Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research

CHI, pp. 3307-3316, 2013.

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We aim to provide HCI researchers with guidance on interpreting, using, and developing behavioral theories

Abstract:

Researchers in HCI and behavioral science are increasingly exploring the use of technology to support behavior change in domains such as health and sustainability. This work, however, remain largely siloed within the two communities. We begin to address this silo problem by attempting to build a bridge between the two disciplines at the l...More

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Introduction
  • HCI researchers are increasingly designing technologies to promote behavior change. A review of the last 10 years of CHI proceedings in the ACM Digital Library found 136 papers that mentioned "behavior change" with 76% of these from the last four years (Figure 1).
  • This work has focused on diverse behaviors from diet [32] and exercise [16] to sustainable water usage [27], a common strategy underlies much of this work: to inform design, HCI researchers draw on theories from behavioral sciences.
  • As HCI research on behavior change technologies matures, Number of Papers
Highlights
  • HCI researchers are increasingly designing technologies to promote behavior change
  • This work has focused on diverse behaviors from diet [32] and exercise [16] to sustainable water usage [27], a common strategy underlies much of this work: to inform design, HCI researchers draw on theories from behavioral sciences
  • USES OF BEHAVIORAL THEORIES IN HCI In our review of HCI literature on behavior change technologies, we have identified three broad uses of behavioral theory
  • Common pitfalls when using behavioral theories we have argued that theory can be helpful to HCI researchers working on behavior change technologies, its use is not without pitfalls
  • Issues that require further explication are numerous such as: (i) best methods for evaluating behavior change technologies in HCI research; (ii) a full understanding of the requisite knowledge each field requires before engaging with the other; (iii) the possibility of distortions that arise from poor translations of concepts between fields; and (iv) the impact of sociocultural differences related to the origin of theories on the interpretability, utility, and generalizability of different behavioral theories within an HCI context
  • Our final goal is a call for behavioral scientists and HCI researchers to work more closely together both on the design of behavior change technologies and the development of better theories
Results
  • In some cases, previously developed theories are insufficient to guide HCI research
  • In such cases, additional empirical work—often in the form of ethnographic and other qualitative approaches—can generate knowledge necessary to establish a starting point for design.
  • In their work with stroke patients, Balaam et al found that household dynamics acted either as barriers or facilitators for patients’ rehabilitation activities [4]
  • Based on this finding, Balaam et al created personalized interventions to motivate regular performance of exercises needed to increase the range of motion in their affected limbs.
  • Its high level of applicability to design makes such empirical work an essential component of HCI research (e.g., [27, 50]
Conclusion
  • CONCLUSIONS AND STEPS

    The authors' goal in this paper was to provide HCI researchers and designers with guidance for interpreting, using, and contributing to behavioral theories.
  • Issues that require further explication are numerous such as: (i) best methods for evaluating behavior change technologies in HCI research; (ii) a full understanding of the requisite knowledge each field requires before engaging with the other; (iii) the possibility of distortions that arise from poor translations of concepts between fields; and (iv) the impact of sociocultural differences related to the origin of theories on the interpretability, utility, and generalizability of different behavioral theories within an HCI context
  • Each of these points requires more careful thought and work from both fields.
  • The authors believe that such collaborations and open exchanges of ideas across disciplines are fundamental to the development of better theories, better systems, better behavioral outcomes, and, to positive societal impact
Summary
  • Introduction:

    HCI researchers are increasingly designing technologies to promote behavior change. A review of the last 10 years of CHI proceedings in the ACM Digital Library found 136 papers that mentioned "behavior change" with 76% of these from the last four years (Figure 1).
  • This work has focused on diverse behaviors from diet [32] and exercise [16] to sustainable water usage [27], a common strategy underlies much of this work: to inform design, HCI researchers draw on theories from behavioral sciences.
  • As HCI research on behavior change technologies matures, Number of Papers
  • Objectives:

    The authors aim to provide HCI researchers with guidance on interpreting, using, and developing behavioral theories.
  • Results:

    In some cases, previously developed theories are insufficient to guide HCI research
  • In such cases, additional empirical work—often in the form of ethnographic and other qualitative approaches—can generate knowledge necessary to establish a starting point for design.
  • In their work with stroke patients, Balaam et al found that household dynamics acted either as barriers or facilitators for patients’ rehabilitation activities [4]
  • Based on this finding, Balaam et al created personalized interventions to motivate regular performance of exercises needed to increase the range of motion in their affected limbs.
  • Its high level of applicability to design makes such empirical work an essential component of HCI research (e.g., [27, 50]
  • Conclusion:

    CONCLUSIONS AND STEPS

    The authors' goal in this paper was to provide HCI researchers and designers with guidance for interpreting, using, and contributing to behavioral theories.
  • Issues that require further explication are numerous such as: (i) best methods for evaluating behavior change technologies in HCI research; (ii) a full understanding of the requisite knowledge each field requires before engaging with the other; (iii) the possibility of distortions that arise from poor translations of concepts between fields; and (iv) the impact of sociocultural differences related to the origin of theories on the interpretability, utility, and generalizability of different behavioral theories within an HCI context
  • Each of these points requires more careful thought and work from both fields.
  • The authors believe that such collaborations and open exchanges of ideas across disciplines are fundamental to the development of better theories, better systems, better behavioral outcomes, and, to positive societal impact
Reference
  • Adams, M.A., Norman, G.J., Hovell, M.F., Sallis, J.F., Patrick, K. (2009). Reconceptualizing decisional balance in an adolescent sun protection intervention: Mediating effects and theoretical interpretations. Healt Psychol, 28, 217-225.
    Google ScholarLocate open access versionFindings
  • Ajzen, I. (1991). The theory of planned behavior, Organizat Behav Hum Dec Proc, 50, 179-211.
    Google ScholarLocate open access versionFindings
  • Ajzen, I. (2002). Perceived Behavioral Control, Self‐Efficacy, Locus of Control, and the Theory of Planned Behavior. J Appl Soc Psych, 32, 665-683
    Google ScholarLocate open access versionFindings
  • Balaam, M. et al. (2011). Motivating mobility: designing for lived motivation in stroke rehabilitation. CHI’11, 3073-3082.
    Google ScholarLocate open access versionFindings
  • Bandura, A.(1986). Social foundations of thought and action. Prentice Hall, Englewood Cliffs, NJ.
    Google ScholarFindings
  • Bandura, A. (1997). Self-Efficacy-The Exercise of Control. Worth Publishers, Inc. New York, NY.
    Google ScholarFindings
  • Bao, L. and Intille, S.S. (2004). Activity Recognition from UserAnnotated Acceleration Data. Most, 1–17.
    Google ScholarFindings
  • Becker, M. (1974). The health belief model and personal health behavior, J Healt Soc Behav, 18, 348-366.
    Google ScholarLocate open access versionFindings
  • Bond, R.M., et al. (2012). A 61-million-person experiment in social influence and political mobilization. Nature 489, 295–298.
    Google ScholarLocate open access versionFindings
  • Buman, M.P., Giacobbi, P.R., Yasova, L.D., and McCrae, C.S. (2009). Using the constructive narrative perspective to understand physical activity reasoning schema in sedentary adults. J Healt Psychol, 14, 1174–83.
    Google ScholarLocate open access versionFindings
  • Collins, L., Dziak, J., and Li, R. (2009). Design of experiments with multiple independent variables: A resource management perspective on complete and reduced factorial designs. Psychol Meth, 14, 202-224.
    Google ScholarLocate open access versionFindings
  • Consolvo, S., Everitt, K., Smith, I., and Landay, J. (2006). Design requirements for technologies that encourage physical activity. CHI’06, 457-466.
    Google ScholarFindings
  • Consolvo, S., Klasnja, P., McDonald, D.W., & Landay, J. (2009). Goal-setting considerations for persuasive technologies that encourage physical activity. Persuasive ’09, Article 8, 8 pgs.
    Google ScholarLocate open access versionFindings
  • Consolvo, S., Klasnja, P., McDonald, D.W., et al. (2008). Flowers or a robot army ? Encouraging awareness & activity with personal, mobile displays. UbiComp’08, 54–63.
    Google ScholarFindings
  • Consolvo, S., McDonald, D.W., and Landay, J. (2009) Theorydriven design strategies for technologies that support behavior change in everyday life. CHI’09, 405-414.
    Google ScholarFindings
  • Consolvo, S., McDonald, D.W., et al. (2008). Activity sensing in the wild: A field trial of UbiFit garden. CHI’08, 1797-1806.
    Google ScholarFindings
  • Corbin, J. and Strauss, A. (2008). Basics of qualitative research: Techniques and procedures for developing grounded theory. 3rd Ed. Sage, Thousand Oaks, CA.
    Google ScholarFindings
  • Deci, E.L. and Ryan, R.M. (1985) Intrinsic motivation and selfdetermination in human behavior. Plenum, New York, NY.
    Google ScholarFindings
  • Dervin, B. (1983). Sense-making theory and practice: An overview of user interests in knowledge seeking and use, J Know Manag, 36-46.
    Google ScholarLocate open access versionFindings
  • Dobson, D. and Cook, T. (1980) Avoiding type III error in program evaluation: Results from a field experiment. Eval Prog Plan, 269-276.
    Google ScholarFindings
  • Dourish, P. (2010) HCI and environmental sustainability: the politics of design and the design of politics. DIS’10, 1-10.
    Google ScholarFindings
  • Dunton, G., Intille, S., Beaudin, J., and Pentz, M.A. (2009). Pilot test of a real-time data capture protocol to assess children’s exposure to and experience of physical activity contexts using mobile phones. Obes 17, S150–S151.
    Google ScholarLocate open access versionFindings
  • Eagle, N. and Pentland, A. (2006). Reality mining: sensing complex social systems. Pers Ubi Comp, 255-268.
    Google ScholarFindings
  • Erickson, T. (2005). Five Lenses: Towards a Toolkit for Interaction Design. Theories and Practice in Interaction Design.
    Google ScholarFindings
  • Fogg, B.J. (2002). Persuasive Technology: Using computers to change what we think and do. Ubiquity, December Issue, A-5.
    Google ScholarFindings
  • Froehlich, J., Findlater, L., and Landay, J. (2010). The design of eco-feedback technology. CHI’10. 1999-2008.
    Google ScholarFindings
  • Froehlich, J., Findlater, L., Ostergren, M. et al. (2012). The design and evaluation of prototype eco-feedback displays for fixture-level water usage data, CHI’12, 2367-2376.
    Google ScholarLocate open access versionFindings
  • Froehlich, J. (2011). Sensing and feedback of everyday activities to promote environmental behaviors, UMI, 3501869.
    Google ScholarFindings
  • Gearing, R. and El-Bassel, N. (2011). Major ingredients of fidelity: A review and scientific guide to improving quality of intervention research implementation. Clin Psychol Rev, 79-88.
    Google ScholarLocate open access versionFindings
  • Glanz, K., Rimer, B., and US- N.C.I. (1995). Theory at a glance: A guide for health promotion practice. NIH-NCI.
    Google ScholarLocate open access versionFindings
  • Goffman, E. (2002). The presentation of self in everyday life.
    Google ScholarFindings
  • Grimes, A., Bednar, M., Bolter, J.D., and Grinter, R.E. (2008). EatWell: Sharing nutrition-related memories in a low-income community, CSCW’08, 87–96.
    Google ScholarLocate open access versionFindings
  • Grimes, A. and Grinter, R. (2007). Designing persuasion: Health technology for low-income African American communities. Persuas Tech, 4744, 24-35.
    Google ScholarLocate open access versionFindings
  • He, H., Greenberg, S., and Huang, E. (2010). One size does not fit all: Applying the transtheoretical model to energy feedback technology design, CHI’10, 927-936.
    Google ScholarLocate open access versionFindings
  • Hekler, E.B., Buman, M.P., Otten, J., et al. (2012) Who responds better to a computer- vs. human-delivered physical activity intervention? Results from the community health advice by telephone (CHAT) trial. In Submission.
    Google ScholarFindings
  • Hekler, E.B., Buman, M.P., Poothakandiyil, N., et al. (2012) Exploring behavioral markers of long-term physical activity maintenance: A case study of system identification modeling within a behavioral intervention. In Submission.
    Google ScholarFindings
  • Hersen, M., and Barlow, D.H. (1976). Single-case experimental designs: Strategies for studying behavior change. Peramon, New York, NY.
    Google ScholarFindings
  • Kim, T., Hong, H., and Magerko, B. (2010). Design requirements for ambient display that supports sustainable lifestyle. DIS’10, 103-112.
    Google ScholarFindings
  • King, A.C., Sallis, J., Frank, L., et al., (2011). Aging in neighborhoods differing in walkability and income: Associations with physical activity and obesity in older adults. Soc Sci Med, 73, 1525-1533.
    Google ScholarLocate open access versionFindings
  • King, A.C., Hekler, E.B., Castro, C.M., et al. (2013). Exercise Advice by Humans versus Computers: Maintenance Effects at 18 Months. Healt Psychol.
    Google ScholarFindings
  • King, A.C., Stokols, D., Talen, E., Brassington, G.S., and Killingsworth, R. (2002). Forging a Transdisciplinary Paradigm. Am J Prev Med 23, 15–25.
    Google ScholarLocate open access versionFindings
  • King, A.C., Toobert, D., et al. (2006). Perceived environments as physical activity correlates and moderators of intervention in five studies. Am J. Healt Prom, 21, 24–35.
    Google ScholarLocate open access versionFindings
  • Klasnja, P., Consolvo, S., and Pratt, W. (2011). How to evaluate technologies for health behavior change in HCI research. CHI’11,3063-3072.
    Google ScholarFindings
  • Kraemer, H.C. and Kiernan, M., Essex, M., and Kupfer, D.J. (2008). How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Healt Psychol, 27, S101-S108.
    Google ScholarLocate open access versionFindings
  • Lee, M., Kiesler, S., and Forlizzi, J. (2011). Mining behavioral economics to design persuasive technology for healthy choices. CHI’11, 325-334.
    Google ScholarFindings
  • Lin, J., Mamykina, L., and Lindtner, S. (2006). Fish’n'Steps: Encouraging physical activity with an interactive computer game. UbiComp’06, 261-278.
    Google ScholarFindings
  • Locke, E. and Latham, G. (1990). A theory of goal setting & task performance, Prentice Hall, Englewood Cliff, NJ USA.
    Google ScholarFindings
  • Maguire, M. (2001). Methods to support human-centered design. Intern J Hum Comp Stud, 55, 587–634.
    Google ScholarLocate open access versionFindings
  • Mamykina, L. and Mynatt, E. (2008). MAHI: investigation of social scaffolding for reflective thinking in diabetes management. CHI’08, 477-486.
    Google ScholarFindings
  • Chetty, M., Tran, D. and Grinter, R.E. (2008). Getting to green: Understanding resource consumption in the home. UbiComp ’08, 242-251.
    Google ScholarLocate open access versionFindings
  • Michie, S. Ashford, S., Sniehotta, F.F., et al., (2011). A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALORE taxonomy. Psychol Healt, 26, 1479-1498.
    Google ScholarLocate open access versionFindings
  • Miluzzo, E., Lane, N., Fodor, K., et al. (2008). Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application, SenSys’08, 337-350.
    Google ScholarFindings
  • Morin, C. and Bootzin, R. (2006). Psychological and behavioral treatment of insomnia: Update of the recent evidence (1998-2004). SLEEP, 29, 1398-1414.
    Google ScholarFindings
  • Moyers, T.B., Martin, T., Manual, J.K., et al. (2005) Assessing competence in the use of motivational intervention. J Sub Abuse Treat, 28, 19-26.
    Google ScholarLocate open access versionFindings
  • Nickerson, R. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Rev Gen Psychol, 2, 175-220.
    Google ScholarLocate open access versionFindings
  • Nigg, C., Allegrante, J., and Ory, M. (2002). Theory-comparison and multiple-behavior research: Common themes advancing health behavior research. Healt Educ Res, 17, 670-679.
    Google ScholarLocate open access versionFindings
  • Ogden, J. (2003). Some problems with social cognition models: a pragmatic and conceptual analysis. Healt Psychol, 22, 424-428.
    Google ScholarLocate open access versionFindings
  • Prochaska, J., Wright, J., and Velicer, W. (2008). Evaluating theories of health behavior change: A hierarchy of criteria applied to the transtheoretical model. Appl Psychol, 57, 561-588.
    Google ScholarLocate open access versionFindings
  • Prochaska, J.O. and DiClemente, C.C. (1983). Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol 51, 390–395.
    Google ScholarLocate open access versionFindings
  • Purpura, S., Schwanda, V., Williams, K., et al. (2011). Fit4Life: The design of a persuasive technology promoting healthy behavior and ideal weight. CHI’11, 423-432.
    Google ScholarFindings
  • Riley, W.T., Rivera, D.E., Atienza, A. et al. (2011). Health behavior models in the age of mobile interventions: Are our theories up to the task? Trans Behav Med 1, 53–71.
    Google ScholarLocate open access versionFindings
  • Rovniak, L.S., Hovell, M.F., and Wojcik, J.R. (2005). Enhancing theoretical fidelity: an email-based walking program demonstration. Am J Healt Prom, 20, 85–95.
    Google ScholarLocate open access versionFindings
  • Sallis, J.F. and Owen, N. (1997). Ecological models. In K. Glanz, et al. eds., Health behavior and health education: Theory, research, and practice. Jossey Bass, San Francisco, 403–424.
    Google ScholarLocate open access versionFindings
  • Sallis, J.F., Saelens, B.E., Frank, L.D., et al. (2009). Neighborhood built environment and income: examining multiple health outcomes. Social Sci Med, 68, 1285–1293.
    Google ScholarLocate open access versionFindings
  • Shove, E. (2010). Beyond the ABC: Climate change policies and theories in social change. Environ and Plan, A, 42(6), 1273.
    Google ScholarLocate open access versionFindings
  • Velicer, W.F. and Prochaska, J.O. (2008). Stage and non-stage theories of behavior and behavior change: A comment on Schwarzer. Appl Psychol Interna Rev 57, 75–83.
    Google ScholarLocate open access versionFindings
  • Behavior Change Techniques Taxonomy. http://www.ucl.ac.uk/healthpsychology/BCTtaxonomy/index.php.
    Findings
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