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Background: Chronic inflammatory arthritis, gout and osteoarthritis are chronic conditions that require lifelong follow-up and long-term treatment. Electronic health (eHealth) systems could provide methods to remotely monitor disease in these conditions, possibly improving patient self-management and lowering the burden of in-person consultations for both patient and healthcare professionals. To use these systems in practice, it is important to understand adherence rates of patients with chronic arthritis to these methods. Objective: To systematically review the literature on adherence to eHealth methods to remotely monitor symptoms or disease activity in chronic arthritis, gout and osteoarthritis. Methods: We systematically searched the following databases: Embase, PubMed, Cochrane Central, CINAHL, Web of Science, Clinicaltrials.gov, and the International Clinical Trials Registry Platform (ICTRP). Eligible articles reported adherence rates to an eHealth method to remotely monitor disease in patients with rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA), spondyloarthritis (SpA), osteoarthritis (OA), or gout. Quality assessment of the remaining articles was done by using the Newcastle-Ottawa quality assessment scale (NOS) and the PEDro scale. Finally, included articles were grouped by eHealth method and patient population, and relevant information on adherence was extracted. Lastly, a linear regression was performed to define the adherence depending on the different study durations. Results: The literature search resulted in 7027 articles. After deduplication, 4219 articles were screened for eligibility, and 47 articles were included. We found the highest adherence rates in studies applying electronic diaries (81.8%), followed by text messages (79.9%), web-based platforms (74.6%), wearable activity trackers (73%), and smartphone applications (65.5%). Highest adherence was reported in studies concerning patients with OA (79.7%), followed by patients with gout (73.7%), RA (71.3%), ankylosing SpA (67%), and JIA (59%). In general, although higher adherence was reported in adult patient populations, demographic characteristics did not seem to affect patient adherence to eHealth monitoring. Based on frequency of data entering, a time interval of once-daily showed highest adherence data (72.6%), followed by entering data more than once a day (72.3%) and once a week (67.7%), or longer (59.2%). A linear regression was performed on adherence levels (%) per study duration (days), and showed a decrease of 0.03% adherence per extra study day (Y = - 0.0332X + 71.84) (figure 2). Only the OA, JIA and SpA studies eventually showed a significant declination in adherence with longer study duration based on the 95%-confidence intervals of the slopes of the linear regressions (table 3). Conclusion: Our literature review identified declining adherence levels with longer study duration, and highest adherence rates to eHealth strategies to remotely monitor disease in studies on patients with OA, making use of electronic diaries as eHealth method, and requesting once-a-day completion. OA studies had an overall shorter duration than RA studies, which could be an explanation as to why OA patients showed higher adherence. Lower adherence rates were reported in studies in juvenile populations, while older age did not affect reported adherence.