|Ahead of print publication
Review: Remote disease monitoring in rheumatoid arthritis
Amy MacBrayne1, William Marsh2, Frances Humby1
1 Department of Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, QMUL, London, England, United Kingdom
2 School of Electronic Engineering and Computer Sciences, QMUL, London, England, United Kingdom
|Date of Submission||28-Jun-2021|
|Date of Acceptance||01-Dec-2021|
|Date of Web Publication||25-Apr-2022|
Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute Barts and The London School of Medicine & Dentistry,2nd Floor, John Vane Science Centre Queen Mary University of London,Charterhouse Square,London
Source of Support: None, Conflict of Interest: None
Rheumatoid arthritis (RA), the archetypal inflammatory arthritis, remains a complex and challenging disease to manage in spite of the abundance of new therapies in the last 20 years. The unpredictable relapsing/remitting nature of RA is at odds with the current prevailing system of scheduled follow-ups, leaving patients with RA to manage pain, flares, and medications between appointments, which may be of little value if occurring during a period of disease control. The rapid progress in the field of mobile health (mHealth) in the last 10 years has led to a proliferation of smartphone applications (apps) targeted at people with RA. Harnessing the power of smartphones to deliver remote monitoring for patients with RA has gone from an exciting possibility to an urgent necessity due to the COVID-19 pandemic. Apps developed solely by commercial providers have been found to be of limited utility in disease monitoring. However, multiple global institutions have developed mHealth technology to support remote monitoring of RA patients, utilizing asynchronous technology for patients to submit indicators of their disease activity, ranging from validated electronic patient-reported outcome measures, to innovative monitoring utilizing smartphone biosensors. This review discusses the current published evidence for mobile applications designed to facilitate remote monitoring of RA, the common barriers faced in implementing mhealth monitoring and strategies to overcome these, and potential areas for future research.
Keywords: Application, mHealth, remote monitoring, rheumatoid arthritis, smartphone
| Introduction|| |
Rheumatoid arthritis (RA) is the most common inflammatory arthritis, affecting 1% of the global population. It is a chronic, relapsing-remitting disease which, if undertreated, leads to permanent joint damage and disability. Patient outcomes have significantly improved in recent years due to the explosion of new biologic and targeted synthetic therapies. However, these treatments require close monitoring to ensure safe prescribing, and the best outcomes are associated with tight control of disease activity, requiring intensive monitoring by a rheumatologist. Globally, demand outstrips capacity in rheumatology services, making mobile Health (mHealth) an attractive prospect with the potential to improve access to care, empower patients to self-manage their disease, and save costs. Globally, smartphone penetration is estimated to have reached 78.05% in 2020, with the number of users growing by 5.9% in the last year. The potential for mobile and wireless technologies to transform the delivery of health services globally has long been recognized, with the immediate need for remote monitoring accelerated by the COVID-19 pandemic and the rapid deployment of telehealth clinics to protect patients. This is particularly relevant for patients with RA, likely to be at risk of severe infection due to immunosuppression and multimorbidity. However, a purely telehealth approach is limited by the inability to examine the patient. mHealth may be able to fill some of those gaps: utilizing validated outcome measures; visualization of painful/swollen joints on body maps; innovative data collection utilizing integrated smartphone biosensors; and asynchronous communication allowing for more regular input of data on disease activity, thus enhancing monitoring from the usual face-to-face review schedule.
While mHealth is a rapidly growing field, and offers an attractive solution to the growing need to deliver care remotely, the evidence base and utility of RA apps remains relatively unknown. The purpose of this review is to examine currently published data evaluating how mHealth can be utilized for remote monitoring of RA. We discuss apps currently available to consumers, review the methods for remote monitoring utilized in clinical trials, the evolving evidence base to support their use, and consider where the future of mHealth monitoring may lead.
| Commercially Available Apps|| |
| Methods of Remote Monitoring of Rheumatoid Arthritis Disease Activity|| |
There are a variety of approaches to measure rheumatoid arthritis (RA) disease activity remotely, ranging from conventional patient-reported outcome measures (PROMs) to more innovative, but as yet unvalidated, biosensor data as surrogate disease activity measures. [Table 1] summarizes the published evidence reviewed.
| Patient-Reported Outcome Measures|| |
PROMs and their digital equivalents (electronic PROMs [ePROMs]) are an attractive prospect for remote monitoring, with patients able to complete the assessment independently, without clinician assessment or laboratory studies. In addition, PROMs provide a more holistic picture of disease activity, capturing information on facets of disease beyond inflammation which are lacking from standard inflammation-driven disease activity assessments such as the DAS-28 or Clinical Disease Activity Index (CDAI).
Routine Assessment of Patient Index Data 3 (RAPID3), the most extensively validated RA-specific PRO tool, is sensitive to change, and can discriminate well between disease activity states. The COmPASS pilot observational study, in which eighty RA patients submitted weekly RAPID3 scores via App, demonstrated that patient's RAPID3 scores correlated well with physician-assessed DAS-28 and Simplified Disease Activity Index at baseline and 3 months. Furthermore, weekly RAPID3 scores demonstrated significant fluctuations into higher severity categories, demonstrating that patients who appear stable at 3- or 6-monthly clinic visits may in fact be experiencing untreated clinically significant flares. The app has subsequently been offered to RA patients in the Swiss clinical quality management in rheumatic diseases registry.
Other ePROMs, such as Rheumatoid Arthritis Disease Activity Index (RADAI), RADAI-5, and patient-derived DAS28 (Pt-DAS28), were determined to have strong levels of evidence and validation in a systematic review, and have been utilized in remote monitoring mHealth studies.,
Pt-DAS28 has been found to be at least as reliable as physician, nurse, or US-derived DAS28, despite poor reliabiity of patient-assessed swollen joint count, & is endorsed for measurement of disease activity by the American College of Rheumatology, particularly in the context of remote assessment necessitated by the COVID-19 pandemic. The importance of patient training at baseline to ensure accuracy of self-assessed swollen joints has been noted.
Several mHealth studies have utilized pt-DAS28 and found it to be acceptable to patients and clinicians with high usability,, and to support enhanced monitoring while minimizing face-to-face clinic visits in a recent randomized controlled trial (RCT).
| Novel Measures for Remote Disease Activity Monitoring|| |
Dramatic advances in consumer-grade digital technology in recent years present the opportunity to measure novel biometric and activity outcomes as a surrogate for disease activity. The following two approaches prevail: set a conscious task for the patient to undertake at a specified intervals; or continuously and passively collect data from the patient using biosensor technology, for example, activity, heart rate, and sleep.
Utilizing smartphone accelerometers, specific gait parameters measured via app were shown to have a moderate correlation to DAS-28 when patients performed a short walk test with their phone worn on an elasticated belt. Wrist range-of-motion tasks utilizing smartphone accelerometers have also been attempted; however, only 45% of motion samples were of high quality, emphasizing the importance of supervised training in set tasks. Handgrip strength (HGS), assessed by a dynamometer connected to a smartphone app, correlated negatively with DAS28 in a trial of 62 patients. However, these measures may not be reliable in patients with comorbidities that impact the measured motion, for example, patients with any other Musculo-skeletal (MSK) or neurological comorbidity were excluded from gait studies, and those with carpal tunnel syndrome were excluded from studies of wrist ROM and HGS, limiting the generalizability of these measures to the wider RA patient population. Patients must remember to do these tasks at specified intervals and consciously engage with them, which may lead to user fatigue and attrition over time. Continuous passive data collection, utilizing biosensors such as wearable activity trackers, may present a pathway to avoid these issues. An observational study of 170 RA and axial spondyloarthropathy patients (91 RA) wearing activity trackers for 3 months found that persistent flares (defined as patient self-reported flare lasting >3 days) correlated to a moderate decrease in physical activity of 12%–21%. While promising, novel objective outputs must be further investigated in order to validate and standardize these measures. Further studies utilizing biosensors for continuous data collection are underway to determine if sleep/wake rhythm impacts the quality of life in RA patients, and an RCT should be designed to determine if passively collected digital measures correlate with disease activity assessed via ePROMs. Passive data collection appears to be acceptable to participants, provided they do not experience a reduction in the battery life of their devices, and that wearable technology is discreet and unobtrusive.
| Utilizing Monitoring Data|| |
Once the methods of remote monitoring have been determined, it must be decided how the generated data are utilized. Consideration must be made as to whether the data will be monitored and responded to continuously with each submission, or whether scores will only be reviewed at a set/agreed time point, for example, a clinic appointment, and patients must be made aware of this in order to manage expectations of mHealth service delivery and ensure patient safety.
While the former strategy is more responsive, it requires substantial health-care resources to review and act on the mHealth data in a timely manner. In the latter strategy, clinicians cannot respond to flares or problems in “real time;” however, the collected mHealth data may enrich the clinical consultation by improving self-efficacy in exchanging information with health professionals and eliminating recall bias, reducing the time required for history taking, and moving focus onto management. Results with this strategy have been mixed. While the qualitative feedback from the twenty patients and two clinicians in the REMORA study was unequivocally positive, two larger studies utilizing the validated “Perceived Efficacy in Patient–Physician Interactions” questionnaire have been less persuasive, with a study of 159 RA patients showing a small (but significant) improvement in scores after 12 months, and another study of 91 RA patients showing no improvement in scores after 6 months. This may be impacted by the views of their clinicians, given 45% involved in the latter study strongly disagreed, or were neutral, that the app improved communication, possibly tied to the fact that the app data were not integrated with the electronic health record (EHR), a notable strength of the REMORA study.
Most studies of mHealth interventions have been pilot studies, with primary outcome measures focused on usability or patient empowerment, rather than whether the intervention might meaningfully improve patient outcomes. However, this has started to change: a RCT of 191 RA patients (91 app users) compared CDAI scores at 6 months of controls and patients enrolled in an app monitoring ePROMs daily, in addition to patient and physician satisfaction. If submitted RADAI-5 scores rose by >30% compared to the previous 2 weeks, and the score was >3, a notification of potential flare was sent to the study coordinator to review questionnaire data and contact the participant to obtain more detailed symptom information. This information, along with the collected PROM scores, was provided to the patient's primary rheumatologist via E-mail within 24 h. However, no differences were found in 6-month median CDAI between the app and control groups. This may be a reflection of the short duration of the study being insufficient to observe changes in disease activity due to disease-modifying antirheumatic drug (DMARD) changes, given a larger proportion of patients in the intervention group due to change DMARD at the 6-month visit.
Most rheumatology clinics schedule follow-up consultations at predetermined intervals, usually 3–6 monthly, to evaluate disease activity. These visits may be of little value if the patient is clinically well, while clinically relevant flares may occur between visits. Remote disease activity monitoring via mHealth has the potential to revolutionize this system, utilizing patient-generated health data (PGHD) to allocate consultations according to clinical need. A Dutch pilot study of 42 patients examined the acceptability of using PROM data collected via a smartphone app (mijnreuma reade), reporting that 87% of participants approved of self-monitoring, and 81% wished to skip hospital visits if their self-reported disease activity was low.
Going further, a recent RCT examined whether using a “connected monitoring” interface on a smartphone reduced the number of physical visits required in a 6-month period following the initiation of DMARD therapy, compared with conventional monitoring. The app combined an e-PROM, auto-DAS28, and a HGS test. An assigned clinical case manager was alerted to significant increases in disease activity, or a drop in data fill rate, prompting contact with the patient to determine the need for a physical or telephone consult. The connected monitoring group required significantly fewer physical visits than controls (0.42 [0.58] vs. 1.93 [0.55]; P < 0.05) with no differences observed in clinical and functional scores, and better quality of life for Short-Form 12 sub-scores, suggesting that this approach could reduce the number of physical visits while maintaining tight control of disease activity, and even improve the quality of life for patients with RA commencing new treatment.
| Medication Monitoring|| |
In addition to disease activity monitoring, mHealth presents an opportunity to optimize medication management and adherence, vital to achieving optimal health outcomes and safe prescribing for individuals with RA. Several apps focused on adherence have been developed in conjunction with pharmaceutical companies around a specific medication, utilizing features such as reminders and push notifications for medications and appointments, and tracking monitoring blood tests. A companion app for patients taking tofacitinib, currently in development, aims to utilize gamification strategies to enhance medication adherence. Innovative approaches, such as the medication event monitoring system, which registers electronically when the cap is removed from a medication bottle, have been shown to be a reliable way to determine adherence to methotrexate for RA patients and may represent a future possibility for remote medication monitoring.
| Frequency of Assessment, Attrition, and Engagement|| |
The opportunity to collect disease activity data with much greater regularity than conventional set follow-up must be offset by the risk of user fatigue and attrition. Many studies request patients to submit data daily,,, representing a significant time commitment for the patient, dependent on the monitoring modality.
Most mHealth studies are of short duration (3–6 months), yet an almost universal feature of these studies is that a substantial proportion of users will drop out, or disengage with the technology prior to the end of the trial. As few as approximately 10% to 25% of the recruited participants have been shown to be engaged in studies lasting between 1 week and 12 weeks by the end of dataentry protocols. Patient-activated approaches have been trialed with mixed results. The Rheumalive proof-of-concept study reported high levels of engagement: patients accessed the app, unprompted, to record PROM a mean of 14.3 times in 3 months, with a retention rate of 71.7%, which was in contrast with the Sanoia app study, in which 26% of the participants never accessed the platform at all, and only 51% of the participants accessed the app twice or more over the 12-month study duration, in spite of high satisfaction scores with the app. The key reason stated by the participants for not accessing the platform was that they were in disease remission and did not feel they needed app support.
High levels of attrition are particularly concerning when aiming for a remote monitoring strategy suitable for patients with a lifelong condition. However, engagement can be significantly improved with mindful study design focused on the usability of technology, including consideration of participants' functional ability, and the workload time commitment; “push” factors such as reminders and data monitoring; and the provision of personal contact and study support. A strong emphasis must be placed on involving key stakeholders, such as patient advisory groups, in study or app design, in order to identify potential barriers and solutions to these. This is supported further by EULAR in their “points to consider” paper for the development, evaluation, and implementation of mHealth applications for self-management of Rheumatic & Musculoskeletal Diseases (RMDs), which states “The design, development, and validation of self-management apps should involve people with RMDs and relevant healthcare providers.”
| Key Stakeholder Involvement: Questionnaire and Qualitative Research|| |
Questionnaire studies reveal that while RA patients in European cohorts have high levels of smartphone usage (82.2%–91.2%), and are eager to utilize mHealth to support care, there remains low e-health literacy and usage of mHealth technologies, with usage of medical apps ranging from 4.1% in a German cohort of 193 patients with RMDs and 8% of a French cohort of 127 patients using apps for RA-related follow-up. A mixed-methods study found that patients felt an app could help them to self-manage their disease, if it was tailored to their needs and co-developed with health professionals. Physician recommendation has been found to significantly influence patients' decision to engage with health technologies. Qualitative interview studies support the acceptability to RA patients of using mHealth for remote monitoring, if their treating rheumatologist uses the information to treat the disease. Whilst seemingly obvious that patients would wish for collected mHealth data to be used to inform their treatment, a recent observational study of 312 patients tracking their RMD activity using a mobile app found that only 55% of patients discussed their remote tracking results with their rheumatologist during consultations. Those who discussed their results found that their clinician was more aware of disease fluctuations and had higher levels of shared decision-making; however, app users who did not discuss their results were no different from nonapp using controls, demonstrating that apps are unlikely to contribute to patient–provider interactions without integration of app data into care processes.
| Barriers|| |
Device factors may reduce the quality or availability of data with passive collection, for example, the “cloudy with a chance of pain” study, which aimed to understand the relationship between pain and the weather by correlating patients' symptoms to geolocation data via smartphone, found that only 17.3% of participant-days had complete geolocation data due to issues with user's devices operating systems, or use of “battery-saving mode.”
While most preliminary studies with bespoke mobile apps suggest that RA-related hand impairments do not hinder data entry, with some patients reporting data entry on a tablet preferable to pen and paper, a pilot study trialing optical imaging as a monitoring modality which required patients to take a calibrated photograph of their hand found that those with more severe hand disabilities struggled to use this feature of the app. In poststudy qualitative interviews, users also reported initial technical difficulties with the app, causing frustration and subsequent attrition. This reinforces the need to extensively test and pilot new technology to ensure that participants' first experience using the app is positive, in order to promote confidence for continual usage.
mHealth monitoring tools must adhere to applicable regulations and ethical principles to ensure that data collected are sufficiently protected and secure. Both the American Medical Association and EULAR have provided guidelines enshrining the need for privacy and security of patient's mHealth data., Concerns over data security is one of the obstacles to incorporating patients' data from EHRs. However, the tide appears to be changing with recent changes in rules in both the USA and Europe, strengthening the rights of patients' to access their health data.
Participants who are clinically well or in remission are less likely to remain engaged with mHealth monitoring, with qualitative studies consistently identifying that while disease is in remission, patients prefer to “forget” their RA.,, It may be that when considering remote monitoring for long-term follow-up, patients should be able to “opt out” for a time period, analogous to an open follow-up appointment. Alternately, adaptive questionnaires for patients in sustained remission which require less time commitment, or utilizing persuasive or gamified designs, may overcome this obstacle.
Future: Harnessing the possibilities of artificial intelligence
It has been noted that remote monitoring strategies create a huge increase in PGHD, and when applied at a large scale, reviewing these data creates a substantial workload. Utilizing artificial intelligence (AI) to filter data and reduce the volume requiring clinician review is a potential strategy. Data collected on physical activity and patient flares in the ActConnect study were applied to machine-learning techniques to create a model to predict flares with a high sensitivity (96% [95% confidence interval [CI]: 94%–97%]), specificity (97% [95% CI: 88%–96%]), positive predictive value (91% [95% CI: 88%–96%]), and negative predictive value (99% [95% CVI: Content Validity Index 98%–100%]). This pilot work is promising for the role of AI supporting clinicians managing mHealth monitoring data.
In our own work, we have collaborated with computer scientists to utilize Bayesian networks, a form of AI which can combine expert knowledge with data, to produce a diagnostic model for RA and a model facilitating personalized advice for patient self-management based on the quality of life issues most needing attention. These models have the potential to support and empower patients to make the complex decisions required for effective self-management, and support clinicians to harness and interpret remote monitoring data.
| Conclusion|| |
Harnessing the power of smartphones to deliver remote monitoring of patients with RA has gone from an exciting possibility to an urgent necessity due to the COVID-19 pandemic. Clinical trials of remote monitoring apps show that these are acceptable to, and usable by patients with RA. Multiple methods of remote monitoring may provide a broader, more holistic picture of disease activity and impact at more regular timepoints than ever possible with conventional follow-up, with novel biosensor approaches having potential to provide innovative new ways to measure RA activity.
While much of the published work thus far are pilot or proof-of-concept studies, the field is moving rapidly with increasing numbers of RCTs underway, and evidence of efficacy of mHealth monitoring is mounting. However, in order for mHealth tools to be of value, they must be developed in conjunction with RA patients and clinicians, be integrated with the EHR, and have sufficient infrastructure for PGHD to be reviewed and responded to in an appropriate and timely manner by clinicians.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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