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

Evaluation of a hybrid telehealth care pathway for patients with axial spondyloarthritis including self-sampling at home: results of a longitudinal proof-of-concept mixed-methods study (TeleSpactive)

Patients with axial spondyloarthritis (axSpA) require close monitoring to achieve the goal of sustained disease remission. Telehealth can facilitate continuous care while relieving scarce healthcare resources. In a mixed-methods proof-of-concept study, we investigated a hybrid telehealth care axSpA pathway in patients with stable disease over 6 months. Patients used a medical app to document disease activity (BASDAI and PtGA bi-weekly, flare questionnaire weekly). To enable a remote ASDAS-CRP (TELE-ASDAS-CRP), patients used a capillary self-sampling device at home. Monitoring results were discussed and a decision was reached via shared decision-making whether a pre-planned 3-month on-site appointment (T3) was necessary. Ten patients completed the study, and eight patients also completed additional telephone interviews. Questionnaire adherence was high; BASDAI (82.3%), flares (74.8%) and all patients successfully completed the TELE-ASDAS-CRP for the T3 evaluation. At T3, 9/10 patients were in remission or low disease activity and all patients declined the offer of an optional T3 on-site appointment. Patient acceptance of all study components was high with a net promoter score (NPS) of +50% (mean NPS 8.8 ± 1.5) for self-sampling, +70% (mean NPS 9.0 ± 1.6) for the electronic questionnaires and +90% for the T3 teleconsultation (mean NPS 9.7 ± 0.6). In interviews, patients reported benefits such as a better overview of their condition, ease of use of telehealth tools, greater autonomy, and, most importantly, travel time savings. To our knowledge, this is the first study to investigate a hybrid approach to follow-up axSpA patients including self-sampling. The positive results observed in this scalable proof-of-concept study warrant a larger confirmatory study.

Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches

BACKGROUND

The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening.

OBJECTIVE

This exploratory study aims to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small data set of daily symptom data collected on a smartphone app.

METHODS

Daily symptoms and weekly flares reported on the Remote Monitoring of Rheumatoid Arthritis (REMORA) smartphone app from 20 patients with RA over 3 months were used. Predictors were several summary features of the daily symptom scores (eg, pain and fatigue) collected in the week leading up to the flare question. We fitted 3 binary classifiers: logistic regression with and without elastic net regularization, a random forest, and naive Bayes. Performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. For the best-performing model, we considered sensitivity and specificity for different thresholds in order to illustrate different ways in which the predictive model could behave in a clinical setting.

RESULTS

The data comprised an average of 60.6 daily reports and 10.5 weekly reports per participant. Participants reported a median of 2 (IQR 0.75-4.25) flares each over a median follow-up time of 81 (IQR 79-82) days. AUCs were broadly similar between models, but logistic regression with elastic net regularization had the highest AUC of 0.82. At a cutoff requiring specificity to be 0.80, the corresponding sensitivity to detect flares was 0.60 for this model. The positive predictive value (PPV) in this population was 53%, and the negative predictive value (NPV) was 85%. Given the prevalence of flares, the best PPV achieved meant only around 2 of every 3 positive predictions were correct (PPV 0.65). By prioritizing a higher NPV, the model correctly predicted over 9 in every 10 non-flare weeks, but the accuracy of predicted flares fell to only 1 in 2 being correct (NPV and PPV of 0.92 and 0.51, respectively).

CONCLUSIONS

Predicting self-reported flares based on daily symptom scorings in the preceding week using machine learning methods was feasible. The observed predictive accuracy might improve as we obtain more data, and these exploratory results need to be validated in an external cohort. In the future, analysis of frequently collected patient-generated data may allow us to predict flares before they unfold, opening opportunities for just-in-time adaptative interventions. Depending on the nature and implication of an intervention, different cutoff values for an intervention decision need to be considered, as well as the level of predictive certainty required.

How to Monitor Disease Activity of Axial Spondyloarthritis in Clinical Practice

PURPOSE OF REVIEW

Treatment guided by periodic and quantitative data assessment results in better outcomes compared to using clinical gestalt. While validated generic as well as specific disease activity measures for axial spondyloarthritis (axSpA) are available, there is vast scope to improve their actual utilization in routine clinical practice. In this review, we discuss available disease activity measures for axSpA, describe results from the survey conducted among general rheumatologists as well as Spondyloarthritis Research and Treatment Network (SPARTAN) members about disease activity measurement in daily practice, and discuss ways to improve axSpA disease activity using technological advances. We also discuss the definitions of active disease and target for the treatment of axSpA.

RECENT FINDINGS

The 2019 American College of Rheumatology (ACR)/Spondylitis Association of America (SAA)/Spondyloarthritis Research and Treatment Network (SPARTAN) axSpA treatment guidelines conditionally recommend the regular monitoring of disease activity using a validated measure such as Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) or Ankylosing Spondylitis Disease Severity Index (ASDAS). Assessment of Spondyloarthritis International Society (ASAS)-European Alliance of Associations for Rheumatology (EULAR) guidelines recommend ASDAS as the most appropriate instrument for the assessment of disease activity, preferably calculated using C-reactive protein (CRP). ASAS has selected a core set of variables which were updated recently and have been endorsed by the Outcome Measures in Rheumatology Clinical Trials (OMERACT) group in order to bring homogeneity in assessment of axSpA. In a recent study, Patient-Reported Outcomes Measurement Information System (PROMIS®) measures were able to discriminate inactive, moderate, and high-very high ASDAS activity groups. A newly developed semi-objective index P4 (pain, physical function, patient global, and physician global) correlates well with BASDAI and ASDAS in axSpA and can also be used for other rheumatic diseases in busy clinical practices. Regular disease activity monitoring is critical for long-term management of axSpA and shared decision-making. The integration of electronic health records and smart devices provides a great opportunity to capture patient-reported data. Automated capture of electronic patient-reported outcome measures (ePROMs) is a highly efficient way and results in consistent regular monitoring and may improve the long-term outcomes. While currently used measures focus only on musculoskeletal symptoms of axSpA, a composite disease activity measure that can also incorporate extra-articular manifestations may provide a better assessment of disease activity.

[Wearables in rheumatology]

As a result of digitalization in medicine wearable computing devices (wearables) are becoming increasingly more important. Wearables are small portable electronic devices with which the user can record data relevant to health, such as number of steps, activity profile, electrocardiogram (ECG), heart and breathing frequency or oxygen saturation. Initial studies on the use of wearables in patients with rheumatological diseases show the opening up of new possibilities for prevention, disease monitoring and treatment. This study provides the current data situation and the implementation of wearables in the discipline of rheumatology. Additionally, future potential fields of application as well as challenges and limits of the implementation of wearables are illustrated.

What does artificial intelligence mean in rheumatology?

Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician's diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.

Advancing Rheumatology Care Through Machine Learning

Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring. Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity. Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms. In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.

Entry point of machine learning in axial spondyloarthritis

Axial spondyloarthritis (axSpA) is a globally prevalent and challenging autoimmune disease. Characterised by insidious onset and slow progression, the absence of specific clinical manifestations and biomarkers often leads to misdiagnosis, thereby complicating early detection and diagnosis of axSpA. Furthermore, the high heterogeneity of axSpA, its complex pathogenesis and the lack of specific drugs means that traditional classification standards and treatment guidelines struggle to meet the demands of personalised treatment. Recently, machine learning (ML) has seen rapid advancements in the medical field. By integrating large-scale data with diverse algorithms and using multidimensional data, such as patient medical records, laboratory examinations, radiological data, drug usage and molecular biology information, ML can be modelled based on real-world clinical issues. This enables the diagnosis, stratification, therapeutic efficacy prediction and prognostic evaluation of axSpA, positioning it as an emerging research topic. This study explored the application and progression of ML in the diagnosis and therapy of axSpA from five perspectives: early diagnosis, stratification, disease monitoring, drug efficacy evaluation and comorbidity prediction. This study aimed to provide a novel direction for exploring rational diagnostic and therapeutic strategies for axSpA.

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis

Digital measures of health status captured during daily life could greatly augment current in-clinic assessments for rheumatoid arthritis (RA), to enable better assessment of disease progression and impact. This work presents results from weaRAble-PRO, a 14-day observational study, which aimed to investigate how digital health technologies (DHT), such as smartphones and wearables, could augment patient reported outcomes (PRO) to determine RA status and severity in a study of 30 moderate-to-severe RA patients, compared to 30 matched healthy controls (HC). Sensor-based measures of health status, mobility, dexterity, fatigue, and other RA specific symptoms were extracted from daily iPhone guided tests (GT), as well as actigraphy and heart rate sensor data, which was passively recorded from patients' Apple smartwatch continuously over the study duration. We subsequently developed a machine learning (ML) framework to distinguish RA status and to estimate RA severity. It was found that daily wearable sensor-outcomes robustly distinguished RA from HC participants (F1, 0.807). Furthermore, by day 7 of the study (half-way), a sufficient volume of data had been collected to reliably capture the characteristics of RA participants. In addition, we observed that the detection of RA severity levels could be improved by augmenting standard patient reported outcomes with sensor-based features (F1, 0.833) in comparison to using PRO assessments alone (F1, 0.759), and that the combination of modalities could reliability measure continuous RA severity, as determined by the clinician-assessed RAPID-3 score at baseline (r2, 0.692; RMSE, 1.33). The ability to measure the impact of the disease during daily life-through objective and remote digital outcomes-paves the way forward to enable the development of more patient-centric and personalised measurements for use in RA clinical trials.

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

Patient perspectives on telemedicine use in rheumatology during the COVID-19 pandemic: survey results from the COVID-19 Global Rheumatology Alliance

OBJECTIVE

The COVID-19 pandemic resulted in rapid adoption of telemedicine in rheumatology. We described perspectives of patients with rheumatic diseases related to telemedicine use.

METHODS

An anonymous online survey for people with rheumatic diseases was launched in January 2021. We collected data on reasons for telemedicine use, perceived benefits, disadvantages and obstacles of telemedicine, perceived telemedicine effectiveness for different clinical tasks, level of satisfaction with telemedicine use, and future preferences for telemedicine. We summarized results with descriptive statistics and identified themes in free text responses to describe perspectives of telemedicine qualitatively.

RESULTS

We received 596 complete responses (85% female and 47% 41-60 years old). During the COVID-19 pandemic, 78% (467/596) of respondents used telemedicine, and 61% (283/467) of telemedicine users reported that telemedicine was as effective or more effective than an in-person visit. Younger participants and those in North America reported effectiveness and satisfaction with telemedicine at higher frequencies. Participants reported similar effectiveness to in-person visits for making medication changes and discussing disease symptoms or complications.

CONCLUSION

Most respondents found telemedicine at least as effective as in-person visits. Participants found telemedicine to be effective for specific scenarios, such as making medication changes and discussion of disease activity. Telemedicine may continue to be of importance in the care of patients with rheumatic diseases post pandemic, but likely for specific subsets of patients for specific visit indications. Key Points • Most patients with rheumatic disease found telemedicine as effective as in-person visits, particularly for some indications.

Follow-Up of Patients With Axial Spondyloarthritis in Specialist Health Care With Remote Monitoring and Self-Monitoring Compared With Regular Face-to-Face Follow-Up Visits (the ReMonit Study): Protocol for a Randomized, Controlled Open-Label Noninferiority Trial

BACKGROUND

Patients with chronic inflammatory joint diseases such as axial spondyloarthritis have traditionally received regular follow-up in specialist health care to maintain low disease activity. The follow-up has been organized as prescheduled face-to-face visits, which are time-consuming for both patients and health care professionals. Technology has enabled the remote monitoring of disease activity, allowing patients to self-monitor their disease and contact health care professionals when needed. Remote monitoring or self-monitoring may provide a more personalized follow-up, but there is limited research on how these follow-up strategies perform in maintaining low disease activity, patient satisfaction, safety, and cost-effectiveness.

OBJECTIVE

The Remote Monitoring in Axial Spondyloarthritis (ReMonit) study aimed to assess the effectiveness of digital remote monitoring and self-monitoring in maintaining low disease activity in patients with axial spondyloarthritis.

METHODS

The ReMonit study is a 3-armed, single-site, randomized, controlled, open-label noninferiority trial including patients with axial spondyloarthritis with low disease activity (Ankylosing Spondylitis Disease Activity Score <2.1) and on stable treatment with a tumor necrosis factor inhibitor. Participants were randomized 1:1:1 to arm A (usual care, face-to-face visits every sixth month), arm B (remote monitoring, monthly digital registration of patient-reported outcomes), or arm C (patient-initiated care, self-monitoring, no planned visits during the study period). The primary end point was disease activity measured with the Ankylosing Spondylitis Disease Activity Score, evaluated at 6, 12, and 18 months. We aimed to include 240 patients, 80 in each arm. Secondary end points included other measures of disease activity, patient satisfaction, safety, and cost-effectiveness.

RESULTS

The project is funded by the South-Eastern Norway Regional Health Authority and Centre for the treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Norway. Enrollment started in September 2021 and was completed with 242 patients by June 2022. The data collection will be completed in December 2023.

CONCLUSIONS

To our knowledge, this trial will be among the first to evaluate the effectiveness, safety, and cost-effectiveness of remote digital monitoring and self-monitoring of patients with axial spondyloarthritis compared with usual care. Hence, the ReMonit study will contribute important knowledge to personalized follow-up strategies for patients with axial spondyloarthritis. These results may also be relevant for other patient groups with inflammatory joint diseases.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05031767; hpps://www.clinicaltrials.gov/study/NCT05031767.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52872.

Artificial intelligence in rheumatoid arthritis: potential applications and future implications

The widespread adoption of digital health records, coupled with the rise of advanced diagnostic testing, has resulted in an explosion of patient data, comparable in scope to genomic datasets. This vast information repository offers significant potential for improving patient outcomes and decision-making, provided one can extract meaningful insights from it. This is where artificial intelligence (AI) tools like machine learning (ML) and deep learning come into play, helping us leverage these enormous datasets to predict outcomes and make informed decisions. AI models can be trained to analyze and interpret patient data, including physician notes, laboratory testing, and imaging, to aid in the management of patients with rheumatic diseases. As one of the most common autoimmune diseases, rheumatoid arthritis (RA) has attracted considerable attention, particularly concerning the evolution of diagnostic techniques and therapeutic interventions. Our aim is to underscore those areas where AI, according to recent research, demonstrates promising potential to enhance the management of patients with RA.

Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals-The OASIS Study

(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for predicting possible disease flares. (2) Methods: Participants were recruited from the LupusCorner online community. Adults self-reporting an SLE diagnosis were consented and given a mobile application to record patient profile (PP), PRO, and QOL metrics, and enlisted participants received smartwatches for digital biometric monitoring. The resulting data were profiled using feature selection and classification algorithms. (3) Results: 550 participants completed digital surveys, 144 (26%) agreed to wear smartwatches, and medical records (MRs) were obtained for 68. Mining of PP, PRO, QOL, and biometric data yielded a 26-feature model for classifying participants according to MR-identified disease flare risk. ROC curves significantly distinguished true from false positives (ten-fold cross-validation: p < 0.00023; five-fold: p < 0.00022). A 25-feature Bayesian model enabled time-variant prediction of participant-reported possible flares (P(true) > 0.85, p < 0.001; P(nonflare) > 0.83, p < 0.0001). (4) Conclusions: Regular profiling of patient well-being and biometric activity may support proactive screening for circumstances warranting clinical assessment.

[Digital rheumatology]

Chronic inflammatory rheumatic diseases mostly run an undulating course and with unspecific symptoms. The initial clarification and timely initiation of treatment are challenging, which is additionally exacerbated by the lack of specialized physicians. Digital approaches, including artificial intelligence (AI), should be of assistance and enable an improved, personalized and needs-based treatment; however, the evidence is currently still very limited. This article provides a compact overview of the current state of digital rheumatology.

Attitudes towards technology supported rheumatoid arthritis care: investigating patient- and clinician-perceived opportunities and barriers

Objectives

Globally, demand outstrips capacity in rheumatology services, making Mobile Health (mHealth) attractive, with the potential to improve access, empower patient self-management and save costs. Existing mHealth interventions have poor uptake by end users. This study was designed to understand existing challenges, opportunities and barriers for computer technology in the RA care pathway.

Methods

People with RA were recruited from Barts Health NHS Trust rheumatology clinics to complete paper questionnaires and clinicians were recruited from a variety of centres in the UK to complete an online questionnaire. Data collected included demographics, current technology use, challenges managing RA, RA medications and monitoring, clinic appointments, opportunities for technology and barriers to technology.

Results

A total of 109 patient and 41 clinician questionnaires were completed. A total of 83.5% of patients and 93.5% of clinicians use smartphones daily. However, only 25% had ever used an arthritis app and only 5% had persisted with one. Both groups identified managing pain, flares and RA medications as areas of existing need. Access to care, medication support and disease education were mutually agreeable opportunities; however, discrepancies existed between groups with clinicians prioritizing education over access, likely due to concerns of data overwhelm (80.6% considered this a barrier).

Conclusions

In spite of high technology use and willingness from both sides, our cohort did not utilize technology to support care, suggesting inadequacies in the existing software. The lack of an objective biomarker for RA disease activity, existing challenges in the healthcare system and the need for integration with existing technical systems were identified as the greatest barriers.

Trial registration

Registered on the Clinical Research Network registry (IRAS ID: 264690).

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

A survey of artificial intelligence in rheumatoid arthritis

The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.

Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review

BACKGROUND

Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity.

OBJECTIVE

This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers.

METHODS

This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed.

RESULTS

This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials.

CONCLUSION

mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.

Physical activity assessment with wearable devices in rheumatic diseases: a systematic review and meta-analysis

OBJECTIVES

In the management of rheumatic musculoskeletal disorders (RMDs), regular physical activity (PA) is an important recognized non-pharmacological intervention. This systematic review and meta-analysis aims to evaluate how the use of wearable devices (WDs) impacts physical activity in patients with noninflammatory and inflammatory rheumatic diseases.

METHODS

A comprehensive search of articles was performed in PubMed, Embase, CINAHL and Scopus. A random-effect meta-analysis was carried out on the number of steps and moderate-vigorous physical activity (MVPA). Univariable meta-regression models were computed to assess the possibility that the study characteristics may act as modifiers on the final meta-analysis estimate.

RESULTS

In the analysis, 51 articles were included, with a total of 7488 participants. Twenty-two studies considered MVPA outcome alone, 16 studies considered the number of steps alone, and 13 studies reported information on both outcomes. The recommended PA threshold was reached for MVPA (36.35, 95% CI 29.39, 43.31) but not for daily steps (-1092.60, -1640.42 to -544.77). Studies on patients with fibromyalgia report a higher number (6290, 5198.65-7381.62) of daily steps compared with other RMDs. Patients affected by chronic inflammatory arthropathies seemed to fare better in terms of daily steps than the other categories. Patients of younger age reported a higher overall level of PA than elderly individuals for both the number of steps and MVPA.

CONCLUSION

Physical activity can be lower than the recommended threshold in patients with RMDs when objectively measured using WD. WDs could be a useful and affordable instrument for daily monitoring physical activity in RMDs and may support an increase in activity levels.

PROSPERO TRIAL REGISTRATION

CRD42021227681, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=227681.

Patient-tailored dose reduction of tumor necrosis factor inhibitors in axial spondyloarthritis

Tumor necrosis factor inhibitors have been widely used in the field of axial spondyloarthritis, with current guidelines now recommending dose reduction instead of withdrawal of biologics. Systemic review and meta-analyses in literature have summarized present tapering strategies and principles in published heterogeneous studies. In this study, we reviewed and provided an update on present evidence based on prospective and retrospective studies from 2008 to 2022 by performing a literature review of related publications on remission or relapse from PubMed. We further stated the core issues concerning dose reduction, including the timing, optimization, intensity, maintenance, monitoring, factors associated with tapering and solutions to de-escalation failure. Remission/relapse should be the principal consideration in dose reduction implementation for individuals without comorbidities. As a treat-to-target scope of this multifaceted systemic disease, extra-articular manifestations such as uveitis, psoriasis, inflammatory bowel disease, cardiovascular complication, hip involvement and progressed structural damage influence patient-tailored dose reduction plans. Safety concerns and costs should be integrated into the decision-making schedule to optimize the individualized dose reduction paradigm.

The challenges of measuring physical activity and sedentary behaviour in people with rheumatoid arthritis

The importance of sufficient moderate-to-vigorous physical activity as a key component of a healthy lifestyle is well established, as are the health risks associated with high levels of sedentary behaviour. However, many people with RA do not undertake sufficient physical activity and are highly sedentary. To start addressing this, it is important to be able to carry out an adequate assessment of the physical activity levels of individual people in order that adequate steps can be taken to promote and improve healthy lifestyles. Different methods are available to measure different aspects of physical activity in different settings. In controlled laboratory environments, respiratory gas analysis can measure the energy expenditure of different activities accurately. In free-living environments, the doubly labelled water method is the gold standard for identifying total energy expenditure over a prolonged period of time (>10 days). To assess patterns of physical activity and sedentary behaviour in daily life, objective methods with body-worn activity monitors using accelerometry are superior to self-reported questionnaire- or diary-based methods.

Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study

Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge.

Digitally-supported patient-centered asynchronous outpatient follow-up in rheumatoid arthritis - an explorative qualitative study

Objective

A steadily increasing demand and decreasing number of rheumatologists push current rheumatology care to its limits. Long travel times and poor accessibility of rheumatologists present particular challenges for patients. Need-adapted, digitally supported, patient-centered and flexible models of care could contribute to maintaining high-quality patient care. This qualitative study was embedded in a randomized controlled trial (TELERA) investigating a new model of care consisting of the use of a medical app for ePRO (electronic patient-reported outcomes), a self-administered CRP (C-reactive protein) test, and joint self-examination in rheumatoid arthritis (RA) patients. The qualitative study aimed to explore experiences of RA patients and rheumatology staff regarding (1) current care and (2) the new care model.

Methods

The study included qualitative interviews with RA patients (n = 15), a focus group with patient representatives (n = 1), rheumatology nurses (n = 2), ambulatory rheumatologists (n = 2) and hospital-based rheumatologists (n = 3). Data was analyzed by qualitative content analysis.

Results

Participants described current follow-up care as burdensome. Patients in remission have to travel long distances. Despite pre-scheduled visits physicians lack questionnaire results and laboratory results to make informed shared decisions during face-to-face visits. Patients reported that using all study components (medical app for ePRO, self-performed CRP test and joint self-examination) was easy and helped them to better assess their disease condition. Parts of the validated questionnaire used in the trial (routine assessment of patient index data 3; RAPID3) seemed outdated or not clear enough for many patients. Patients wanted to be automatically contacted in case of abnormalities or at least have an app feature to request a call-back or chat. Financial and psychological barriers were identified among rheumatologists preventing them to stop automatically scheduling new appointments for patients in remission. Rheumatology nurses pointed to the potential lack of personal contact, which may limit the holistic care of RA-patients.

Conclusion

The new care model enables more patient autonomy, allowing patients more control and flexibility at the same time. All components were well accepted and easy to carry out for patients. To ensure success, the model needs to be more responsive and allow seamless integration of education material.

Trial registration

The study was prospectively registered on 2021/04/09 at the German Registry for Clinical Trials (DRKS00024928).

Remote self-collection of capillary blood using upper arm devices for autoantibody analysis in patients with immune-mediated inflammatory rheumatic diseases

Objectives

To evaluate the feasibility, accuracy, usability and acceptability of two upper arm self-sampling devices for measurement of autoantibodies and C reactive protein (CRP) levels in patients with immune-mediated rheumatic diseases (IMRDs).

Methods

70 consecutive patients with IMRD with previously documented autoantibodies were assigned to supervised and unsupervised self-collection of capillary blood with the Tasso+ or TAP II device. Interchangeability of 17 biomarkers with standard venesection was assessed by: concordance, correlation, paired sample hypothesis testing and Bland-Altman plots. Patients completed an evaluation questionnaire, including the System Usability Scale (SUS) and Net Promoter Score (NPS).

Results

While 80.0% and 77.0% were able to safely and successfully collect capillary blood using the Tasso+ and TAP II within the first attempt, 69 of 70 (98.6%) patients were successful in collecting capillary blood within two attempts. Concordance between venous and capillary samples was high; 94.7% and 99.5% for positive and negative samples, respectively. For connective tissue disease screen, anti-Ro52 and anti-proteinase 3 autoantibody levels, no significant differences were observed. Self-sampling was less painful than standard venesection for the majority of patients (Tasso+: 71%; TAP II: 63%). Both devices were well accepted (NPS; both: +28%), usability was perceived as excellent (SUS; Tasso+: 88.6 of 100; TAP II: 86.0 of 100) and 48.6 %/62.9% of patients would prefer to use the Tasso+/TAP II, respectively, instead of a traditional venous blood collection.

Conclusions

Remote self-collection of capillary blood using upper arm-based devices for autoantibody and CRP analysis in patients with autoimmune rheumatic diseases is feasible, accurate and well accepted among patients.

Trial registration number

WHO International Clinical Trials Registry (DRKS00024925).

Wearables for Measuring the Physical Activity and Sedentary Behavior of Patients With Axial Spondyloarthritis: Systematic Review

Background

Axial spondyloarthritis (axSpA) is an inflammatory rheumatic disease associated with chronic back pain and restricted mobility and physical function. Increasing physical activity is a viable strategy for improving the health and quality of life of patients with axSpA. Thus, quantifying physical activity and sedentary behavior in this population is relevant to clinical outcomes and disease management. However, to the best of our knowledge, no systematic review to date has identified and synthesized the available evidence on the use of wearable devices to objectively measure the physical activity or sedentary behavior of patients with axSpA.

Objective

This study aimed to review the literature on the use of wearable activity trackers as outcome measures for physical activity and sedentary behavior in patients with axSpA.

Methods

PubMed, PEDro, and Cochrane electronic databases were searched in July 2021 for relevant original articles, with no limits on publication dates. Studies were included if they were original articles, targeted adults with a diagnosis of axSpA, and reported wearable device–measured physical activity or sedentary behavior among patients with axSpA. Data regarding the study’s characteristics, the sample description, the methods used for measuring physical activity and sedentary behavior (eg, wearable devices, assessment methods, and outcomes), and the main results of the physical activity and sedentary behavior assessments were extracted.

Results

A total of 31 studies were initially identified; 13 (13/31, 42%) met the inclusion criteria, including 819 patients with axSpA. All the studies used accelerometer-based wearable devices to assess physical activity. Of the 13 studies, 4 (4/31, 31%) studies also reported outcomes related to sedentary behavior. Wearable devices were secured on the wrists (3/13 studies, 23%), lower back (3/13, 23%), right hip (3/13, 23%), waist (2/13, 15%), anterior thigh (1/13, 8%), or right arm (1/13, 8%). The methods for reporting physical activity and sedentary behavior were heterogeneous. Approximately 77% (10/13) of studies had a monitoring period of 1 week, including weekend days.

Conclusions

To date, few studies have used wearable devices to quantify the physical activity and sedentary behavior of patients with axSpA. The methodologies and results were heterogeneous, and none of these studies assessed the psychometric properties of these wearables in this specific population. Further investigation in this direction is needed before using wearable device–measured physical activity and sedentary behavior as outcome measures in intervention studies in patients with axSpA.

Trial Registration

PROSPERO CRD42020182398; https://tinyurl.com/ec22jzkt

International Registered Report Identifier (IRRID)

RR2-10.2196/23359

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs)

Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.

Smartphones for musculoskeletal research - hype or hope? Lessons from a decennium of mHealth studies

BACKGROUND

Smartphones provide opportunities for musculoskeletal research: they are integrated in participants' daily lives and can be used to collect patient-reported outcomes as well as sensor data from large groups of people. As the field of research with smartphones and smartwatches matures, it has transpired that some of the advantages of this modern technology are in fact double-edged swords. BODY: In this narrative review, we illustrate the advantages of using smartphones for data collection with 18 studies from various musculoskeletal domains. We critically appraised existing literature, debunking some myths around the advantages of smartphones: the myth that smartphone studies automatically enable high engagement, that they reach more representative samples, that they cost little, and that sensor data is objective. We provide a nuanced view of evidence in these areas and discuss strategies to increase engagement, to reach representative samples, to reduce costs and to avoid potential sources of subjectivity in analysing sensor data.

CONCLUSION

If smartphone studies are designed without awareness of the challenges inherent to smartphone use, they may fail or may provide biased results. Keeping participants of smartphone studies engaged longitudinally is a major challenge. Based on prior research, we provide 6 actions by researchers to increase engagement. Smartphone studies often have participants that are younger, have higher incomes and high digital literacy. We provide advice for reaching more representative participant groups, and for ensuring that study conclusions are not plagued by bias resulting from unrepresentative sampling. Costs associated with app development and testing, data storage and analysis, and tech support are substantial, even if studies use a 'bring your own device'-policy. Exchange of information on costs, collective app development and usage of open-source tools would help the musculoskeletal community reduce costs of smartphone studies. In general, transparency and wider adoption of best practices would help bringing smartphone studies to the next level. Then, the community can focus on specific challenges of smartphones in musculoskeletal contexts, such as symptom-related barriers to using smartphones for research, validating algorithms in patient populations with reduced functional ability, digitising validated questionnaires, and methods to reliably quantify pain, quality of life and fatigue.

Future challenges in rheumatology – is telemedicine the solution?

The COVID-19 pandemic has become an unprecedented facilitator of rapid telehealth expansion within rheumatology. Due to demographic shifts and workforce shortages in the future, new models of rheumatology care will be expected to emerge, with a growing footprint of telehealth interventions. Telehealth is already being used to monitor patients with rheumatic diseases and initial studies show good results in terms of safety and disease progression. It is being used as a tool for appointment prioritization and triage, and there is good evidence for using telehealth in rehabilitation, patient education and self-management interventions. Electronic patient-reported outcomes (ePROs) offer a number of long-term benefits and opportunities, and a routine collection of ePROs also facilitates epidemiological research that can inform future healthcare delivery. Telehealth solutions should be developed in close collaboration with all stakeholders, and the option of a telehealth visit must not deprive patients of the possibility to make use of a conventional ‘face-to-face’ visit. Future studies should especially focus on optimal models for rheumatology healthcare delivery to patients living in remote areas who are unable to use or access computer technology, and other patient groups at risk for disparity due to technical inequity and lack of knowledge.

Using patient-reported data from a smartphone app to capture and characterise real-time patient-reported flares in rheumatoid arthritis

Objective

We aimed to explore the frequency of self-reported flares and their association with preceding symptoms collected through a smartphone app by people with RA.

Methods

We used data from the Remote Monitoring of RA study, in which patients tracked their daily symptoms and weekly flares on an app. We summarized the number of self-reported flare weeks. For each week preceding a flare question, we calculated three summary features for daily symptoms: mean, variability and slope. Mixed effects logistic regression models quantified associations between flare weeks and symptom summary features. Pain was used as an example symptom for multivariate modelling.

Results

Twenty patients tracked their symptoms for a median of 81 days (interquartile range 80, 82). Fifteen of 20 participants reported at least one flare week, adding up to 54 flare weeks out of 198 participant weeks in total. Univariate mixed effects models showed that higher mean and steeper upward slopes in symptom scores in the week preceding the flare increased the likelihood of flare occurrence, but the association with variability was less strong. Multivariate modelling showed that for pain, mean scores and variability were associated with higher odds of flare, with odds ratios 1.83 (95% CI, 1.15, 2.97) and 3.12 (95% CI, 1.07, 9.13), respectively.

Conclusion

Our study suggests that patient-reported flares are common and are associated with higher daily RA symptom scores in the preceding week. Enabling patients to collect daily symptom data on their smartphones might, ultimately, facilitate prediction and more timely management of imminent flares.

Technische Alltagshilfen in der Rheumatologie – Was ist sinnvoll, was ist bewiesen, welche Perspektiven gibt es?

Einschränkungen der Alltagsaktivität sind ein relevantes gesundheitliches Problem bei Patienten mit entzündlich-rheumatischen Systemerkrankungen. Technische Alltagshilfen nehmen in der Rehabilitation von diesen Teilhabeeinschränkungen einen hohen Stellenwert ein. Wir erläutern Evidenz für den Einsatz von Alltagshilfen und die neuen Entwicklungen auf diesem Gebiet.

The Digital Way to Intercept Psoriatic Arthritis

Psoriasis (PsO) and Psoriatic Arthritis (PsA) are chronic, immune-mediated diseases that share common etiopathogenetic pathways. Up to 30% of PsO patient may later develop PsA. In nearly 75% of cases, skin psoriatic lesions precede arthritic symptoms, typically 10 years prior to the onset of joint symptoms, while PsO diagnosis occurring after the onset of arthritis is described only in 15% of cases. Therefore, skin involvement offers to the rheumatologist a unique opportunity to study PsA in a very early phase, having a cohort of psoriatic “risk patients” that may develop the disease and may benefit from preventive treatment. Progression from PsO to PsA is often characterized by non-specific musculoskeletal symptoms, subclinical synovio-entheseal inflammation, and occasionally asymptomatic digital swelling such as painless toe dactylitis, that frequently go unnoticed, leading to diagnostic delay. The early diagnosis of PsA is crucial for initiating a treatment prior the development of significant and permanent joint damage. With the ongoing development of pharmacological treatments, early interception of PsA has become a priority, but many obstacles have been reported in daily routine. The introduction of digital technology in rheumatology may fill the gap in the physician-patient relationship, allowing more targeted monitoring of PsO patients. Digital technology includes telemedicine, virtual visits, electronic health record, wearable technology, mobile health, artificial intelligence, and machine learning. Overall, this digital revolution could lead to earlier PsA diagnosis, improved follow-up and disease control as well as maximizing the referral capacity of rheumatic centers.

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

INTRODUCTION

Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA.

AREAS COVERED

In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed.

EXPERT OPINION

ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.

[Telemedical care and IT-based systems in rheumatology]

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic and also the ever-increasing demands on the healthcare system, have led to a focus on the further development of telemedical services in rheumatology.

OBJECTIVE

What is the evidence for telemedical services in rheumatology?

MATERIAL AND METHODS

Narrative review of existing literature on telemedicine in rheumatology.

RESULTS

Electronic patient reported outcomes (ePROs) can be determined by patients from their home and sent electronically to the rheumatologist. In future, ePROs may help with the decision whether a patient needs to attend the clinic for a visit or the visit can be rescheduled due to remission and well-being. Telemedicine has already been used for well-controlled patients with rheumatic diseases with good results in terms of safety and disease activity compared to conventional face-to-face visits. Telemedicine represents an interesting tool for appointment prioritization and triaging, while automated algorithm-based applications are currently too imprecise for routine clinical use. The role of smartphone applications in the care of patients with rheumatic diseases is still unclear.

DISCUSSION

Telemedicine represents an interesting option for certain patient populations with rheumatic diseases. Apart from research on the effectiveness and safety of telemedical interventions, decision makers need to set clear rules on how telemedicine should be used to provide the best possible care for the individual patient.

Understanding flare in axial spondyloarthritis: novel insights from daily self-reported flare experience

OBJECTIVES

Our objective was to explore daily self-reported experiences of axial SpA (axSpA) flare based on data entered into the Project Nightingale smartphone app (www.projectnightingale.org), between 5 April 2018 and 1 April 2020.

METHODS

Paired t-tests were conducted for mean_flare_on and mean_flare_off scores for each recorded variable. The mean estimated difference between flare and non-flare values for each variable was calculated with 95% CIs. Mean, S.d. and range were reported for flare duration and frequency. Participants with ≥10 days of data entry were included for affinity propagation cluster analysis. Baseline characteristics and mean flare on vs mean flare off values were reported for each cluster. Welch's t-test was used to assess differences between clusters.

RESULTS

A total of 143/189 (75.7%) participants recorded at least one flare. Each flare lasted a mean of 4.30 days (S.d. 6.82, range 1-78), with a mean frequency of once every 35.32 days (S.d. 65.73, range 1-677). Significant relationships were identified between flare status and variable scores. Two clusters of participants were identified with distinct flare profiles. Group 1 experienced less severe worsening of symptoms during flare in comparison to group 2 (P < 0.01). However, they experienced significantly longer flare duration (7.2 vs 3.5 days; P < 0.01), perhaps indicating a prolonged, yet less intense flare experience. Groups were similar in terms of flare frequency and clinical characteristics.

CONCLUSIONS

Two clusters of participants were identified with distinct flare experiences but similar baseline clinical characteristics. Smartphone technologies capture subtle changes in disease experience not currently considered in clinical practice.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

The Challenges of Telemedicine in Rheumatology

During the past 20 years, the development of telemedicine has accelerated due to the rapid advancement and implementation of more sophisticated connected technologies. In rheumatology, e-health interventions in the diagnosis, monitoring and mentoring of rheumatic diseases are applied in different forms: teleconsultation and telecommunications, mobile applications, mobile devices, digital therapy, and artificial intelligence or machine learning. Telemedicine offers several advantages, in particular by facilitating access to healthcare and providing personalized and continuous patient monitoring. However, some limitations remain to be solved, such as data security, legal problems, reimbursement method, accessibility, as well as the application of recommendations in the development of the tools.

Wearable activity trackers and artificial intelligence in the management of rheumatic diseases : Where are we in 2021?

Wearable activity trackers are playing an increasingly important role in healthcare. In the field of rheumatic and musculoskeletal diseases (RMDs), various applications are currently possible. This review will present the use of activity trackers to promote physical activity levels in rheumatology, as well as the use of trackers to measure health parameters and detect flares using artificial intelligence. Challenges and limitations of the use of artificial intelligence will be discussed, as well as technical issues when using activity trackers in clinical practice.

[Artificial intelligence-supported treatment in rheumatology : Principles, current situation and perspectives]

Computer-guided clinical decision support systems have been finding their way into practice for some time, mostly integrated into electronic medical records. The primary goals are to improve the quality of treatment, save time and avoid errors. These are mostly rule-based algorithms that recognize drug interactions or provide reminder functions. Through artificial intelligence (AI), clinical decision support systems can be disruptively further developed. New knowledge is constantly being created from data through machine learning in order to predict the individual course of a patient's disease, identify phenotypes or support treatment decisions. Such algorithms already exist for rheumatological diseases. Automated image recognition and disease prediction in rheumatoid arthritis are the most advanced; however, these have not yet been sufficiently tested or integrated into existing decision support systems. Rather than dictating the AI-assisted choice of treatment to the doctor, future clinical decision systems are seen as hybrid decision support, always involving both the expert and the patient. There is also a great need for security through comprehensible and auditable algorithms to sustainably guarantee the quality and transparency of AI-assisted treatment recommendations in the long term.

Digital Approaches for a Reliable Early Diagnosis of Psoriatic Arthritis

Psoriatic arthritis (PsA) is a chronic inflammatory disease that develops in up to 30% of patients with psoriasis. In the vast majority of cases, cutaneous symptoms precede musculoskeletal complaints. Progression from psoriasis to PsA is characterized by subclinical synovio-entheseal inflammation and often non-specific musculoskeletal symptoms that are frequently unreported or overlooked. With the development of increasingly effective therapies and a broad drug armamentarium, prevention of arthritis development through careful clinical monitoring has become priority. Identifying high-risk psoriasis patients before PsA onset would ensure early diagnosis, increased treatment efficacy, and ultimately better outcomes; ideally, PsA development could even be averted. However, the current model of care for PsA offers only limited possibilities of early intervention. This is attributable to the large pool of patients to be monitored and the limited resources of the health care system in comparison. The use of digital technologies for health (eHealth) could help close this gap in care by enabling faster, more targeted and more streamlined access to rheumatological care for patients with psoriasis. eHealth solutions particularly include telemedicine, mobile technologies, and symptom checkers. Telemedicine enables rheumatological visits and consultations at a distance while mobile technologies can improve monitoring by allowing patients to self-report symptoms and disease-related parameters continuously. Symptom checkers have the potential to direct patients to medical attention at an earlier point of their disease and therefore minimizing diagnostic delay. Overall, these interventions could lead to earlier diagnoses of arthritis, improved monitoring, and better disease control while simultaneously increasing the capacity of referral centers.

Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network

Background

Deep neural networks learn from former experiences on a large scale and can be used to predict future disease activity as potential clinical decision support. AdaptiveNet is a novel adaptive recurrent neural network optimized to deal with heterogeneous and missing clinical data.

Objective

We investigate AdaptiveNet for the prediction of individual disease activity in patients from a rheumatoid arthritis (RA) registry.

Methods

Demographic and disease characteristics from over 9500 patients and 65.000 visits from the Swiss Quality Management (SCQM) database were used to train and evaluate the network. Patient characteristics, clinical and patient reported outcomes, laboratory values and medication were used as input features. DAS28-BSR served as a target to predict active RA and future numeric individual disease activity by classification and regression.

Results

AdaptiveNet predicted active disease defined as DAS28-BSR >2.6 at the next visit with an overall accuracy of 75.6% (SD +- 0.7%) and a sensitivity and specificity of 84.2% (SD +- 1.6%) and 61.5% (SD +- 3.6%), respectively. Prediction performance was significantly higher in patients with a disease duration >3 years and positive rheumatoid factor. Regression allowed forecasting individual DAS28-BSR values with a mean squared error (MSE) of 0.9 (SD +- 0.05). This corresponds to a 8% deviation between estimated and real DAS28-BSR values. Compared to linear regression, random forest and support vector machines, AdaptiveNet showed an increased performance of over 7% in MSE. Medication played a minor role in the prediction of RA disease activity.

Conclusion

AdaptiveNet has a superior capacity to predict numeric RA disease activity compared to classical machine learning architectures. All investigated models had limitations in low specificity.

Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.

A Real-World Rheumatology Registry and Research Consortium: The German RheumaDatenRhePort (RHADAR) Registry

Real-world data are crucial to continuously improve the management of patients with rheumatic and musculoskeletal diseases (RMDs). The German RheumaDatenRhePort (RHADAR) registry encompasses a network of rheumatologists and researchers in Germany providing pseudonymized real-world patient data and allowing timely and continuous improvement in the care of RMD patients. The RHADAR modules allow automated anamnesis and adaptive coordination of appointments regarding individual urgency levels. Further modules focus on the collection and integration of electronic patient-reported outcomes in between consultations. The digital RHADAR modules ultimately allow a patient-centered adaptive approach to integrated medical care starting as early as possible in the disease course. Such a closed-loop system consisting of various modules along the whole patient pathway enables comprehensive and timely patient management in an unprecedented manner.

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.