PERSIST enables, for the first time, a more complete and frequent update of the survivorship care plans based on the aggregation and processing of real-time prospective data altogether with existing retrospective data. This new knowledge (based on wellbeing, biomarkers, health and social factors) enables a better overview of the individual trajectory and the individual long-term side effects of the cancer treatment and highlights the importance of the adherence to the care plans for cancer survivors. This new knowledge generated will contribute to the expected impact listed in the work plan by:

Indicators: (1) #New clinical pathways and guidelines used to facilitate the preparation of personalised care plans and documentation; (2) #New actions included in the care plan to personalise it and prevent future events; (3) Scoring of the quality of care received (QoC-Q).

The patient trajectories learned from Big Data (data extracted from EHRs) will be reviewed taking into consideration the data collected from monitored patients during the duration of the pilot study. These data include the clinical history of these patients (baseline data, including social aspects) and prospective data, clinical events (including a new biomarker gathered through liquid biopsy techniques) and data collected using the mHealth application (lifestyle, mood estimation, PROs and PREs, etc.). These innovations will support the creation of new methods and evidence in treatment and follow-up strategies. Furthermore, additional innovations developed under PERSIST, such as the patient sensing network and the improved conversational agent and chatbot, will pave the way for more effective coaching-style mHealth applications to support, motivate and encourage the adoption of healthier lifestyles, the learning of self-management strategies and increased treatment adherence. The mHealth application will facilitate the process of gathering relevant data necessary to analyse risks with a holistic perspective and develop follow up strategies oriented to reducing the risks of developing further complications or new diseases.

Indicators: (1) Evaluation of the clinical utility of the objective and subjective data acquired during the pilot clinical study; (2) mHealth application usage ratio per patient; (3) Usability scores; (4) Patient adherence to the care plan, including lifestyle changes recommended, evaluated through questionnaires.

At this point, PERSIST aims to contribute to the research on behavioural determinants of return to work in cancer patients and survivors. Several scientific articles have concluded that there is a significant association between attitude, social support and willingness to expend effort in completing the behaviour change with a shorter time to return to work in employees on long-term sickness absence. PERSIST will contribute to the improvement of the illness perception for the patient. On the other hand, the data models to be developed will enable to explore how theories and models used in behavioural research in cancer survivors can encourage a behavioural approach in the development of work-related interventions for cancer survivors. PERSIST will also enable clinicians more effectively address risks and consequently develop personalized care plans that may shorten the recovery period and mitigate negative effect of the treatment. As a result, patients may recover faster and will face fewer negative after-treatments effects (physical or psychological). This will significantly improve their employability and economic sustainability.
Moreover, focused and personalized follow-up strategies will contribute to reduce the burden of care.

Indicator: (1) #Research activities on employability of cancer survivors based on the project outcomes; (2) Scoring by survivors. As the main long-term impact, PERSIST will contribute to set a patient-centred care path with specific strategies to improve quality of life of cancer survivors by providing a fully integrated picture of what influences progress and setbacks in treatment and follow up strategies. During the pilot clinical study, we will monitor several indicators to give initial evidence of the expected long-term effect of the developed technology.
Indicator: The score of scales of quality of life (e.g., EuroQol 5-D https://euroqol.org/) will be measured during the clinical study. The 160 patients involved in the clinical study will complete this questionnaire three times during the clinical study (M15, M30, M36). Clinicians and patients will critically evaluate the results.