1. ABOUT THE DATASET
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Title:   Dataset supporting a study of cognition, emotion regulation, 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: 2025
 
Description: This dataset contains anonymised data from 103 individuals diagnosed with Parkinson’s Disease (PD), collected as part of a remote study investigating the relationships between cognitive functioning, emotion regulation 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; performance-based cognitive tasks (Trail Making Test, Digit Span, Go/No-Go), self-reported functional cognitive impairment, and validated questionnaires assessing anxiety, depression, mental wellbeing and both cognitive and behavioural emotion regulation 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 cognition-emotion interactions, executive functioning, or non-motor symptoms in PD.
 
Cite as: Seton, Caroline (2025): Dataset supporting a study of cognition, emotion regulation, and mental health in Parkinson’s Disease. University of Reading. Dataset. https://doi.org/10.17864/1947.001467
 
Preprint: Seton, C., van Reekum, C. M., & Ho, A. (2025, October 28). Every day and Task-based Cognition in Parkinson’s Disease Predicts Emotion Regulation and Wellbeing. https://doi.org/10.31234/osf.io/jmu3c_v1

Contact: 
 
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 
 
Caroline Seton
School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
Email: c.seton@pgr.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 2025 Caroline Seton. This dataset is licensed under a Creative Commons Attribution 4.0 International Licence: 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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Dataset Summary
 
Number of variables: 42
 
Number of records (rows): 103 participants
 
Missing data: Represented as empty cells (i.e., NA in R).

Abbreviations Used
 
MDS-UPDRS: Movement Disorder Society – Unified Parkinson’s Disease Rating Scale 
 
CERQ: Cognitive Emotion Regulation Questionnaire
 
BERQ: Behavioural Emotion Regulation Questionnaire
 
EF: Executive Functioning 
 
FCI: Functional Cognitive Impairment
 
ER: Emotion Regulation

File Listings
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.
	
df_transformed.csv
	Dataset with Yeo-Johnson transformed variables.	
df_standardised.csv	Transformed and z-standardised variables for analysis.
	
transformation_summary.csv
	Summary of skewness, kurtosis, and Shapiro-Wilk test results across transformation types for each variable.
	
df2.csv	Final analytic dataset, including PCA components for task-based EF. 
	
Path_Coefficients_Unadjusted.csv	Path coefficients (standardised beta, SE, p-values, confidence intervals) extracted from the unadjusted mediation models.
	
Path_Coefficients_Adjusted.csv	Path coefficients (standardised beta, SE, p-values, confidence intervals) extracted from the mediation models adjusted for covariates.
	
data_processing_script.r	Performs preprocessing on raw data to assess and address non-normality across 24 variables. Applies multiple transformation methods and produces z-standardised outputs used in later analyses.
	
PCA_script.r	Runs a Principal Component Analysis (PCA) on task-based EF measures. Extracts two interpretable cognitive components retained for use in subsequent models.
	
1A_Correlations_script.r	Computes partial correlations between cognitive functioning and mental health outcomes while adjusting for demographic and clinical covariates. Results form the basis of the first correlation matrix (Question 1A). 
	
1B_Correlations_script.r	Extends the correlation analyses to include ER strategies alongside cognitive and mental health measures. Produces a correlation matrix reflecting broader variable associations (Question 1B).
	
mediation_unadjusted_script.r	Implements bootstrapped mediation models testing whether ER strategies mediate the relationship between cognitive predictors and mental health outcomes. Outputs detailed path estimates and significance values for unadjusted models.
	
mediation_adjusted_script.r	Re-estimates all mediation models with covariate adjustment. 
	
mediation_path_coefficients_script.r	Generates clear visual diagrams of mediation pathways. Accepts external .csv inputs of model results, allowing flexible visualisation of adjusted or unadjusted effects.
	
moderation_unadjusted_script.r	Tests unadjusted moderation models examining interactions between cognitive functioning and ER strategies. Identifies significant interaction terms and checks statistical assumptions.
	
moderation_adjusted_script.r	Re-estimates the unadjusted moderation models by including covariate. Outputs adjusted model summaries and robust standard errors.

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 (T-MoCA) measuring 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

CFRS	Parkinsons Disease-Cognitive Functional Rating Scale (PD-CFRS)
	Total Score	
GAD	Generalised Anxiety Disorder-7 (GAD-7)
	Total Score	
GDS	Geriatric Depression Scale-15 (GDS-15)
	Total Score	
WEMWBS	Warwick Edinburgh Mental Wellbeing Scale
	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 Subscale  
	Average rating on items 9 – 12	
BERQ_SocialSupport	BERQ Seeking Social Support Subscale
	Average rating on items 13 – 16	
BERQ_Ignoring	BERQ Ignoring Subscale	Average rating on items17 – 20
	
TMT_A	Trail Making Task Part A	Total errors
	
TMT_A_RT	Trail Making Task Part A Reaction Time
	Seconds	
TMT_B	Trial Making Task Part B 
	Total errors
	
TMT_B_RT	Trail Making Task Part B Reaction Time
	Seconds	
Forwards_Total	Forward Digit Span 	Total Correct
	
Backwards_Total	Backward Digit Span 	Total Correct
	
Go	Correct Go trials on Go/No-Go task 
	Percentage	
NoGo	Correct No-Go trials on Go/No-Go task 
	Percentage	
FalseAlarm	Incorrect No-Go trials on Go/No-Go task 
	Percentage	
Go_RT	Reaction Time on correct Go trials of Go/No-Go task
	Seconds	
PCA_RC1	Principle Component Analysis Variable 1: Inhibitory Control
	Standardised factor score	
PCA_RC2	Principle Component Analysis Variable 2: Executive Control 
	Standardised factor score	
 
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), and three EF tasks including the Trail Making Test (Sánchez-Cubillo, 2009), the Digit Span forward and backward tasks (Wechsler, 2008), and the Go/No-Go task (Redick et al., 2011) were administered. 

Within one week of the session, participants independently completed the remaining self-report questionnaires online, including the PD-CFRS (Kulisevsky et al., 2013), 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 online platforms, 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 and EF tasks 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 (df_subscales.csv and df.csv) were preprocessed in R with the objective of addressing non-normality in continuous variables prior to statistical analyses.
 
A set of 24 continuous variables were transformed using the Yeo-Johnson method to reduce skewness and improve normality. Variables included demographic (e.g., PD_Duration), clinical (e.g., UPDRS scores), cognitive (e.g., Digit Span, TMT, Go/No-Go), and self-report outcomes (e.g., GAD, GDS, 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.
 
Invalid responses (e.g., Backward Digit Span score of 0 when Forward was attempted) were set to missing (NA) prior to transformation.
 
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.
 
Principal Component Analysis 
To reduce dimensionality and extract composite indicators of cognitive performance, a PCA with Promax (oblique) rotation was conducted on the eight task-based EF variables: Trail Making Test A and B reaction times, Forward and Backward Digit Span totals, Go accuracy, NoGo accuracy, False Alarm rate, and Go reaction time.
Assumption Checks and Data Adequacy
Sample size: 103 participants and 8 variables resulted in a participant-to-variable ratio of 12.88:1, exceeding the recommended threshold of 5:1.
 
Bartlett’s test of sphericity: χ²(28) = 397.67, p < .001, indicating sufficient intercorrelations.
 
Kaiser-Meyer-Olkin (KMO) measure: KMO = 0.61 (acceptable).
 
Determinant of the correlation matrix: 0.018, suggesting no multicollinearity.

Missing data: Five participants were missing data for Backward Digit Span; missingness was handled via pairwise complete observations.

Model Fit and Extraction
The PCA was performed using the psych::principal() function, specifying two factors and rotate = "promax".
 
Fit statistics indicated good model adequacy:
 
Root Mean Square Residual (RMSR) = 0.12
 
Model χ²(28) = 81.79, p < .001
 
Two components were extracted, accounting for 59.3% of total variance, and were weakly negatively correlated (r = –.07), supporting their use as distinct constructs.
 
Component Interpretation
1. RC1 – Inhibitory Control
High positive loadings: NoGo Accuracy (λ = .93), Go RT (λ = .76)
High negative loading: False Alarm rate (λ = –.93)
Moderate negative cross-loading: Go Accuracy (λ = –.45)
 
2. RC2 – Executive Control
High negative loadings: TMT-A RT (λ = –.74), TMT-B RT (λ = –.63)
Positive loadings: Forward Digit Span (λ = .66), Backward Digit Span (λ = .61), Go Accuracy (λ = .53)
 
Component scores were calculated using the regression method and saved in the final dataset as:
PCA_RC1 – Inhibitory Control
PCA_RC2 – Executive Control
 
These scores were retained for use in regression, mediation, and moderation analyses.
A scree plot and parallel analysis were used to support the 2-factor solution.
 
Associated R Script:  PCA_script.r 
R Packages used: psych, performance, car, GGally, writexl. 
 
Output: df2.csv - final dataset following PCA used for all analyses 
 Correlation Analyses
Two sets of Spearman rank-order correlation analyses were conducted using the Hmisc::rcorr() function to examine associations between task-based EF, 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). 
 
1A: Cognition and Mental Health Outcomes
The first analysis examined associations between mental health outcomes and cognitive functioning measures, including:
	Mental health outcomes: Anxiety (GAD); Depression (GDS); Mental Wellbeing (WEMWBS).
 
	Cognitive measures: Functional cognitive impairment (PD-CFRS) and PCA-derived EF components (PCA_RC1 – Inhibitory Control, PCA_RC2 – Executive Control).
 
	Covariates: Age; Education; Illness duration; Motor symptom severity (MDS-UPDRS-II); Global cognition (T-MoCA).
 
Bonferroni correction was applied to control for multiple comparisons: 55 unique comparisons → adjusted α = 0.00091. 
 
Associated R Script: 1A_Correlations_script.r 
R Packages used: psych, Hmisc, corrplot, GGally, openxlsx
 
1B: Cognition, Emotion Regulation Strategies, and Mental Health
The second analysis extended the scope to include self-reported ER strategies, with correlations computed among:
	Mental health outcomes and cognitive measures (as in 1A).
	ER strategy subscales from CERQ and BERQ. 
	Covariates (as in 1A).
 
Bonferroni correction was applied to control for multiple comparisons: 300 unique comparisons → adjusted α = 0.00017
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 cognitive functioning and mental health outcomes in individuals with PD. 
 
Model Specification
Cognitive predictors: Functional Cognitive Impairment (CFRS) and Executive Control (PCA_RC2). Inhibitory Control (PCA_RC1) was excluded due to non-significant correlations with mental health outcomes.
 
Potential mediators: Six ER strategies selected based on significant correlations with at least one predictor and one outcome:
	BERQ: Actively Approaching, Withdrawal, Ignoring
	CERQ: Self-Blame, Refocus on Planning, Positive Reappraisal
 
Mental health outcomes: Anxiety (GAD), Depression (GDS), 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 (FCI or Executive Control), 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 Functional Cognitive Impairment - CFRS and PCA_RC2 – Executive Control), change the selected_predictor variable in line 17 of the script. 
To visualise the adjusted vs. unadjusted models, change the input file path in line 22.
The resulting plot will show:
Blue box: Predictor
Yellow ellipses: Mediating ER strategies
Pink boxes: Mental health outcomes
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 cognitive functioning 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 cognitive predictor and ER strategy on mental health outcomes. Analyses followed a two-step approach, described below:
 
Model Structure
Cognitive predictors: Functional Cognitive Impairment (CFRS) and PCA-derived components of executive functioning (PCA_RC1 – Inhibitory Control, PCA_RC2 – Executive Control).
 
Moderators (ER strategies): CERQ and BERQ Subscales
 
Mental health outcomes (dependent variables): Anxiety (GAD), Depression (GDS), and Mental Wellbeing (WEMWBS)
 
Unadjusted Models
In unadjusted models, each outcome was regressed on:
The cognitive 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. 

References

Garnefski, N., Rieffe, C., Jellesma, F., Terwogt, M. M., & Kraaij, V. (2007). Cognitive emotion regulation strategies and emotional problems in 9 - 11-year-old children: the development of an instrument. Eur Child Adolesc Psychiatry, 16(1), 1-9. https://doi.org/10.1007/s00787-006-0562-3 
Goetz, C. G., Tilley, B. C., Shaftman, S. R., Stebbins, G. T., Fahn, S., Martinez‐Martin, P., Poewe, W., Sampaio, C., Stern, M. B., & Dodel, R. (2008). Movement Disorder Society‐sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS‐UPDRS): scale presentation and clinimetric testing results. Movement disorders: official journal of the Movement Disorder Society, 23(15), 2129-2170. https://doi.org/10.1002/mds.22340 
Kraaij, V., & Garnefski, N. (2019). The Behavioral Emotion Regulation Questionnaire: Development, psychometric properties and relationships with emotional problems and the Cognitive Emotion Regulation Questionnaire. Personality and Individual Differences, 137, 56-61. https://doi.org/https://doi.org/10.1016/j.paid.2018.07.036 
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Lee, M. (2010). The Capacity to Consent to Research Among Older Adults. Educational Gerontology, 36, 592-603. https://doi.org/10.1080/03601270903324461 
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Sánchez-Cubillo, I., Periáñez, J., Adrover-Roig, D., Rodríguez-Sánchez, J., Ríos-Lago, M., Tirapu, J., & Barceló, F. (2009). Construct validity of the Trail Making Test: Role of task-switching, working memory, inhibition/interference control, and visuomotor abilities. Journal of the International Neuropsychological Society, 15(3), 438-450. https://doi.org/10.1017/S1355617709090626 
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Swinson, R. P. (2006). The GAD-7 scale was accurate for diagnosing generalised anxiety disorder. Evid Based Med, 11(6), 184. https://doi.org/10.1136/ebm.11.6.184 
Tennant, R., Hiller, L., Fishwick, R., Platt, S., Joseph, S., Weich, S., Parkinson, J., Secker, J., & Stewart-Brown, S. (2007). The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation. Health and Quality of Life Outcomes, 5(1), 63. https://doi.org/10.1186/1477-7525-5-63 
Wechsler, D. (2008). Wechsler Adult Intelligence Scale - Fourth Edition (4th ed.). Pearson. 
Yesavage, J. A., & Sheikh, J. I. (1986). Geriatric Depression Scale (GDS). Clinical Gerontologist, 5(1-2), 165-173. https://doi.org/10.1300/J018v05n01_09