nach oben

Erschienen in:

01.08.2016

Behavior change interventions: the potential of ontologies for advancing science and practice

verfasst von: Kai R. Larsen, Susan Michie, Eric B. Hekler, Bryan Gibson, Donna Spruijt-Metz, David Ahern, Heather Cole-Lewis, Rebecca J. Bartlett Ellis, Bradford Hesse, Richard P. Moser, Jean Yi

Erschienen in: Journal of Behavioral Medicine | Ausgabe 1/2017

Einloggen, um Zugang zu erhalten

Abstract

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using “ontologies.” In information science, an ontology is a systematic method for articulating a “controlled vocabulary” of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine’s Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science.

Arp, R., Smith, B., & Spear, A. D. (2015). Building ontologies with basic formal ontology. Cambridge: The MIT Press.CrossRef

Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015). Correlational effect size benchmarks. Journal of Applied Psychology, 100, 431–449. doi:10.1037/a0038047 CrossRefPubMed

BCTTv1 Team. (2016). Behaviour change techniques and theory: Feedback on BCTTv1. Retrieved from: https://www.ucl.ac.uk/behaviour-change-techniques/BCTTv1Feedback

Cane, J., O’Connor, D., & Michie, S. (2012). Validation of the theoretical domains framework for use in behaviour change and implementation research. Implementation Science, 7, 37. doi:10.1186/1748-5908-7-37 CrossRefPubMedPubMedCentral

Cane, J., Richardson, M., Johnston, M., Ladha, R., & Michie, S. (2015). From lists of behaviour change techniques (BCTs) to structured hierarchies: Comparison of two methods of developing a hierarchy of BCTs. British Journal of Health Psychology, 20, 130–150. doi:10.1111/bjhp.12102 CrossRefPubMed

Chinn, P., & Kramer, M. (1991). Theory and nursing: A systematic approach (3rd ed.). St. Louis, MO: Mosby Year Book.

Chorpita, B. F., Rotheram-Borus, M. J., Daleiden, E. L., Bernstein, A., Cromley, T., Swendeman, D., et al. (2011). The old solutions are the new problem how do we better use what we already know about reducing the burden of mental illness? Perspectives on Psychological Science, 6, 493–497. doi:10.1177/1745691611418240 CrossRefPubMed

Cobb, N. K., Graham, A. L., Byron, M. J., Niaura, R. S., & Abrams, D. B. (2011). Online social networks and smoking cessation: A scientific research agenda. Journal of Medical Internet Research, 13, e119. doi:10.2196/jmir.1911 CrossRefPubMedPubMedCentral

Connell, L., Johnston, M., Carey, R., Rothman, A., Kelly, M., & De Bruin, et al. (2015). Linking behaviour change techniques with theory. In S. Michie (Ed.), Making sense of behaviour change: problems, methods and applications. Symposium conducted at the meeting of the European Health Psychology Society, Nicosia, Cyprus.

Davis, R. E., Campbell, R., Hildon, Z., Hobbs, L., & Michie, S. (2015). Theories of behaviour and behaviour change across the social and behavioural sciences: A scoping review. Health Psychology Review, 9, 323–344. doi:10.1080/17437199.2014.941722 CrossRefPubMed

Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., & Harshman, R. A. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science., 41, 391–407. doi:10.1002/(SICI)1097-4571(199009)41:6<391:AID-ASI1>3.0.CO;2-9 CrossRef

Fiannaca, A., La Rosa, M., Rizzo, R., Urso, A., & Gaglio, S. (2012). An ontology design methodology for knowledge-based systems with application to bioinformatics. In 2012 IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB) (pp. 85–91). IEEE.

French, S. D., Green, S. E., O’Connor, D. A., McKenzie, J. E., Francis, J. J., Michie, S., et al. (2012). Developing theory-informed behaviour change interventions to implement evidence into practice: A systematic approach using the theoretical domains framework. Implementation Science, 7, 38. doi:10.1186/1748-5908-7-38 CrossRefPubMedPubMedCentral

Gennari, J. H., Musen, M. A., Fergerson, R. W., Grosso, W. E., Crubézy, M., Eriksson, H., et al. (2003). The evolution of Protégé: an environment for knowledge-based systems development. International Journal of Human-Computer Studies, 58, 89–123. doi:10.1016/S1071-5819(02)00127-1 CrossRef

Hall, D., Huerta, M. F., McAuliffe, M. J., & Farber, G. K. (2012). Sharing heterogeneous data: The national database for autism research. Neuroinformatics, 10, 331–339. doi:10.1007/s12021-012-9151-4 CrossRefPubMedPubMedCentral

Hanna, J., Joseph, E., Brochhausen, M., & Hogan, W. R. (2013). Building a drug ontology based on RxNorm and other sources. Journal of Biomedical Semantics, 4, 44. Retrieved from: http://www.jbiomedsem.com/content/4/1/44

Hardeman, W., Sutton, S., Griffin, S., Johnston, M., White, A., Wareham, N. J., et al. (2005). A causal modelling approach to the development of theory-based behaviour change programmes for trial evaluation. Health Education Research, 20, 676–687. doi:10.1093/her/cyh022 CrossRefPubMed

Hesse, B. W., Moser, R. P., & Riley, W. T. (2015). From big data to knowledge in the social sciences. The Annals of the American Academy of Political and Social Science, 659, 16–32. doi:10.1177/0002716215570007 CrossRefPubMedPubMedCentral

Hunter, J. (2003). Enhancing the semantic interoperability of multimedia through a core ontology. IEEE Transactions on Circuits and Systems for Video Technology, 13, 49–58. doi:10.1109/TCSVT.2002.808088 CrossRef

ICPSR. (2016). Membership in ICPSR. Retrieved from: https://www.icpsr.umich.edu/icpsrweb/content/membership/index.html

IOM (Institute of Medicine). (2008). Committee on reviewing evidence to identify highly effective clinical services. Knowing what works in health care: A roadmap for the nation. Washington, DC: National Academies Press. doi:10.17226/12038

IOM (Institute of Medicine). (2011). Patients charting the course: Citizen engagement and the learning health system—Workshop summary. Washington, D.C.: National Academies Press.

Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52, 36–44. doi:10.1145/1536616.1536632 CrossRef

Kelder, S. H., Hoelscher, D., & Perry, C. L. (2015). How individuals, environment, and health behaviors interact. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health behavior: Theory, research, and practice (5th ed., pp. 159–182). San Francisco: Wiley.

Kelley, T. L. (1927). Interpretation of educational measurements. Oxford, England: World Book Company.

Kelly, M. P., & Moore, T. A. (2012). The judgment process in evidence-based medicine and health technology assessment. Social Theory and Health, 10, 1–19. doi:10.1057/sth.2011.21 CrossRefPubMed

Kolenikov, S., & Angeles, G. (2009). Socioeconomic status measurement with discrete proxy variables: Is principal component analysis a reliable answer? Review of Income and Wealth, 55, 128–165. doi:10.1111/j.1475-4991.2008.00309.x CrossRef

Larsen, K. (2016). Inter-nomological network. Retrieved from: http://inn.theorizeit.org

Larsen, K. R., & Bong, C. H. (2016). A tool for addressing construct identity in literature reviews and meta-analyses. MIS Quarterly, 40, 529–551.

Larsen, K. R., Michie, S., West, R., & Gurshuny, V. (2015). Developing an interdisciplinary taxonomy of behavior. In W. T. Riley (Ed.), 37: Toward an ontology of behavior change—An innovative approach to intervention development. Symposium conducted at the annual meeting of the society of behavioral medicine, San Antonio, TX.

Li, J., & Larsen, K. R. (2011). Establishing nomological networks for behavioral science: A natural language processing based approach. In 2011 Proceedings of the international conference on information systems, paper 24. Available at: http://aisel.aisnet.org/icis2011/proceedings/knowledge/24

Litwin, M. S. (1995). How to measure survey reliability and validity. London, England: Sage Publications.CrossRef

Liu, Y., Hinds, P. S., Wang, J., Correia, H., Du, S., Ding, J., et al. (2013). Translation and linguistic validation of the pediatric patient-reported outcomes measurement information system measures into simplified Chinese using cognitive interviewing methodology. Cancer Nursing, 36, 368–376. doi:10.1097/NCC.0b013e3182962701 CrossRefPubMed

Lorencatto, F., West, R., Bruguera, C., Brose, L., & Michie, S. (2015). Assessing quality of goal-setting in behavioural support for smoking cessation and the association with outcomes. Annals of Behavioral Medicine,. doi:10.1007/s12160-015-9755-7 PubMedCentral

Lowe, H. J., & Barnett, O. G. (1994). Understanding and using the medical subject headings (MeSH) vocabulary to perform literature searches. JAMA, 271, 1103–1108. doi:10.1001/jama.1994.03510380059038 CrossRefPubMed

McGinnis, J. M., Williams-Russo, P., & Knickman, J. R. (2002). The case for more active policy attention to health promotion. Health Affairs, 21, 78–93. doi:10.1377/hlthaff.21.2.78 CrossRefPubMed

McGuinness, D. L., & Van Harmelen, F. (2004). OWL web ontology language overview. W3C recommendation 10 February. Retrieved from: http://www.w3.org/TR/2004/REC-owl-features-20040210/

Memidex. (2013). Domains. Retrieved from: http://www.memidex.com/domains

Meriam-Webster. (2015). Domain. Available at: http://www.merriam-webster.com/dictionary/domain

Michie, S., & Johnston, M. (2012). Theories and techniques of behaviour change: Developing a cumulative science of behaviour change. Health Psychology Review, 6, 1–6. doi:10.1080/17437199.2012.654964 CrossRef

Michie, S., Johnston, M., Abraham, C., Lawton, R., Parker, D., & Walker, A. (2005). Making psychological theory useful for implementing evidence based practice: A consensus approach. Quality and Safety in Health Care, 14, 26–33. doi:10.1136/qshc.2004.011155 CrossRefPubMedPubMedCentral

Michie, S., Johnston, M., Francis, J., Hardeman, W., & Eccles, M. (2008). From theory to intervention: mapping theoretically derived behavioural determinants to behaviour change techniques. Applied Psychology, 57, 660–680. doi:10.1111/j.1464-0597.2008.00341.x CrossRef

Michie, S., Johnston, M., Rothman, A., Kelly, M., & de Bruin, M. (2014). Behaviour change techniques and theory: The theories and techniques of behavior change project. Retrieved from: https://www.ucl.ac.uk/behaviour-change-techniques

Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., et al. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine, 46, 81–95. doi:10.1007/s12160-013-9486-6 CrossRefPubMed

Michie, S., West, R., Sheals, K., Carey, R., & Connell, L. (2015a). Integrating constructs across 83 theories of behaviour change: Development of a method. Paper presented at the meeting of the society of behavioral medicine, annual meeting, San Antonio, USA.

Michie, S., Wood, C., Johnston, M., Abraham, C., Francis, J., & Hardeman, W. (2015b). Behaviour change techniques: The development and evaluation of a taxonomic method for reporting and describing behaviour change interventions. Health Technology Assessment,. doi:10.3310/hta19990

Moser, R. P., Hesse, B. W., Shaikh, A. R., Courtney, P., Morgan, G., Augustson, E., et al. (2011). Grid-enabled measures: Using science 2.0 to standardize measures and share data. American Journal of Preventive Medicine, 40, S134–S143. doi:10.1016/j.amepre.2011.01.004 CrossRefPubMedPubMedCentral

NCI (National Cancer Institute). (2016). GEM (Grid-enabled measures database). Retrieved from: https://www.gem-beta.org/Public/Home.aspx

National Research Council. (2011). Toward precision medicine: Building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: The National Academies Press. doi:10.17226/13284

Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., et al. (2015). Promoting an open research culture: Author guidelines for journals could help to promote transparency, openness, and reproducibility. Science, 348, 1422–1425. doi:10.1126/science.aab2374 CrossRefPubMedPubMedCentral

Open Geospatial Consortium. (2016). Retrieved from: http://www.opengeospatial.org

Open Science Framework. (2016). Open science framework: A scholarly commons to connect the entire research cycle. Retrieved from: https://osf.io

Oxford English Dictionary. (2016). Class. Retrieved from: http://www.oed.com/view/Entry/33874?rskey=Wk0x4G&result=1#eid

Paz, S. H., Spritzer, K. L., Morales, L. S., & Hays, R. D. (2013). Evaluation of the patient-reported outcomes information system (PROMIS^®) Spanish-language physical functioning items. Quality of Life Research, 22, 1819–1830. doi:10.1007/s11136-012-0292-6 CrossRefPubMed

Pennebaker, J. W., & Graybeal, A. (2001). Patterns of natural language use: Disclosure, personality, and social integration. Current Directions in Psychological Science, 10, 90–93.CrossRef

Poldrack, R. (2016). The cognitive atlas. Available at: http://www.cognitiveatlas.org

President’s Council of Advisors on Science and Technology (PCAST). (2010). Designing a digital future: Federally funded research and development in networking and information technology. Washington, DC: Executive Office of the President of the United States. Retrieved from: https://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-nitrd-report-2010.pdf

PROsetta Stone^®. (2016). What is PROsetta stone? Retrieved from: http://www.prosettastone.org

Schalet, B. D., Cook, K. F., Choi, S. W., & Cella, D. (2014). Establishing a common metric for self-reported anxiety: Linking the MASQ, PANAS, and GAD-7 to PROMIS Anxiety. Journal of Anxiety Disorders, 28, 89–96. doi:10.1016/j.janxdis.2013.11.006 CrossRef

Staunton, L., Gellert, P., Knittle, K., & Sniehotta, F. F. (2014). Perceived control and intrinsic vs. extrinsic motivation for oral self-care: A full factorial experimental test of theory-based persuasive messages. Annals of Behavioral Medicine, 49, 258–268. doi:10.1007/s12160-014-9655-2 CrossRef

Stavri, Z., & Michie, S. (2012). Classification systems in behavioural science: Current systems and lessons from the natural, medical and social sciences. Health Psychology Review, 6, 113–140. doi:10.1080/17437199.2011.641101 CrossRef

The Gene Ontology Consortium. (2015a). Gene ontology consortium: Going forward. Nucleic Acids Research, 43, D1049–D1056. doi:10.1093/nar/gku1179 CrossRef

The Gene Ontology Consortium. (2015b). What is the gene ontology? Retreived from: http://geneontology.org

Thorndike, E. (1904). An introduction to the theory of mental and social measurements. New York: The Science Press.CrossRef

U.S. National Library of Medicine. (1999). Fact sheet: Medical subject headings (MeSH^®). Available at: https://www.nlm.nih.gov/pubs/factsheets/mesh.html

U.S. National Library of Medicine. (2012). Common data element (CDE) resource portal. Retrieved at: https://www.nlm.nih.gov/cde/

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478. Retrieved from: http://www.jstor.org/stable/30036540

Walls, R. L., Athreya, B., Cooper, L., Elser, J., Gandolfo, M. A., Jaiswal, P., et al. (2012). Ontologies as integrative tools for plant science. American Journal of Botany, 99, 1263–1275. doi:10.3732/ajb.1200222 CrossRefPubMedPubMedCentral

Weber, R. (2012). Evaluating and developing theories in the information systems discipline. Journal of the Association for Information Systems, 13, 1–30.

Weisz, J. R., Ng, M. Y., & Bearman, S. K. (2014). Odd couple? Reenvisioning the relation between science and practice in the dissemination-implementation era. Clinical Psychological Science, 2, 58–74. doi:10.1177/2167702613501307 CrossRef

West, R., & Michie, S. (2016). A guide to development and evaluation of digital behaviour change interventions in healthcare (version 1). London: Silverback Publishing.

Wong, W., Liu, W., & Bennamoun, M. (2012). Ontology learning from text: A look back and into the future. ACM Computing Surveys (CSUR), 44, 20. doi:10.1145/2333112.2333115 CrossRef

World Health Organization. (2013). How to use the ICF: A practical manual for using the international classification of functioning. Disability and health (ICF). Exposure draft for comment. October 2013. Geneva: WHO. Retrieved from: http://www.who.int/classifications/drafticfpracticalmanual.pdf

Titel: Behavior change interventions: the potential of ontologies for advancing science and practice
verfasst von: Kai R. Larsen
Susan Michie
Eric B. Hekler
Bryan Gibson
Donna Spruijt-Metz
David Ahern
Heather Cole-Lewis
Rebecca J. Bartlett Ellis
Bradford Hesse
Richard P. Moser
Jean Yi
Publikationsdatum: 01.08.2016
Verlag: Springer US
Erschienen in: Journal of Behavioral Medicine / Ausgabe 1/2017
Print ISSN: 0160-7715
Elektronische ISSN: 1573-3521
DOI: https://doi.org/10.1007/s10865-016-9768-0

Neu im Fachgebiet Psychiatrie

18.04.2024 | Arterielle Hypertonie | Nachrichten

Update Psychiatrie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Behavior change interventions: the potential of ontologies for advancing science and practice

Abstract

Neu im Fachgebiet Psychiatrie

Antihypertensiva schützen auch alte Menschen noch vor Demenz

Delir kann Demenz begünstigen

Zunahme von Geschlechtsinkongruenz und -dysphorie registriert

Vitiligo: Prävalenz steigt und viele Erkrankte werden spät behandelt

Update Psychiatrie

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 1/2017

Technology-based interventions for weight management: current randomized controlled trial evidence and future directions

Integration of behavioral health and primary care: current knowledge and future directions

Future directions of multiple behavior change research

Motivating future directions of behavioral medicine

Preparing tomorrow’s behavioral medicine scientists and practitioners: a survey of future directions for education and training

Military and veteran health behavior research and practice: challenges and opportunities

Neu im Fachgebiet Psychiatrie

Antihypertensiva schützen auch alte Menschen noch vor Demenz

Delir kann Demenz begünstigen

Zunahme von Geschlechtsinkongruenz und -dysphorie registriert

Vitiligo: Prävalenz steigt und viele Erkrankte werden spät behandelt

Update Psychiatrie