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1.
Artigo em Inglês | MEDLINE | ID: mdl-33807952

RESUMO

The global COVID-19 pandemic has led to drastic changes in the management of patients with rheumatic diseases. Due to the imminent risk of infection, monitoring intervals of rheumatic patients have prolonged. The aim of this study is to present insights from patients, rheumatologists, and digital product developers on the ongoing digital health transition in rheumatology. A qualitative and participatory semi-structured fishbowl approach was conducted to gain detailed insights from a total of 476 participants. The main findings show that digital health and remote care are generally welcomed by the participants. Five key themes emerged from the qualitative content analysis: (1) digital rheumatology use cases, (2) user descriptions, (3) adaptation to different environments of rheumatology care, and (4) potentials of and (5) barriers to digital rheumatology implementation. Codes were scaled by positive and negative ratings as well as on micro, meso, and macro levels. A main recommendation resulting from the insights is that both patients and rheumatologists need more information and education to successfully implement digital health tools into clinical routine.

2.
J Med Internet Res ; 23(3): e23742, 2021 03 29.
Artigo em Inglês | MEDLINE | ID: mdl-33690147

RESUMO

BACKGROUND: The worldwide burden of musculoskeletal diseases is increasing. The number of newly registered rheumatologists has stagnated. Primary care, which takes up a key role in early detection of rheumatic disease, is working at full capacity. COVID-19 and its containment impede rheumatological treatment. Telemedicine in rheumatology (telerheumatology) could support rheumatologists and general practitioners. OBJECTIVE: The goal of this study was to investigate acceptance and preferences related to the use of telerheumatology care among German rheumatologists and general practitioners. METHODS: A nationwide, cross-sectional, self-completed, paper-based survey on telerheumatology care was conducted among outpatient rheumatologists and general practitioners during the pre-COVID-19 period. RESULTS: A total of 73.3% (349/476) of survey participants rated their knowledge of telemedicine as unsatisfactory, poor, or very poor. The majority of survey participants (358/480, 74.6%) answered that they do not currently use telemedicine, although 62.3% (291/467) would like to. Barriers to the implementation of telemedicine include the purchase of technology equipment (182/292, 62.3%), administration (181/292, 62.0%), and poor reimbursement (156/292, 53.4%). A total of 69.6% (117/168) of the surveyed physicians reckoned that telemedicine could be used in rheumatology. Surveyed physicians would prefer to use telemedicine to communicate directly with other physicians (370/455, 81.3%) than to communicate with patients (213/455, 46.8%). Among treatment phases, 64.4% (291/452) of participants would choose to use telemedicine during follow-up. Half of the participants would choose telecounseling as a specific approach to improve rheumatology care (91/170, 53.5%). CONCLUSIONS: Before COVID-19 appeared, our results indicated generally low use but high acceptance of the implementation of telerheumatology among physicians. Participants indicated that the lack of a structural framework was a barrier to the effective implementation of telerheumatology. Training courses should be introduced to address the limited knowledge on the part of physicians in the use of telemedicine. More research into telerheumatology is required. This includes large-scale randomized controlled trials, economic analyses, and the exploration of user preferences.

3.
Arthritis Res Ther ; 23(1): 67, 2021 02 27.
Artigo em Inglês | MEDLINE | ID: mdl-33640008

RESUMO

BACKGROUND: Biological disease-modifying anti-rheumatic drugs (bDMARDs) can be tapered in some rheumatoid arthritis (RA) patients in sustained remission. The purpose of this study was to assess the feasibility of building a model to estimate the individual flare probability in RA patients tapering bDMARDs using machine learning methods. METHODS: Longitudinal clinical data of RA patients on bDMARDs from a randomized controlled trial of treatment withdrawal (RETRO) were used to build a predictive model to estimate the probability of a flare. Four basic machine learning models were trained, and their predictions were additionally combined to train an ensemble learning method, a stacking meta-classifier model to predict the individual flare probability within 14 weeks after each visit. Prediction performance was estimated using nested cross-validation as the area under the receiver operating curve (AUROC). Predictor importance was estimated using the permutation importance approach. RESULTS: Data of 135 visits from 41 patients were included. A model selection approach based on nested cross-validation was implemented to find the most suitable modeling formalism for the flare prediction task as well as the optimal model hyper-parameters. Moreover, an approach based on stacking different classifiers was successfully applied to create a powerful and flexible prediction model with the final measured AUROC of 0.81 (95%CI 0.73-0.89). The percent dose change of bDMARDs, clinical disease activity (DAS-28 ESR), disease duration, and inflammatory markers were the most important predictors of a flare. CONCLUSION: Machine learning methods were deemed feasible to predict flares after tapering bDMARDs in RA patients in sustained remission.

4.
RMD Open ; 7(1)2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33622673

RESUMO

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.


Assuntos
Doenças Musculoesqueléticas/terapia , Aceitação pelo Paciente de Cuidados de Saúde/estatística & dados numéricos , Reumatologistas/estatística & dados numéricos , Telemedicina , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Conhecimentos, Atitudes e Prática em Saúde , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Inquéritos e Questionários , Adulto Jovem
5.
J Clin Endocrinol Metab ; 106(4): 1062-1073, 2021 Mar 25.
Artigo em Inglês | MEDLINE | ID: mdl-33382877

RESUMO

CONTEXT: Type 2 diabetes is associated with a greater risk for musculoskeletal disorders, yet its impact on joint function remains unclear. OBJECTIVE: We hypothesized that patients with type 2 diabetes and osteoarthritis would exhibit musculoskeletal impairment, which would associate with insulin resistance and distinct microRNA profiles. METHODS: Participants of the German Diabetes Study with type 2 diabetes (T2D, n = 39) or normal glucose tolerance (CON, n = 27), both with (+OA) or without osteoarthritis (-OA) underwent intravenous glucose tolerance and hyperinsulinemic-euglycemic clamp tests. Musculoskeletal function was assessed by isometric knee extension strength (KES), grip strength, range of motion (ROM), and balance skills, while neural function was measured by nerve conductance velocity (NCV). Arthritis-related symptoms were quantified using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire, serum arthritis-related microRNA using quantitative polymerase chain reaction. RESULTS: Insulin sensitivity was lower in T2D+OA vs T2D-OA (4.4 ±â€…2.0 vs 5.7 ±â€…3.0 mg* kg-1*min-1) and in CON+OA vs CON-OA (8.1 ±â€…2.0 vs 12.0 ±â€…2.6 mg*kg-1,*min-1, both P < .05). In T2D+OA, KES and ROM were 60% and 22% lower than in CON+OA, respectively (both P < .05). Insulin sensitivity correlated positively with KES (r = 0.41, P < .05) among T2D, and negatively with symptom severity in CON and T2D (r = -0.60 and r = -0.46, respectively, P < .05). CON+OA and T2D+OA had inferior balance skills than CON-OA, whereas NCV was comparable in T2D+OA and T2D-OA. Expression of arthritis-related microRNAs was upregulated in T2D compared to CON, but downregulated in CON+OA compared to CON-OA (P < .05), and did not differ between T2D+OA and T2D-OA. CONCLUSION: Musculoskeletal impairment and osteoarthritis-related symptoms are associated with insulin resistance. Type 2 diabetes can mask changes in arthritis-related microRNA profiles.

7.
JMIR Med Inform ; 8(11): e23930, 2020 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-33252349

RESUMO

BACKGROUND: Financial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries. OBJECTIVE: The aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records. METHODS: Two electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation. RESULTS: For the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97). CONCLUSIONS: We demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

8.
PLoS One ; 15(11): e0241480, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33137123

RESUMO

BACKGROUND: Mobile health apps (MHA) have the potential to improve health care. The commercial MHA market is rapidly growing, but the content and quality of available MHA are unknown. Instruments for the assessment of the quality and content of MHA are highly needed. The Mobile Application Rating Scale (MARS) is one of the most widely used tools to evaluate the quality of MHA. Only few validation studies investigated its metric quality. No study has evaluated the construct validity and concurrent validity. OBJECTIVE: This study evaluates the construct validity, concurrent validity, reliability, and objectivity, of the MARS. METHODS: Data was pooled from 15 international app quality reviews to evaluate the metric properties of the MARS. The MARS measures app quality across four dimensions: engagement, functionality, aesthetics and information quality. Construct validity was evaluated by assessing related competing confirmatory models by confirmatory factor analysis (CFA). Non-centrality (RMSEA), incremental (CFI, TLI) and residual (SRMR) fit indices were used to evaluate the goodness of fit. As a measure of concurrent validity, the correlations to another quality assessment tool (ENLIGHT) were investigated. Reliability was determined using Omega. Objectivity was assessed by intra-class correlation. RESULTS: In total, MARS ratings from 1,299 MHA covering 15 different health domains were included. Confirmatory factor analysis confirmed a bifactor model with a general factor and a factor for each dimension (RMSEA = 0.074, TLI = 0.922, CFI = 0.940, SRMR = 0.059). Reliability was good to excellent (Omega 0.79 to 0.93). Objectivity was high (ICC = 0.82). MARS correlated with ENLIGHT (ps<.05). CONCLUSION: The metric evaluation of the MARS demonstrated its suitability for the quality assessment. As such, the MARS could be used to make the quality of MHA transparent to health care stakeholders and patients. Future studies could extend the present findings by investigating the re-test reliability and predictive validity of the MARS.

9.
Rheumatol Int ; 40(12): 2031-2037, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32995894

RESUMO

Social media services, such as Twitter, offer great potential for a better understanding of rheumatic and musculoskeletal disorders (RMDs) and improved care in the field of rheumatology. This study examined the content and stakeholders associated with the Twitter hashtag #Covid4Rheum during the COVID-19 pandemic. The content analysis shows that Twitter connects stakeholders of the rheumatology community on a global level, reaching millions of users. Specifically, the use of hashtags on Twitter assists digital crowdsourcing projects and scientific collaboration, as exemplified by the COVID-19 Global Rheumatology Alliance registry. Moreover, Twitter facilitates the distribution of scientific content, such as guidelines or publications. Finally, digital data mining enables the identification of hot topics within the field of rheumatology.


Assuntos
Infecções por Coronavirus/terapia , Pneumonia Viral/terapia , Doenças Reumáticas/terapia , Mídias Sociais/estatística & dados numéricos , Betacoronavirus , Comportamento Cooperativo , Saúde Global , Humanos , Disseminação de Informação/métodos , Pandemias , Reumatologia
10.
JMIR Mhealth Uhealth ; 8(8): e19661, 2020 Aug 12.
Artigo em Inglês | MEDLINE | ID: mdl-32678796

RESUMO

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.

11.
Ann Rheum Dis ; 79(9): 1139-1140, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32527863

RESUMO

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.


Assuntos
Pesquisa Biomédica/métodos , Infecções por Coronavirus/terapia , Crowdsourcing/métodos , Pneumonia Viral/terapia , Reumatologia/métodos , Betacoronavirus , Pesquisa Biomédica/normas , Infecções por Coronavirus/virologia , Crowdsourcing/normas , Humanos , Pandemias , Pneumonia Viral/virologia , Reumatologia/normas
12.
JMIR Mhealth Uhealth ; 8(7): e18117, 2020 07 20.
Artigo em Inglês | MEDLINE | ID: mdl-32390592

RESUMO

BACKGROUND: The use of patient-reported outcomes (PROs) allows for patient-centered, measurable, and transparent care. Electronic PROs (ePROs) have many benefits and hold great potential to improve current usage of PROs, yet limited evidence exists regarding their acceptance, usage, and barriers among rheumatologists. OBJECTIVE: This study aims to evaluate the current level of acceptance, usage, and barriers among German rheumatologists regarding the use of ePROs. The importance of different ePRO features for rheumatologists was investigated. Additionally, the most frequently used PROs for patients with rheumatoid arthritis (RA) were identified. METHODS: Data were collected via an online survey consisting of 18 questions. The survey was completed by members of the Working Group Young Rheumatology of the German Society for Rheumatology (Arbeitsgemeinschaft Junge Rheumatologie der Deutschen Gesellschaft für Rheumatologie [DGRh]) at the 2019 annual DGRh conference. Only members currently working in clinical adult rheumatology were eligible to complete the survey. RESULTS: A total of 119 rheumatologists completed the survey, of which 107 (89.9%) reported collecting PROs in routine practice and 28 (25.5%) already used ePROs. Additionally, 44% (43/97) were planning to switch to ePROs in the near future. The most commonly cited reason for not switching was the unawareness of suitable software solutions. Respondents were asked to rate the features of ePROs on a scale of 0 to 100 (0=unimportant, 100=important). The most important features were automatic score calculation and display (mean 77.50) and simple data transfer to medical reports (mean 76.90). When asked about PROs in RA, the respondents listed pain, morning stiffness, and patient global assessment as the most frequently used PROs. CONCLUSIONS: The potential of ePROs is widely seen and there is great interest in them. Despite this, only a minority of physicians use ePROs, and the main reason for not implementing them was cited as the unawareness of suitable software solutions. Developers, patients, and rheumatologists should work closely together to help realize the full potential of ePROs and ensure a seamless integration into clinical practice.

13.
JMIR Mhealth Uhealth ; 8(5): e17507, 2020 05 15.
Artigo em Inglês | MEDLINE | ID: mdl-32348258

RESUMO

Outcomes of patients with inflammatory rheumatic diseases have significantly improved over the last three decades, mainly due to therapeutic innovations, more timely treatment, and a recognition of the need to monitor response to treatment and to titrate treatments accordingly. Diagnostic delay remains a major challenge for all stakeholders. The combination of electronic health (eHealth) and serologic and genetic markers holds great promise to improve the current management of patients with inflammatory rheumatic diseases by speeding up access to appropriate care. The Joint Pain Assessment Scoring Tool (JPAST) project, funded by the European Union (EU) European Institute of Innovation and Technology (EIT) Health program, is a unique European project aiming to enable and accelerate personalized precision medicine for early treatment in rheumatology, ultimately also enabling prevention. The aim of the project is to facilitate these goals while at the same time, reducing cost for society and patients.

14.
Ultraschall Med ; 41(4): 410-417, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-29797308

RESUMO

PURPOSE: To create current fetal biometry reference ranges and to compare them with references published in 1999, from the same local area in order to generate data for secular trend in fetal size. MATERIALS AND METHODS: Applying the same methodology as previously published, we calculated reference ranges for biparietal diameter (BPD), occipitofrontal diameter (OFD), head circumference (HC), abdominal circumference (AC) and femur length (FL) in 7863 patients examined at the obstetric clinics in a cross-sectional, prospective study in a university setting from January 2008 to December 2014. In order to compare the new reference ranges with our previously published data, we used Z-Scores and displayed the pick-up of fetal biometry data below the 5th and above the 95th percentile using the previously published reference charts. RESULTS: The comparison of the charts showed a minimal but clinically relevant increase in mean fetal body measures (BPD, HC, AC). Applying the 1999 charts to the new dataset, we would classify only 162 of 339 fetuses (47.8 %) to be correctly below the 5th percentile for AC and only 134 of 349 (38.4 %) fetuses were correctly below the 5th percentile for HC. On the other hand, the 1999 charts classified 426 instead of 332 fetuses to be above the 95th percentile for AC, which means an overestimation of 28.3 %. CONCLUSION: Applying a similar methodology, study collective and clinical setting, our new charts showed clinically relevant differences compared to the 1999 charts. The data suggest that within one generation fetuses are getting bigger and regular updates of fetal reference charts are needed.


Assuntos
Feto , Ultrassonografia Pré-Natal , Biometria , Estudos Transversais , Feminino , Feto/anatomia & histologia , Idade Gestacional , Humanos , Gravidez , Estudos Prospectivos , Valores de Referência , Suíça
17.
18.
Ultraschall Med ; 40(6): 772, 2019 12.
Artigo em Alemão | MEDLINE | ID: mdl-31703237
19.
JMIR Mhealth Uhealth ; 7(8): e14991, 2019 08 05.
Artigo em Inglês | MEDLINE | ID: mdl-31381501

RESUMO

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.


Assuntos
Aplicativos Móveis/normas , Reumatologia/instrumentação , Alemanha , Humanos , Aplicativos Móveis/estatística & dados numéricos , Reumatologia/métodos , Reumatologia/estatística & dados numéricos , Avaliação da Tecnologia Biomédica/métodos
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