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Healful Dataset: Integrating Wearable Data with Self-Reported Quality of Life Assessments

Topics: Databases and Datawarehousing; Ehealth; eHealth Applications; Mobile Technologies for Healthcare Applications; Wearable Health Informatics

Pedro Oliveira 1 ; 2 ; Rossana Andrade 2 ; Pedro A. Santos Neto 3 ; Evilasio Costa Junior 2 ; 4 ; Ismayle S. Santos 2 ; 5 ; Victoria T. Oliveira 2 ; Wilson Castro 2 and Leonan Carneiro 2

Affiliations: 1 Laboratory of Innovation and Scientific Computing (LICC), Federal Institute of Maranhão, Pedreiras, Brazil ; 2 Group of Computer Networks, Software Engineering, and Systems (GREat), Federal University of Ceará (UFC), Fortaleza, Brazil ; 3 Laboratory of Software Optimization and Testing (LOST), Federal University of Piauí, Teresina, Brazil ; 4 Federal University of Ceará (UFC), Sobral, Brazil ; 5 Ceará State University (UECE), Fortaleza, Brazil

Keyword(s): Quality of Life, Self-Reported Questionnaires, Internet of Health Things, Dataset.

Abstract: This paper proposes a novel dataset – called Healful Dataset – correlating real data acquired from wearable health-tracking devices with Self-reported Quality of Life (SRQoL) measures collected using the WHOQOL-BREF questionnaire. Recently, increasing interest has been shown in using technology for Quality of Life (QoL) monitoring and improvement, significantly leveraging the Internet of Health Things (IoHT). Although several tools have been developed to quantify QoL, such as the SF-36 and WHOQOL-BREF, most are based on static and bothersome questionnaires rather than ubiquitous real-time data collection. Our database addresses this gap by integrating sensor-generated data with QoL assessment, enhancing the research path focused on intelligent models for QoL monitoring that use Machine Learning techniques to predict and improve QoL. In this paper, we describe the methodology used to build this database, the scenarios in which it can be applied, and discuss its relevance for future Io HT-driven health solutions toward improving people’s QoL through personalized monitoring and interventions. (More)

This paper proposes a novel dataset – called Healful Dataset – correlating real data acquired from wearable health-tracking devices with Self-reported Quality of Life (SRQoL) measures collected using the WHOQOL-BREF questionnaire. Recently, increasing interest has been shown in using technology for Quality of Life (QoL) monitoring and improvement, significantly leveraging the Internet of Health Things (IoHT). Although several tools have been developed to quantify QoL, such as the SF-36 and WHOQOL-BREF, most are based on static and bothersome questionnaires rather than ubiquitous real-time data collection. Our database addresses this gap by integrating sensor-generated data with QoL assessment, enhancing the research path focused on intelligent models for QoL monitoring that use Machine Learning techniques to predict and improve QoL. In this paper, we describe the methodology used to build this database, the scenarios in which it can be applied, and discuss its relevance for future IoHT-driven health solutions toward improving people’s QoL through personalized monitoring and interventions.

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Paper citation in several formats:

Oliveira, P., Andrade, R., Neto, P. A. S., Costa Junior, E., Santos, I. S., Oliveira, V. T., Castro, W. and Carneiro, L. (2025). Healful Dataset: Integrating Wearable Data with Self-Reported Quality of Life Assessments. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF; ISBN 978-989-758-731-3; ISSN 2184-4305, SciTePress, pages 611-622. DOI: 10.5220/0013175200003911

@conference{healthinf25,
author={Pedro Oliveira and Rossana Andrade and Pedro A. Santos Neto and Evilasio {Costa Junior} and Ismayle S. Santos and Victoria T. Oliveira and Wilson Castro and Leonan Carneiro},
title={Healful Dataset: Integrating Wearable Data with Self-Reported Quality of Life Assessments},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
year={2025},
pages={611-622},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013175200003911},
isbn={978-989-758-731-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF
TI - Healful Dataset: Integrating Wearable Data with Self-Reported Quality of Life Assessments
SN - 978-989-758-731-3
IS - 2184-4305
AU - Oliveira, P.
AU - Andrade, R.
AU - Neto, P.
AU - Costa Junior, E.
AU - Santos, I.
AU - Oliveira, V.
AU - Castro, W.
AU - Carneiro, L.
PY - 2025
SP - 611
EP - 622
DO - 10.5220/0013175200003911
PB - SciTePress