Wearable HRV and Sleep Dataset with Mental Health Assessments (Nature Scientific Data 2025)

Overview

This Wearable HRV and Sleep Dataset published in Nature Scientific Data (August 2025) provides continuous real-world physiological and behavioral data from Samsung Galaxy Active 2 smartwatches alongside mental health assessments.

Data Collection

• 49 healthy participants (mean age 28.35 ± 5.87 years, 51% female) wore smartwatches continuously for 4 weeks.
• Raw PPG, accelerometer, gyroscope, and pedometer data sampled at 10 Hz (every 100 ms) during waking hours.
• Participants completed daily sleep diaries and biweekly clinical questionnaires (PHQ-9 for depression, GAD-7 for anxiety, ISI for insomnia).
• Total of 33,600 hours of sensor data collected, averaging 672 hours per participant.

Variables and Measurements

• Physiological signals: PPG (photoplethysmography), heart rate, and computed HRV metrics (SDNN, RMSSD, LF/HF ratio) in 5-minute segments.
• Motion data: Tri-axial accelerometer, gyroscope, gyroscope rotational vector, step count, distance, and calories burned.
• Environmental: Ambient light intensity measured by smartwatch sensor.
• Sleep diaries: Self-reported bedtime, sleep onset, wake time, WASO (wake after sleep onset), sleep duration, and sleep efficiency.
• Mental health: PHQ-9 (depression), GAD-7 (anxiety), ISI (insomnia), and MEQ (morningness-eveningness) scores at baseline, week 2, and week 4.

Data Format

• Processed 5-minute HRV and sensor aggregates in CSV format with quality scores (missingness threshold 0.35).
• Raw PPG, accelerometer, gyroscope, pedometer, and light sensor data in compressed CSV files.
• Daily sleep diary entries and clinical questionnaire responses linked by participant ID.

Use Cases

• Mental health monitoring: Predicting depression, anxiety, and insomnia severity from wearable HRV and activity patterns.
• Sleep research: Analyzing relationships between nightly sleep metrics and next-day HRV and mood.
• Wearable algorithm development: Benchmarking continuous in-the-wild HRV extraction from PPG signals.
• Longitudinal health analytics: Studying daily and weekly patterns in physiological and behavioral markers.