Development and Implementation of an eHealth Oncohematonootric Program: Descriptive, Observational, Prospective Cohort Pilot Study
Bibliographic record
Abstract
BACKGROUND: In oncohematology, both the development of the disease and the side effects of antineoplastic treatment often take a toll on patients' physical and nutritional well-being. In this era of digital transformation, we launched a pioneering project for oncohematologic patients to promote adherence to a healthy lifestyle and improve their physical and nutritional well-being. We aim to achieve this goal by involving doctors and nutritionists through the Nootric app. OBJECTIVE: This study aims to assess the impact of the use of eHealth tools to facilitate nutrition and well-being in oncohematologic patients. We also aim to determine the usefulness of physical-nutritional management in improving tolerance to chemotherapy treatments within routine clinical practice. METHODS: We designed a descriptive, observational, longitudinal, prospective cohort pilot study that included a total of 22 patients from March to May 2022 in the Vinalopó University Hospital. The inclusion criteria were adults over 18 years of age diagnosed with oncohematological pathology in active chemotherapy treatment. An action plan was created to generate alerts between the doctor and the nutritionist. In the beginning, the patients were trained to use the app and received education highlighting the importance of nutrition and physical exercise. Sociodemographic, clinical-biological-analytical (eg, malnutrition index), health care impact, usability, and patient adherence data were collected. Tolerance to chemotherapy treatment and its health care impact were evaluated. RESULTS: We included 22 patients, 11 (50%) female and 11 (50%) male, ranging between 42 and 84 years of age. Among them, 13 (59%) were adherents to the program. The most frequent diseases were lymphoproliferative syndromes (13/22, 59%) and multiple myeloma (4/22, 18%). Moreover, 15 (68%) out of 22 patients received immunochemotherapy, while 7 (32%) out of 22 patients received biological treatment. No worsening of clinical-biological parameters was observed. Excluding dropouts and abandonments (n=9/22, 41%), the adherence rate was 81%, established by calculating the arithmetic mean of the adherence rates of 13 patients. No admission was observed due to gastrointestinal toxicity or discontinuation of treatment related to alterations in physical and nutritional well-being. In addition, only 5.5% of unscheduled consultations were increased due to incidents in well-being, mostly telematic (n=6/103 consultation are unscheduled). Additionally, 92% of patients reported an improvement in their nutritional habits (n=12/13), and up to 45% required adjustment of medical supportive treatment (n=5/11). There were no cases of grade 3 or greater gastrointestinal toxicity. All of this reflects improved tolerance to treatments. Patients reported a satisfaction score of 4.3 out of 5, while professionals rated their satisfaction at 4.8 out of 5. CONCLUSIONS: We demonstrated the usefulness of integrating new technologies through a multidisciplinary approach. The Nootric app facilitated collaboration among the medical team, nutritionists, and patients. It enabled us to detect health issues related to physical-nutritional well-being, anticipate major complications, and mitigate potentially avoidable risks. Consequently, there was a decrease in unscheduled visits and admissions related to this condition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".