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Updated: Jul 31, 2025
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
A Smart Digital Health Platform to Enable Monitoring of Quality of Life and Frailty in Older Patients with Cancer: A
Nikolaos Papachristou1, Panagiotis Kartsidis1, Alexandra Anagnostopoulou1
1Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Objectives:
LifeChamps is an EU Horizon 2020 project that aims to create a digital platform to enable monitoring of health-related quality of life and frailty in patients with cancer over the age of 65. Our primary objective is to assess feasibility, usability, acceptability, fidelity, adherence, and safety parameters when implementing LifeChamps in routine cancer care. Secondary objectives involve evaluating preliminary signals of efficacy and cost-effectiveness indicators.
Data Sources:
This will be a mixed-methods exploratory project, involving four study sites in Greece, Spain, Sweden, and the United Kingdom. The quantitative component of LifeChamps (single-group, pre-post feasibility study) will integrate digital technologies, home-based motion sensors, self-administered questionnaires, and the electronic health record to (1) enable multimodal, real-world data collection, (2) provide patients with a coaching mobile app interface, and (3) equip healthcare professionals with an interactive, patient-monitoring dashboard. The qualitative component will determine end-user usability and acceptability via end-of-study surveys and interviews.
Conclusion:
The first patient was enrolled in the study in January 2023. Recruitment will be ongoing until the project finishes before the end of 2023.
Implications For Nursing Practice:
LifeChamps provides a comprehensive digital health platform to enable continuous monitoring of frailty indicators and health-related quality of life determinants in geriatric cancer care. Real-world data collection will generate "big data" sets to enable development of predictive algorithms to enable patient risk classification, identification of patients in need for a comprehensive geriatric assessment, and subsequently personalized care.
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