Mobile Apps for Oncology Health Care Professionals: Mapping and Assessment Study
Bibliographic record
Abstract
BACKGROUND: The use of mobile apps in oncology has been expanding rapidly, encompassing prevention, treatment, and patient support. These technologies hold significant potential to improve care delivery and enhance the efficiency of health care services. However, their integration into clinical practice faces important challenges. A key issue lies in the difficulties health care professionals (HCPs) encounter when selecting apps that adequately meet their specific needs and comply with appropriate standards of quality and clinical effectiveness. This lack of robust evidence on the availability, adoption, and evaluation of mobile apps designed for cancer care professionals not only hinders their wider adoption but also restricts their potential to serve as reliable tools in oncology practice. OBJECTIVE: This study aims to map the landscape of free mobile apps for cancer prevention, treatment, therapy, or support for HCPs, and assess the quality of the apps identified. METHODS: A systematic search was conducted on Google Play and Apple App Store in June 2023 and December 2024 using predefined oncology- and professional-related keywords, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two independent reviewers (DL and AC) assessed the selected apps using the Mobile App Rating Scale (MARS), which evaluates engagement, functionality, aesthetics, information quality, and subjective quality on a 5-point Likert scale. Discrepancies in ratings were resolved by a third reviewer. Descriptive statistics summarized the app quality and characteristics. RESULTS: Out of 221 apps initially identified, 20 met the inclusion criteria and were evaluated. Most apps (15/20, 75%) supported both Android and iOS platforms, with 90% (18/20) commercially developed. The mean overall MARS score was 3.51 (SD 0.54), indicating moderate quality but with room for improvement. Only 2 apps, ONCOassist (Portable Medical Technology Ltd.) (mean 4.25, SD 0.26) and Oncology Board Review (mean 4.03, SD 0.39), surpassed the threshold of 4.0, considered good quality. ONCOassist stood out for its comprehensive functionality and high information quality, offering clinical decision support tools such as treatment protocols, prognostic calculators, and toxicity grading aligned with professional oncology practice. Prevention and support apps generally scored lower, particularly in engagement and interactive features. No app achieved a high score across all MARS domains. CONCLUSIONS: The study highlights a fragmented landscape of free mobile apps for cancer care professionals, with predominantly low to moderate quality and limited evidence to support clinical effectiveness. ONCOassist emerges as a promising tool warranting further investigation. This underscores the urgent need for standardized evaluation frameworks, regulatory oversight, and sustainable development strategies to ensure the creation and adoption of reliable, evidence-based digital health tools in oncology.
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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 | 0.039 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".