Clinical trial · Interventional
Advanced Patient Monitoring and A.I. Supported Outcomes Assessment in Lung Cancer Using Internet of Things Technologies (A.I. - APALITT)
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The use of advanced technological tools able to exploit patient-centered "Real World Data", represents an innovative and fascinating challenge for the most modern personalized medicine paradigms. Monitoring oncological patients during multimodal cancer therapies may represent a significant step towards a comprehensive and reliable quality of life assessment, prevention of toxicity before its clinical onset and treatment outcomes prediction. The big data approach, being able to collect, manage and interpret large volumes of health data, eventually supported by artificial intelligence (A.I.) is therefore fundamental in this setting and may be translated in the next future in tangible advantages for the patients. Primary aim of the project is to assess patients experience of using portable monitoring systems during multimodal oncological therapies and follow up period, through the use of a dedicated app and wearable technology (i.e. monitoring bracelet), as Electronic Health Record data harvesting devices. More specifically, the patients report experience measure of man/women affected by locally advanced non-small-cell lung cancer undergoing chemo(radio)therapy followed either by surgery or immunotehrapy (e.g. describing toxicity, instrumental activities of daily living and stress/coping levels) will be analyzed. The machine learning assisted analysis of these data will allow to identify patients profile that may be used as risk categories to optimize assistance and follow up practices. This is an observational study with device, co-financed, monocentric study with a foreseen study duration of 36 months.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Non-small-cell Lung Cancer | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Fitbit charge | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Patients affected by locally advanced non-small-cell lung cancer
- description
- Patients affected by locally advanced non-small-cell lung cancer (staged III according to 8th TNM classification), undergoing induction therapy (IT) followed by either radical surgery or immunotherapy boost and treated in Fondazione Policlinico Universitario "A. Gemelli" IRCCS of Rome, Italy, will be enrolled in this study.
- interventionNames
- Device: Fitbit charge
Primary outcomes (3)
- measure
- Daily collection of basis real world data such as physical activity in non-small-cell lung cancer patients by portable monitoring systems
- timeFrame
- 8-52 weeks
- description
- Patients will receive a state-of -the-art wearable device (electronic wrist bracelet) that will collect real world data everyday such as physical activity that will be established on the basis of the number of steps per day. The device will be delivered to patients during the first appointment prior to the beginning of chemotherapy and/or radiotherapy. The collected data will be then transferred to the patient's paired device through the application Healthentia downloaded on appropriate instruments (e.g. smartphone). Data will be collected during multimodal oncological therapies and follow-up period.
Eligibility
Eligibility (as posted)
- Sex
- All
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: * Aged \< 75 * Clinically able to use portable technologies * Able to understand and sign informed consent Exclusion Criteria: * Major psychiatric disorder * ECOG\>2 performance status * Not able to use portable technologies
References
Publications (0)
Data not yet available