Neoplasms

All Conditions

Behaviors and Mental Disorders

Head and Neck Neoplasms

Quality of Life

Study participants will be followed up as per clinical practice and will receive the investigational electronic platform, including an Android-based app. Study subjects will be asked to fill in questionnaires at study entry and during follow-up.

No specific mobile apps will be provided to study participants, who will be followed as per clinical practice. Study subjects will be asked to fill in questionnaires at study entry and during follow-up.

The BD4QoL platform consists of a set of services to allow patient monitoring and empowerment, through two main tools: Point of Care application to manage all patients data and follow-up by clinical investigators, and a mobile application (App) installed on participating subject's smartphone. Also, a web-form tool is delivered to allow the QoL questionnaire completion.

BD4QoL platform

Lisa Licitra, MD, Prof

Antonello Manocchio, SC

Stefano Cavalieri, MD

Luca M Lacerenza, MD

Hisham Mehanna, MD, Prof

Paul Nankivell, MD

Luca Lacerenza, MD

Depending on disease stage, head and neck cancer (HNC) can be cured either with a single modality or with multimodal treatments, consisting of various combinations of surgery, radiotherapy, and chemotherapy. Despite treatment with curative intent, loco-regional recurrences and/or distant relapses are frequent. Moreover, these therapeutic approaches result in significant acute toxicities and late sequelae. Therefore, quality of life (QoL) is often impaired in these survivors. It is known that QoL is a prognostic factor because it is related to overall survival in cancer patients and to loco-regional control in HNC patients.

The adoption of mobile technologies of common use (i.e. embedded into standard mobile phones) for behavior reconstruction and linkage of behavior modifications to quality of life indicators, and the realization of predictive models for quality of life modifications will allow seamless and unobtrusive data capture over time, making the execution of clinical investigations more precise and less burdensome as compared to standard (manual) data capture.

The main aim of the present study is to reduce and to anticipate, with the use of the non-invasive Big data for quality of life (BD4QoL) platform, the proportion of HNC survivors experiencing a clinically meaningful reduction in QoL.

The BD4QoL platform consists of a set of services to allow patient monitoring and empowerment, through two main tools: Point of Care application to manage all patients data and follow-up by clinical investigators, and a mobile application (App) installed on participating subject's smartphone. Also, a web-form tool is delivered to allow the QoL questionnaire completion.

To achieve the study objectives, the BD4QoL platform will use the following sensors embedded in a smartphone to collect data which will be used to identify behavioral and affective traits associated with study outcomes. Sensors at mobile phone used to collect relevant data are the following:

* Accelerometer (x,y,z measurements)
* Global Positioning System - GPS (Lat, Long)
* Ambient light (measurement of light in the room / area where the mobile device is located)
* Screen (Status ON or OFF for smartphone device screen)
* Activity (type of activity with which the person engages,which can be one of these: Still, Walking, Running, On_Bicycle, In_Vehicle)
* Steps (total number of steps per day and per hour)
* Daily connections to wifi networks (naming of wifi connections as well as corresponding duration)
* Data detected from the mobile device's operating system (phone usage logs, phone applications usage) and from external datasets (steps, identification of places (Points of Interest) visited based on participant's permissions, through the correlation of one's GPS signal with external datasets from Foursquare and OpenStreet maps) The above data will be used to detect activities and behaviors which have a high likelihood of being meaningfully related with participants' QoL trajectories.

The BD4QoL App, available for Android, will be able to collect and store data about the following domains: mobility, physical activity, activities of daily living, instrumental activities of daily living, socialization, cognitive function, health related activities as well as affective personal data. A summary of the findings and the supporting data will be available to the patient and clinical investigators , through a dashboard available on mobile devices for patients and through the Point of Care (PoC) web tool for clinical investigators. The data collected by the mobile App will not be available to the technology manufacturer and will be transferred in almost-real-time (real-time when possible, as soon as data transfer is available) to the central BD4QoL repository (as long as there is available storage in the local memory storage of the mobile device).

In the interval between visits, study participants, allocated in the intervention arm, will be able to interact electronically with a chatbot, which will be part of the BD4QoL platform, implementation based on IBM Watson technology. The chatbot is an application to empower patients to manage their QoL and health, under the supervision of clinical investigators. The chatbot will have a series of e-coaching (electronic coaching) functions that include: (i) dialogue management that allows the patient to be counselled by chatting electronically in a structured and effective way; (ii) management of two-way communications with healthcare professionals \[e.g. for the patient to request specific support in case of special needs, or for the chatbot to invite the patient for a visit in case of an early detection of health-related QoL (HRQoL) or health issues; identified people will have to be listed on the delegation log by the Principal Investigator (PI)\]; (iii) detection of affective traits embodied in the e-coach / patient dialogue, through sentiment analysis and emotion analysis technologies to gather data about the participant mood. The latter element can be used to both re-adapt the chatbot counselling strategy, as well as to provide additional information on subjects mood to clinical investigators. The adverse events that the chatbot will be able to recognise will be the following: fatigue, malaise, fever, excessive sleepiness, difficulty sleeping, depression, change in social circumstances, neck swelling, facial pain, difficulty breathing, nose bleeds, difficulty speaking, dry mouth, tooth loss, muscle weakness, ear pain, difficulty hearing, tinnitus, vertigo, nausea, diarrhea, constipation, difficulty seeing, dry eye, eye pain, nervous eyelid, eye floaters, swollen eye, bleeding eyes, eye watering, sexuality issues, weight loss, difficulty swallowing, mouth sores, appetite loss, difficulty opening mouth, difficulty eating, increased sensitivity to smells, no taste.

The platform will provide the investigators with real time data of device usage (e.g number and type of alerts and chatbot interactions by patients) and it will integrate the electronic case report forms (eCRFs) as a study monitoring dashboard.

The BD4QoL platform used in this trial is not to be considered a medical device and is used for experimental assessment only. No drugs will be suggested by the automated chatbot responses. Tips provided by the chatbot regarding detected symptoms are also not to be considered clinical advice by any means and should not be a substitute for conversations with a member of a trained medical personnel.

Multicenter, international, randomized (2:1 ratio), open-label, superiority trial

Big Data for Quality of Life in Head and Neck Cancer

A Multicenter Randomized Trial for Quality of Life Evaluation by Non-invasive Intelligent Tools During Post-curative Treatment Follow-up for Head and Neck Cancer

Association of DFS, EFS and OS with clinically relevant variations of EORTC QLQ-C30 and HN43 scales

Correlation of DFS with time-dependent clinically relevant variations of EORTC QLQ-C30 and HN43 scales (variations \> +10 points or \< -10 points in each previously cited questionnaire scale)

Correlation of EFS with time-dependent clinically relevant variations of EORTC QLQ-C30 and HN43 scales (variations \> +10 points or \< -10 points in each previously cited questionnaire scale)

Correlation of OS with time-dependent clinically relevant variations of EORTC QLQ-C30 and HN43 scales (variations \> +10 points or \< -10 points in each previously cited questionnaire scale)

Correlation of DFS with time-dependent clinically relevant variations of EORTC QLQ-C30 and HN43 scales (variations \> +10 points or \< -10 points in each previously cited questionnaire scale) in nasopharyngeal carcinoma patients

Correlation of EFS with time-dependent clinically relevant variations of EORTC QLQ-C30 and HN43 scales (variations \> +10 points or \< -10 points in each previously cited questionnaire scale) in nasopharyngeal carcinoma patients

Correlation of OS with time-dependent clinically relevant variations of EORTC QLQ-C30 and HN43 scales (variations \> +10 points or \< -10 points in each previously cited questionnaire scale) in nasopharyngeal carcinoma patients

Pre-specified HN43 scales non included in secondary endpoints (pain, senses problems, body image, dry mouth, sticky saliva, coughing, trouble social contact, neurological problems, less sexuality, problems with shoulder, skin problems). Score range: 0-100 (higher score = higher level of symptomatology / problems)

Association of health-related data recorded by the BD4QoL platform registered continuously within the study observation period (Data detected from the mobile device's operating system: Phone usage logs, Phone applications usage; Data from external datasets: steps, identification of places through the correlation of one's GPS signal per day with external datasets from Foursquare and OpenStreet maps) with clinically relevant variations of EORTC QLQ-C30 and HN43 scales (variations \> +10 points or \< -10 points in each previously cited questionnaire scale)

Correlation of clinically relevant variations of EORTC QLQ-C30 and HN43 scales (variations \> +10 points or \< -10 points in each previously cited questionnaire scale) and CBI-B item scores. CBI-B consists of 12 items (rated 1 = not at all confident to 7 = totally confident) and is derived from the longer version of the Cancer Behavior Inventory (CBI). The CBI-B total score can be computed by averaging single item scores through arithmetic mean and, thus, ranges in value from 1 to 7.

Analysis of the primary endpoint stratifying subjects according to timing after study completion (less than 12 months versus more than 12 months)

Incremental Cost-Effectiveness Ratio measured in €/QALY. Measures of viability, usability, and trust

Proportion of HNC survivors experiencing a clinically meaningful global health-related EORTC QLQ-C30 QoL deterioration (decrease ≥10 points)

Time to first clinically relevant deterioration of EORTC QLQ-C30 global score (decrease ≥10 points)

Pre-specified C30 scales (emotional functioning, role functioning, insomnia). Score range: 0-100 (higher score = higher/healthier level of functioning for emotional and role functioning; higher score = higher level of symptomatology / problems for insomnia)

Pre-specified HN43 scales (swallowing, teeth, opening mouth, speech problems, trouble with social eating, fear of progression, emotional functioning, fatigue). Score range: 0-100 (higher score = higher/healthier level of functioning for emotional functioning; higher score = higher level of symptomatology / problems for the remaining specified scales)

EQ-5D-5L domains (mobility, self-care, usual activities, pain/discomfort, anxiety/depression). For each dimension, levels of perceived problems are coded from level 1 (minimum) to level 5 (maximum): no problems (1), slight problems (2), moderate problems (3), severe problems (4), and extreme problems (5).

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
Big Data for Quality of Life in Head and Neck Cancer	NCT05315570	Head and Neck C... Survivorship Quality of Life	BD4QoL platform	18 Years -	Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Big Data for Quality of Life in Head and Neck Cancer	NCT05315570	Head and Neck C... Survivorship Quality of Life	BD4QoL platform	18 Years -	Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

Spots Global Cancer Trial Database for bd4qol platform