Real-Life Pilot Feasibility Study (LC_RLP)

Overview

The LifeChamps Real-Life pilot's objective is to ascertain the feasibility of the use of the LifeChamps solution, previously developed in another clinical trial, in a real-world environment. This solution integrates a health advisory system, and technical intelligence, based on behavioural science, to provide personalized recommendations to follow a healthier lifestyle, to act as a nutrition and physical activity coach, and to guide on social inclusion, among other issues, with the aim of increasing the quality of life. This study is conducted to investigate the feasibility of such an undertaking, assess the clinical impact it may have, as well as evaluate the applicability, usability, and effectiveness of the solution.

"The LifeChamps project (https://lifechamps.eu/) is creating a digital platform to support clinical teams to provide more integrated follow-up care to older patients with cancer. The digital platform will integrate data coming directly from the patient (patient-reported outcomes and sensor data from wearable devices), from the home environment (home sensors, weight scales), and from the clinical environment (data routinely collected via the Electronic Health Record). The digital platform will use big data analytics (machine learning) to process all data as part of predictive clinical algorithms for frailty and quality of life for older patients with cancer. Development of each clinical algorithm requires that the prototype model (or analytics engine) is trained using abundant real-world data to help consolidate the predictive ability and validity of the algorithms before the algorithms are deployed in a larger scale feasibility trial. A pre-post design will be employed, whereby the LifeChamps platform will be deployed for a total of 3 months. The main research questions of the study are: 1. Is patient recruitment possible in terms of numbers and rates within the recruitment period? 2. Is participant retention in the study possible in terms of numbers and rates? 3. Do participants adhere to the protocol? 4. What are the views/experiences of study participants (patients and healthcare professionals) using the Lifechamps solution (mHealth app and dashboard respectively)? 5. What is the adherence with the usage of the different digital health patients' tools (e.g., usage of mHealth app, smartwatch, smart weight scale)? Healthcare professionals related to oncology (oncologists, nurses etc.) will also be recruited to use the LifeChamps dashboard and evaluate its usefulness, usability and acceptability. They will have access to and asked to use the LC Dashboard. The LC Dashboard will provide pseudonymised information regarding the patients' physical activity and PROMs for the purpose of the pilot feasibility trial. They will not be able to view real time processed information. After the patients have exited the study, the HCPs will be shown the anonymised outputs of the engine per patient and asked their opinions. Older patients with a cancer diagnosis will be the target population for this study. Target number of participants is 200. Each study participant will be involved in the study for 3 months in total. A 4-month recruitment period will be allowed, bringing the total study duration to 7 months (from first patient being enrolled until last patient finishing data collection). After written informed consent has been provided, the mini-COG will be used to evaluate study participants' cognitive function and impairment at baseline. The mini-COG consists of a 3-word recall and a clock-drawing test, and can be completed within 5 minutes. A score of less than 3/5 indicates the need to refer the patient for full cognitive assessment. Study endpoints will be collected at this point. The researcher will also arrange for study participants to receive study equipment, i.e. home sensors, wearable activity sensors, smart weight scale, and mobile app. The researcher will arrange a suitable time for a home visit to install the home sensors and test functionality. The researcher will demonstrate use of study equipment to the participant, and reiterate that support with use of the technology will be available. Data collection will involve a variety of sources, including the patient (patient-reported outcomes and sensor data from wearable devices), the home environment (home sensors, weight scales), and the clinical site (data routinely collected via the Electronic Health Record). The following technology will be used: Mobile devices: Activity tracker wristband (FitBit charge 4). It will be used to passively monitor and collect data on heart rate, heart rate variability, steps, activity tracking, sleep monitoring, breathing rate, skin temperature and SpO2. mHealth app (SALUMEDIA). It will be used to enable collection of patient-reported outcome measures (PROMs) and to forward this information along with the data gathered by the activity tracker and the smart scale to the Raspberry Pi Kit at home. At home sensors / devices: LOCS Home sensors: They will be used to monitor participants' daily activities e.g., to track ambulation and functioning. Study participants will be provided with 4 motion sensors, 1 door contact sensor, 2 corridor sensors, and a tag device. Smart Scale (Withings Body+): It will be used to measure weekly body weight, body composition and body mass index. Raspberry Pi (RPI) kit: As an edge gateway, RPI is hosting LOCS gateway, Movesense Gateway and data ingestion service. RPI will enable data collection and edge analytics and transfer of data to the LIFECHAMPS platform. Smart plug: It will be used to collect data about use home appliances and thereby data about active daily living. Selected study participant clinical and demographic data from the local EHRs will be collected and loaded onto the LifeChamps analytics engine. The data will be loaded by technical partners via the LifeChamps dashboard for processing and analysis. Data regarding recruitment rate (patients consenting / patients approached), participant retention in the study, reasons for study discontinuation (if offered), participant adherence with technology, issues with technology and need for troubleshooting will be recorded. These data will (a) be recorded by local researchers using bespoke 'recording logs' in the form of an Excel spreadsheet, and (b) remotely monitored and logged by technical partners involved in the distribution / management of the technology to be used in the trial as described above."