Real-world usage of digital health applications (DiGA) in rheumatology: results from a German patient survey

Abstract

Mobile health applications and digital therapeutics (DTx) aim to improve current patient care. Real-world data on DTx are, however, scarce. The aim of this study was to evaluate the adherence, acceptance, and efficacy of DTx in a clinical routine rheumatology setting. We conducted a prospective observational cohort study assessing the use, adherence, acceptance, and efficacy of the DTx DiGA (Digitale Gesundheitsanwendungen) by survey over 12 weeks. Patients included had to have a rheumatic disease and had been prescribed a DiGA. Acceptance was assessed using the Net promoter score (NPS). 48 patients were prescribed DiGA. Of these, 39/48 (81%) completed the follow-up survey. 21/39 (54%) patients downloaded the DTx and 20/39 (51%) used the DTx at least once. 9/39 (23%) of patients stopped quickly afterward and 5/39 (13%) reported having completed the whole DTx program. Lack of time and commitment were reported as the main reasons for non-use. Overall acceptance of DiGA was high (Net promoter score (NPS) mean (SD) 7.8/10 (2.3)). While the majority of patients (60%) reported no improvement, one subgroup of patients (7/20, 35%) who regularly used an exercise-based DTx for back pain reported symptom improvement. Acceptance of DTx in patients with rheumatic diseases is high, however onboarding to DTx use and adherence to DTx is still challenging in patients with rheumatic diseases. In a subgroup of patients with back pain, however, the use of an exercise-based DTx led to symptom improvement.

Keywords: Adherence; Apps; DTx; Digital health; Digital therapeutics; Interviews; Questionnaire; Survey; eHealth.

Fig. 1

Patient diseases, reason for DTX prescription, and DTX prescribed. The percentage distribution of primary rheumatologic conditions (A), comorbidities or symptoms or behaviors relevant to DTx prescription (B), and DTx prescribed (C) for all 48 study patients is shown. RA, rheumatoid arthritis; axSpA, axial Spondyloarthritis; PsA, psoriatic arthritis; FM, fibromyalgia; CP, chronic pain; BP, back pain; AD; anxiety disorder; Dep; depression; Smo, smoking; SD, sleep disorder; HBCP, HelloBetter chronic pain; NRH NichtRaucherHelden (for smoking cessation)

Fig. 2

Study flow. The study flow of all 48 patients shows drop-outs, follow-ups, downloads, and non-downloads (A). The usage behavior of all 20 users is shown in the bar graphs in (B) (usage period) and in (C) (frequency of use)

Fig. 3

Efficacy and acceptance of DTX. The percentage distribution of efficacy (A) and acceptance as NPS categorical distribution (B) and NPS single values (C) is demonstrated. NPS, Net promoter score

Fig. 4

Clusters of DTx users. Data were obtained from the interview in a correlation-based hierarchical clustering algorithm to separate different patient groups (online tool ClustVis) (clustering method, columns, and rows: Euclidian; clustering distance, columns, and rows: Ward). Limited clinical data separated by cluster are provided below the heatmap. P values were determined by Kruskal–Wallis test (continuous variables) or the chi-square test (categorical variables). The disease distribution (absolute number of patients) is shown as axSpA/PsA/RA/FM/others. Recomm, Recommendation