Mobile Health Features Supporting Self-Management Behavior in Patients With Chronic Arthritis: Mixed-Methods Approach on Patient Preferences

• Published in: JMIR mHealth and uHealth 2019-03, Vol.7 (3), p.e12535-e12535
• Main Authors: Geuens, Jonas, Geurts, Luc, Swinnen, Thijs W, Westhovens, Rene, Vanden Abeele, Vero
• Format: Article
• Language: English
• Subjects: Adult; Aged; Arthritis - psychology; Arthritis - therapy; Consumer Behavior - statistics & numerical data; Female; Health Promotion - methods; Health Promotion - standards; Health Promotion - statistics & numerical data; Humans; Interviews as Topic - methods; Male; Middle Aged; Mobile Applications - standards; Mobile Applications - statistics & numerical data; Original Paper; Patients - psychology; Patients - statistics & numerical data; Qualitative Research; Self-Management - methods; Self-Management - psychology; Self-Management - statistics & numerical data
• ISSN: 2291-5222; 2291-5222
• DOI: 10.2196/12535

Patients 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. The 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. A 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. In 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. We 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.