Healful Dataset: Integrating Wearable Data with Self-Reported Quality of Life (HEALTHINF 2025)

Overview

The Healful Dataset presented at HEALTHINF 2025 is a pioneering dataset that bridges wearable health-tracking device measurements with validated Self-Reported Quality of Life (SRQoL) assessments.

Data Collection

• Real data acquired from wearable health-tracking devices (fitness trackers, smartwatches) worn by participants during their daily activities.
• Quality of Life assessments collected using the WHOQOL-BREF questionnaire, a validated instrument from the World Health Organization measuring physical health, psychological health, social relationships, and environment domains.
• Data collected over an extended period to capture variability in both physiological parameters and subjective well-being.

Wearable Measurements

• Heart rate: Continuous or periodic heart rate monitoring throughout the day.
• Physical activity: Step counts, active minutes, distance traveled, and activity intensity levels.
• Sleep metrics: Sleep duration, sleep stages, and sleep quality indicators.
• Caloric expenditure: Estimated energy burned based on activity and basal metabolic rate.
• Other physiological signals: Depending on device capabilities, may include SpO2, skin temperature, or stress indicators.

Quality of Life Assessment

• WHOQOL-BREF questionnaire responses covering four domains: physical health (pain, energy, sleep), psychological (positive feelings, cognition, self-esteem), social relationships, and environment (safety, resources, healthcare access).
• Temporal alignment between wearable data windows and QoL questionnaire completion dates.

Use Cases

• Quality of Life prediction: Building machine learning models to predict QoL scores from objective wearable data.
• Identifying physiological and behavioral correlates of well-being across different QoL domains.
• Developing Internet of Health Things (IoHT) applications for continuous QoL monitoring and personalized health interventions.
• Research on the relationship between daily activity patterns, sleep, heart rate variability, and subjective well-being.