Skip to main content
SpringerLink
Log in

Innovative Ansätze der Gesundheitsprävention chronischer Erkrankungen am Beispiel der muskuloskelettalen Erkrankungen

  • Chapter
  • First Online:
Innovationen in der Gesundheitsversorgung

  • 775 Accesses

Zusammenfassung

Ein Viertel der deutschen Bevölkerung weist Funktionseinschränkungen am Bewegungsapparat auf, davon sind es zehn Millionen mit klinisch manifesten, behandlungsbedürftigen chronischen Erkrankungen des Stütz- und Bewegungsapparates. Zu diesem zählen sieben Millionen mit schweren chronischen Rückenschmerzen, fünf Millionen mit symptomatischen degenerativen Skeletterkrankungen (sog. Arthrosen).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Literatur

  • Abaci, H. E., Guo, Z., Doucet, Y., Jacków, J., & Christiano, A. (2017). Next generation human skin constructs as advanced tools for drug development. Experimental Biology and Medicine (Maywood, N.J.), 242, 1657–1668.

    Article  Google Scholar 

  • Akram, A., Georgiou, P., Shi, W., et al. (2021). Impact of change in lifestyle and exercise on cognitive function in patients with rheumatoid arthritis: A systematic review. Cureus, 13, e18268.

    Google Scholar 

  • Albrecht, K., Callhoff, J., & Strangfeld, A. (2022). Steigt die Prävalenz der rheumatoiden Arthritis wirklich an? Zeitschrift für Rheumatologie, 81, 323–327.

    Google Scholar 

  • Allen, D. (2019). Getting things done: The art of stress-free productivity. Piatkus.

    Google Scholar 

  • Amdie, F. Z., Sawhney, M., & Woo, K. (2022). The weakness of will: The role of free will in treatment adherence. Patient Preference and Adherence, 16, 1131–1139.

    Article  Google Scholar 

  • Andonian, B. J., Johannemann, A., Hubal, M. J., et al. (2021). Altered skeletal muscle metabolic pathways, age, systemic inflammation, and low cardiorespiratory fitness associate with improvements in disease activity following high-intensity interval training in persons with rheumatoid arthritis. Arthritis Research & Therapy, 23, 187.

    Article  Google Scholar 

  • Ash, C., Kiberstis, P., Marshall, E., & Travis, J. (2012). Disease prevention. It takes more than an apple a day. Introduction. Science, 337, 1466–1467.

    Google Scholar 

  • Assidi, M., Buhmeida, A., & Budowle, B. (2022). Medicine and health of 21st century: Not just a high biotech-driven solution. NPJ Genomic Medicine, 7, 67.

    Article  Google Scholar 

  • Balchin, C., Tan, A. L, & Golding, J., et al. (2022). Acute effects of exercise on pain symptoms, clinical inflammatory markers and inflammatory cytokines in people with rheumatoid arthritis: A systematic literature review. Therapeutic Advances in Musculoskeletal Disorder, 14, 1759720X221114104.

    Google Scholar 

  • Barrios, J. P, & Tison, G. H. (2022). Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective. Cell Reports Medicine, 3.

    Google Scholar 

  • Bellocchi C, Carandina A, Montinaro B, et al. The interplay between autonomic nervous system and inflammation across systemic autoimmune diseases. Int J Mol Sci 2022; 23

    Google Scholar 

  • Boffetta, P., Farioli, A., & Rizzello, E. (2020). Application of epidemiological findings to individuals*. Medicina del Lavoro, 111, 10–21.

    Google Scholar 

  • Bradford, N., Caffery, L., & Smith, A. (2016). Telehealth services in rural and remote Australia: A systematic review of models of care and factors influencing success and sustainability. RRH.

    Google Scholar 

  • Bunea A-C, Dediu V, Laszlo EA, et al. E-skin: The dawn of a new era of on-body monitoring systems. Micromachines (Basel) 2021; 12

    Google Scholar 

  • Canfell, O. J., Kodiyattu, Z., Eakin, E., et al. (2022). Real-world data for precision public health of noncommunicable diseases: A scoping review. BMC Public Health, 22, 2166.

    Article  Google Scholar 

  • Casali, P. G. (2014). Successes and limitations of targeted cancer therapy in gastrointestinal stromal tumors. Prog Tumor Res, 41, 51–61.

    Article  Google Scholar 

  • Celis-Morales, C., Marsaux, C. F., Livingstone, K. M., et al. (2017). Can genetic-based advice help you lose weight? Findings from the food4me European randomized controlled trial. American Journal of Clinical Nutrition, 105, 1204–1213.

    Article  Google Scholar 

  • Cerasa, A., Gaggioli, A., Marino, F., Riva, G., & Pioggia, G. (2022). The promise of the metaverse in mental health: The new era of medverse. Heliyon, 8.

    Google Scholar 

  • Charles, C., Whelan, T., & Gafni, A. (1999). What do we mean by partnership in making decisions about treatment? BMJ, 319, 780–782.

    Article  Google Scholar 

  • Chiappim, W., Fraga, M. A., Furlan, H., Ardiles, D. C., & Pessoa, R. S. (2022). The status and perspectives of nanostructured materials and fabrication processes for wearable piezoresistive sensors. Microsystem Technologies, 28, 1561–1580.

    Article  Google Scholar 

  • Ciolac, E. G., & Rodrigues-da-Silva, J. M. (2016). Resistance training as a tool for preventing and treating musculoskeletal disorders. Sports Medicine (Auckland, N. Z.), 46, 1239–1248.

    Article  Google Scholar 

  • Cock D de, Myasoedova E, Aletaha D, Studenic P. Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (rmds). Ther Adv Musculoskelet Dis 2022; 14: 1759720X221105978

    Google Scholar 

  • Collatuzzo, G., & Boffetta, P. (2022). Application of S. 4 (predictive, preventive, personalized, participatory) approach to occupational medicine1. Medical Lav, 113.

    Google Scholar 

  • Combe, B., Landewe, R., Lukas, C., et al. (2007). Eular recommendations for the management of early arthritis: Report of a task force of the European standing committee for international clinical studies including therapeutics (escisit). Annals of the Rheumatic Diseases, 66, 34–45.

    Article  Google Scholar 

  • Connolly J, Condell J, Curran K, Gardiner P. Improving data glove accuracy and usability using a neural network when measuring finger joint range of motion. Sensors (Basel) 2022; 22

    Google Scholar 

  • Cooney, J. K., Ahmad, Y. A., Moore, J. P., Sandoo, A., & Thom, J. M. (2019). The impact of cardiorespiratory fitness on classical cardiovascular disease risk factors in rheumatoid arthritis: A cross-sectional and longitudinal study. Rheumatology International, 39, 1759–1766.

    Article  Google Scholar 

  • Crepaldi, G., Scirè, C. A., Carrara, G., et al. (2016). Cardiovascular comorbidities relate more than others with disease activity in rheumatoid arthritis. PLoS ONE, 11, e0146991.

    Article  Google Scholar 

  • Crowson, C. S., Liao, K. P., Davis, J. M., et al. (2013). Rheumatoid arthritis and cardiovascular disease. American Heart Journal, 166, 622-628.e1.

    Article  Google Scholar 

  • D’Antoni, F., Russo, F., & Ambrosio, L., et al. (2021). Artificial intelligence and computer vision in low back pain: A systematic review. International Journal of Environmental Research Public Health, 18.

    Google Scholar 

  • Dahlke, J., Bogner, K., Becker, M., Schlaile, M. P., Pyka, A., & Ebersberger, B. (2021). Crisis-driven innovation and fundamental human needs: A typological framework of rapid-response covid-19 innovations. Technol Forecast Soc Change, 169, 120799.

    Article  Google Scholar 

  • Davergne, T., Kedra, J., & Gossec, L. (2021). Tragbare Aktivitätstracker und Künstliche Intelligenz in der Behandlung rheumatischer Erkrankungen: Stand der Dinge im Jahr 2021. Zeitschrift für Rheumatologie, 80, 928–935.

    Google Scholar 

  • Denecke, K., & Baudoin, C. R. (2022). A review of artificial intelligence and robotics in transformed health ecosystems. Frontiers of Medicine (Lausanne), 9.

    Google Scholar 

  • [Der Titel „Pinkstone J 2018 – Thousands of Swedes are getting“ kann nicht dargestellt werden. Die Vorlage „Literaturverzeichnis – Internetdokument – (Standardvorlage)“ beinhaltet nur Felder, welche bei diesem Titel leer sind.]

    Google Scholar 

  • Detert, J., Pischon, N., Burmester, G. R., & Buttgereit, F. (2010). The association between rheumatoid arthritis and periodontal disease. Arthritis Research & Therapy, 12, 218.

    Article  Google Scholar 

  • Dougados, M., Soubrier, M., Antunez, A., et al. (2014). Prevalence of comorbidities in rheumatoid arthritis and evaluation of their monitoring: Results of an international, cross-sectional study (comora). Annals of the Rheumatic Diseases, 73, 62–68.

    Article  Google Scholar 

  • Doumen, M., Westhovens, R., Pazmino, S., et al. (2021). The ideal mhealth-application for rheumatoid arthritis: Qualitative findings from stakeholder focus groups. BMC Musculoskeletal Disorders, 22, 746.

    Article  Google Scholar 

  • Esbensen, B. A., Kennedy, N., & Brodin, N. (2020). Prevention and adherence in rheumatic and musculoskeletal disease. Best Practice & Research Clinical Rheumatology, 34, 101525.

    Article  Google Scholar 

  • Fiehn, C., Holle, J., Iking-Konert, C., et al. (2018). S2e-Leitlinie: Therapie der rheumatoiden Arthritis mit krankheitsmodifizierenden Medikamenten. Zeitschrift für Rheumatologie, 77, 35–53.

    Google Scholar 

  • Filipp, F. V. (2019). Opportunities for artificial intelligence in advancing precision medicine. Curr Genet Med Rep, 7, 208–213.

    Article  Google Scholar 

  • Foguet, C., Xu, Y., & Ritchie, S. C. et al. (2022). Genetically personalised organ-specific metabolic models in health and disease. Nature Communications, 13.

    Google Scholar 

  • Forgas, J. P. (2011). Psychology of self-regulation: Cognitive, affective, and motivational processes. Taylor and Francis.

    Book  Google Scholar 

  • Forgas, J. P. (2014). The social self: Cognitive, interpersonal and intergroup perspectives. Taylor and Francis.

    Book  Google Scholar 

  • Frazzei, G., van Vollenhoven, R. F., Jong, B. A. de, Siegelaar, S. E., & van Schaardenburg, D. (2022). Preclinical autoimmune disease: A comparison of rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis and type 1 diabetes. Front Immunol, 13.

    Google Scholar 

  • Froschauer, S., Muth, T., Bredow, L., et al. (2021). Versorgungsatlas Rheumatologie: Ansätze und Konzepte zur Verbesserung der Versorgung in der ambulanten Rheumatologie. Zeitschrift für Rheumatologie, 80, 819–826.

    Google Scholar 

  • Gaubitz, M. (2019). Osteoporose – häufige Komorbidität bei Rheumapatienten. Zeitschrift für Rheumatologie, 78, 249–254.

    Google Scholar 

  • GBD. (2017). Disease and injury incidence and prevalence collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the global burden of disease study. Lancet, 392, 1789–858.

    Google Scholar 

  • Gerlich, C., Andreica, I., Küffner, R., et al. (2020). Evaluation einer Basisschulung für Patienten mit rheumatoider Arthritis. Zeitschrift für Rheumatologie, 79, 737–748.

    Google Scholar 

  • Griffiths, A. J., White, C. M., Thain, P. K., & Bearne, L. M. (2018). The effect of interactive digital interventions on physical activity in people with inflammatory arthritis: A systematic review. Rheumatology International, 38, 1623–1634.

    Article  Google Scholar 

  • Grzybowski, A. (Hrsg.). (2021). Artificial intelligence in ophthalmology. Springer International Publishing; Imprint Springer.

    Google Scholar 

  • Guemghar, I., Pires de Oliveira Padilha, P., Abdel-Baki, A., Jutras-Aswad, D., Paquette, J., & Pomey, M.-P. (2022). Social robot interventions in mental health care and their outcomes, barriers, and facilitators: Scoping review. JMIR Mental Health, 9, E36094.

    Google Scholar 

  • Gwinnutt, J. M., Wieczorek, M., & Balanescu, A., et al. (2021). Eular recommendations regarding lifestyle behaviours and work participation to prevent progression of rheumatic and musculoskeletal diseases. Ann Rheum Dis 2022.

    Google Scholar 

  • Gwinnutt, J. M., Wieczorek, M., & Rodríguez-Carrio, J., et al. (2022). Effects of diet on the outcomes of rheumatic and musculoskeletal diseases (rmds): Systematic review and meta-analyses informing the 2021a eular recommendations for lifestyle improvements in people with rmds. RMD Open, 8.

    Google Scholar 

  • Hartmann, A. M., Dell’Oro, M., Spoo, M., et al. (2022). To eat or not to eat—an exploratory randomized controlled trial on fasting and plant-based diet in rheumatoid arthritis (nutrifast-study). Frontiers of Nutrition, 9.

    Google Scholar 

  • Henderson J, Condell J, Connolly J, Kelly D, Curran K. Review of wearable sensor-based health monitoring glove devices for rheumatoid arthritis. Sensors (Basel) 2021; 21

    Google Scholar 

  • Hood, L. (2019). How technology, big data, and systems approaches are transforming medicine. Research-Technology Management, 62, 24–30.

    Article  Google Scholar 

  • Horesh, D., Kohavi, S., & Shilony-Nalaboff, L., et al. (2022). Virtual reality combined with artificial intelligence (vr-ai) reduces hot flashes and improves psychological well-being in women with breast and ovarian cancer: A pilot study. Healthcare (Basel), 10.

    Google Scholar 

  • Hügle M, Omoumi P, van Laar JM, Boedecker J, Hügle T. Applied machine learning and artificial intelligence in rheumatology. Rheumatol Adv Pract 2020; 4: Rkaa005

    Google Scholar 

  • Ibrahim NFA, Sabani N, Johari S, et al. A comprehensive review of the recent developments in wearable sweat-sensing devices. Sensors (Basel) 2022; 22

    Google Scholar 

  • Jeon, C. Y., Lokken, R. P., Hu, F. B., & van Dam, R. M. (2007). Physical activity of moderate intensity and risk of type 2 diabetes: A systematic review. Diabetes Care, 30, 744–752.

    Article  Google Scholar 

  • Jones, K., Kimble, R., Baker, K., & Tew, G. A. (2022). Effects of structured exercise programmes on physiological and psychological outcomes in adults with inflammatory bowel disease (ibd): A systematic review and meta-analysis. PLoS ONE, 17, e0278480.

    Article  Google Scholar 

  • Keefe DMK, BEH (2019). Potential successes and challenges of targeted cancer therapies. JNCI Monographs, 53.

    Google Scholar 

  • Kim J, Chun K, McGowan J, et al. 14–3–3ζ: A suppressor of inflammatory arthritis. Proc Natl Acad Sci U S A 2021; 118

    Google Scholar 

  • Kondratʹev, N. D. (2021). Die langen Wellen der Konjunktur (3. Aufl.). Brendow.

    Google Scholar 

  • Klareskog, L., Stolt, P., Lundberg, K., et al. (2006). A new model for an etiology of rheumatoid arthritis: Smoking May trigger hla-dr (shared epitope)-restricted immune reactions to autoantigens modified by citrullination. Arthritis and Rheumatism, 54, 38–46.

    Article  Google Scholar 

  • Knevel, R., & Hügle, T. (2022). E-health as a sine qua non for modern healthcare. RMD Open, 8.

    Google Scholar 

  • Koller-Smith, L., Mehdi, A. M., March, L., Tooth, L., Mishra, G. D., & Thomas, R. (2022). Rheumatoid arthritis is a preventable disease: 11 ways to reduce your patients’ risk. Internal Medicine Journal, 52, 711–716.

    Article  Google Scholar 

  • Krause, A., & Lakomek, H.-J. (2022). Die rheumatologische Versorgung in Deutschland – Sachstand und Perspektiven. Zeitschrift für Rheumatologie, 81, 443–444.

    Google Scholar 

  • Krusche, M., Klemm, P., Grahammer, M., et al. (2020). Acceptance, usage, and barriers of electronic patient-reported outcomes among german rheumatologists: Survey study. JMIR mHealth and uHealth, 8, e18117.

    Article  Google Scholar 

  • Lambert, S. A., Abraham, G., & Inouye, M. (2019). Towards clinical utility of polygenic risk scores. Human Molecular Genetics, 28, R133–R142.

    Article  Google Scholar 

  • Lorig, K., Ritter, P. L., & Plant, K. (2005). A disease-specific self-help program compared with a generalized chronic disease self-help program for arthritis patients. Arthritis and Rheumatism, 53, 950–957.

    Article  Google Scholar 

  • Lee, S., Oh, H., Kim, S., et al. (2022). Factors that influence sleep disturbance and the mediating effects of depression on sleep disturbance in patients with rheumatoid arthritis. Orthopaedic Nursing, 41, 335–344.

    Article  Google Scholar 

  • Li Yim, A. Y. F., Ferrero, E., Maratou, K., et al. (2021). Novel insights into rheumatoid arthritis through characterization of concordant changes in dna methylation and gene expression in synovial biopsies of patients with differing numbers of swollen joints. Frontiers in Immunology, 12, 651475.

    Article  Google Scholar 

  • Li, C., Chen, X., & Bi, X. (2021). Wearable activity trackers for promoting physical activity: A systematic meta-analytic review. International Journal of Medical Informatics, 152, 104487.

    Article  Google Scholar 

  • Løppenthin, K., Esbensen, B. A, Østergaard, M., Ibsen, R., Kjellberg, J., & Jennum, P. (2019). Morbidity and mortality in patients with rheumatoid arthritis compared with an age- and sex-matched control population: A nationwide register study. Journal of Comorbidity, 9, 2235042X19853484.

    Google Scholar 

  • Litvinova O, Klager E, Tzvetkov NT, et al. Digital pills with ingestible sensors: Patent landscape analysis. Pharmaceuticals (Basel) 2022; 15

    Google Scholar 

  • Maaß, L., Freye, M., Pan, C.-C., Dassow, H.-H., Niess, J., & Jahnel, T. (2022). The definitions of health apps and medical apps from the perspective of public health and law: Qualitative analysis of an interdisciplinary literature overview. JMIR mHealth and uHealth, 10, e37980.

    Article  Google Scholar 

  • Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60.

    Article  Google Scholar 

  • Manickam, P., Mariappan, S. A, & Murugesan, S. M., et al. (2022). Artificial intelligence (ai) and internet of medical things (iomt) assisted biomedical systems for intelligent healthcare. Biosensors (Basel), 12.

    Google Scholar 

  • Mayer-Schönberger, V., & Cukier, K. (2014). Big data: A revolution that will transform how we live, work, and think. Mariner Books Houghton Mifflin Harcourt.

    Google Scholar 

  • Matcham, F., Scott, I. C., Rayner, L., et al. (2014). The impact of rheumatoid arthritis on quality-of-life assessed using the sf-36: A systematic review and meta-analysis. Seminars in Arthritis and Rheumatism, 44, 123–130.

    Article  Google Scholar 

  • Mascarenhas, M., Afonso, J., Andrade, P., Cardoso, H., & Macedo, G. (2021). Artificial intelligence and capsule endoscopy: Unravelling the future. Annals of Gastroenterology, 34, 300–309.

    Google Scholar 

  • Martins, P., & Fonseca, J. E. (2019). How to investigate: Pre-clinical rheumatoid arthritis. Best Practice & Research Clinical Rheumatology, 33, 101438.

    Article  Google Scholar 

  • Maarseveen, T. D., Meinderink, T., Reinders, M. J. T., et al. (2020). Machine learning electronic health record identification of patients with rheumatoid arthritis: Algorithm pipeline development and validation study. JMIR Medical Informatics, 8, e23930.

    Article  Google Scholar 

  • Mayrink, N. N. V., Alcoforado, L., & Chioro, A., et al. (2022). Translational research in health technologies: A scoping review. Front Digit Health, 4.

    Google Scholar 

  • McDowell, A., Kang, J., Yang, J., et al. (2022). Machine-learning algorithms for asthma, copd, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects. Experimental & Molecular Medicine, 54, 1586–1595.

    Article  Google Scholar 

  • McKenna, S. G., Donnelly, A., Esbensen, B. A., et al. (2021). The feasibility of an exercise intervention to improve sleep (time, quality and disturbance) in people with rheumatoid arthritis: A pilot rct. Rheumatology International, 41, 297–310.

    Article  Google Scholar 

  • Milling, M., Pokorny, F., Bartl-Pokorny, K. D., & Schuller, B. (2022). Is speech the new blood? Recent progress in ai-based disease detection from audio in a nutshell. Augsburg: Universität Augsburg; Frontiers Media SA.

    Book  Google Scholar 

  • Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart wearables for the detection of occupational physical fatigue: A literature review. Sensors (Basel) 2022; 22

    Google Scholar 

  • Mucke, J., Krusche, M., & Burmester, G. R.(2022). A broad look into the future of rheumatoid arthritis. Therapeutic Advances in Musculoskeletal Disease, 14, 1759720X221076211.

    Google Scholar 

  • Mühlensiepen, F., Kurkowski, S., & Krusche, M., et al. (2021). Digital health transition in rheumatology: A qualitative study. International Journal of Environmental Resource Public Health, 18.

    Google Scholar 

  • Mun, S., Lee, J., Park, M., Shin, J., Lim, M.-K., & Kang, H.-G. (2021). Serum biomarker panel for the diagnosis of rheumatoid arthritis. Arthritis Research & Therapy, 23, 31.

    Article  Google Scholar 

  • Najm, A., Nikiphorou, E., Kostine, M., et al. (2019). Eular points to consider for the development, evaluation and implementation of mobile health applications aiding self-management in people living with rheumatic and musculoskeletal diseases. RMD Open, 5, e001014.

    Article  Google Scholar 

  • Ndosi, M., Tennant, A., Bergsten, U., et al. (2011). Cross-cultural validation of the educational needs assessment tool in ra in 7 European countries. BMC Musculoskeletal Disorders, 12, 110.

    Article  Google Scholar 

  • Nielen, M. M. J., van Schaardenburg, D., Reesink, H. W., et al. (2004). Increased levels of c-reactive protein in serum from blood donors before the onset of rheumatoid arthritis. Arthritis and Rheumatism, 50, 2423–2427.

    Article  Google Scholar 

  • Norgeot, B., Glicksberg, B. S., Trupin, L., et al. (2019). Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis. JAMA Network Open, 2, e190606.

    Article  Google Scholar 

  • Obayya, M., Alamgeer, M. S., & Alzahrani, J., et al. (2022). Artificial intelligence driven biomedical image classification for robust rheumatoid arthritis classification. Biomedicines, 10.

    Google Scholar 

  • O’Dwyer, T., Rafferty, T., O’Shea, F., Gissane, C., & Wilson, F. (2014). Physical activity guidelines: Is the message getting through to adults with rheumatic conditions? Rheumatology (Oxford), 53, 1812–1817. https://www.ideals.illinois.edu/items/73856. Zugegriffen: 30. Dez. 2022.

  • Onuora, S. (2016). Rheumatoid arthritis: Vagus nerve stimulation reduces ra severity in patients. Nature Reviews Rheumatology, 12, 500.

    Article  Google Scholar 

  • Organization, W. H. (2016). World health statistics 2016: Monitoring health for the sustainable development goals (sdgs). World Health Organization.

    Google Scholar 

  • Ory, M. G., Ahn, S., Jiang, L., et al. (2013). Successes of a national study of the chronic disease self-management program: Meeting the triple aim of health care reform. Medical Care, 51, 992–998.

    Article  Google Scholar 

  • Palumbo A. Microsoft hololens 2 in medical and healthcare context: State of the art and future prospects. Sensors (Basel) 2022; 22

    Google Scholar 

  • Patermann, J., Ehlebracht-König, I., & Lind-Albrecht, G., et al. (2016). EULAR-Empfehlungen für die Schulung von Patienten mit entzündlich-rheumatischen Gelenkerkrankungen. Übersetzung und Bewertung für Deutschland. Zeitschrift Rheumatol, 75, 187–99.

    Google Scholar 

  • Pecere, S., Milluzzo, S. M., Esposito, G., Dilaghi, E., Telese, A., & Eusebi, L. H. (2021). Applications of artificial intelligence for the diagnosis of gastrointestinal diseases. Diagnostics (Basel), 11.

    Google Scholar 

  • Rausch Osthoff, A.-K., Juhl, C. B., Knittle, K., et al. (2018a). Effects of exercise and physical activity promotion: Meta-analysis informing the 2018 eular recommendations for physical activity in people with rheumatoid arthritis, spondyloarthritis and hip/knee osteoarthritis. RMD Open, 4, e000713.

    Article  Google Scholar 

  • Rausch Osthoff, A.-K., Niedermann, K., Braun, J., et al. (2018b). 2018 eular recommendations for physical activity in people with inflammatory arthritis and osteoarthritis. Annals of the Rheumatic Diseases, 77, 1251–1260.

    Article  Google Scholar 

  • Reusch, A., Braun, J., Ehlebracht-König, I., et al. (2016). Rahmenkonzept für rheumatologische Patientenschulungen. Zeitschrift für Rheumatologie, 75, 950–953.

    Google Scholar 

  • Rodarte, C. (2017). Pharmaceutical perspective: How digital biomarkers and contextual data will enable therapeutic environments. Digit Biomark, 1, 73–81.

    Article  Google Scholar 

  • Richter, J. G., Chehab, G., Stachwitz, P., et al. (2022). One year of digital health applications (diga) in Germany – rheumatologists’ perspectives. Front Med (Lausanne), 9, 1000668.

    Article  Google Scholar 

  • Richter, J. G., Chehab, G., & Reiter, J., et al. (2022). Evaluation of the use of video consultation in german rheumatology care before and during the covid-19 pandemic. Frontiers of Medicine (Lausanne), 9. https://ada.com/de/. Zugegriffen: 31. Dez. 2022.

  • Ritschl, V., Stamm, T. A., & Aletaha, D., et al. (2020). Prevention, screening, assessing and managing of non-adherent behaviour in people with rheumatic and musculoskeletal diseases: Systematic reviews informing the 2020 eular points to consider. RMD Open, 6.

    Google Scholar 

  • Roelsgaard, I. K., Esbensen, B. A, & Østergaard, M., et al. (2019). Smoking cessation intervention for reducing disease activity in chronic autoimmune inflammatory joint diseases. Cochrane Database of Systematics Reviews, 9, CD012958.

    Google Scholar 

  • Safiri, S., Kolahi, A. A., Cross, M., et al. (2021). Global, regional, and national burden of other musculoskeletal disorders 1990–2017: Results from the global burden of disease study 2017. Rheumatology (Oxford), 60, 855–865.

    Article  Google Scholar 

  • Sagner, M., McNeil, A., Puska, P., et al. (2017). The p4 health spectrum – a predictive, preventive, personalized and participatory continuum for promoting healthspan. Progress in Cardiovascular Diseases, 59, 506–521.

    Article  Google Scholar 

  • Schäfer, C., & Keyßer, G. (2022). Lifestyle factors and their influence on rheumatoid arthritis: A narrative review. Journal of Clinical Medicine, 11.

    Google Scholar 

  • Schüssler-Fiorenza Rose, S. M., Contrepois, K., Moneghetti, K. J., et al. (2019). A longitudinal big data approach for precision health. Nature Medicine, 25, 792–804.

    Article  Google Scholar 

  • Schett, G., Tanaka, Y., & Isaacs, J. D. (2021). Why remission is not enough: Underlying disease mechanisms in ra that prevent cure. Nature Reviews Rheumatology, 17, 135–144.

    Article  Google Scholar 

  • Schwarze, M., Fieguth, V., Schuch, F., et al. (2020). Krankheitsbezogener Wissenserwerb durch strukturierte Patienteninformation bei rheumatoider Arthritis (strupi-ra): Erste Ergebnisse der Strupi-ra-Studie in Deutschland. Zeitschrift für Rheumatologie, 80, 364–372.

    Google Scholar 

  • Sharif, K., Watad, A., Bragazzi, N. L., Lichtbroun, M., Amital, H., & Shoenfeld, Y. (2018). Physical activity and autoimmune diseases: Get moving and manage the disease. Autoimmunity Reviews, 17, 53–72.

    Article  Google Scholar 

  • Shilo, S., Rossman, H., & Segal, E. (2020). Axes of a revolution: Challenges and promises of big data in healthcare. Nature Medicine, 26, 29–38. https://med.stanford.edu/ipop.html. Zugegriffen: 30. Dez. 2022.

  • https://med.stanford.edu/ipop.html (letzter Zugriff am 30.12.2022)

  • Shuai Z, Zheng S, Wang K, Wang J, Leung PSC, Xu B. Reestablish immune tolerance in rheumatoid arthritis. Front Immunol 2022; 13

    Google Scholar 

  • Smolen, J. S. (2019). Treat to target in rheumatology: A historical account on occasion of the 10th anniversary. Rheumatic Diseases Clinics of North America, 45, 477–485.

    Article  Google Scholar 

  • Solomon, D. H., & Rudin, R. S. (2020). Digital health technologies: Opportunities and challenges in rheumatology. Nature Reviews Rheumatology, 16, 525–535.

    Article  Google Scholar 

  • Song, Y., Bernard, L., Jorgensen, C., Dusfour, G., & Pers, Y-M. (2021). The challenges of telemedicine in rheumatology. Frontiers of Medicine (Lausanne), 8, 746219. https://www.who.int/europe/publications/i/item/EUR-RC72-R2. Zugegriffen: 29. Dez. 2022.

  • Tam, J., Lacaille, D., Liu-Ambrose, T., et al. (2019). Effectiveness of an online self-management tool, operas (an on-demand program to empower active self-management), for people with rheumatoid arthritis: A research protocol. Trials, 20, 712.

    Article  Google Scholar 

  • Verma, A. K., Bhatt, D., Goyal, Y., et al. (2021). Association of rheumatoid arthritis with diabetic comorbidity: Correlating accelerated insulin resistance to inflammatory responses in patients. Journal of Multidisciplinary Healthcare, 14, 809–820.

    Article  Google Scholar 

  • Vodencarevic, A., Tascilar, K., Hartmann, F., et al. (2021). Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs. Arthritis Research & Therapy, 23, 67.

    Article  Google Scholar 

  • Vohs, K. D., & Baumeister, R. F. (Hrsg.). (2016). Handbook of self-regulation: Research, theory, and applications. The Guilford Press.

    Google Scholar 

  • Wang, F., Zheng, J., Cheng, J., et al. (2022). Personalized nutrition: A review of genotype-based nutritional supplementation. Frontiers in Nutrition, 9, 992986.

    Article  Google Scholar 

  • Wieczorek, M., Gwinnutt, J. M, & Ransay-Colle, M., et al. (2022). Smoking, alcohol consumption and disease-specific outcomes in rheumatic and musculoskeletal diseases (rmds): Systematic reviews informing the 2021 eular recommendations for lifestyle improvements in people with rmds. RMD Open.

    Google Scholar 

  • Yuan Y, Liu B, Li H, et al. Flexible wearable sensors in medical monitoring. Biosensors (Basel) 2022; 12

    Google Scholar 

  • https://www.who.int/europe/publications/i/item/EUR-RC72-R2 (letzter Zugriff am 29.12.2022)

  • https://ada.com/de/ (letzter Zugriff am 31.12.2022)

  • La Torre-Díez I de, López-Coronado, M., Vaca, C., Aguado, J. S., Castro, C. de. (2015). Cost-utility and cost-effectiveness studies of telemedicine, electronic, and mobile health systems in the literature: A systematic review. Journal of Telemedicine & Telecare Health, 21, 81–85.

    Google Scholar 

  • https://www.ideals.illinois.edu/items/73856 (letzter Zugriff am 30.12.2022)

  • Tao, W., Concepcion, A. N., Vianen, M., et al. (2021). Multiomics and machine learning accurately predict clinical response to adalimumab and etanercept therapy in patients with rheumatoid arthritis. Arthritis & Rhematology, 73, 212–222.

    Article  Google Scholar 

  • de Thurah, A., Stengaard-Pedersen, K., Axelsen, M., et al. (2018). Tele-health followup strategy for tight control of disease activity in rheumatoid arthritis: Results of a randomized controlled trial. Arthritis Care Res (Hoboken), 70, 353–360.

    Article  Google Scholar 

  • Vanderlinden, L. A., Bemis, E. A & Seifert, J., et al. (2022). Relationship between a vitamin d genetic risk score and autoantibodies among first-degree relatives of probands with rheumatoid arthritis and systemic lupus erythematosus. Frontiers in Immunology, 13. https://www.profbanafa.com/2021/03/technology-under-your-skin.html. Zugegriffen: 30. Dez. 2022.

  • Wand, H., Lambert, S. A., Tamburro, C., et al. (2021). Improving reporting standards for polygenic scores in risk prediction studies. Nature, 591, 211–219.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Templin, Deutschland

    Jacqueline Detert & Markus Detert

Authors
  1. Jacqueline Detert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Markus Detert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jacqueline Detert .

Editor information

Editors and Affiliations

  1. Köln, Deutschland

    Julia Plugmann

  2. Praxis Prof. Dr. Dr. Philipp Plugmann, Leverkusen, Deutschland

    Philipp Plugmann

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Detert, J., Detert, M. (2023). Innovative Ansätze der Gesundheitsprävention chronischer Erkrankungen am Beispiel der muskuloskelettalen Erkrankungen. In: Plugmann, J., Plugmann, P. (eds) Innovationen in der Gesundheitsversorgung. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-41681-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-41681-2_10

  • Published:

  • Publisher Name: Springer Gabler, Wiesbaden

  • Print ISBN: 978-3-658-41680-5

  • Online ISBN: 978-3-658-41681-2

  • eBook Packages: Medicine (German Language)

Publish with us

Policies and ethics

Search

Navigation