Wilk p-values) were calculated for each transformation. Yeo-Johnson was selected as the default due to consistent improvements in normality across variables.

All transformed variables were z-standardised using the scale() function to allow comparability across metrics. Only numeric columns were standardised.

Associated R Script: data_processing_script.r 

R Packages Used: bestNormalize, e1071, dplyr, openxlsx

Outputs:
transformation_summary.csv - Summary of skewness, kurtosis, and Shapiro-Wilk test results across transformation types for each variable.

df_transformed.csv - Transformed dataset using the Yeo-Johnson method.

df_standardised.csv - Z-standardised dataset used for statistical analyses.

Correlation Analyses
Two sets of Spearman rank-order correlation analyses were conducted using the Hmisc::rcorr() function to examine associations between apathy, alexithymia, ER strategies, and mental health outcomes in individuals with PD. Results were presented in matrices with correlation coefficients (rounded to 3 decimals) and significance levels marked with asterisks (i.e., * p ≤ .05, ** p ≤ .01, *** p ≤ .001). Correlation matrices are provided for exploratory purposes and were used to guide the selection of candidate mediators in subsequent analyses. Uncorrected p-values and effect size thresholds were used, consistent with a hypothesis-generating approach.

1A: Apathy, Alexithymia and Mental Health Outcomes

The first analysis examined associations between apathy, alexithymia and mental health outcomes measures, including:
Mental health outcomes: Anxiety (GAD-7); Depression (GDS-15); Mental Wellbeing (WEMWBS).

Apathy (Starkstein Apathy Scale) and Alexithymia (TAS-20) 

Covariates: Age; Education; Illness duration; Motor symptom severity (MDS-UPDRS-II); Global cognition (T-MoCA).

Associated R Script: 1A_Correlations_script.r 
R Packages used: psych, Hmisc, corrplot, GGally, openxlsx

1B: Apathy, Alexithymia, Emotion Regulation Strategies, and Mental Health Outcomes

The second analysis extended the scope to include self-reported ER strategies, with correlations computed among:
Mental health outcomes and Apathy/ Alexithymia variables (as in 1A).

ER strategy subscales from CERQ and BERQ. 

	Covariates (as in 1A).

Associated R Script: 1B_Correlations_script.r 
R Packages used: psych, Hmisc, corrplot, GGally, openxlsx

Mediation Analysis 
Mediation analyses were conducted to examine whether ER strategies mediated the relationship between Apathy or Alexithymia and mental health outcomes in individuals with PD. Separate models were run for each predictor, focusing only on mediators (CERQ and BERQ subscales) that showed a minimum correlation of |rho| ≥ 0.2 with at least one predictor and one outcome.

Model Specification
Predictors: Apathy (Starkstein Apathy Scale) and Alexithymia (TAS-20)

Potential mediators: Eight ER strategies selected based on significant correlations with at least one predictor and one outcome:
BERQ: Actively Approaching, Withdrawal, Ignoring, Seeking Social Support
CERQ: Self-Blame, Positive Refocusing, Focus on Planning, Positive Reappraisal

Mental health outcomes: Anxiety (GAD-7), Depression (GDS-15), and Mental Wellbeing (WEMWBS).

Each model estimated the a-path (predictor → mediator), b-path (mediator → outcome), c′-path (direct effect), and indirect effect (a × b). Indirect effects were computed using 1,000 bootstrapped samples with Full Information Maximum Likelihood (FIML) to handle missing data. Significance was determined via 95% bootstrapped confidence intervals (CIs not containing zero), supplemented by the Aroian test for indirect effects.

Associated R Script: mediation_unadjusted_script.r
R Packages Used: lavaan, car, lmtest, sandwich, readxl, openxlsx.

Adjusted Models
All mediation models as described above were also estimated controlling for covariates including Age, Education, Illness duration and Motor symptom severity (MDS-UPDRS II). Path coefficients were interpreted in the context of these covariates.

Associated R Script: mediation_adjusted_script.r
R Packages Used: lavaan, car, lmtest, sandwich, readxl, openxlsx. 

Path Coefficient Visualisation
A separate script was used to generate visual diagrams of the mediation paths between predictors (Apathy or Alexithymia), mediators (ER strategies), and mental health outcomes. This script requires as input a .csv file containing path coefficients (standardised beta, SE, p-values, confidence intervals) extracted from the mediation models. Two versions are provided.
Path coefficients from mediation models without covariates:
	Path_Coefficients_Unadjusted.csv
Path coefficients from models including covariates (age, education, illness duration, UPDRS-II):
	Path_Coefficients_Adjusted.csv
To switch between predictors (e.g., from Apathy to Alexithymia), change the selected_predictor variable in line 14 of the script. 
To visualise the adjusted vs. unadjusted models, change the input file path in line 18.
The resulting plot will show:
	•	o	Blue box: Predictor
	•	o	Yellow ellipses: Mediating ER strategies
	•	o	Pink boxes: Mental health outcomes
	•	o	Each edge is labelled with the corresponding standardised path coefficient and significance level (*, **, or ***)

Associated R Script: mediation_path_coefficients_script.r 
R Packages used: DiagrammeR, dplyr 

Moderation Analysis 
To investigate whether the effect of Apathy or Alexithymia on mental health outcomes depends on ER strategy use, a comprehensive series of moderation analyses were conducted. Linear models were used to test the interaction between each predictor and ER strategy on mental health outcomes. Analyses followed a two-step approach, described below:

Model Structure
Predictors: Apathy (Starkstein Apathy Scale) and Alexithymia (TAS-20)
Moderators (ER strategies): CERQ and BERQ Subscales
Mental health outcomes (dependent variables): Anxiety (GAD-7), Depression (GDS-15), and Mental Wellbeing (WEMWBS)

Unadjusted Models
In unadjusted models, each outcome was regressed on:
The Predictor
The ER strategy
The interaction term (predictor × ER strategy)

Associated R Script: moderation_unadjusted_script.r 
R Packages Used: stats, car, lmtest, sandwich, performance, readxl, openxlsx. 

Adjusted Models
Adjusted models included the same terms as above, with the addition of covariates to account for covariates including Age, Education, Illness duration and Motor symptom severity (MDS-UPDRS II).

Associated R Script: moderation_adjusted_script.r 
R Packages Used: stats, car, lmtest, sandwich, performance, readxl, openxlsx. 

Significance and Interpretation
Significant interactions (p < .05)

Robust standard errors were computed using the HC1 estimator via the sandwich package to improve reliability where required. 

Data sources

All data were collected firsthand for the purposes of this study. No secondary or pre-existing data sources were used.

People involved in activities that contributed to the production, processing or analysis of the dataset:

Caroline Seton: production, processing and analysis of the dataset.