ArticleJMIR mHealth and uHealth2018
Supply and Demand in mHealth Apps for Persons With Multiple Sclerosis: Systematic Search in App Stores and Scoping Literature Review.
Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04244214 (Pilot Study for the Evaluation of the More Stamina Mobile Application for Fatigue Management in Persons With Multiple Sclerosis), which is not on this map. Cited by 40 papers, 6 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Pilot Study for the Evaluation of the More Stamina Mobile Application for Fatigue Management in Persons With Multiple Sclerosis
Who cites it
40 citing papers in PubMed, 6 syntheses or guidelines pooled it, 90 citations in OpenAlex.
- mHealth Apps for Dementia, Alzheimer Disease, and Other Neurocognitive Disorders: Systematic Search and Environmental Scan.JMIR mHealth and uHealth · 2024Pooled it
- Cocreating an Automated mHealth Apps Systematic Review Process With Generative AI: Design Science Research Approach.JMIR medical education · 2024Pooled it
- Conceptual Ambiguity Surrounding Gamification and Serious Games in Health Care: Literature Review and Development of Game-Based Intervention Reporting Guidelines (GAMING).Journal of medical Internet research · 2021Pooled it
- Assessment of the Fairness of Privacy Policies of Mobile Health Apps: Scale Development and Evaluation in Cancer Apps.JMIR mHealth and uHealth · 2020Pooled it
- Patients' Perceptions of mHealth Apps: Meta-Ethnographic Review of Qualitative Studies.JMIR mHealth and uHealth · 2019Pooled it
- Evaluation of Mobile Apps Targeted to Parents of Infants in the Neonatal Intensive Care Unit: Systematic App Review.JMIR mHealth and uHealth · 2019Pooled it
- Functionality Review of Mobile Apps for the Tracking and Self-Management of Fatigue: Systematic Search in App Stores and Content Analysis.JMIR mHealth and uHealth · 2026Article
- Key Components of Participatory Design Workshops for Digital Health Solutions: Nominal Group Technique and Feasibility Study.Journal of healthcare informatics research · 2025Article
- Impact of family-oriented gamification on self-management of people with multiple sclerosis: a mixed-methods study protocol.BMJ open · 2025Article
- Exploring caregiver challenges, digital health technologies, and healthcare support: a qualitative study.Frontiers in digital health · 2025Article
- Health Maintenance Organization-mHealth Versus Face-to-Face Interaction for Health Care in Israel: Cross-Sectional Web-Based Survey Study.Journal of medical Internet research · 2024Article
- A scoping review assessing the usability of digital health technologies targeting people with multiple sclerosis.NPJ digital medicine · 2024Article
- An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care.Methods of information in medicine · 2023Article
- A Proposal for a Robust Validated Weighted General Data Protection Regulation-Based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: An eDelphi Study.Methods of information in medicine · 2023Article
- Mobile applications in the Philippines during the COVID-19 pandemic: systematic search, use case mapping, and quality assessment using the Mobile App Rating Scale (MARS).BMC digital health · 2023Article
- Digital health for chronic disease management: An exploratory method to investigating technology adoption potential.PloS one · 2023Article
- A mobile app (IDoThis) for multiple sclerosis self-management: development and initial evaluation.BMC medical informatics and decision making · 2022Article
- Validation of the German eHealth impact questionnaire for online health information users affected by multiple sclerosis.BMC medical informatics and decision making · 2022Article
- User Requirements for Comanaged Digital Health and Care: Review.Journal of medical Internet research · 2022Review
- Creating a Digital Toolkit to Reduce Fatigue and Promote Quality of Life in Multiple Sclerosis: Participatory Design and Usability Study.JMIR formative research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple 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.
objectiveThe main objective of this review was to assess how the features present in MS apps meet the reported needs of persons with MS.
methodsWe 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.
resultsAn 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.
conclusionsCurrently 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.