ArticleJMIR mHealth and uHealth2019
Mobile Health Features Supporting Self-Management Behavior in Patients With Chronic Arthritis: Mixed-Methods Approach on Patient Preferences.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Design Features for Improving Mobile Health Intervention User Engagement: Systematic Review and Thematic Analysis.Journal of medical Internet research · 2020Pooled it
- Experiences of activity monitoring and perceptions of digital support among working individuals with hip and knee osteoarthritis - a focus group study.BMC public health · 2022Trial
- Preferences towards digital health technologies: a scoping review.Health economics review · 2026Review
- Evaluating the Impact on Pain Perceptions, Pain Intensity, and Physical Activity of a Mobile App to Empower Employees With Musculoskeletal Pain: Mixed Methods Pilot Study.JMIR formative research · 2025Article
- Feasibility of remote measurement in intensive longitudinal data collection for rheumatoid arthritis patients commencing a new treatment.Rheumatology advances in practice · 2025Article
- "How can we help you?": results of a scoping review on the perceived needs of people living with chronic pain regarding physiotherapy.BMC health services research · 2024Article
- Development and Use of Mobile Messaging for Individuals With Musculoskeletal Pain Conditions: Scoping Review.JMIR mHealth and uHealth · 2024Article
- Young adult preferences for digital health interventions to support adherence to inhaled corticosteroids in asthma: a qualitative study.Health psychology and behavioral medicine · 2022Article
- Monitoring Lower Back Activity in Daily Life Using Small Unintrusive Sensors and Wearable Electronics in the Context of Rheumatic and Musculoskeletal Diseases.Sensors (Basel, Switzerland) · 2021Article
- Mobile App-based documentation of patient-reported outcomes - 3-months results from a proof-of-concept study on modern rheumatology patient management.Arthritis research & therapy · 2021Article
- Investigator Experiences Using Mobile Technologies in Clinical Research: Qualitative Descriptive Study.JMIR mHealth and uHealth · 2021Article
- Assessing Physical Activity and Sleep in Axial Spondyloarthritis: Measuring the Gap.Rheumatology and therapy · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with chronic arthritis (CA) ideally apply self-management behaviors between consultations. This enduring, tedious task of keeping track of disease-related parameters, adhering to medication schemes, and engaging in physical therapy may be supported by using a mobile health (mHealth) app. However, further research is needed to determine which self-management features are valued most by adult patients with CA patients.
objectiveThe aim of this study was to determine the preference of features for an mHealth app to support self-management behavior in patients with CA. In addition, we aimed to explore the motives behind these ratings.
methodsA mixed-methods approach was used to gather information from 31 adult patients (14 females), aged 23 to 71 years (mean 51 [SD 12.16]), with CA. Structured interviews were conducted to gather data pertaining to preferences of app features. Interviews were analyzed qualitatively, whereas ratings for each of the 28 features studied were analyzed quantitatively.
resultsIn general, patients with CA favored the use of features pertaining to supporting active and direct disease management, (eg, medication intake and detecting and alarming of bad posture), helping them to keep a close watch on their disease status and inform their health care professional (eg, providing a means to log and report disease-related data) and receiving personalized information (eg, offering tailored information based on the patient's health data). Patients strongly disliked features that provide a means of social interaction or provide incentivization for disease-related actions (eg, being able to compare yourself with other patients, cooperating toward a common goal, and receiving encouragement from friends and/or family). Driving these evaluations is the finding that every patient with CA hurts in his/her own way, the way the disease unfolds over time and manifests itself in the patient and social environment is different for every patient, and patients with CA are well aware of this.
conclusionsWe have offered an insight into how patients with CA favor mHealth features for self-management apps. The results of this research can inform the design and development of prospective self-management apps for patients with CA.
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