Mobile Apps for Hematological Conditions: Review and Content Analysis Using the Mobile App Rating Scale
Bibliographic record
Abstract
BACKGROUND: Hematological conditions are prevalent disorders that are associated with significant comorbidities and have a major impact on patient care. Concerning new tools for the care of these patients, the number of health apps aimed at hematological patients is growing. Currently, there are no quality analyses or classifications of apps for patients diagnosed with hematological conditions. OBJECTIVE: The aim of this study is to analyze the characteristics and quality of apps designed for patients diagnosed with hematological conditions by using the Mobile App Rating Scale (MARS). METHODS: We performed an observational, cross-sectional descriptive study of all smartphone apps for patients diagnosed with hematological conditions. A search was conducted in March 2021 using the following terms: anemia, blood cancer, blood disorder, hematological cancer, hematological malignancy, hematological tumor, hematology, hemophilia, hemorrhage, lymphoma, leukemia, multiple myeloma, thalassemia, thrombocytopenia, and thrombosis. The apps identified were downloaded and evaluated by 2 independent researchers. General characteristics were registered, and quality was analyzed using MARS scores. Interrater reliability was measured by using the Cohen κ coefficient. RESULTS: We identified 2100 apps in the initial search, and 4.19% (88/2100) of apps met the inclusion criteria and were analyzed. Of the 88 apps, 61% (54/88) were available on Android, 30% (26/88) were available on iOS, and 9% (8/88) were available on both platforms. Moreover, 7% (6/88) required payment, and 49% (43/88) were updated in the last year. Only 26% (23/88) of the apps were developed with the participation of health professionals. Most apps were informative (60/88, 68%), followed by preventive (23/88, 26%) and diagnostic (5/88, 6%). Most of the apps were intended for patients with anemia (23/88, 26%). The mean MARS score for the overall quality of the 88 apps was 3.03 (SD 1.14), ranging from 1.19 (lowest-rated app) to 4.86 (highest-rated app). Only 47% (41/88) of the apps obtained a MARS score of over 3 points (acceptable quality). Functionality was the best-rated section, followed by aesthetics, engagement, information, and app subjective quality. The five apps with the highest MARS score were the following: Multiple Myeloma Manager, Hodgkin Lymphoma Manager, Focus On Lymphoma, ALL Manager, and CLL Manager. The analysis by operating system, developer, and cost revealed statistically significant differences in MARS scores (P<.001, P<.001, and P=.049, respectively). The interrater agreement between the 2 reviewers was substantial (k=0.78). CONCLUSIONS: There is great heterogeneity in the quality of apps for patients with hematological conditions. More than half of the apps do not meet acceptable criteria for quality and content. Most of them only provide information about the pathology, lacking interactivity and personalization options. The participation of health professionals in the development of these apps is low, although it is narrowly related to better quality.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".