Detection of Flares by Decrease in Physical Activity, Collected Using Wearable Activity Trackers in Rheumatoid Arthritis or Axial Spondyloarthritis: An Application of Machine Learning Analyses in Rheumatology

A Virtual Reality-Based App to Educate Health Care Professionals and Medical Students About Inflammatory Arthritis: Feasibility Study

Background

Inflammatory arthritides (IA) such as rheumatoid arthritis or psoriatic arthritis are disorders that can be difficult to comprehend for health professionals and students in terms of the heterogeneity of clinical symptoms and pathologies. New didactic approaches using innovative technologies such as virtual reality (VR) apps could be helpful to demonstrate disease manifestations as well as joint pathologies in a more comprehensive manner. However, the potential of using a VR education concept in IA has not yet been evaluated.

Objective

We evaluated the feasibility of a VR app to educate health care professionals and medical students about IA.

Methods

We developed a VR app using data from IA patients as well as 2D and 3D-visualized pathological joints from X-ray and computed tomography–generated images. This VR app (Rheumality) allows the user to interact with representative arthritic joint and bone pathologies of patients with IA. In a consensus meeting, an online questionnaire was designed to collect basic demographic data (age, sex); profession of the participants; and their feedback on the general impression, knowledge gain, and potential areas of application of the VR app. The VR app was subsequently tested and evaluated by health care professionals (physicians, researchers, and other professionals) and medical students at predefined events (two annual rheumatology conferences and academic teaching seminars at two sites in Germany). To explore associations between categorical variables, the χ² or Fisher test was used as appropriate. Two-sided P values ≤.05 were regarded as significant.

Results

A total of 125 individuals participated in this study. Among them, 56% of the participants identified as female, 43% identified as male, and 1% identified as nonbinary; 59% of the participants were 18-30 years of age, 18% were 31-40 years old, 10% were 41-50 years old, 8% were 51-60 years old, and 5% were 61-70 years old. The participants (N=125) rated the VR app as excellent, with a mean rating of 9.0 (SD 1.2) out of 10, and many participants would recommend use of the app, with a mean recommendation score of 3.2 (SD 1.1) out of 4. A large majority (120/125, 96.0%) stated that the presentation of pathological bone formation improves understanding of the disease. We did not find any association between participant characteristics and evaluation of the VR experience or recommendation scores.

Conclusions

The data show that IA-targeting innovative teaching approaches based on VR technology are feasible.

Patient Reported Outcomes in Chronic Inflammatory Diseases: Current State, Limitations and Perspectives

Chronic inflammatory diseases (CID) are emerging disorders which do not only affect specific organs with respective clinical symptoms but can also affect various aspects of life, such as emotional distress, anxiety, fatigue and quality of life. These facets of chronic disease are often not recognized in the therapy of CID patients. Furthermore, the symptoms and patient-reported outcomes often do not correlate well with the actual inflammatory burden. The discrepancy between patient-reported symptoms and objectively assessed disease activity can indeed be instructive for the treating physician to draw an integrative picture of an individual's disease course. This poses a challenge for the design of novel, more comprehensive disease assessments. In this mini-review, we report on the currently available patient-reported outcomes, the unmet needs in the field of chronic inflammatory diseases and the challenges of addressing these.

Digital rheumatology in the era of COVID-19: results of a national patient and physician survey

OBJECTIVE

To analyse the impact of the COVID-19 pandemic on rheumatic patients' and rheumatologists' usage, preferences and perception of digital health applications (DHAs).

METHODS

A web-based national survey was developed by the Working Group Young Rheumatology of the German Society for Rheumatology and the German League against Rheumatism. The prospective survey was distributed via social media (Twitter, Instagram and Facebook), QR code and email. Descriptive statistics were calculated, and regression analyses were performed to show correlations.

RESULTS

We analysed the responses of 299 patients and 129 rheumatologists. Most patients (74%) and rheumatologists (76%) believed that DHAs are useful in the management of rheumatic and musculoskeletal diseases (RMDs) and felt confident in their own usage thereof (90%; 86%). 38% of patients and 71% of rheumatologists reported that their attitude had changed positively towards DHAs and that their usage had increased due to COVID-19 (29%; 48%). The majority in both groups agreed on implementing virtual visits for follow-up appointments in stable disease conditions. The most reported advantages of DHAs were usage independent of time and place (76.6%; 77.5%). The main barriers were a lack of information on suitable, available DHAs (58.5%; 41.9%), poor usability (42.1% of patients) and a lack of evidence supporting the effectiveness of DHAs (23.2% of rheumatologists). Only a minority (<10% in both groups) believed that digitalisation has a negative impact on the patient-doctor relationship.

CONCLUSION

The COVID-19 pandemic instigated an increase in patients' and rheumatologists' acceptance and usage of DHAs, possibly introducing a permanent paradigm shift in the management of RMDs.

Identifying chronic disease patients using predictive algorithms in pharmacy administrative claims: an application in rheumatoid arthritis

OBJECTIVE

To evaluate the predictive performance of logistic and linear regression versus machine learning (ML) algorithms to identify patients with rheumatoid arthritis (RA) treated with target immunomodulators (TIMs) using only pharmacy administrative claims.

METHODS

Adults aged 18-64 years with ≥1 TIM claim in the IBM MarketScan commercial database were included in this retrospective analysis. The predictive ability of logistic regression to identify RA patients was compared with supervised ML classification algorithms including random forest (RF), decision trees, linear support vector machines (SVMs), neural networks, naïve Bayes classifier, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and K-nearest neighbors (k-NN). Model performance was evaluated using F1 score, accuracy, precision, sensitivity, area under the receiver operating characteristic curve (AUROC), and Matthews correlation coefficient (MCC). Analyses were conducted in all-patient and etanercept-only samples.

RESULTS

In the all-patients sample, ML approaches did not outperform logistic regression. RF showed small improvements versus logistic regression that were not considered remarkable, respectively: F1 score (84.55% vs 83.96%), accuracy (84.05% vs 83.79%), sensitivity (84.53% vs 82.20%), AUROC (84.04% vs 83.85%), and MCC (68.07% vs 67.66%). Findings were similar in the etanercept samples.

CONCLUSION

Logistic regression and ML approaches successfully identified patients with RA in a large pharmacy administrative claims database. The ML algorithms were no better than logistic regression at prediction. RF, SVMs, LDA, and ridge classifier showed comparable performance, while neural networks, decision trees, naïve Bayes classifier, and QDA underperformed compared with logistic regression in identifying patients with RA.

A pilot study of a machine-learning tool to assist in the diagnosis of hand arthritis

OBJECTIVE

Arthritis is a common condition, which frequently involves the hands. Patients with inflammatory arthritis have been shown to experience significant delays in diagnosis. We sought to develop and test a screening tool combining an image of a patient's hands, a short series of questions, and a single examination technique, to determine the most likely diagnosis in a patient presenting with hand arthritis. Machine learning techniques were used to develop separate algorithms for each component, which were combined to produce a diagnosis.

METHODS

280 consecutive new patients presenting to a Rheumatology practice with hand arthritis were enrolled. Each patient completed a 9-part questionnaire, had photographs taken of each hand, and had a single examination result recorded. The Rheumatologist diagnosis was recorded following a 45-minute consultation. The photograph algorithm was developed from 1000 previous hand images, and machine learning techniques were applied to the questionnaire results, training several models against the diagnosis from the Rheumatologist.

RESULTS

The combined algorithms in this study were able to predict inflammatory arthritis with an accuracy, precision, recall and specificity of 96·8%, 97·2%, 98·6% and 90·5% respectively. Similar results were found when inflammatory arthritis was subclassified into rheumatoid arthritis and psoriatic arthritis. The corresponding figures for osteoarthritis were 79·6%, 85·9%, 61·9% and 92·6%.

CONCLUSION

This study demonstrates a novel application combining image-processing and a patient questionnaire with applied machine-learning methods, to facilitate the diagnosis of patients presenting with hand arthritis. Preliminary results are encouraging for the application of such techniques in clinical practice. This article is protected by copyright. All rights reserved.

[Perspectives for rheumatological health services research at the German Rheumatism Research Center]

In diesem Übersichtsartikel werden aktuelle Projekte und Perspektiven der rheumatologischen Versorgungsforschung am Programmbereich Epidemiologie des DRFZ (Deutsches Rheuma-Forschungszentrum) zusammengefasst. Versorgungsforschung wird mithilfe verschiedener Datenquellen betrieben. Neben den klassischen rheumatologischen Krankheitsregistern werden zunehmend auch Krankenkassendaten und bevölkerungsbezogene Kohorten für Analysen verwendet. Von der Datenerfassung über das Monitoring bis zu Analysealgorithmen verändern digitale Anwendungen die Versorgungsforschung der nächsten Jahre. Kollaborative Analysen mit nationalen und internationalen Kooperationspartnern unter Einbindung von Biomarkern komplettieren die Forschung am Programmbereich Epidemiologie. Die Digitalisierung der Forschungsprojekte ist ein zentraler Baustein, der die Versorgungsforschung im kommenden Jahrzehnt weiter verändern wird.

Asynchronous mHealth Interventions in Rheumatoid Arthritis: Systematic Scoping Review

BACKGROUND

Mobile devices such as smartphones and tablets have surged in popularity in recent years, generating numerous possibilities for their use in health care as mobile health (mHealth) tools. One advantage of mHealth is that it can be provided asynchronously, signifying that health care providers and patients are not communicating in real time. The integration of asynchronous mHealth into daily clinical practice might therefore help to make health care more efficient for patients with rheumatoid arthritis (RA). The benefits have been reviewed in various medical conditions, such as diabetes and asthma, with promising results. However, to date, it is unclear what evidence exists for the use of asynchronous mHealth in the field of RA.

OBJECTIVE

The objective of this study was to map the different asynchronous mHealth interventions tested in clinical trials in patients with RA and to summarize the effects of the interventions.

METHODS

A systematic search of Pubmed, Scopus, Cochrane, and PsycINFO was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Studies were initially screened and later assessed by two independent researchers. Disagreements on inclusion or exclusion of studies were resolved by discussion.

RESULTS

The literature search yielded 1752 abstracts. After deduplication and screening, 10 controlled intervention studies were included. All studies were assessed to be at risk for bias in at least one domain of the Cochrane risk-of-bias tool. In the 10 selected studies, 4 different types of mHealth interventions were used: SMS reminders (to increase medication adherence or physical activity; n=3), web apps (for disease monitoring and/or to provide medical information; n=5), smartphone apps (for disease monitoring; n=1), and pedometers (to increase and track steps; n=1). Measured outcomes varied widely between studies; improvements were seen in terms of medication compliance (SMS reminders), reaching rapid remission (web app), various domains of physical activity (pedometer, SMS reminders, and web apps), patient-physician interaction (web apps), and self-efficacy (smartphone app).

CONCLUSIONS

SMS reminders, web apps, smartphone apps, and pedometers have been evaluated in intervention studies in patients with RA. These interventions have been used to monitor patients or to support them in their health behavior. The use of asynchronous mHealth led to desirable outcomes in nearly all studies. However, since all studies were at risk of bias and methods used were very heterogeneous, high-quality research is warranted to corroborate these promising results.

[Telemedicine in rheumatology]

Der Ausbruch der COVID-19-Pandemie geht mit tief greifenden Einschnitten im Alltag und im Berufsleben einher – sowohl gesamtgesellschaftlich als auch speziell im Gesundheitswesen. Im Fokus der Pandemieeindämmung haben sich vielerorts rheumatologische Routineabläufe verändert. Um den entsprechenden Infektionsschutz der Patienten und des medizinischen Personals gewährleisten zu können, wurde hier verstärkt Telemedizin (insbesondere Telefon- und Videosprechstunde) eingesetzt. Weiterhin stehen durch die Digitale-Gesundheitsanwendungen-Verordnung (DiGAV) voraussichtlich in den kommenden Monaten neue, abrechnungsfähige telemedizinische Anwendungsmöglichkeiten wie Apps und Wearables zur Verfügung. Der Artikel soll einen Überblick über telemedizinische Versorgungsmöglichkeiten in der Rheumatologie (mit besonderem Fokus auf die Videosprechstunde) geben. Weiterhin wird Bezug auf die vorhandene Evidenzlage sowie Chancen und Limitation der Telemedizin im Fachgebiet genommen.

A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data

Background

Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently.

Objective

This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30).

Methods

Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups.

Results

No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t = -4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different.

Conclusion

This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.

[Mobile apps and their usage in rheumatology]

The increasing use of smartphones is accompanied by a significant increase in the use of mobile applications (apps). Chronically ill patients could permanently profit from this development.This development is fuelled by the Digital Healthcare Act (DVG), whereby patients have a legal claim to certain apps, so-called digital health applications (DiGAs), which are reimbursed by the statutory health insurance companies. Especially in the field of rheumatology, there are various opportunities to implement apps in the management of chronic diseases and their comorbidities. Furthermore, rheumatic patients and rheumatologists are becoming interested in apps and are willing to use them in the daily routine. This article tries to shed light on the chances and risks of apps and gives a first insight into the digital landscape of rheumatology apps in Germany.

Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?

In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers.

Mobile Health Usage, Preferences, Barriers, and eHealth Literacy in Rheumatology: Patient Survey Study

Background

Mobile health (mHealth) defines the support and practice of health care using mobile devices and promises to improve the current treatment situation of patients with chronic diseases. Little is known about mHealth usage and digital preferences of patients with chronic rheumatic diseases.

Objective

The aim of the study was to explore mHealth usage, preferences, barriers, and eHealth literacy reported by German patients with rheumatic diseases.

Methods

Between December 2018 and January 2019, patients (recruited consecutively) with rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis were asked to complete a paper-based survey. The survey included questions on sociodemographics, health characteristics, mHealth usage, eHealth literacy using eHealth Literacy Scale (eHEALS), and communication and information preferences.

Results

Of the patients (N=193) who completed the survey, 176 patients (91.2%) regularly used a smartphone, and 89 patients (46.1%) regularly used social media. Patients (132/193, 68.4%) believed that using medical apps could be beneficial for their own health. Out of 193 patients, only 8 (4.1%) were currently using medical apps, and only 22 patients (11.4%) stated that they knew useful rheumatology websites/mobile apps. Nearly all patients (188/193, 97.4%) would agree to share their mobile app data for research purposes. Out of 193 patients, 129 (66.8%) would regularly enter data using an app, and 146 patients (75.6%) would welcome official mobile app recommendations from the national rheumatology society. The preferred duration for data entry was not more than 15 minutes (110/193, 57.0%), and the preferred frequency was weekly (59/193, 30.6%). Medication information was the most desired app feature (150/193, 77.7%). Internet was the most frequently utilized source of information (144/193, 74.6%). The mean eHealth literacy was low (26.3/40) and was positively correlated with younger age, app use, belief in benefit of using medical apps, and current internet use to obtain health information.

Conclusions

Patients with rheumatic diseases are very eager to use mHealth technologies to better understand their chronic diseases. This open-mindedness is counterbalanced by low mHealth usage and competency. Personalized mHealth solutions and clear implementation recommendations are needed to realize the full potential of mHealth in rheumatology.

Physical activity measured using wearable activity tracking devices associated with gout flares

Objective

To determine the feasibility and validity of using wearable activity trackers to test associations between gout flares with physical activity and sleep.

Methods

Participants with physician-diagnosed gout, hyperuricemia (≥ 6.8 mg/dl), current smartphone use, and ≥ 2 self-reported flares in the previous 6 months were enrolled. Physical activity, heart rate, and sleep data were obtained from wearable activity trackers (Fitbit Charge HR2). Daily compliance was defined by the availability of sufficiently complete activity data at least 80% of the day. Associations of weekly gout flares with sleep and activity were measured by comparing flare-related values to average sleep and steps per day. We used mixed linear models to account for repeated observations.

Results

Forty-four participants enrolled; 33 met the criteria for minimal wear time and flare reporting, with activity tracker data available for 60.5% of all total study days. Mean ± SD age was 48.8 ± 14.9 years; 85% were men; 15% were black; 88% were on allopurinol or febuxostat, and 30% reported ≥ 6 flares in the prior 6 months. Activity trackers captured 204 (38%) person-weeks with flares and 340 (62%) person-weeks without flares. Mean ± SD daily step count was significantly lower (p < 0.0001) during weeks with gout flares (5900 ± 4071) than during non-flare periods (6972 ± 5214); sleep however did not differ.

Conclusion

The pattern of wear in this study illustrates reasonable feasibility of using such devices in future arthritis research. The use of these devices to passively measure changes in physical activity patterns may provide an estimate of gout flare occurrence and duration.

Trial registration

NCT, NCT02855437. Registered 4 August 2016

Digital health technologies: opportunities and challenges in rheumatology

The past decade in rheumatology has seen tremendous innovation in digital health technologies, including the electronic health record, virtual visits, mobile health, wearable technology, digital therapeutics, artificial intelligence and machine learning. The increased availability of these technologies offers opportunities for improving important aspects of rheumatology, including access, outcomes, adherence and research. However, despite its growth in some areas, particularly with non-health-care consumers, digital health technology has not substantially changed the delivery of rheumatology care. This Review discusses key barriers and opportunities to improve application of digital health technologies in rheumatology. Key topics include smart design, voice enablement and the integration of electronic patient-reported outcomes. Smart design involves active engagement with the end users of the technologies, including patients and clinicians through focus groups, user testing sessions and prototype review. Voice enablement using voice assistants could be critical for enabling patients with hand arthritis to effectively use smartphone apps and might facilitate patient engagement with many technologies. Tracking many rheumatic diseases requires frequent monitoring of patient-reported outcomes. Current practice only collects this information sporadically, and rarely between visits. Digital health technology could enable patient-reported outcomes to inform appropriate timing of face-to-face visits and enable improved application of treat-to-target strategies. However, best practice standards for digital health technologies do not yet exist. To achieve the potential of digital health technology in rheumatology, rheumatology professionals will need to be more engaged upstream in the technology design process and provide leadership to effectively incorporate the new tools into clinical care.

Digital crowdsourcing: unleashing its power in rheumatology

The COVID-19 pandemic forces the whole rheumatic and musculoskeletal diseases community to reassemble established treatment and research standards. Digital crowdsourcing is a key tool in this pandemic to create and distil desperately needed clinical evidence and exchange of knowledge for patients and physicians alike. This viewpoint explains the concept of digital crowdsourcing and discusses examples and opportunities in rheumatology. First experiences of digital crowdsourcing in rheumatology show transparent, accessible, accelerated research results empowering patients and rheumatologists.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Assessment of the many faces of PsA: single and composite measures in PsA clinical trials

PsA is a complex, heterogeneous disease that can place a large burden on patients' psychological and physical well-being. The multifaceted nature of PsA poses a significant assessment challenge, both in randomized control trials and in clinical practice. In recent years, there has been much progress in the development of unidimensional and composite measures of disease activity, as well as of questionnaires that capture the patient's perspective of the condition. Despite these advances, there remains uncertainty around which tools to implement within a research setting. This review aims to summarize the currently available clinical and patient-derived assessment tools, providing a practical and informative resource for the assessment of PsA. This review will also explore recent advancements in digital approaches to the assessment of rheumatological conditions. This will highlight the potential for digitalization in the assessment and monitoring of PsA, outlining innovative means of capturing disease activity and treatment response.

Big data and data processing in rheumatology: bioethical perspectives

Big data analytics and processing through artificial intelligence (AI) are increasingly being used in the health sector. This includes both clinical and research settings, and newly in specialties like rheumatology. It is, however, important to consider how these new methodologies are used, and particularly the sensitivities associated with personal information. Based on current applications in rheumatology, this article provides a narrative review of the bioethical perspectives of big data. It presents examples of databases, data analytic methods, and AI in this specialty to address four main ethical issues: privacy and confidentiality, informed consent, the impact on the medical profession, and justice. The use of big data and AI processing in healthcare has great potential to improve the quality of clinical care, including through better diagnosis, treatment, and prognosis. They may also increase patient and societal participation and engagement in healthcare and research. Developing these methodologies and using the information generated from them in line with ethical standards could positively affect the design of global health policies and introduce a new phase in the democratization of health.Key Points• Current applications of big data, data analytics, and AI in rheumatology-including registries, machine learning algorithms, and consumer-facing platforms-raise issues in four main bioethical areas: privacy and confidentiality, informed consent, the impact on the medical profession, and justice.• Bioethical concerns about rheumatology registries require careful consideration of privacy provisions, set within the context of local, national, and regional law.• Machine learning and big data aid diagnosis, treatment, and prognosis, but the final decision about the use of information from algorithms should be left to rheumatology specialists to maintain the promise of fiduciary obligations in the physician-patient relationship.• International collaboration in big data projects and increased patient engagement could be ways to counteract health inequalities in the practice of rheumatology, even on a global scale.

Assessing Physical Activity and Sleep in Axial Spondyloarthritis: Measuring the Gap

Patients with axial spondyloarthritis (axSpA) frequently report pain, stiffness, fatigue, and sleep problems, which may lead to impaired physical activity. The majority of reported-on measures evaluating physical activity and sleep disturbance in axSpA are self-reported questionnaires, which can be impacted by patient recall (reporting bias). One objective measure, polysomnography, has been employed to evaluate sleep in patients with axSpA; however, it is an intrusive measure and cannot be used over the long term. More convenient objective measures are therefore needed to allow for the long-term assessment of both sleep and physical activity in patients' daily lives. Wearable technology that utilizes actigraphy is increasingly being used for the objective measurement of physical activity and sleep in various therapy areas, as it is unintrusive and suitable for continuous tracking to allow longitudinal assessment. Actigraphy characterizes sleep disruption as restless movement while sleeping, which is particularly useful when studying conditions such as axSpA in which chronic pain and discomfort due to stiffness may be evident. Studies have also shown that actigraphy can effectively assess the impact of disease on physical activity. More research is needed to establish the usefulness of objective monitoring of sleep and physical activity specifically in axSpA patients over time. This review summarizes the current perspectives on physical activity and sleep quality in patients with axSpA, and the possible role of actigraphy in the future to more accurately evaluate the impact of treatment interventions on sleep and physical activity in axSpA.Funding: Novartis Pharmaceuticals Corporation.Plain Language Summary: Plain language summary available for this article.

German Mobile Apps in Rheumatology: Review and Analysis Using the Mobile Application Rating Scale (MARS)

Background

Chronic rheumatic diseases need long-term treatment and professional supervision. Mobile apps promise to improve the lives of patients and physicians. In routine practice, however, rheumatology apps are largely unknown and little is known about their quality and safety.

Objective

The aim of this study was to provide an overview of mobile rheumatology apps currently available in German app stores, evaluate app quality using the Mobile Application Rating Scale (MARS), and compile brief, ready-to-use descriptions for patients and rheumatologists.

Methods

The German App Store and Google Play store were systematically searched to identify German rheumatology mobile apps for patient and physician use. MARS was used to independently assess app quality by 8 physicians, 4 using Android and 4 using iOS smartphones. Apps were randomly assigned so that 4 apps were rated by all raters and the remaining apps were rated by two Android and two iOS users. Furthermore, brief app descriptions including app developers, app categories, and features were compiled to inform potential users and developers.

Results

In total, 128 and 63 apps were identified in the German Google Play and App Store, respectively. After removing duplicates and only including apps that were available in both stores, 28 apps remained. Sixteen apps met the inclusion criteria, which were (1) German language, (2) availability in both app stores, (3) targeting patients or physicians as users, and (4) clearly including rheumatology or rheumatic diseases as subject matter. Exclusion criteria were (1) congress apps and (2) company apps with advertisements. Nine apps addressed patients and 7 apps addressed physicians. No clinical studies to support the effectiveness and safety of apps could be found. Pharmaceutical companies were the main developers of two apps. Rheuma Auszeit was the only app mainly developed by a patient organization. This app had the highest overall MARS score (4.19/5). Three out of 9 patient apps featured validated questionnaires. The median overall MARS score was 3.85/5, ranging from 2.81/5 to 4.19/5. One patient-targeted and one physician-targeted app had MARS scores >4/5. No significant rater gender or platform (iOS/Android) differences could be observed. The overall correlation between app store ratings and MARS scores was low and inconsistent between platforms.

Conclusions

To our knowledge, this is the first study that systematically identified and evaluated mobile apps in rheumatology for patients and physicians available in German app stores. We found a lack of supporting clinical studies, use of validated questionnaires, and involvement of academic developers. Overall app quality was heterogeneous. To create high-quality apps, closer cooperation led by patients and physicians is vital.

A portfolio of biologic self-injection devices in rheumatology: how patient involvement in device design can improve treatment experience

Biologic drugs (e.g. anti-tumor necrosis factors) are effective treatments for multiple chronic inflammatory diseases including rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis. Administration of biologic drugs is usually via subcutaneous self-injection, which provides many patient benefits compared to infusions including increased flexibility, reduced costs, and reduced caregiver burden. However, it is also associated with challenges such as needle phobia, patient treatment misconceptions and incorrect drug administration, and can be impacted by dexterity problems. Evidence suggests these problems, along with other drug administration challenges (e.g. patient forgetfulness, busy lifestyles, and polypharmacy), can reduce patient adherence to treatment. To combat these challenges, patient feedback has been used to develop a range of self-injection devices, including pre-filled syringes, pre-filled pens, and electronic injection devices. Providing different devices for drug administration gives patients the opportunity to choose a device that addresses the challenges they face as an individual. Research suggests involving patients in medical device development, providing patients with a choice of devices and enrolling individuals in patient support programs can empower patients to take control of their treatment journey. By providing a portfolio of self-injection devices, designed based on patient needs, patient experience will improve, potentially improving adherence and hence, long-term treatment outcomes.

Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations

Objective

To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).

Methods

A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs.

Results

Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000–5 billion) in RMDs, and 9.1 billion (range 100 000–200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs).

Conclusions

Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.

EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases

BACKGROUND

Tremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs).

METHODS

A multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated.

RESULTS

Three overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice.

CONCLUSION

These EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.

Detection of Familial Mediterranean Fever attacks by using a connected activity tracker and assessment of impact of attacks to daily physical activities: a pilot study

OBJECTIVE

The objective of this study was to assess the impact of Familial Mediterranean Fever (FMF) attacks on daily physical activity and detect FMF attacks using a connected activity tracker.

METHODS

Patients with FMF according to the Tel-Hashomer criteria were included in this prospective observational study. Attack-related data were collected weekly via phone call to avoid memory bias. Median steps in attack and attack-free days were calculated and compared using the Wilcoxon rank test. Sensitivity and specificity threshold for capturing attacks was set to two thirds of median steps per day in the whole observation period.

RESULTS

Twelve patients participated in the study. The median age of patients was 26 (18-32) years, and 7 (58.3%) of them were male. Patients with attacks (n = 10) walked a median of 6990 (4552-11,531) steps per day in attack-free days, whereas this number decreased to a median of 1841 (590-4783) steps in attack days (p = 0.005). The activity tracker captured 42 of 45 attack days and 312 of 361 attack-free days. The cutoff value had 93% sensitivity and 86% specificity for capturing attacks.

CONCLUSIONS

FMF attacks significantly impair the physical activity of patients. Activity tracking may be a reasonable method to document FMF attacks. This might prevent errors due to memory bias and help accurately identify and treat patients with FMF.