Journal of Behavioral Medicine

, Volume 40, Issue 1, pp 6–22 | Cite as

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

  • Kai R. LarsenEmail author
  • 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


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.


Behavior change interventions Ontologies Controlled vocabularies Taxonomies Mechanisms of action Behaviors 



We are grateful for the helpful suggestions and edits provided by the metaBUS team, specifically Frank Bosco, Krista Uggerslev, and Piers Steel. We further appreciate the help from from George Alter at the Inter-University Consortium for Political and Social Research, William Riley, Office of Behavioral and Social Sciences Research, the National Institutes of Health, as well as from Robert West at the Department of Epidemiology and Public Health, University College London.

Compliance with ethical standards

Conflict of interest

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, and Jean Yi declare that they do not have any conflict of interest.

Human and animal rights and Informed consent

All procedures followed were in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.


  1. Arp, R., Smith, B., & Spear, A. D. (2015). Building ontologies with basic formal ontology. Cambridge: The MIT Press.CrossRefGoogle Scholar
  2. 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 CrossRefPubMedGoogle Scholar
  3. BCTTv1 Team. (2016). Behaviour change techniques and theory: Feedback on BCTTv1. Retrieved from:
  4. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 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 CrossRefPubMedGoogle Scholar
  6. Chinn, P., & Kramer, M. (1991). Theory and nursing: A systematic approach (3rd ed.). St. Louis, MO: Mosby Year Book.Google Scholar
  7. 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 CrossRefPubMedGoogle Scholar
  8. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 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.Google Scholar
  10. 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 CrossRefPubMedGoogle Scholar
  11. 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 CrossRefGoogle Scholar
  12. 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.Google Scholar
  13. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 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 CrossRefGoogle Scholar
  15. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 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:
  17. 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 CrossRefPubMedGoogle Scholar
  18. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 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 CrossRefGoogle Scholar
  20. ICPSR. (2016). Membership in ICPSR. Retrieved from:
  21. 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 Google Scholar
  22. IOM (Institute of Medicine). (2011). Patients charting the course: Citizen engagement and the learning health system—Workshop summary. Washington, D.C.: National Academies Press.Google Scholar
  23. Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52, 36–44. doi: 10.1145/1536616.1536632 CrossRefGoogle Scholar
  24. 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.Google Scholar
  25. Kelley, T. L. (1927). Interpretation of educational measurements. Oxford, England: World Book Company.Google Scholar
  26. 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 CrossRefPubMedGoogle Scholar
  27. 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 CrossRefGoogle Scholar
  28. Larsen, K. (2016). Inter-nomological network. Retrieved from:
  29. Larsen, K. R., & Bong, C. H. (2016). A tool for addressing construct identity in literature reviews and meta-analyses. MIS Quarterly, 40, 529–551.Google Scholar
  30. 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.Google Scholar
  31. 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:
  32. Litwin, M. S. (1995). How to measure survey reliability and validity. London, England: Sage Publications.CrossRefGoogle Scholar
  33. 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 CrossRefPubMedGoogle Scholar
  34. 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 PubMedCentralGoogle Scholar
  35. 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 CrossRefPubMedGoogle Scholar
  36. 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 CrossRefPubMedGoogle Scholar
  37. McGuinness, D. L., & Van Harmelen, F. (2004). OWL web ontology language overview. W3C recommendation 10 February. Retrieved from:
  38. Memidex. (2013). Domains. Retrieved from:
  39. Meriam-Webster. (2015). Domain. Available at:
  40. 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 CrossRefGoogle Scholar
  41. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 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 CrossRefGoogle Scholar
  43. 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:
  44. 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 CrossRefPubMedGoogle Scholar
  45. 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.Google Scholar
  46. 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 Google Scholar
  47. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  48. NCI (National Cancer Institute). (2016). GEM (Grid-enabled measures database). Retrieved from:
  49. 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 Google Scholar
  50. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  51. Open Geospatial Consortium. (2016). Retrieved from:
  52. Open Science Framework. (2016). Open science framework: A scholarly commons to connect the entire research cycle. Retrieved from:
  53. Oxford English Dictionary. (2016). Class. Retrieved from:
  54. 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 CrossRefPubMedGoogle Scholar
  55. Pennebaker, J. W., & Graybeal, A. (2001). Patterns of natural language use: Disclosure, personality, and social integration. Current Directions in Psychological Science, 10, 90–93.CrossRefGoogle Scholar
  56. Poldrack, R. (2016). The cognitive atlas. Available at:
  57. 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:
  58. PROsetta Stone®. (2016). What is PROsetta stone? Retrieved from:
  59. 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 CrossRefGoogle Scholar
  60. 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 CrossRefGoogle Scholar
  61. 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 CrossRefGoogle Scholar
  62. The Gene Ontology Consortium. (2015a). Gene ontology consortium: Going forward. Nucleic Acids Research, 43, D1049–D1056. doi: 10.1093/nar/gku1179 CrossRefGoogle Scholar
  63. The Gene Ontology Consortium. (2015b). What is the gene ontology? Retreived from:
  64. Thorndike, E. (1904). An introduction to the theory of mental and social measurements. New York: The Science Press.CrossRefGoogle Scholar
  65. U.S. National Library of Medicine. (1999). Fact sheet: Medical subject headings (MeSH®). Available at:
  66. U.S. National Library of Medicine. (2012). Common data element (CDE) resource portal. Retrieved at:
  67. 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:
  68. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  69. Weber, R. (2012). Evaluating and developing theories in the information systems discipline. Journal of the Association for Information Systems, 13, 1–30.Google Scholar
  70. 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 CrossRefGoogle Scholar
  71. West, R., & Michie, S. (2016). A guide to development and evaluation of digital behaviour change interventions in healthcare (version 1). London: Silverback Publishing.Google Scholar
  72. 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 CrossRefGoogle Scholar
  73. 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:

Copyright information

© Springer Science+Business Media New York (outside the USA) 2016

Authors and Affiliations

  • Kai R. Larsen
    • 1
    Email author
  • Susan Michie
    • 2
  • Eric B. Hekler
    • 3
  • Bryan Gibson
    • 4
  • Donna Spruijt-Metz
    • 5
  • David Ahern
    • 6
  • Heather Cole-Lewis
    • 7
  • Rebecca J. Bartlett Ellis
    • 8
  • Bradford Hesse
    • 9
  • Richard P. Moser
    • 10
  • Jean Yi
    • 11
  1. 1.Leeds School of BusinessUniversity of ColoradoBoulderUSA
  2. 2.Centre for Behaviour ChangeUniversity College LondonLondonUK
  3. 3.School of Nutrition and Health PromotionArizona State UniversityPhoenixUSA
  4. 4.Department of Biomedical InformaticsUniversity of UtahSalt Lake CityUSA
  5. 5.Department of Psychology, Center for Economic and Social ResearchUniversity of Southern CaliforniaLos AngelesUSA
  6. 6.Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA
  7. 7.Johnson and Johnson Health and Wellness SolutionsNew BrunswickUSA
  8. 8.Indiana University School of NursingIndianapolisUSA
  9. 9.National Cancer InstituteNational Institutes of HealthBethesdaUSA
  10. 10.Behavioral Research ProgramNational Cancer InstituteRockvilleUSA
  11. 11.Clinical Research DivisionFred HutchSeattleUSA

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