Advertisement

Advancing Models and Theories for Digital Behavior Change Interventions

      To be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. The proposed framework stipulates the use of a state-space representation to define when, where, for whom, and in what state for that person, an intervention will produce a targeted effect. The “state” is that of the individual based on multiple variables that define the “space” when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to American Journal of Preventive Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Davis R.
        • Campbell R.
        • Hildon Z.
        • Hobbs L.
        • Michie S.
        Theories of behaviour and behaviour change across the social and behavioural sciences: a scoping review.
        Health Psychol Rev. 2015; 9: 323-344https://doi.org/10.1080/17437199.2014.941722
        • Noar S.M.
        • Zimmerman R.S.
        Health behavior theory and cumulative knowledge regarding health behaviors: are we moving in the right direction?.
        Health Educ Res. 2005; 20: 275-290https://doi.org/10.1093/her/cyg113
        • Rothman A.J.
        “Is there nothing more practical than a good theory?” Why innovations and advances in health behavior change will arise if interventions are used to test and refine theory.
        Intern J Behav Nut Phys Act. 2004; 1: 11https://doi.org/10.1186/1479-5868-1-11
        • Michie S.
        • Campbell R.
        • Brown J.
        • West R.R.
        • Gainsforth H.
        ABC of Behaviour Change Theories: An Essential Resource for Researchers, Policy Makers, and Practitioners.
        Silverback Publishing, London, UK2014
        • Prestwich A.
        • Sniehotta F.F.
        • Whittington C.
        • Dombrowski S.U.
        • Rogers L.
        • Michie S.
        Does theory influence the effectiveness of health behavior interventions? Meta-analysis.
        Health Psychol. 2014; 33: 465https://doi.org/10.1037/a0032853
        • Spruijt-Metz D.
        • Hekler E.B.
        • Saranummi N.
        • et al.
        Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.
        Transl Behav Med. 2015; 5: 335-346https://doi.org/10.1007/s13142-015-0324-1
        • Shadish W.R.
        • Cook T.D.
        • Campbell D.T.
        Experimental and Quasi-Experimental Designs for Generalized Causal Inference.
        Wadsworth Cengage Learning, Belmont, CA2002
        • Molenaar P.
        • Campbell C.
        The new person-specific paradigm in psychology.
        Curr Dir Psychol Sci. 2009; 18: 112-117https://doi.org/10.1111/j.1467-8721.2009.01619.x
        • Molenaar P.C.
        A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever.
        Measurement (Mahwah N J). 2004; 2: 201-218https://doi.org/10.1207/s15366359mea0204_1
        • Yardley L.
        • Patrick K.
        • Choudhury T.
        • Michie S.
        Current issues and future directions for research into digital behavior change interventions.
        Am J Prev Med. 2016;
        • Riley W.T.
        • Rivera D.E.
        • Atienza A.A.
        • Nilsen W.
        • Allison S.M.
        • Mermelstein R.
        Health behavior models in the age of mobile interventions: are our theories up to the task?.
        Transl Behav Med. 2011; 1: 53-71https://doi.org/10.1007/s13142-011-0021-7
        • Patrick K.
        • Griswold W.G.
        • Raab F.
        • Intille S.S.
        Health and the mobile phone.
        Am J Prev Med. 2008; 35: 177-181https://doi.org/10.1016/j.amepre.2008.05.001
      1. Intille SS, Kukla C, Farzanfar R, Bakr W. Just-in-time technology to encourage incremental, dietary behavior change. In: AMIA Annual Symposium Proceedings. Bethesda, MD: American Medical Informatics Association; 2003:874.

        • Nahum-Shani I.
        • Hekler E.B.
        • Spruijt-Metz D.
        Building health behavior models to guide the development of just-in-time adaptive interventions: a pragmatic framework.
        Health Psychol. 2016; 34: 1209-1219https://doi.org/10.1037/hea0000306
        • Hekler E.B.
        • Klasnja P.
        • Riley W.T.
        • et al.
        Agile Science: creating useful products for behavior change in the real-world.
        Transl Behav Med. February 26, 2016; (Online)https://doi.org/10.1007/s13142-016-0395-7
        • Christmas S.
        • Michie S.
        • West R.
        Thinking About Behaviour Change: An Interdisciplinary Dialogue.
        Silverback Publishing, London, UK2016
        • Michie S.
        • Atkins L.
        • West R.
        The Behaviour Change Wheel: A Guide to Designing Interventions.
        Silverback Publishing, London, UK2014
        • Dunton G.F.
        • Atienza A.A.
        The need for time-intensive information in healthful eating and physical activity research: a timely topic.
        J Am Diet Assoc. 2009; 109: 30-35https://doi.org/10.1016/j.jada.2008.10.019
        • Hekler E.B.
        • Buman M.P.
        • Poothakandiyl N.
        • et al.
        Exploring behavioral markers of long-term physical activity maintenance: a case study of system identification modeling within a behavioral intervention.
        Health Educ Res. 2013; 40: 51S-62Shttps://doi.org/10.1177/1090198113496787
        • Klasnja P.
        • Hekler E.B.
        • Shiffman S.
        • et al.
        Micro-randomized trials: an experimental design for developing just-in-time adaptive interventions.
        Health Psychol. 2016; 34: 1220-1228https://doi.org/10.1037/hea0000305
        • Patrick K.
        • Hekler E.B.
        • Estrin D.
        • et al.
        Rapid rate of technological development and its implications for research on digital health behavior interventions.
        Am J Prev Med. 2016;
        • Rivera D.E.
        • Pew M.D.
        • Collins L.M.
        Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction.
        Drug Alcohol Depend. 2007; 88: S31-S40https://doi.org/10.1016/j.drugalcdep.2006.10.020
        • Rothman A.J.
        Exploring connections between moderators and mediators: commentary on subgroup analyses in intervention research.
        Prev Sci. 2013; 14: 189-192https://doi.org/10.1007/s11121-012-0333-y
        • Spruijt-Metz D.
        • Nilsen W.
        Dynamic models of behavior for just-in-time adaptive interventions.
        IEEE Pervasive Comput. 2014; 13: 13-17https://doi.org/10.1109/MPRV.2014.46
        • Riley W.T.
        • Martin C.A.
        • Rivera D.E.
        • et al.
        The development of a control systems model of social cognitive theory.
        Transl Behav Med. November 9, 2016; (Online)https://doi.org/10.1007/s13142-015-0356-6
        • Anderson J.C.
        • Gerbing D.W.
        Structural equation modeling in practice: a review and recommended two-step approach.
        Psychol Bull. 1988; 103: 411https://doi.org/10.1037/0033-2909.103.3.411
        • Deshpande S.
        • Rivera D.E.
        • Younger J.W.
        • Nandola N.N.
        A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention.
        Transl Behav Med. 2014; 4 (Errratum in 4(3), p439): 275-289
        • Bandura A.
        Social Foundations of Thought and Action: A Social Cognitive Theory.
        Prentice Hall, Englewood Cliffs, NJ1986
        • Olander E.K.
        • Fletcher H.
        • Williams S.
        • Atkinson L.
        • Turner A.
        • French D.P.
        What are the most effective techniques in changing obese individuals’ physical activity self-efficacy and behaviour: a systematic review and meta-analysis.
        Int J Behav Nutr Phys Act. 2013; 10: 1-15https://doi.org/10.1186/1479-5868-10-29
        • Van Bavel J.J.
        • Mende-Siedlecki P.
        • Brady W.J.
        • Reinero D.A.
        Contextual sensitivity in scientific reproducibility.
        Proc Natl Acad Sci U S A. 2016; 113: 6454-6459
        • Lawson P.J.
        • Flocke S.A.
        Teachable moments for health behavior change: a concept analysis.
        Patient Educ Couns. 2009; 76: 25-30https://doi.org/10.1016/j.pec.2008.11.002
        • Resnick P.
        • Varian H.R.
        Recommender systems.
        Commun ACM. 1997; 40: 56-58https://doi.org/10.1145/245108.245121
      2. Golbeck J, Robles C, Turner K. Predicting personality with social media. In: CHI׳11 Extended Abstracts on Human Factors in Computing Systems. ACM; 2011:253–262. http://dx.doi.org/10.1145/1979742.1979614.

      3. Zhou MX, Nichols J, Dignan T, Lohr S, Golbeck J, Pennebaker JW. Opportunities and risks of discovering personality traits from social media. In: Proceedings of the extended abstracts of the 32nd annual ACM Conference on Human Factors in Computing Systems (CHI׳14). ACM; 2014:1081–1086. http://dx.doi.org/10.1145/2559206.2579408.

        • Estrin D.
        Small data, where n = me.
        Commun ACM. 2014; 57: 32-34https://doi.org/10.1145/2580944
        • Witten I.H.
        • Frank E.
        Data Mining: Practical Machine Learning Tools and Techniques.
        Morgan Kaufmann, Amsterdam2005
      4. Ravichandran R, Saba E, Chen K-Y, Goel M, Gupta S, Patel SN. WiBreathe: estimating respiration rate using wireless signals in natural settings in the home. In: IEEE Pervasive Computing and Communications (PerCom), 2015 IEEE International Conference on. http://dx.doi.org/10.1109/PERCOM.2015.7146519.

        • Kumar S.
        • Nilsen W.
        • Pavel M.
        • Srivastava M.
        Mobile health: revolutionizing healthcare through trans-disciplinary research.
        Computer (Long Beach Calif). 2013; 46: 28-35
        • Kumar S.
        • Nilsen W.J.
        • Abernethy A.
        • et al.
        Mobile health technology evaluation: the mHealth evidence workshop.
        Am J Prev Med. 2013; 45: 228-236https://doi.org/10.1016/j.amepre.2013.03.017
        • Shiffman S.
        • Stone A.A.
        • Hufford M.R.
        Ecological momentary assessment.
        Annu Rev Clin Psychol. 2008; 4: 1-32https://doi.org/10.1146/annurev.clinpsy.3.022806.091415
        • Dunton G.F.
        • Dzubur E.
        • Kawabata K.
        • Yanez B.
        • Bo B.
        • Intille S.
        Development of a smartphone application to measure physical activity using sensor-assisted self-report.
        Front Public Health. 2014; 2: 12https://doi.org/10.3389/fpubh.2014.00012
        • Ljung L.
        System Identification: Theory for the User.
        PTR Prentice Hall Information and System Sciences Series, Englewood Cliff, NJ1987: 198
        • Nandola N.N.
        • Rivera D.E.
        An improved formulation of hybrid model predictive control with application to production-inventory systems.
        IEEE Trans Control Syst Technol. 2013; 1: 121-135https://doi.org/10.1109/TCST.2011.2177525
        • Dong Y.
        • Rivera D.E.
        • Downs D.S.
        • Savage J.S.
        • Thomas D.M.
        • Collins L.M.
        Hybrid model predictive control for optimizing gestational weight gain behavioral interventions.
        Proc Am Control Conf. 2013; : 1970-1975
        • Martin C.A.
        • Desphande S.
        • Hekler E.B.
        • Rivera D.E.
        A system identification approach for improving behavioral interventions based on social cognitive theory.
        Proc Am Control Conf. 2015; : 5878-5883https://doi.org/10.1109/ACC.2015.7172261
        • Martin C.A.
        • Rivera D.E.
        • Riley W.T.
        • et al.
        A dynamical systems model of social cognitive theory.
        Proc Am Control Conf. 2014; : 2407-2412https://doi.org/10.1109/ACC.2014.6859463
      5. Larsen K, Michie S, Hekler EB, et al. Behavior change interventions: the potential of ontologies for advancing science and practice. J Behav Med. In press.

        • Arp R.
        • Smith B.
        • Spear A.D.
        Building Ontologies With Basic Formal Ontology.
        MIT Press, Cambridge, MA2015https://doi.org/10.7551/mitpress/9780262527811.001.0001
        • Michie S.
        • Wood C.
        • Johnston M.
        • Abraham C.
        • Francis J.
        • Hardeman W.
        Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions.
        Health Technol Assess. 2015; 19: 1-188https://doi.org/10.3310/hta19990
        • Singer J.D.
        • Willett J.B.
        Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence.
        Oxford University Press, New York2003https://doi.org/10.1093/acprof:oso/9780195152968.001.0001
        • Hekler E.B.
        • Buman M.P.
        • Ahn D.
        • Dunton G.F.
        • Atienza A.A.
        • King A.C.
        Are daily fluctuations in perceived environment associated with walking?.
        Psychol Health. 2012; 27: 1009-1020https://doi.org/10.1080/08870446.2011.645213
        • Ljung L.
        System Identification: Theory for the User.
        2nd ed. Prentice Hall, Upper Saddle River, NJ1999https://doi.org/10.1002/047134608x.w1046
        • Sutton R.S.
        • Barto A.G.
        Reinforcement Learning: An Introduction.
        MIT Press, Cambridge, MA1998

      CHORUS Manuscript

      View Open Manuscript