Supply and Demand in mHealth Apps for Persons With Multiple Sclerosis: Systematic Search in App Stores and Scoping Literature Review
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a non-curable chronic inflammatory disease of the central nervous system that affects more than 2 million people worldwide. MS-related symptoms impact negatively on the quality of life of persons with MS, who need to be active in the management of their health. mHealth apps could support these patient groups by offering useful tools, providing reliable information, and monitoring symptoms. A previous study from this group identified needs, barriers, and facilitators for the use of mHealth solutions among persons with MS. It is unknown how commercially available health apps meet these needs. OBJECTIVE: The main objective of this review was to assess how the features present in MS apps meet the reported needs of persons with MS. METHODS: We followed a combination of scoping review methodology and systematic assessment of features and content of mHealth apps. A search strategy was defined for the two most popular app stores (Google Play and Apple App Store) to identify relevant apps. Reviewers independently conducted a screening process to filter apps according to the selection criteria. Interrater reliability was assessed through the Fleiss-Cohen coefficient (k=.885). Data from the included MS apps were extracted and explored according to classification criteria. RESULTS: An initial total of 581 potentially relevant apps was found. After removing duplicates and applying inclusion and exclusion criteria, 30 unique apps were included in the study. A similar number of apps was found in both stores. The majority of the apps dealt with disease management and disease and treatment information. Most apps were developed by small and medium-sized enterprises, followed by pharmaceutical companies. Patient education and personal data management were among the most frequently included features in these apps. Energy management and remote monitoring were often not present in MS apps. Very few contained gamification elements. CONCLUSIONS: Currently available MS apps fail to meet the needs and demands of persons with MS. There is a need for health professionals, researchers, and industry partners to collaborate in the design of mHealth solutions for persons with MS to increase adoption and engagement.
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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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.035 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".