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      Digital Guardian Angel Supported by an Artificial Intelligence System to Improve Quality of Life, Well-being, and Health Outcomes of Patients With Cancer (ONCORELIEF): Protocol for a Single Arm Prospective Multicenter Pilot Study

      research-article
      , PhD 1 , , , PhD 1 , , PhD 2 , , MSc 3 , , MSc 4 , , BA 5 , , MSc 6 , , MD 6 , , MD 7 , , MD 7 , , MSc 7 , , MD 7 , , PhD 7 , , MSc 7 , , PhD 8 , , MSc 8 , , MSc 9 , , MSc 10 , , MSc 10 , , MD 7 , , MD 7
      JMIR Research Protocols
      JMIR Publications
      eHealth, artificial intelligence, quality of life and well-being, supportive cancer care, mobile phone, cancer support, artificial intelligence–based recommendations

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          Abstract

          Background

          According to Europe’s Beating Cancer Plan, the number of cancer survivors is growing every year and is now estimated at over 12 million in Europe. A main objective of the European Commission is to ensure that cancer survivors can enjoy a high quality of life, underlining the role of digital technology and eHealth apps and tools to achieve this.

          Objective

          The main objective of this study is the development of a user-centered artificial intelligence system to facilitate the input and integration of patient-related biopsychosocial data to improve posttreatment quality of life, well-being, and health outcomes and examine the feasibility of this digitally assisted workflow in a real-life setting in patients with colorectal cancer and acute myeloid leukemia.

          Methods

          A total of 60 patients with colorectal cancer and 30 patients with acute myeloid leukemia will be recruited from 2 clinical centers: Universitätsmedizin der Johannes Gutenberg-Universität Mainz (Mainz, Germany) and IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori” (IRST, Italy). Psychosocial data (eg, emotional distress, fatigue, quality of life, subjective well-being, sleep problems, and appetite loss) will be collected by questionnaires via a smartphone app, and physiological data (eg, heart rate, skin temperature, and movement through step count) will be collected by a customizable smart wrist-worn sensor device. Each patient will be assessed every 2 weeks over their 3-month participation in the ONCORELIEF study. Inclusion criteria include patients with the diagnosis of acute myeloid leukemia or colorectal cancer, adult patients aged 18 years and older, life expectancy greater than 12 months, Eastern Cooperative Oncology Group performance status ≤2, and patients who have a smartphone and agree to use it for the purpose of the study. Exclusion criteria include patients with a reduced cognitive function (such as dementia) or technological illiteracy and other known active malignant neoplastic diseases (patients with a medical history of treated neoplastic disease are included).

          Results

          The pilot study started on September 1, 2022. As of January 2023, we enrolled 33 patients with colorectal cancer and 7 patients with acute myeloid leukemia. As of January 2023, we have not yet started the data analysis. We expect to get all data in June 2023 and expect the results to be published in the second semester of 2023.

          Conclusions

          Web-based and mobile apps use methods from mathematical decision support and artificial intelligence through a closed-loop workflow that connects health professionals and patients. The ONCORELIEF system has the potential of continuously identifying, collecting, and processing data from diverse patient dimensions to offer health care recommendations, support patients with cancer to address their unmet needs, and optimize survivorship care.

          Trial Registration

          German Clinical Trials Register (DRKS) 00027808; https://drks.de/search/en/trial/DRKS00027808

          International Registered Report Identifier (IRRID)

          DERR1-10.2196/45475

          Related collections

          Most cited references28

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            The PHQ-9: validity of a brief depression severity measure.

            While considerable attention has focused on improving the detection of depression, assessment of severity is also important in guiding treatment decisions. Therefore, we examined the validity of a brief, new measure of depression severity. The Patient Health Questionnaire (PHQ) is a self-administered version of the PRIME-MD diagnostic instrument for common mental disorders. The PHQ-9 is the depression module, which scores each of the 9 DSM-IV criteria as "0" (not at all) to "3" (nearly every day). The PHQ-9 was completed by 6,000 patients in 8 primary care clinics and 7 obstetrics-gynecology clinics. Construct validity was assessed using the 20-item Short-Form General Health Survey, self-reported sick days and clinic visits, and symptom-related difficulty. Criterion validity was assessed against an independent structured mental health professional (MHP) interview in a sample of 580 patients. As PHQ-9 depression severity increased, there was a substantial decrease in functional status on all 6 SF-20 subscales. Also, symptom-related difficulty, sick days, and health care utilization increased. Using the MHP reinterview as the criterion standard, a PHQ-9 score > or =10 had a sensitivity of 88% and a specificity of 88% for major depression. PHQ-9 scores of 5, 10, 15, and 20 represented mild, moderate, moderately severe, and severe depression, respectively. Results were similar in the primary care and obstetrics-gynecology samples. In addition to making criteria-based diagnoses of depressive disorders, the PHQ-9 is also a reliable and valid measure of depression severity. These characteristics plus its brevity make the PHQ-9 a useful clinical and research tool.
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              Validation of the Insomnia Severity Index as an outcome measure for insomnia research.

              C. Bastien (2001)
              Background: Insomnia is a prevalent health complaint that is often difficult to evaluate reliably. There is an important need for brief and valid assessment tools to assist practitioners in the clinical evaluation of insomnia complaints.Objective: This paper reports on the clinical validation of the Insomnia Severity Index (ISI) as a brief screening measure of insomnia and as an outcome measure in treatment research. The psychometric properties (internal consistency, concurrent validity, factor structure) of the ISI were evaluated in two samples of insomnia patients.Methods: The first study examined the internal consistency and concurrent validity of the ISI in 145 patients evaluated for insomnia at a sleep disorders clinic. Data from the ISI were compared to those of a sleep diary measure. In the second study, the concurrent validity of the ISI was evaluated in a sample of 78 older patients who participated in a randomized-controlled trial of behavioral and pharmacological therapies for insomnia. Change scores on the ISI over time were compared with those obtained from sleep diaries and polysomnography. Comparisons were also made between ISI scores obtained from patients, significant others, and clinicians.Results: The results of Study 1 showed that the ISI has adequate internal consistency and is a reliable self-report measure to evaluate perceived sleep difficulties. The results from Study 2 also indicated that the ISI is a valid and sensitive measure to detect changes in perceived sleep difficulties with treatment. In addition, there is a close convergence between scores obtained from the ISI patient's version and those from the clinician's and significant other's versions.Conclusions: The present findings indicate that the ISI is a reliable and valid instrument to quantify perceived insomnia severity. The ISI is likely to be a clinically useful tool as a screening device or as an outcome measure in insomnia treatment research.
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                Author and article information

                Contributors
                On behalf of : ONCORELIEF Team
                Journal
                JMIR Res Protoc
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                1929-0748
                2023
                21 April 2023
                : 12
                : e45475
                Affiliations
                [1 ] Institute of Biophysics and Biomedical Engineering Faculty of Sciences University of Lisbon Lisboa Portugal
                [2 ] Institute for Industrial Mathematics Fraunhofer-Institut für Techno- und Wirtschaftsmathematik (ITWM) Kaiserslautern Germany
                [3 ] Careacross London United Kingdom
                [4 ] Suite5, Data Intelligence Solutions Limited Limassol Cyprus
                [5 ] Maggioli SPA Santarcangelo di Romagna Italy
                [6 ] Universitaetsmedizin der Johannes Gutenberg-Universitaet Mainz Mainz Germany
                [7 ] IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori”, IRST S.r.L. Meldola Italy
                [8 ] Exus Software London United Kingdom
                [9 ] Innosystems Athens Greece
                [10 ] MCS Datalabs Berlin Germany
                Author notes
                Corresponding Author: Joaquim Reis jdcreis@ 123456fc.ul.pt
                Author information
                https://orcid.org/0000-0003-0737-0955
                https://orcid.org/0000-0003-3509-3035
                https://orcid.org/0000-0003-4552-1427
                https://orcid.org/0000-0002-4646-448X
                https://orcid.org/0000-0002-3711-8569
                https://orcid.org/0000-0001-7934-0566
                https://orcid.org/0000-0003-1682-9377
                https://orcid.org/0000-0003-4071-8562
                https://orcid.org/0009-0007-7972-1650
                https://orcid.org/0000-0002-7099-240X
                https://orcid.org/0000-0001-9265-6610
                https://orcid.org/0000-0002-1025-4210
                https://orcid.org/0000-0001-9456-3514
                https://orcid.org/0000-0002-9421-176X
                https://orcid.org/0000-0001-9932-5160
                https://orcid.org/0000-0002-5511-6057
                https://orcid.org/0000-0002-4210-1008
                https://orcid.org/0000-0002-0277-649X
                https://orcid.org/0000-0002-1258-1194
                https://orcid.org/0000-0002-6478-0638
                https://orcid.org/0009-0003-7380-1255
                Article
                v12i1e45475
                10.2196/45475
                10163393
                37083563
                b746a0f6-6825-4c70-b41f-1feaa2eeb486
                ©Joaquim Reis, Luzia Travado, Alexander Scherrer, Thanos Kosmidis, Stefanos Venios, Paris Emmanouil Laras, Gabrielle Oestreicher, Markus Moehler, Margherita Parolini, Alessandro Passardi, Elena Meggiolaro, Giovanni Martinelli, Elisabetta Petracci, Chiara Zingaretti, Sotiris Diamantopoulos, Maria Plakia, Charalampos Vassiliou, Suheib Mousa, Robert Zifrid, Francesco Giulio Sullo, Chiara Gallio. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 21.04.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

                History
                : 12 January 2023
                : 24 January 2023
                : 31 January 2023
                : 31 January 2023
                Categories
                Protocol
                Protocol
                Custom metadata
                The proposal of this study was peer reviewed by Horizon 2020 - Research and Innovation Framework Programme - Research and Innovation Action - European Commission. See the Multimedia Appendix for the peer-review report;

                ehealth,artificial intelligence,quality of life and well-being,supportive cancer care,mobile phone,cancer support,artificial intelligence–based recommendations

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