Clinical trial NCT05815472: Advanced Patient Monitoring and A.I. Supported Outcomes Assessment in Lung Cancer Using Internet of Things Technologies (A.I. - APALITT)
Official title: Advanced Patient Monitoring and A.I. Supported Outcomes Assessment in Lung Cancer Using Internet of Things Technologies (A.I. - APALITT)
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Non-small-cell Lung Cancer
Lead sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Sponsor class: OTHER
First posted: 2023-04-18
Enrollment: 50
Sex: ALL
Maximum age (years): 75
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief summary: 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.


Eligibility criteria: 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

Primary outcomes:
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
measure: Daily collection of basis real world data such as sleep habits in non-small-cell lung cancer patients by portable monitoring systems
timeFrame: 8-52 weeks
measure: Daily collection of basis real world data such as vital signs in non-small-cell lung cancer patients by portable monitoring systems
timeFrame: 8-52 weeks
Secondary outcomes:
measure: The completion of Lawton Instrumental Activities of Daily Living questionnaire
timeFrame: 8-52 weeks
measure: The completion of the EORTC QLQ-C30 questionnaire
timeFrame: 8-52 weeks
measure: The completion of the Malnutrition Screening Tool
timeFrame: 8-52 weeks
measure: The completion of the Distress thermometer - DT6 test
timeFrame: 8-52 weeks
measure: The completion of Mental Adjustment to Cancer Scale - MINI-MAC 7 questionnaire
timeFrame: 8-52 weeks
Other outcomes:
Locations:
city: Rome
country: Italy
facility: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Study officials:
References: