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Published June 22, 2026 | Version 1.0.0
Dataset Open

QoL_Stress: A Multimodal Dataset of Physiological and Self-Reported Stress Responses

Authors/Creators

  • 1. Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Basque Country, Spain
  • 2. Faculty of Engineering, University of Deusto, Bilbao, Spain
  • 3. e-Health Group, Biogipuzkoa Health Research Institute, Paseo Dr Begiristain s/n, 20014

Description

A multimodal dataset containing ECG, EDA, heart rate, and self-reported stress/anxiety measures from 66 participants during relaxation and stress phases, designed for stress detection research.

The QoL_Stress dataset is organized into collections exported as CSV files. Each file contains data for all participants; phase labels (RELAX/STRESS) and participant identifiers allow linking physiological signals with self-reported measures, enabling multimodal analyses.

Collection Records Description Key Fields
ecg 132 Continuous ECG during RELAX/STRESS phases, HR features and alerts waveform_samples, heart_rate (bpm), heart_rate_alert, phase
eda 124 Electrodermal activity capturing SCL and SCR timestamp, scl_avg (µS), activation (SCR), valid_data, phase
fitbit_heart_rate_per_second 66 HR per second time, value (bpm)
fitbit_heart_rate_per_minute 66 HR averaged per minute time, value (bpm)
fitbit_heart_rate_summary 66 Daily HR summaries including calories and HR zones heartRateZones, caloriesOut, min, max
participants 66 Demographics and professional category sex, age, education, professional_category
pss-14 66 Perceived Stress Scale total and item-level scores total_score, item-level responses
stai 132 STAI-S by phase total_score, item-level responses, phase

File Naming:
Files are named after the collection, e.g.,ecg.csv, eda.csv, fitbit_heart_rate_per_second.csv, participants.csv. Each file contains all participants’ data for that collection. Phase labels (RELAX/STRESS) and participant identifiers allow linking physiological signals with self-reported measures.

Data Format:
All files are in CSV format, ready for analysis with standard tools such as Python (Pandas, NumPy), R, or MATLAB. Time stamps are provided where appropriate to enable precise temporal alignment between physiological and self-reported measures.

Methods (English)

Measurement Device
The Fitbit Charge 6 is a wrist‑worn wearable device designed for continuous real‑time physiological monitoring, suitable for research applications. It includes an optical photoplethysmography (PPG) sensor providing continuous heart‑rate measurements and enabling derivation of cardiovascular features such as HR and basic HRV summaries. It also incorporates multipurpose electrical sensors for single‑lead ECG and EDA for assessing sympathetic arousal.

Physiological and Self‑Reported Measures
Physiological signals were recorded as follows:

  • Electrocardiogram (ECG): single‑lead ECG recordings at the end of each phase to capture phase‑specific physiological responses and derive HR and HRV indices.

  • Electrodermal Activity (EDA): tonic (SCL) and phasic responses (SCRs) were recorded to characterize sympathetic activation.

  • Heart Rate (HR): recorded continuously throughout the session at per‑second and per‑minute resolution.

Self‑reported measures included:

  • Perceived Stress Scale (PSS‑14): 14-item questionnaire evaluating perceived stress over the past month.

  • State Anxiety (STAI‑S): 20-item scale assessing state anxiety; threshold of 36 distinguishes low/high state anxiety.

All measures were linked to participant IDs, timestamps, and experimental phase (RELAX/STRESS), enabling synchronized analyses.

Population
Sixty‑six healthy adults (39 males, 27 females; mean age 29.7 ± 6.9 years) participated. Inclusion criteria included age ≥ 18 years and ability to provide informed consent. Exclusion criteria included cardiovascular or dermatological conditions, psychoactive medication use, or inability to complete the protocol. Written informed consent was obtained. The study was approved by the Ethics Committee for Research of the Gipuzkoa Health Area in accordance with applicable ethical standards and data protection laws.

Experimental Protocols
Sessions lasted ~30 minutes and followed a structured two‑phase protocol:

  • Relaxation Phase: guided relaxation video, baseline physiological recordings, and partial STAI‑S.

  • Stress Induction Phase: Stroop Color Test and Geometry Dash gameplay; ECG/EDA recorded pre‑ and post‑tasks; HR continuously. Remaining STAI‑S administered post‑stress.

Data Collection and Quality Control
Signals were visually inspected and processed with automated quality control. Less than 2% of data were removed or interpolated due to artifacts.

Data Preparation for Analysis
Data were anonymized, time-aligned, and labeled by experimental phase and participant ID. All data are exported in CSV format, organized by collection (e.g., ecg.csv, eda.csv, fitbit_heart_rate_per_second.csv), and structured to enable linking physiological signals with self-reported measures.

Abstract (English)

This dataset contains physiological signals and self-reported measures collected during a controlled stress protocol. Sixty-six healthy adults participated in standardized experimental sessions consisting of a relaxation phase followed by stress induction using cognitive and interactive tasks (Stroop Color Test and Geometry Dash). Physiological recordings include electrocardiogram (ECG, 250 Hz), electrodermal activity (EDA), and heart rate (HR, 1 Hz) obtained from wearable devices (Fitbit Charge 6). Participants also completed validated self-reported questionnaires, including the Perceived Stress Scale (PSS-14) and the State-Trait Anxiety Inventory (STAI-S). All recordings are time-aligned, phase-annotated (RELAX/STRESS), anonymized, and provided in structured collections. The dataset supports research on stress detection, physiological signal analysis, multimodal modeling of stress responses, and machine learning applications for emotion and stress recognition.

Notes (English)

The dataset can be used to:

  • Develop machine learning models for stress detection.

  • Analyze ECG, EDA, and heart rate responses to stress.

  • Benchmark physiological signal preprocessing methods.

  • Study relationships between perceived stress and physiological responses.

Files

QoL_Stress.zip

Files (2.2 MB)

Name Size
md5:24212a80bf7fe4532ec1a205b997d4c7
2.2 MB Preview Download

Additional details

References

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