Supportive Digital Health Service During Cancer Chemotherapy: Single-Arm Before-and-After Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Digital supportive cancer care is recommended to improve patient outcomes. A portal was designed and embedded within the electronic medical record and public health portal of Iceland, consisting of symptom and needs monitoring, educational material, and messaging. OBJECTIVE: This study aims to assess (1) portal feasibility (adoption, engagement, usability, and acceptability), (2) potential predictors of usability and acceptability, and (3) the potential impact of the portal on patient-reported outcomes. METHODS: This was a single-arm, before-and-after feasibility study at a university hospital among patients with cancer who were undergoing chemotherapy. Participation included filling out the Edmonton Symptom Assessment System-Revised (ESASr) weekly and the Distress Thermometer and Problem List (DT&PL) 3 times; reading educational material and messaging; and completing study questionnaires. Clinical and portal engagement data were collected from medical records. Data from patients were collected electronically at baseline and 7 to 10 days after the third chemotherapy round. Usability was assessed using the System Usability Scale (score 0-100), and acceptability was assessed using a 35-item survey (score 1-5). Patient-reported outcome measures included ESASr and DT&PL; a single-item scale for quality of life, family support, and quality of care; and multi-item scales for health literacy (Brief Health Literacy Screener), health engagement (Patient Health Engagement Scale), self-care self-efficacy (Self-Care Self-Efficacy scale), symptom interference (MD Anderson Symptom Inventory), knowledge expectations (Hospital Patients' Knowledge Expectations), and received knowledge (Hospital Patients' Received Knowledge). Health care professionals were interviewed regarding portal feasibility. RESULTS: The portal adoption rate was 72% (103/143), and the portal use rate was 76.7% (79/103) over a mean 8.6 (SD 2.7) weeks. The study completion rate was 67% (69/103). The combined completion rate of the ESASr and DT&PL was 78.4% (685/874). Patients received a mean 41 (SD 13) information leaflets; 33% (26/79) initiated messaging, 73% (58/79) received messages, and 85% (67/79) received follow-up phone calls. The mean System Usability Scale score was 72.3 (SD 14.7), indicating good usability. Usability was predicted by age (β=-.45), ESASr engagement (β=.5), symptom interference (β=.4), and received knowledge (β=.41). The mean acceptability score, 3.97 (SD 0.5), was above average and predicted by age (β=-.31), ESASr engagement (β=.37), symptom interference (β=.60), self-care self-efficacy (β=.37), and received knowledge (β=.41). ESASr scores improved for total symptom distress (P=.003; Cohen d=0.36), physical symptoms (P=.01; Cohen d=0.31), and emotional symptoms (P=.01; Cohen d=0.31). Daily symptom interference increased (P=.03; Cohen d=0.28), quality of life improved (P=.03; Cohen d=0.27) and health engagement (P=.006; Cohen d=0.35) improved, while knowledge expectations decreased (P≤.001; Cohen d=2.57). Health care professionals were positive toward the portal but called for clearer role delineation and follow-up. CONCLUSIONS: This study supports the feasibility of a support portal and the results indicate the possibility of improving patient outcomes, but further developments are warranted.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".